research in health information management

In this Winter/Spring 2024 issue we are pleased to present a variety of manuscripts for you, addressing vital topics such as recording sex gender identity, ICD-11 innovation, professional ethics, patient portals, utilizing EHR data for quality improvement, the creation of a SDOH platform, and the validation of COVID-19 data in a research database.

Kathy L. Giannangelo, MA, RHIA, CCS, FAHIMA, Michael B. Pine, MD, MBA, Christopher P. Tompkins, PhD

Egondu R. Onyejekwe PhD, Dasantila Sherifi, PhD, MBA, RHIA, and Hung Ching, PhD, DABR

This qualitative study focused on better understanding the reality of big data and big data anayltics management and usage by healthcare organizations.

Jennifer L. Peterson, PhD, RHIA, CTR, and Shannon H. Houser, PhD, MPH, RHIA, FAHIMA

Adam Baus, PhD, MA, MPH, Andrea Calkins, MPH, Cecil Pollard, MA, Craig Robinson, MPH, Robin Seabury, MS, Marcus Thygeson, MD, MPH, Curt Lindberg, MHA, DMan, Andrya Durr, PhD  

Whitney Linsenmeyer, PhD, RD, LD, Katie Heiden-Rootes, PhD, LMFT, Michelle R. Dalton, PhD, LPC, and Timothy Chrusciel, MPH

Oliver Astasio, MD, PhD, Belén Castillo-Cano, MSc, Beatriz Sánchez Delgado, MSc, PharmD, Fabio Riefolo, PhD, Rosa Gini, PhD Elisa Martín-Merino, PhD, PharmD   

This study aims to estimate and describe the validation parameters of the collected SARS-CoV-2 disease information among vaccinated patients and their unvaccinated controls in Spain's public health database. 

• Technical Support
• Find My Rep

You are here

Health Information Management Journal

Preview this book.

• Description
• Aims and Scope
• Editorial Board
• Abstracting / Indexing
• Submission Guidelines

The Health Information Management Journal (HIMJ) is the official peer-reviewed research journal of the Health Information Management Association of Australia (HIMAA) providing a forum for the dissemination of original research and opinions related to the management and communication of health information. Papers published in HIMJ will be of interest to researchers, policy makers and governments, health practitioners, teachers, consumers and others with an interest in improving health service delivery and health outcomes for patients and the community. We welcome contributions from national and international researchers as HIMJ provides a critical role in advancing the knowledge-base in this dynamic field. HIMJ is published three times per year with an Online First facility that facilitates speedy publication.

HIMJ publishes research, article commentaries, professional practice papers and reviews covering a broad range of topics related to the management and communication of health information. Topic areas include: e-health and personal health records; privacy and confidentiality; health classifications, terminologies and clinical coding; data quality; data linkage; consumer health informatics; public and population health information management; health information policy and governance; health information systems; and health information management education.

HIMJ statement for use of AI in HIMJ submissions

For the information of authors, the Editorial Board has created a position statement to advise on the use of Artificial Intelligence in research publications.

‘HIMJ aligns with the position statement of the Committee of Publication Ethics (COPE) regarding the use of Large Language Models (LLMs) and other artificial intelligence (AI) tools (e.g. ChatGPT, GIX.AI, Chatsonic, Google Bard, Microsoft Bing, Auto-GPT, or similar) in research publications, in particular that AI tools cannot be listed as an author of a paper. “AI tools cannot meet the requirements for authorship as they cannot take responsibility for the submitted work. As non-legal entities, they cannot assert the presence or absence of conflicts of interest nor manage copyright and license agreements.” Authors who us AI tools in the writing of a manuscript, production of images or graphical elements of the paper, or in the collection and analysis of data, must be transparent in disclosing in the Methods section of the paper which AI tool was used and how it was used. Authors are fully responsible for the content of their manuscript, even those parts produced by an AI tool and are thus liable for any breach of publication ethics.

Further information

• Clarivate Analytics: Science Citation Index Expanded (SCIE)
• Clarivate Analytics: Social Sciences Citation Index (SSCI)
• Informit's Humanities and Social Sciences Collection
• Web of Science (Clarivate Analytics)

Manuscript submission guidelines can be accessed on Sage Journals.

• Read Online
• Sample Issues
• Current Issue
• Email Alert
• Permissions
• Foreign rights
• Reprints and sponsorship
• Advertising

Institutional Subscription, E-access

Institutional Backfile Purchase, E-access (Content through 1998)

Institutional Subscription & Backfile Lease, E-access Plus Backfile (All Online Content)

Institutional Subscription, Print Only

Institutional Subscription, Combined (Print & E-access)

Institutional Subscription & Backfile Lease, Combined Plus Backfile (Current Volume Print & All Online Content)

Institutional, Single Print Issue

To order single issues of this journal, please contact SAGE Customer Services at 1-800-818-7243 / 1-805-583-9774 with details of the volume and issue you would like to purchase.

• Open access
• Published: 15 August 2024

A framework for health information governance: a scoping review

• Somayeh Ghaffari Heshajin 1 ,
• Shahram Sedghi   ORCID: orcid.org/0000-0001-6457-7464 1 ,
• Sirous Panahi 1 &
• Amirhossein Takian 2  

Health Research Policy and Systems volume  22 , Article number:  109 ( 2024 ) Cite this article

Metrics details

As a newly emerged concept and a product of the twenty-first century, health information governance is expanding at a rapid rate. The necessity of information governance in the healthcare industry is evident, given the significance of health information and the current need to manage it. The objective of the present scoping review is to identify the dimensions and components of health information governance to discover how these factors impact the enhancement of healthcare systems and services.

PubMed, Scopus, Web of Science, ProQuest and the Google Scholar search engine were searched from inception to June 2024. Methodological study quality was assessed using CASP checklists for selected documents. Endnote 20 was utilized to select and review articles and manage references, and MAXQDA 2020 was used for content analysis.

A total of 37 documents, including 18 review, 9 qualitative and 10 mixed-method studies, were identified by literature search. Based on the findings, six core categories (including health information governance goals, advantages and applications, principles, components or elements, roles and responsibilities and processes) and 48 subcategories were identified to form a unified general framework comprising all extracted dimensions and components.

Conclusions

Based on the findings of this scoping review, health information governance should be regarded as a necessity in the health systems of various countries to improve and achieve their goals, particularly in developing and underdeveloped countries. Moreover, in light of the undesirable effects of the coronavirus disease 2019 (COVID-19) pandemic in various countries, the development and implementation of health information governance models at organizational, national and international levels are among the pressing concerns. Researchers can use the present findings as a comprehensive model for developing health information governance models. A possible limitation of this study is our limited access to some databases.

Peer Review reports

Introduction

The value of information in the healthcare industry.

The healthcare industry is rapidly evolving while many new demands are emerging, among which there is a fundamental need for accurate and applicable information [ 1 ]. The value and importance of information in health organizations stem from their dual missions and goals. Health data and patient information are regarded as valuable sources for researchers to enhance healthcare provision in terms of efficiency, safety and quality [ 2 , 3 , 4 , 5 ]. It is acknowledged that high-quality data and information facilitate high-quality care, accurate research, favorable patient outcomes, cost-effective risk assessment and strategic decision-making [ 6 ]. Consequently, managing and controlling data and information in health organizations are regarded as the core fundamental requirement in these organizations.

What is information governance?

Timely and effective management of crucial information constitutes a pillar of support for any organization [ 7 ]. In this regard, most organizations have devoted time and resources to the development of information governance systems to provide specific solutions at any time or location [ 7 , 8 ]. The concept of information governance has been around since the early twentieth century when organizations began to develop effective and comprehensive management of data and information. Many consider it to be the effective management of knowledge assets [ 9 , 10 ]. Information governance is an enterprise-wide accountability framework that promotes appropriate behavior when handling information-related matters [ 8 , 10 , 11 ]. This concept encompasses the processes, rules, standards and criteria that guarantee an organization’s effective and efficient use of information to achieve its goals. Information governance also encompasses the entire information life cycle, including how information is created, stored, used, archived and discarded. In addition, this concept determines who should have access to specific information when and how [ 1 , 4 , 6 , 12 , 13 , 14 , 15 ].

Health information governance (HIG)

Information governance in the healthcare industry is a relatively new concept. Primary efforts in this field date back to 1997, when the National Health Service of England (NHS) developed the Caldicott Principles [ 3 ]. They initiated the practice of information governance in the health sector in 2002 [ 16 ]. Legal, regulatory and information security requirements shape the primary drivers for developing information governance programs in various organizations [ 16 , 17 ]. In healthcare organizations, however, quality control and confidentiality of the ever-increasing volumes of information are crucial. Therefore, creating information governance programs is essential to improve care quality and achieve satisfactory results for patients and other stakeholders [ 1 , 16 ].

The necessity of HIG

According to Smallwood: “Bad information [in health] means people could die.” [ 16 ]. The United States has the most expensive healthcare in the world; however, medical mistakes are the third reason for death in this country [ 18 ]. To explain the necessity of HIG, it is important to consider some experts’ opinions; Smallwood explained in 2019 that one possible reason for the over 250,000 people dying from medical mistakes each year in the U.S. [ 18 ] is poor information governance [ 16 ]. Moreover, Riegner believes that the cause of major failures and problems during the coronavirus disease 2019 (COVID-19) pandemic is the lack of global information governance [ 19 ]. Conversely, a recent book published by OCED Library highlights South Korea, one of the countries with the best results against COVID-19, has one of the strongest health data and information governance [ 20 ].

Information governance is essential for enhancing healthcare outcomes in several ways; accurate, reliable and current information greatly benefits population health and care provision by enabling better clinical decision-making and reducing medical mistakes [ 8 , 16 , 21 ]. An example is the electronic health record system that assists medical specialists in accessing information about a patient’s medications, allergies and more [ 22 ]. In addition, HIG enables seamless sharing of patient data among different healthcare providers, facilitating better care coordination, especially for patients with complex or chronic conditions who may see multiple specialists [ 23 ]. Furthermore, HIG can lead to (1) more efficient healthcare delivery through effective data management [ 24 ], (2) enhanced population health management by analyzing big data to identify trends, risk factors and opportunities for preventive care [ 25 ], (3) advancements in medical research and treatment protocols [ 26 ] and (4) empowerment of patients to play a more active role in their healthcare decisions [ 21 , 24 ].

HIG best practices

Despite the brief history of HIG, numerous studies have emphasized its significance [ 16 , 27 ]. In addition to England, some other countries, such as Canada, Australia and the United States, have developed and implemented HIG models [ 2 ]. Information Governance Principles for Healthcare (IGPHC) and the associated maturity model, developed in 2014 by the American Health Information Management Association (AHIMA), are among the most recent and comprehensive efforts in this field. IGPHC is a framework that includes eight fundamental principles for HIG [ 8 , 28 ]. In addition, various models of HIG have been developed based on research reports. Each model introduces specific dimensions and components, mostly built upon the fundamental principles proposed by AHIMA. Slight nuances depend on the study background, aim and geographical location.

Apart from the models presented and used by the pioneering countries, no other comprehensive resources were found for studying and obtaining ideas for using or developing novel models of HIG; indeed, despite the booming growth of the healthcare industry, concerns have been raised about the lack of information governance programs [ 2 , 10 , 16 ]. Therefore, the present study aims to:

Map the existing literature on HIG models to identify the types of models used by pioneering counties and explore the available resources for developing novel models.

Identify the dimensions and components of existing HIG models and identify any potential knowledge gaps.

Explore the relationship between HIG factors and the enhancement of healthcare systems and services.

By achieving these objectives, this scoping review will provide a clear understanding of the current landscape of HIG models and their impact on healthcare. It will also identify areas for further research and development of more comprehensive and effective HIG programs.

Methodology

This scoping review was conducted based on the five steps outlined by Arksey and O’Malley [ 29 ]: (1) formulating the research question, (2) searching for relevant literature, (3) selection of eligible studies, (4) data extraction and (5) analysing and describing the results. In addition, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) reporting guidelines [ 30 ]. No protocol was registered for this review. The scoping review methodology was selected due to its relevance to the clarification of key concepts in literature and identifying key characteristics or factors related to the concept of HIG [ 31 ].

Search strategy and information sources

The search strategy for electronic databases was developed, piloted and refined by the team’s librarians. After finalizing our search in PubMed through an iterative process involving pilot tests, we completed a systematic search of PubMed, Scopus, Web of Science, ProQuest and Google Scholar for relevant published articles up to July 2022 and updated until June 2024; there was no time constraints for publications and records. Furthermore, the reference lists of all included studies were manually scanned to identify any relevant investigations suitable for inclusion. Search strategies by the following two categories of keywords using Boolean operators are presented in ‘Supplementary Table 1, Additional file 1 ’:

(1) (‘information governance’ OR ‘data governance’ OR ‘knowledge governance’ OR ‘information policy’).

AND (2) (health* OR medical OR clinical OR hospital*).

Eligibility criteria

The criteria for the inclusion and exclusion of articles are listed below (Table  1 ).

Study selection

After conducting a literature search, we imported the results into Endnote 20 (Thomson Reuters, New York, NY). Two reviewers used Endnote 20 to screen the articles. After removing duplicates, two reviewers independently read and reviewed the title and abstract of each document to determine if it met the inclusion criteria. Publications that were deemed potentially relevant were retrieved in full text and screened by two independent reviewers. Any disagreements between the reviewers were resolved through discussion. If consensus could not be reached, a third reviewer made the final decision.

Data quality assessment

After the selected articles were rechecked, two independent authors assessed each document using the CASP quality assessment checklists. We have used CASP checklists for review articles, case–control articles and qualitative research, which have 10, 12 and 10 questions, respectively. The validity, results and clinical relevance are the three main areas covered by CASP checklists [ 32 ]. We changed the possible answers for each item from yes or no to yes and no or unclear to reflect methodological quality (Supplementary Fig. 1, Additional file 1 ). All documents with a total score of six or more were considered as the research population. Two of the articles, which scored five, did not meet the required score. However, to prevent potential bias and to include a diverse range of related literature, the reviewers decided to include these articles in the final collection of selected articles.

Data extraction

Eventually, 37 articles underwent in-depth analysis and information extraction after their quality was confirmed. A data charting form was developed and the first 10 articles were piloted by the reviewers. Data extracted included study characteristics (first author, year, country of affiliation, article type, study setting), type of health governance investigated and the summary of the findings. Excel-formatted integrated data charting form was used to compare, combine and classify the results and findings. Data were extracted by a single reviewer and validated by the second reviewer. If any disagreement happened, it was resolved by discussion.

Statistical analysis

The content analysis results of the reviewed articles were arranged in chronological order, and the qualitative data analysis software MAXQDA 2020 was used for the thematic analysis of the findings to achieve more accurate results and to extract valid and documented themes. MAXQDA is suitable for content analysis due to its strong coding capabilities, powerful visual tools, advanced search features and sharing and collaboration features [ 33 ]. We primarily have used this software for its ability to share data among reviewers, visually organize codes (especially important due to the wide range and complexity of our study’s data) and help to identify key themes. To prevent any bias, we manually coded the literature and did not use electronic coding tools in MAXQDA to generate the codes. The codes were generated based on the concepts in the text. Then, related codes were grouped together based on their similarities and differences and labelled to form descriptive themes. The main themes were then identified. Data analysis and grouping were independently carried out by two reviewers. Any discrepancies were resolved through discussion with a third reviewer.

Search results

The initial database search resulted in the retrieval of 3955 records. After eliminating inappropriate documents, remaining 37 titles aligned with the objectives were chosen for in-depth inspection, extraction of the dimensions and components and content analysis (see Fig.  1 ).

PRISMA flow diagram of included studies

Publication characteristics

The characteristics of the articles reviewed are summarized in ‘Supplementary Table 2, Additional file 1 .’ The publication date of the chosen articles fell between 2003 and 2024 despite the absence of a certain time limit during the document search phase. The majority of the articles (over 70%) were published between 2011 and 2020. Additionally, the fewest articles were published between 2003 and 2010. The document types were review, mixed-methods and qualitative, respectively. In a scoping review, a wide range of articles can be included, such as review articles, as selected sources. Using a variety of sources can help provide a more comprehensive and in-depth view of the topic under discussion [ 34 , 35 , 36 , 37 ]. In most articles, the data collection tool was a literature review, an interview guide, a questionnaire, a data collection (charting) form, or a combination of these. Furthermore, there were eight articles where no information was available about the data collection tool, and these were labelled as ‘not specified’. More than 60% of the articles analysed their data using the content analysis method. Descriptive statistics, inferential statistics, framework analysis and thematic analysis were the other used data analysis methods. Additionally, seven articles did not demonstrate their analysing method, labelled as ‘not specified’ in the table. Sixteen of the Included studies dealt with information governance, 12 with data governance, and the remaining nine examined various aspects of health-related IT governance, digital data governance, indigenous data governance, clinical governance and information security governance.

The process of classifying and codifying the results yielded six themes or main components (including information governance goals, applications of information governance, principles, components or elements, roles and responsibilities and information governance processes) (Fig.  2 ).

HIG main components

The goals of information governance in the health system

The first theme extracted from the literature review introduced ‘the goals of HIG’ based on the needs of the health system and information governance stakeholders (see Table  2 ).

According to the results, ‘providing high-quality health care’ is the primary goal of health information governance. This goal can subsume and serve as a precondition for the other goals. Effective and efficient management leads to high-quality care, which in turn generates high-quality data, boosts productivity and lowers healthcare costs [ 6 , 38 , 39 , 40 ]. Numerous studies have emphasized that access, security and privacy of highly sensitive health data and information are among the most important goals of HIG [ 6 , 22 , 40 , 41 , 44 , 47 ]. According to our findings, the common objectives of HIG programs in various contexts are aligned with the organizational objectives of healthcare systems, which ultimately lead to client satisfaction and trust. Several studies have stated that gaining and maintaining the clients’ trust is the ultimate goal of HIG and its effectiveness [ 22 , 41 , 42 , 44 , 45 , 48 ].

Advantages and applications of HIG

The second theme derived from the review includes their ‘advantages and applications’, which are related to the system goals and contribute to the realization of those goals. Table 3 presents five primary applications of the HIG systems and their respective constituents.

Cost reduction and economic improvement

The first identified category in the theme of advantages and applications of HIG, can be viewed as an application and primary goal not only in health organizations but in all organizations that use information governance programs. This theme is divided into eight subcategories, such as ‘savings in service provision, resource allocation and procurement, time and information costs’ [ 8 , 22 , 38 , 39 , 49 , 51 , 52 ]. These categories ultimately emerge within the eighth subcategory labelled ‘business intelligence’ [ 38 ]. In fact, business intelligence can be viewed as a concept encompassing the seven preceding subcategories.

Improved quality of and access to healthcare services

According to the second category identified under the advantages and applications, HIG can improve the quality of service delivery in diverse ways within health organizations: planning for the management and optimization of community health by increasing the potential for high-quality health service delivery and fair access to services for different segments of society, improving the ability to follow up on high-quality results, increasing cooperation and interaction with doctors and, thus, reducing medical errors, improving and optimizing the health services received by patients, enhancing the efficiency and effectiveness from various perspectives as regards the health organizations services and interaction with all potential stakeholders, as well as identifying defects and risk management [ 8 , 22 , 38 , 39 , 49 ].

Management and policymaking at different levels of healthcare organizations

The present study divided the levels of health care organizations for managing and policymaking into macro levels, inter-organizational and organizational levels. At the macro-organizational level, HIG leads to the planning, determination and implementation of rules, policies and standards. Moreover, the specification of roles and responsibilities, approaches related to medical equipment management, restrictions on information access and decision-making processes are among the additional advantages of information governance at the macro level of healthcare organizations [ 8 , 22 , 38 , 39 ]. One of the benefits of HIG in inter-organizational management is the monitoring and assessing compliance with rules, as well as the cooperation and competition between organizations [ 22 , 40 , 49 ]. Finally, issues such as improving organizational performance, supporting strategic decisions, resource management, reducing repetitive actions and enhancing patient interaction have been cited as benefits of HIG at the basic organizational level [ 8 , 22 , 45 , 51 ].

‘Creation of a culture of trust’ and ‘Information and knowledge management’

These are the last two categories identified in relation to the advantages and applications in the present study. Among the subcategories associated with the theme of establishing a ‘culture of trust’ are the ‘sharing of data and information’ and the ‘consideration of privacy, security and reliability’ [ 8 , 40 , 45 , 51 ]. In the realm of ‘information and knowledge management’, data and knowledge are treated as assets, and their potential benefits to an organization are discussed at length. These benefits include, but are not limited to, increased productivity, better decision-making and new avenues for health research [ 8 , 22 , 51 , 52 ].

In reviewing the selected studies, we found a consensus regarding the applications of HIG programs; in fact, most studies have mentioned all five applications listed in the table, along with the corresponding components, the only differences being in their scope and depth. The compatibility of applications with the stated HIG goals and definitions is one of the most important aspects of this section’s findings. Gartner’s definition pertains to the applications of information management and the establishment of a culture of trust, which includes roles, policies, standards and criteria that considers effective use of information as a prerequisite for ensuring the achievement of organizational goals [ 22 , 55 ]. Smallwood also included the accuracy and security of the data in his definition [ 6 , 16 ]. Donaldson and Walker introduced information governance in 2004 as an organization-wide movement towards confidentiality, integrity and secure access to information [ 55 ]. In addition, Panian’s (2010) definition emphasizes adopting management and policy applications, fostering a culture of trust and enhancing the quality and accessibility of healthcare services [ 43 ]. The Association of Records Managers and Administrators (ARMA) highlights the policy and management aspects of information governance [ 39 ]. Additionally, the AHIMA has pinpointed the importance of information management in the health sector, as reflected in Briggs’ (2013) definition [ 39 ].

HIG principles

The principles of HIG, comprising 13 components, emerged as the third theme in this analysis. In this regard, the majority of reviewed studies reflected a consensus. These principles are presented in Table  4 .

The eight IGPHC principles developed by AHIMA are accountability, transparency, integrity, protection, compliance, availability, retention and disposal [ 6 , 8 , 49 , 56 , 57 , 59 , 65 ]. In addition to these eight principles, the developed HIG programs have also developed concepts such as consent, participation, continuous quality improvement, independence and justice and effectiveness and efficiency. In practice, however, there are minor differences in the principles based on the goals and approaches of the programs.

Transparency

The first category under the principles theme is transparency, which presents all decisions, policies and measures related to the use of data in a way that is accessible to stakeholders and the public in an effort to gain and maintain trust [ 6 , 8 , 40 , 44 , 56 ]. However, it is emphasized that maintaining the confidentiality and controlling access to confidential information does not conflict with transparency, and healthcare organizations should consider their obligations in this regard [ 56 ].

Accountability

‘Accountability’ is predicated on the presence of a senior leader who should assist various groups in developing, implementing and updating a comprehensive HIG program [ 56 ]. Two applicable digital health governance principles, noted by Marcelo et al. [ 38 ], are ‘responsibility and accountability’, where an accountable person is defined as someone who is responsible for making decisions and taking actions related to digital health. The principle of accountability also involves digital health responsiveness to the health system priorities and its ability to balance the competing needs of various stakeholders [ 38 ]. Laurie and Sethi have defined responsibility and accountability as fundamental principles in the framework of good health governance. According to their view, this principle refers to the responsible use of health data in scientific studies directed by the goals of the relevant organizations and includes 15 key subareas [ 46 ].

The third principle of HIG identified in this study is ‘integrity’, ensuring a reasonable and adequate level of information authenticity and reliability for the organization. This principle seeks to ensure the accuracy of information through the design and implementation of governance processes and procedures that govern the production, use and maintenance of information [ 40 , 57 , 58 ].

‘Protection’ is the fourth category under the theme of HIG principles. It involves ensuring the confidentiality and security of sensitive information, which is essential for strong information governance programs. In various studies, it is emphasized as protection [ 6 , 40 , 57 , 61 , 62 , 65 ], confidentiality [ 38 ] or security and confidentiality [ 58 ]. According to the principle of protection, information has varying degrees of sensitivity that must be classified and safeguarded throughout its lifetime. Additionally, this information must be protected at the source and throughout the ecosystem of the healthcare organization [ 57 ]. The six principles developed by the Caldicott Committee address the use of patients’ personal data and compliance with their security and confidentiality, demonstrating the significance of health data protection [ 60 , 66 ]. Also, protection is regarded as an essential component of the digital health governance [ 38 , 64 ].

The next principle is ‘compliance’, which requires the information governance system to operate legally and ethically. Neglecting compliance can result in the organization’s inability to deliver quality services [ 59 ]. In line with the compliance principle, Willison et al. developed the principle of obedience to the rule of law to gain and maintain public trust [ 44 ]. The same definition further highlights the importance of compliance with the rule of law in digital health governance [ 38 ].

Availability

In theory, the most important goal of availability is to gain the organization’s trust, as a lack of access to the right information at the right time can put patient care at risk [ 59 ]. Marcelo et al. believe that timely access to reliable and high-quality health data improves the surveillance of infectious diseases, enables more targeted allocation of health resources, expedites the response to the community’s healthcare needs and facilitates the monitoring of care quality [ 38 ].

Retention and disposal

The ‘retention’ principle can contribute significantly to the success of HIG programs. An organization’s ability to maintain all the necessary information is of utmost importance in light of the fact that organizations produce and store vast amounts of data (mostly electronically) [ 65 ]. Retention is one of the accentuated principles in NHS’s HOURS model [ 58 ]. Part of the principles of digital data governance refers to the establishment of an independent, long-term data storage and management program [ 64 ], which contrasts with HIG’s principle of retention in certain ways. To reduce potential losses and expenses, IGPHC states that certain types of data must be deleted after their retention periods have expired [ 65 ]. This highlights the importance of ‘disposal’ as the next HIG principle. Based on this principle, information has a shelf life, and when the organization no longer requires it, it becomes a burden and must be disposed of in accordance with the rules of the retention plan [ 65 ].

‘Consent’ is a route for voicing preferences and the need for being treated with dignity [ 40 ]. If consent cannot be obtained for the use of personal data, according to Laurie and Sethi, two specific actions can be taken: anonymizing the data as much reasonably as possible and obtaining permission from an appropriate regulatory body [ 46 ]. Anonymization involves removing clients’ identity information from data sets to protect privacy so that they can be used legally for other legitimate purposes [ 47 ].

Participation

‘Participation’ is a further category identified in this review under the principles theme, by which anyone affected by the health sector decisions can make their own contributions to this process [ 38 ]. When individuals are unable to make decisions about their personal information, it is crucial for them, including patients and other stakeholders, to have the opportunity to have input throughout the governance process [ 44 ]. The primary objective is to gain and maintain the stakeholders’ trust.

Continuous quality improvement, independence and effectiveness

According to the ‘continuous quality improvement’ principle, the process of information governance deals with the provision of accurate and up-to-date data and services to establish and uphold trust [ 44 , 51 , 64 ]. Impartiality, fairness, independence and inclusiveness, with the same objective as the quality improvement principle, are intertwined with the fair presentation of the information governance program’s benefits [ 38 , 44 ]. Finally, ‘effectiveness and efficiency’ were the last category identified in the theme of principles in HIG, which deals with ensuring the fulfilment of the organization’s comprehensive goals and its efficiency in obtaining the highest efficiency as a result of its activities. The ultimate goal of this principle is to gain and maintain the stakeholders’ trust and achieve the organization’s business goals [ 38 , 44 ].

HIG components or elements

The fourth theme resulting from the study review (i.e. HIG components or elements) consists of 11 components that characterize the fundamental components of information governance models according to the established principles (Table  5 ).

Rules, standards and policies

The focus of HIG programs is on the categories of laws, standards and policies, which have been occasionally discussed either as distinct categories or complementary components in some studies. Due to their fundamental proximity and alignment, ‘laws and standards’ were determined to be the first category in this study, followed by ‘policies and guidelines’. Legal requirements and standards are also introduced as the fundamental components of information governance in the ARMA and AHIMA definitions [ 39 , 49 ]. The Data Governance Institute (DGI) has introduced the laws and rules of interaction, which include policies and standards, as one of its three core categories of governance components in data governance [ 6 ]. In addition, other studies have identified legal requirements, policies, standards and implementation of standards as the principal components of HIG programs [ 48 , 54 , 61 , 62 ].

Compliance with information governance policies and procedures enables healthcare organizations to meet legal and regulatory requirements and ensures the safety and quality of patient care [ 57 ]. Consequently, policies and strategies may be conceived as including rules and standards, the prominent aspects of which may include data protection, freedom of information, confidentiality and information security. Other categories of interest are document and records management, policy for determining the responsibilities of key stakeholders, operational and training directives, the framework for organizational costs, policies related to setting objectives and developing strategic plans [ 6 , 40 , 49 , 53 , 62 , 63 , 67 ].

Information management

‘Information management’ addresses the management of the life cycle of information, from production to disposal, which is a crucial issue for health organizations and all organizations. Information management can handle the entire life cycle of information, including how to create, store, use and archive information. In addition, information management determines who should have access to particular information, when and how [ 6 ]. Notably, ‘document management’ and ‘quality assurance’ are listed as one of the subcategories of information management in the current study, because information management can also encompass documents. In addition, information life cycle management comprises the following steps: generation and collection, analysis, access and use, storage and organization, dissemination, disposal, exchange, quality management and integrity of information [ 22 , 39 , 50 ].

The remaining three categories in the current study introduced as essential categories for HIG elements are the governance program types. Due to the expansive nature of the concept of information governance, data governance, IT governance and information security governance are introduced as the subsets of information governance in several studies. Moreover, it has been acknowledged that the umbrella term ‘information governance’ subsumes these three governance concepts [ 22 , 40 , 49 , 58 , 62 ].

Data governance

Data governance is the processes, policies, standards and technologies necessary for an organization to manage and ensure data availability, quality, consistency, auditability and security [ 43 ]. Data managers establish policies and procedures governing the definition, accessibility, protection, archiving, ownership and integrity of data to ensure the precision and security of them [ 6 , 16 , 52 ]. Furthermore, since health data is the foundation of any governance process, it is logical to prioritize data governance as one of the primary categories within the HIG elements theme.

Information technology governance

Dong et al. have emphasized that information governance and information technology governance are inseparable in nature. Effective information governance programs require IT assistance to manage information governance policies and processes, engage stakeholders and guarantee data quality. Additionally, information in the IT sector is crucial for identifying the appropriate technology that can support information governance, and technology investments should support the mission and vision of information governance [ 6 ]. According to Datskovsky et al., information cannot be trusted unless the technology infrastructure on which it is created, used, maintained and stored is reliable by itself [ 57 ]. The category of information technology governance in this study differs from other studies [ 40 , 57 ] in treating information technology management as a subcategory of information technology governance. This is because the information technology governance category encompasses all other aspects of the concept of information technology management.

Information security governance and risk management

‘Information security governance’ is the third aspect of governance patterns identified in the current study as one of the categories related to the theme of HIG elements. The objective of information security governance in healthcare is to safeguard all health-related data to ensure their confidentiality, availability and integrity. This is crucial to maintain business continuity, reduce risks and demonstrate best practices and compliance [ 62 ]. Furthermore, information security governance tended to fully incorporate information security management in an attempt to comply with legal and professional requirements [ 62 ]. The first part of the Information Security Management Standard in the NHS HOURS series highlights the information security best practices such as security policy, security organization, asset classification, control, communication, operations, management, access control, systems development and maintenance, business management and compliance. Numerous studies have repeatedly referred to information security aspects, either as a separate category or in conjunction with such categories as laws, policies and standards [ 39 , 51 , 58 , 60 , 62 , 66 ]. Some studies have also recommended information security as a subcategory of risk management [ 22 , 39 , 40 , 49 , 53 , 57 , 60 , 62 , 66 ]. Information security is ascertained as a distinct category from risk management in the present investigation due to its high rate of sensitivity and salience as well as the increasing emphasis on these two facets of information governance. Risk assessment is a security process that entails considering potential threats and risks to data, creating policies and procedures for security officials and other staff to follow and designing appropriate protective measures in the healthcare sector [ 62 ]. Recommended methods for risk management involve clear reporting culture, regular risk recording, risk reduction in patient-related processes, quality impact assessments, continuous risk reduction, service speed and scale development and innovation and transformation [ 53 ].

Human resource

‘Human resource management’ is another category identified as an element of HIG models that encompasses all processes related to employees and human resources; it is also regarded as an essential and valuable aspect for both the health sector and other organizations. Among the significant issues that must be addressed in this category are employee knowledge and skills, knowledge expansion and training and strategic orientation [ 50 , 53 , 54 , 62 ]. In addition, time management and the optimal utilization of employees’ knowledge, skills and competencies are considered as important factors in this field [ 53 ]. This category has a direct relationship with the principle of compliance, as workforce training enables individuals to align their activities with policies and help appreciate their significance [ 57 ]. Alternatives for participation and consensus may include open meetings, public workshops, national associations, advisory committees, satisfaction surveys, conferences and national health associations [ 53 ].

Quality management

In light of the significance of assuring the quality and integrity of healthcare information [ 22 ], the next theme of the elements of HIG patterns is ‘the quality management’, which can be characterized by factors such as reducing and adjusting mortality data, improving clinical results, improving research results, positive patient feedback, providing fruitful services and enhancing the treatment goals for appropriate and timely care [ 53 ]. Notably, adhering to information governance policies and procedures can assist the organizations in meeting legal requirements and ensuring the safety and quality of patient care [ 57 ].

Project and change management

Since the modern era necessitates routine monitoring of the organizational structures and infrastructures [ 57 ] to identify and modify possible shortcomings and lower the rate of related risks, ‘the project management and change’ category emerged in the present study as a defining category within the elements of HIG. This category is a combination of ‘the monitoring category’ and ‘audit and change management category’ Rouzbahani et al. [ 40 ] reported in their study; in the present study, it is merged into a single component due to overlapping major themes.

‘The audit category’ is the final category mentioned in the theme of the elements of HIG patterns identified in the current review. In addition to emphasizing the financial and commercial aspects of the organization, this category documents the information-related activities, thereby enhancing the reliability and integrity of the desired information [ 57 ]. Better system performance and gaining the satisfaction and trust of stakeholders are the end results of audit cycles in the areas of service provision, financial affairs, research results and information assets, as well as audits of changes adopted in practice.

Roles and responsibilities (of individuals) in HIG programs

Officials, policymakers and executives make up the backbone of ‘the roles and responsibilities’ theme. Table 6 describes the levels and responsibilities of each official, as well as their respective duties.

Based on the present study, the roles and responsibilities of HIG are presented separately at three organizational levels, as shown in Figs.  3 , 4 and 5 .

Roles and responsibilities at the organizational senior level

Roles and responsibilities at the organizational middle level

Roles and responsibilities at the organizational operational level

Senior level

The executive director is the first and most crucial role at the senior level. This position is central to the accountability principle of the HIG program and is regarded as the primary position accountable for the program’s design and implementation [ 56 ]. Baskaran et al. believe that information governance principles should be communicated downward through a more robust leadership structure than at the board level [ 68 ]. The key responsibilities of executive director include: ensuring timely and budget-conscious project completion, taking responsibility for regulatory compliance policies and, most importantly, overseeing the development, implementation and revision of policies and procedures to maintain the organization’s integrity [ 22 , 40 , 69 ]. Chief Executive Officers, Chief Information Officers, Chief Legal Officers and Chief Medical Officers are examples of executive directors who may be accountable for smaller task-related departments [ 56 ].

In most cases, the second role and responsibility at the organization’s senior level falls on the senior director of the information governance program. In some studies, this position is referred to as the Caldicott guardian [ 66 , 67 ], who is typically a senior expert in the health field and has the most significant responsibility for protecting the confidentiality of patient information.

The third senior-level role is the core team with executive leadership, composed of representatives from clinical, business and technology domains. This group is responsible for making final decisions on proposed policy or procedure changes and ensuring the proper resolution of operational or data issues [ 22 , 40 ]. Principal members oversee the decision-making principles and protocols, organizational barriers, expansion and strengthening of partnerships and interaction with institutions, the needs of stakeholders, as well as the implementation of governance mechanisms [ 52 , 70 ].

Senior information risk management is the final role identified in the present study for the organization’s senior level. This role, also known as the manager of information-threatening risks, is highly reliant on the regulations and policies of countries. There is a critical emphasis on the importance of stressing context-specific confidentiality and information security protection [ 49 , 67 ].

Middle-level

Managers of organizational information governance must foster an environment conducive to change and provide employees with precise descriptions of individual responsibilities and penalties for violations. In addition, these managers are responsible for assessing the efficacy of training on information governance and identifying the training needs of employees [ 68 ]. Data steward [ 22 , 69 ], data manager or controller [ 40 ] and data protection officer [ 66 , 67 ] are all terms that have been used to refer to the role of data manager. The data manager or steward reports to superiors on all matters concerning data protection. Among these factors, we can mention information governance risks for the organization, privacy concerns and suggestions for potential changes or updates involving personal data processing [ 67 ]. Management of information assets or owners of information assets deals specifically with managing people’s information assets and ensures compliance with policies and laws pertaining to their protection.

Information technology management [ 22 , 67 ], managing the legal and financial department [ 22 , 68 ] and quality and compliance management [ 22 ] are a few examples of roles at the middle level of an organization sporadically mentioned in various studies. The definition of each responsibility depends on each organization’s context and target policies. Information technology management is responsible for developing and implementing appropriate information security methods and protocols to ensure compliance with data protection laws [ 67 ].

Operational-level

The operational level is the third and final organizational level identified in this review, which consists of operations managers and employees who, in practice, must abide by the laws and policies of HIG in conducting their tasks and execute and implement the principles of information governance at this organizational level [ 40 , 67 , 68 ].

Processes in HIG programs

‘The process’, as the final theme emerging from the present review, is a lesser-studied and less-mentioned component of HIG programs. What appears to be the root cause of this phenomenon is the dependence of the process dimensions to the geographical, activity, goal and organizational contexts in which the HIG program is being developed. Renaud’s point of view can be used to corroborate this assertion; he thinks the process is more similar to a delicate tool that needs to be built with care, deployed selectively and used under close supervision in a supportive setting so that human elements are not dehumanized [ 55 ]. Therefore, one could argue that the definition of a process and procedure in information governance and HIG programs depends on the activity’s context, the desired field and the organization’s policies. Indeed, it is impossible to determine a fixed and specific procedure for all programs of HIG. The current review has identified four core categories and nine subcategories within the theme of HIG processes based on different processes narrowly developed and reported in previous studies (Table 7 ). These core categories and subcategories have specified the development and implementation of the information governance program in a comprehensive manner. Policy making, decision-making, planning and implementation begin with an objective assessment of relevant factors such as assets, risk, capability and criteria and progress by a logical sequence that culminates in the monitoring of outcomes following policy implementation and outcome monitoring [ 6 , 38 , 55 , 62 ].

This review compiled and analysed previous research on HIG-related programs in an effort to unravel its various facets and constituents. The objective was providing a comprehensive picture of the studies conducted and the programs developed, as well as suggesting a framework encompassing all existing dimensions. The study was conducted with 37 articles selected from the review of related studies, and the results led to the development of six core categories and 48 subcategories for HIG programs. Figure  6 provides a summary of the findings from the review of the articles.

Summary of dimensions and components of HIG programs

The first theme derived from the review of studies identifies ‘the HIG goals’, comprising six subcategories: providing quality healthcare, providing affordable health services, ensuring equitable access to healthcare information and services, preserving data security, meeting legal obligations and fostering trust. Smallwood defines information governance as ‘comprehensive policies and processes to optimize and use information while keeping it secure and complying with legal and privacy obligations, in line with stated organizational business goals.’ [ 16 ] Moreover, according to Willison et al., the three primary objectives of HIG are to optimize the use of data to achieve business objectives, to maintain data security and to comply with legal and privacy requirements. In addition, gaining and maintaining the trust of patients, stakeholders, data providers and the general public are described as the objectives for using data in public interest research [ 44 ]. According to Kadlec, the main objective of HIG programs is to proactively and effectively manage the increasing volume of information collected and maintained daily [ 22 ]. Various studies have pointed to broader goals for HIG programs, such as improving and maintaining the health of the community [ 38 ], establishing effective and efficient management of information, improving productivity and effectiveness of services [ 39 ], enhancing the desire to maintain a competitive advantage, ensuring better performance and results of organizations and promptly responding to information requests [ 22 ]. As reflected by the focal points of the studies as well as goals focused on local and specific fields and after eliminating some overlaps, the current study has identified six comprehensive goals as categories associated with this theme.

The second theme derived from the studies analysed in this review is ‘the advantages and applications of HIG’, comprised five core categories and 39 subcategories. The core areas of focus for this theme are ‘cost containment and economic growth’, ‘healthcare quality and availability’, ‘healthcare management and policymaking at the macro, inter-organizational and organizational levels’, ‘trust building’ and ‘knowledge management’. It is conceivable that the benefits and applications of HIG are logically consistent with the goals of these programs, and the existence of some overlap between these two primary categories is not unanticipated. In his study, Kloss argues that improving organizational performance, reducing costs, and minimizing risks are the true benefits of information governance in organizations [ 71 ]. Moreover, according to Willison and colleagues, the expectations and, consequently, the applications of HIG programs from the users’ perspective fall into three primary categories: meeting expectations regarding how to perform and provide services, gaining trust in institutions and individuals, and creating belief in the accuracy and value of health services [ 44 ]. Rouzbahani et al. categorized the applications of HIG programs into six categories: improving healthcare and patient safety, reducing costs, enhancing the quality of health information, improving the security and confidentiality of health information, enhancing health information management and boosting the management of healthcare organizations [ 39 ]. Additionally, the results of AHIMA’s case studies identified some other applications of the HIG program’s used by the investigated centres [ 52 ]. The review of the current literature and the examination of the extracted categories indicate the breadth and frequency of applications and benefits of HIG. Given the young age of governance programs in the health field, it can be acknowledged that some potential benefits have not yet been identified. Therefore, it is anticipated that by expanding the application and use of this important strategy, additional benefits will be identified and implemented over time.

The third theme identified from the present review concerns ‘HIG principles’, with 13 categories as follows: transparency, accountability, integrity, protection, compliance, availability, retention, disposal, consent, participation, continuous quality improvement, independence and justice and effectiveness and efficiency. It is acknowledged that the theme of principles and related categories provide a comprehensive set of common speech and behavioural points for a diverse range of HIG program beneficiaries, allowing everyone to progress in line with the information governance project [ 8 ]. The first eight categories were those developed by AHIMA, regarded as fundamental principles in most of the previous studies; the rest of the categories were cumulatively added to literature over time. These principles are among the fundamental topics that have been investigated by research and developed as models of information governance. Accountability, participation and transparency have been cited as principles of health governance by Ibrahimova and Korjonen [ 53 ]. Likewise, Lauriea et al. emphasized the principles of transparency and consent as obvious criteria for protecting privacy [ 47 ]. Informed by the conceptual work of Lauriea and Sethi, Willison et al. developed eight principles for their governance model: transparency, accountability, obedience to the rule of law, honesty, participation and inclusion, impartiality and independence, effectiveness and accountability and continuous quality improvement [ 44 ]. In addition, Rouzbahani et al. have presented a model comprising 12 HIG principles [ 40 ]. In the present study, the categories associated with the theme of HIG principles are presented as exhaustively as possible by incorporating all categories highlighted in literature and models developed, as well as by eliminating their likely overlaps with other categories close to other themes or specific domains. Notably, ethical principles are emphasized alongside professional principles in HIG models, with no weighting or differentiation between the categories presented [ 56 , 59 , 67 ].

‘Components or elements of HIG programs’ is the fourth theme identified in the present review, with 11 distinct categories: laws and standards, policies and guidelines, information management, data governance, information technology governance, information security governance, management risk, human resource management, quality management, project and change management and auditing. In his article, Kadlec introduced several HIG components considered by AHIMA, including quality management, regulations, risk reduction, patient participation and business intelligence [ 22 ]. Williams considered audit and control, risk management and compliance to be essential components of information governance [ 62 ]. Rouzbahani et al. have introduced 13 elements as HIG model components [ 40 ]. Ibrajimova and Korjonen noted seven components of clinical governance, including patient participation, staff management, clinical effectiveness, use of information and information technology, education, risk management and audit, in relation to other governance programs [ 53 ]. In the present review, an attempt was made to consider all these categories associated with elements of HIG programs, and it appeared that all these elements indeed played a determining role. Given the scope of the introduced elements, it is reasonable to conclude that HIG, as an all-encompassing strategy and umbrella term, embraces other governance programs.

The fifth theme associated with HIG programs is ‘the roles and responsibilities’, denoting the introduction of HIG officials and policymakers at three organizational levels: senior, middle and operational levels. At the senior level, four categories and their respective responsibilities are identified: executive director, senior information governance program manager, core team and senior information risk manager. The middle organizational level includes the categories of the information asset manager, data manager and organizational information governance manager. The operational level of an organization consists of operations managers and employees. According to the model proposed and developed by Baskaran et al., the information governance hierarchy consists of six levels: executive director, financial and functional manager, information governance manager, team leaders of operations management, line managers and employees [ 68 ]. Rouzbahani et al. developed a model for Iran’s HIG and incorporated 14 roles and responsibilities into this model, with the Minister of Health assuming the highest role [ 40 ]. Haarbrandt et al. introduced the HiGHmed governance platform, where some of the roles considered included the executive board, supervisory board, technical coordination board, project management office, educational board, support and access committee, ethics working group, advisory board and the general assembly [ 70 ]. With a different view, Ibragimova and Korjonen detailed three groups of library activities that supported clinical and health governance in healthcare organizations: infrastructure (staff and resources); program management (library products and services); and direct participation (needs assessment, committees, audits, HTA, etc.) [ 53 ]. Given that the introduced studies developed their models in distinct domains, the disparity in the hierarchy of responsibilities seems reasonable. The current literature review introduces three levels and nine roles for HIG officials and policymakers, which are the sets of categories introduced in the reviewed studies after eliminating duplicate items and merging the overlapping items.

The final theme introduced in this literature review is that of ‘the processes’ by which HIG programs are developed, implemented and monitored. The associated categories are assessing strategic options, formulating policies, developing plans and tracking progress. In addition, nine subcategories were identified, including asset assessment, risk assessment, capability assessment, criterion assessment, policy development and implementation, internal and external validation, monitoring and change management, stakeholder support, results assessment and reporting. Several studies have described various processes linked to the developed programs in a very limited manner. Governance processes identified in the study by Marcelo et al. include policy and decision-making, planning, resource allocation, coordination and monitoring and evaluation [ 38 ]. While asset identification, risk assessment, policy implementation, capability assessment, procedure development, protection and compatibility, criteria assessment and possible external validation are among the six processes introduced by Williams [ 62 ], Dong et al. have introduced eight further key processes for information governance: data element definition, data integration, information sharing and accountability, information to information and information from information [ 6 ]. Although ‘the processes’ constitute an integral part of HIG programs, it has received less attention than other principles in academic research, because ‘the process’ is highly dependent on the location, activity, goals and overall vision of the organization in which the HIG program is being developed and implemented.

Despite its short history, health information governance has been the focus of several studies which have emphasized its significance, value and necessity. In fact, the development and implementation of national HIG models, particularly in developed nations, is evidence of this claim. The conclusions drawn from a review of the present articles reflect a number of specific aspects. Primarily, the extent and diversity of HIG-related dimensions and components are quite extensive, due to the fact that information governance encompasses the entire health system in the desired area, taking into account all advantages and disadvantages, with the goal of improving the system. Therefore, it requires the experts’ consideration in order to develop impeccable models that function as comprehensively as possible. Second, due to the unique significance and sensitivity of the information within the health organizations, the need to develop HIG models and programs becomes evident, particularly in the present age. Therefore, it can be concluded that developing and underdeveloped nations require the development of information governance models to manage and optimally utilize their health data and information to achieve the national health system goals. Finally, as the COVID-19 pandemic has led to unprecedented death toll since 2020, it appears logical to develop HIG models in order to maintain health system preparation for potential crises in future and to help prevent such tragic outbreaks. For the development of organizational, national and international models, it is our hope that the current literature review serve as a tentative road map and a comprehensive overview by describing the general framework of existing HIG models developed by experts and scientists.

Limitations

Although the scoping review is a valuable tool for comprehensively examining a broad topic such as HIG, it is essential to note the following possible limitations:

A scoping review provides an overview of the dimensions and components of the subject. However, a deeper understanding of these dimensions and components may require more focused studies.

The findings from the scoping review may not directly address a specific problem or answer a focused question.

We encountered difficulties for accessing some databases, but we made a comprehensive effort to search for articles in as many databases as possible.

The study tried to include different types of articles to prevent potential bias.

Additionally, it is important to consider that various factors such as technology, policy, regulation and health system structure influence the HIG landscape and related definitions. Therefore, these definitions may vary depending on the context.

The statistical analysis tool used in the study was not considered a limitation, as our purpose was to organize and structure the studies to more accurately identify the concepts. It is also worth noting that the authors manually coded the entire process in the software.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Kadlec L. Coming soon to your healthcare facility: Information governance. A look at healthcare information governance trends through practical case studies. J AHIMA. 2014;85(8):26–32.

PubMed   Google Scholar  

Hovenga EJ, Grain H. Health information governance in a digital environment. Ios Press; 2013. ( ISBN: 97816149929162912 ).

Google Scholar  

Caldicott F. Information: To share or not to share. Information governance review. Health Do; 2013. http://collections.crest.ac.uk/id/eprint/9560

Warner D. IG 101: What is information governance? Jurnal Of AHIMA; 2013. https://journal.ahima.org/page/ig-101-what-is-information-governance

Pierotti D, Meehan A. Information governance: an interview with Ann Meehan. RHIA Home Healthc Now. 2017;35(7):402–3. https://doi.org/10.1097/nhh.0000000000000566 .

Article   PubMed   Google Scholar  

Dong L, Keshavjee K. Why is information governance important for electronic healthcare systems? A Canadian experience. J Adv Hum Soc Sci. 2016;2(5):250–60. https://doi.org/10.20474/jahss-2.5.1 .

Article   Google Scholar  

Dogiparthi H. History of information governance. Cumberlands: University of the Cumberlands; 2018. p. 1–20.

Empel S. The way forward. AIHMA develops information governance principles to lead healthcare toward better data management. J Ahima. 2014;85(10):30–2 ( quiz 4 ).

Butler M. Keeping information clean: new information governance efforts challenge HIM to sort out dirty data. J AHIMA. 2013;84(11):28–31.

Ellington NN. Impact information governance has on healthcare organizations: University of Tennessee Health Science Center; 2018. https://core.ac.uk/download/pdf/234018953.pdf

Beach T, Rana O, Rezgui Y, Parashar M. Governance model for cloud computing in building information management. IEEE Trans Serv Comput. 2013;8(2):314–27. https://doi.org/10.1109/TSC.2013.50 .

Kwan H, Riley M, Prasad N, Robinson K. An investigation of the status and maturity of hospitals’ health information governance in Victoria. Aust Health Inf Manag J. 2020. https://doi.org/10.1177/1833358320938309 .

Kooper MN, Maes R, Lindgreen ER. On the governance of information: introducing a new concept of governance to support the management of information. Int J Inf Manage. 2011;31(3):195–200. https://doi.org/10.1016/j.ijinfomgt.2010.05.009 .

Proença D, Vieira R, Borbinha J. Information governance maturity model. Springer International Publishing Switzerland; 2016. https://doi.org/10.1007/978-3-319-43997-6_2 .

Book   Google Scholar  

Bunker D, Ehnis C, Seltsikas P, Levine L, editors. Crisis management and social media: Assuring effective information governance for long term social sustainability. In: IEEE international conference on technologies for homeland security (HST) . IEEE; 2013. https://doi.org/10.1109/THS.2013.6699008

Smallwood RF. Information governance for healthcare professionals: a practical approach. Boca Raton: Routledge/CRC Press, Taylor & Francis Group; 2019. ( 978-0-203-70524-7 ).

Maxcer C. Measuring success of an information governance program not so simple: IronMountain; 2011. http://docs.media.bitpipe.com/io_10x/io_105560/item_570545/Iron%20Mountain_sContentMgt_IO%23105560_EGuide_082012.pdf .

Christensen J, Cohen E. Medical errors may be third leading cause of death in the U.S.: cnn.com; 2016. https://edition.cnn.com/2016/05/03/health/medical-error-a-leading-cause-of-death .

Riegner M. Global information governance in pandemic times: in the geopolitics of global health information, international institutional law is more important than ever. Völkerrechtsblog; 2020. https://doi.org/10.17176/20200410-152747-0 .

OECD. Towards an integrated health information system in Korea. OECD; 2022.

Mastaneh Z, Mouseli L. Information governance in heath sector. J Health Manag. 2012;3(3):29–36.

Kadlec L. Coming soon to your healthcare facility: information governance. J Ahima. 2014;85(8):26–32.

Buckbee M. Data governance in healthcare: Your complete guide; 2022. https://www.varonis.com/blog/data-governance-in-healthcare .

Tinker A. The top seven healthcare outcome measures and three measurement essentials; 2019. https://www.healthcatalyst.com/insights/top-7-healthcare-outcome-measures .

Seditas K, Dobson H. Data access systems in NHS Scotland: experiences of cancer data utilisation projects. Scotland: Innovative Healthcare Delivery Programme(IHDP); 2019. https://www.ed.ac.uk/usher/ihdp/our-work/information-governance/information-governance-case-studies

Bougnague S. Cloudficient; 2023. https://www.cloudficient.com/blog/what-is-information-governance-in-healthcare-an-overview .

Donaldson A, Walker P. Information governance—a view from the NHS. Int J Med Inf. 2004;73(3):281–4. https://doi.org/10.1016/j.ijmedinf.2003.11.009 .

Rouzbahani F. Developing a model of health information governance program for healthcare system of Iran [PhD Thesis]. Tehran: Shahid Beheshti University of Medical Sciences; 2018.

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. https://doi.org/10.1080/1364557032000119616 .

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, Moher D. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73. https://doi.org/10.7326/M18-0850 .

Munn Z, Peters MD, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:1–7. https://doi.org/10.1186/s12874-018-0611-x .

Critical Appraisal Skills Programme. Critical appraisal tools and resources; 2023. https://casp-uk.net/critical-appraisal-tools-and-resources/ .

Literature Review with MAXQDA: VERBI GmbH; 2024. https://www.maxqda.com/literature-review .

Verdejo C, Tapia-Benavente L, Schuller-Martínez B, Vergara-Merino L, Vargas-Peirano M, Silva-Dreyer AM. What you need to know about scoping reviews. Medwave. 2021. https://doi.org/10.5867/medwave.2021.02.8144 .

Kazi MR, Chowdhury N, Chowdhury M, Turin TC. Conducting comprehensive scoping reviews to systematically capture the landscape of a subject matter. Popul Med. 2021;3:1–9. https://doi.org/10.18332/popmed/143831 .

Gottlieb M, Haas MRC, Daniel M, Chan TM. The scoping review: a flexible, inclusive, and iterative approach to knowledge synthesis. AEM Educ Train. 2021;5(3): e10609. https://doi.org/10.1002/aet2.10609 .

Article   PubMed   PubMed Central   Google Scholar  

Peters MDJ, Marnie C, Colquhoun H, Garritty CM, Hempel S, Horsley T, et al. Scoping reviews: reinforcing and advancing the methodology and application. Syst Rev. 2021;10(1):263. https://doi.org/10.1186/s13643-021-01821-3 .

Marcelo A, Medeiros D, Ramesh K, Roth S, Wyatt P. Transforming health systems through good digital health governance. Asian Development Bank; 2018. Contract No.: 51. https://doi.org/10.22617/WPS189244-2

Rouzbahani F, Rabiei R, Asadi F, Moghaddasi H, Emami H. Information governance program: a review of applications in healthcare. J Paramed Sci. 2019;10(1):47–55. https://doi.org/10.22037/jps.v9i1.16927 .

Rouzbahani F, Asadi F, Rabiei R, Moghaddasi H, Emami H. Developing a model for national health information governance program in Iran. J Med Life. 2020;13(4):510–6. https://doi.org/10.25122/jml-2020-0036 .

Holly L, Thom S, Elzemety M, Murage B, Mathieson K, Iñigo Petralanda MI. Strengthening health data governance: new equity and rights-based principles. Int J Health Governance. 2023;28(3):225–37. https://doi.org/10.1108/IJHG-11-2022-0104 .

Foy M, Martyn D, Daly D, Byrne A, Aguneche C, Brennan R. Blockchain-based governance models for COVID-19 digital health certificates: a legal, technical, ethical and security requirements analysis. Procedia Comput Sci. 2022;198:662–9. https://doi.org/10.1016/j.procs.2021.12.303 .

Bruhn J. Identifying useful approaches to the governance of indigenous data. Int Indigenous Policy J. 2014;5(2):1–32. https://doi.org/10.18584/iipj.2014.5.2.5 .

Willison DJ, Trowbridge J, Greiver M, Keshavjee K, Mumford D, Sullivan F. Participatory governance over research in an academic research network: the case of Diabetes Action Canada. BMJ Open. 2019;9(4):1–9. https://doi.org/10.1136/bmjopen-2018-026828 .

Were V, Moturi C. Toward a data governance model for the Kenya health professional regulatory authorities. TQM J. 2017. https://doi.org/10.1108/TQM-10-2016-0092 .

Laurie G, Sethi N. Towards principles-based approaches to governance of health-related research using personal data. Eur J Risk Regul. 2013;4(1):43–57. https://doi.org/10.1017/S1867299X00002786 .

Laurie G, Ainsworth J, Cunningham J, Dobbs C, Jones KH, Kalra D, et al. On moving targets and magic bullets: Can the UK lead the way with responsible data linkage for health research? Int J Med Inform. 2015;84(11):933–40. https://doi.org/10.1016/j.ijmedinf.2015.08.011 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Li Q, Lan L, Zeng N, You L, Yin J, Zhou X, Meng Q. A framework for big data governance to advance RHINs: a case study of China. IEEE Access. 2019;7:50330–8. https://doi.org/10.1109/ACCESS.2019.2910838 .

Rabiei R, Rouzbahani F, Asadi F, Moghaddasi H, Emami H, Rahimi F. Health information governance program: a review on components and principles. Crescent J Med Biol Sci. 2021;8(1):10–4.

Tumulak A, Tin J, Keshavjee K. Towards a unified framework for information and interoperability governance. In: The role of digital health policy and leadership. IOS Press; 2024. https://doi.org/10.3233/shti231310 .

Chapter   Google Scholar  

Paolino AR, McGlynn EA, Lieu T, Nelson AF, Prausnitz S, Horberg MA, et al. Building a governance strategy for CER: the patient outcomes research to advance learning (PORTAL) network experience. Generat Evid Methods Improve Patient Outcomes. 2016;4(2):17. https://doi.org/10.13063/2327-9214.1216 .

Nunn S. Driving compliance through data governance. J Am Health Inf Manag Assoc. 2009;80(3):50–1.

Ibragimova I, Korjonen MH. The value of librarians for clinical and health governance (a view from Europe). Int J Health Governance. 2019;24(1):66–88. https://doi.org/10.1108/IJHG-11-2018-0062 .

Faridoon A, Kechadi MT. Healthcare Data Governance, Privacy, and Security-A Conceptual Framework. arXiv preprint arXiv:240317648; 2024. https://doi.org/10.48550/arXiv.2403.17648

Renaud K. Clinical and information governance proposes; human fallibility disposes. Clin Governance. 2014;19(2):94–109. https://doi.org/10.1108/CGIJ-01-2014-0001 .

Datskovsky G, Hedges R, Empel S. Evaluating the Information governance principles for healthcare: accountability and transparency. J Ahima. 2015;86(2):52–3.

Datskovsky G, Hedges R, Empel S, Washington L. Evaluating the information governance principles for healthcare: integrity and protection. J Ahima. 2015;86(4):48–9.

Lillywhite TP. Implementing BS7799 in the UK National Health Service. Comput Fraud Secur. 2004;2004(2):4–8. https://doi.org/10.1016/S1361-3723(04)00027-2 .

Datskovsky G, Hedges R, Empel S, Washington L. Evaluating the information governance principles for healthcare: compliance and availability. J Ahima. 2015;86(6):54–5.

Ryan FS, Cedro MK, Pabari S, Davenport-Jones L, Noar JH. Clinicians’ knowledge and practice of data protection legislation and information management. Br Dent J. 2009;206(2):E4. https://doi.org/10.1038/sj.bdj.2009.53 . ( discussion 90-1 ).

Article   CAS   PubMed   Google Scholar  

Welle Donker F, van Loenen B. How to assess the success of the open data ecosystem? Int J Digital Earth. 2017;10(3):284–306. https://doi.org/10.1080/17538947.2016.1224938 .

Williams P. Information governance: a model for security in medical practice. J Dig For Secur Law. 2007;2(1):57–74.

Stahl BC, Rainey S, Harris E, Fothergill BT. The role of ethics in data governance of large neuro-ICT projects. J Am Med Inform Assoc. 2018;25(8):1099–107. https://doi.org/10.1093/jamia/ocy040 .

Tiffin N, George A, LeFevre AE. How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries. BMJ Glob Health. 2019;4(2): e001395. https://doi.org/10.1136/bmjgh-2019-001395 .

Datskovsky G, Hedges R, Empel S, Washington L. Evaluating the information governance principles for healthcare: retention and disposition. J Ahima. 2015;86(9):50–1.

Roch-Berry C. What is a Caldicott guardian? Postgrad Med J. 2003;79(935):516–8. https://doi.org/10.1136/pmj.79.935.516 .

Corporate Information Governance. Information governance policy; 2019. https://www.england.nhs.uk/wp-content/uploads/2019/10/information-governance-policy-v5.1.pdf

Baskaran V, Davis K, Bali RK, Naguib RN, Wickramasinghe N. Managing information and knowledge within maternity services: privacy and consent issues. Inform Health Soc Care. 2013;38(3):196–210. https://doi.org/10.3109/17538157.2012.735732 .

Shaw-Taylor Y. Making quality improvement programs more effective. Int J Health Care Qual Assur. 2014;27(4):264–70. https://doi.org/10.1108/IJHCQA-02-2013-0017 .

Haarbrandt B, Schreiweis B, Rey S, Sax U, Scheithauer S, Rienhoff O, et al. HiGHmed–an open platform approach to enhance care and research across institutional boundaries. Methods Inf Med. 2018;57(S01):e66–81.

PubMed   PubMed Central   Google Scholar  

Kloss LL. Leading innovation in enterprise information governance. J Am Health Inf Manag Assoc. 2013;84(9):34–8.

Download references

Acknowledgements

This work is part of a PhD thesis in Medical Library and Information Science supported and founded by the Iran University of Medical Science, Tehran, Iran. The ethical code is IR.IUMS.REC.1400.1158. We thank Iran University of Medical Science for supporting this research.

The present study is the funded by Iran University of Medical Sciences(IUMS) (IR.IUMS.REC.1400.1158).

Author information

Authors and affiliations.

Department of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

Somayeh Ghaffari Heshajin, Shahram Sedghi & Sirous Panahi

Department of Global Health & Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Amirhossein Takian

You can also search for this author in PubMed   Google Scholar

Contributions

S.G.H. performed several tasks, including conceptualization, methodology development, using software, formal analysis, investigation, resource allocation, original draft writing and data curation. S.S. undertakes various activities and roles, including supervision, project administration, funding acquisition and writing reviews. S.P. is involved in various activities, including writing – review and editing, as well as validation, conceptualization, data curation and methodology development. A.T. undertakes various activities and roles, including methodology development, conceptualization and writing – review and editing.

Corresponding author

Correspondence to Shahram Sedghi .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1, supplementary material 2, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ghaffari Heshajin, S., Sedghi, S., Panahi, S. et al. A framework for health information governance: a scoping review. Health Res Policy Sys 22 , 109 (2024). https://doi.org/10.1186/s12961-024-01193-9

Download citation

Received : 10 February 2024

Accepted : 20 July 2024

Published : 15 August 2024

DOI : https://doi.org/10.1186/s12961-024-01193-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health information governance
• Scoping review
• Healthcare industry
• Health information

Health Research Policy and Systems

ISSN: 1478-4505

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Scientific Computing
• Computer Science
• Computing in Mathematics, Natural Science, Engineering and Medicine
• Medical Informatics

Research in health information management: An expanding field of enquiry

• Health information management: journal of the Health Information Management Association of Australia 46(3):183335831771080
• 46(3):183335831771080

• The University of Sydney

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• BMC MED INFORM DECIS
• Byung Kwan Choi
• Young-Taek Park
• Hyeoun-Ae Park
• Sunghong Kang

• HEALTH INF MANAG J

• Razzaq Adetona Adio

• Mohammad Fararouei

• Deirdre Murphy

• Bronwyn Hemsley

• Ming-Ling Sher
• Paul C. Talley
• Tain-Junn Cheng

• Laura Newstead

• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

What is health information?

Health information is the data related to a person's medical history, including symptoms, diagnoses, procedures, and outcomes. A health record includes information such as: a patient's history, lab results, X-rays, clinical information, demographic information, and notes.

A patient's health information can be viewed individually to see how the patient's health has changed; it can also be viewed as a part of a larger data set to understand how a population's health has changed, and how medical interventions can change health outcomes.

But most importantly... Health Information is human information.

Health information is the patient's story. AHIMA-certified professionals hold an intimate relationship with health information. While our patients don't often see us, we see our patients in a way no other healthcare professional does. This perspective is critical to the success of all modern health organizations. We see the person connected to the data, ensuring their information stays human.Because when information stays human, it stays relevant.

What is Health Information Management?

Health information management (HIM) is the practice of acquiring, analyzing, and protecting digital and traditional medical information vital to providing quality patient care. It is a combination of business, science, and information technology.

What Does a Health Information Professional Do?

Health information (HI) professionals are highly trained in the latest information management technology applications. They understand the workflow process in healthcare provider organizations, from large hospital systems to private physician practices, and are vital to the daily operations management of health information and electronic health records (EHRs). They ensure a patient's health information is complete, accurate, and protected. Hl professionals have an extraordinary impact. They are the link between clinicians, administrators, technology designers, operations, and information technology professionals.

These professionals affect the quality of patient information and patient care at every touch point in the healthcare delivery cycle. HI professionals work on the classification of diseases and treatments to ensure they are standardized for clinical, financial, and legal uses in healthcare. HI professionals care for patients by caring for their medical data and are responsible for the quality, integrity, security, and protection of patients health information.

Visit the AHIMA Career Map to learn about Health Information job roles.

Why Choose a Career in Health Information?

Versatile education.

HIM students acquire a versatile yet focused skillset incorporating clinical, information technology, leadership, and management skills. Hl professionals use their knowledge of information technology and records management to form the link between clinicians, administrators, technology designers, and information technology professionals.

HIM programs incorporate the disciplines of medicine, management, finance, information technology, and law into one curriculum. Because of this unique mixture, graduates can choose from a variety of work settings across an array of healthcare environments.

Visit the CAHIIM Program Directory to view accredited Health Information Management Associate, Health Information Management Baccalaureate, Health Information Management Baccalaureate (Certificate of Degree) and Health Information Management Masters Degree Programs

Dynamic Career Opportunities

Constantly evolving regulations and technologies allow for lifelong learning and continued professional development. As healthcare advances, HI provides the patient data needed to successfully navigate the changes. As a result, HI professionals can expect to be in high demand as the health sector continues to expand. Demand is on the rise at all levels of education and credentialing, and the US Bureau of Labor Statistics (BLS) cites medical records and health information technicians as one of the fastest growing occupations in the US, with an anticipated growth of 11 percent between 2018-2028.

The median annual salary for medical records and health information technicians was $40,350 in May 2018.

Salaries rise for health information administrators. In 2019, the median salary was $100,980 per year for healthcare administrators and the 2028 outlook anticipates an 18 percent increase in jobs for these individuals possessing a baccalaureate or master's in health information management.

Industries with an increased demand for health information professionals include healthcare organizations, academic institutions, consulting agencies, government agencies, and healthcare software companies.

HI practitioners continue to be critical component of the electronic health record (EHR) workforce.

Visit the  AHIMA Career Assist career planning resources to begin preparing for your HI career.

A Health Information Career is Right for You if You:

Real HI Stories

Health information (HI) students prepare for a number of potential health information careers, and AHIMA members hold positions in more than 40 job categories and 200 job titles. Watch the videos below to hear from a student and recent graduates who are beginning their careers.

Health Information Administration Research Guide

• How to Use this Guide
• Research health information management topics
• Research medical/disease topics
• Introduction to Research in HIA / HIM
• Evaluate health/medical information
• Get Full Text
• Search Techniques
• HIM 150 activities
• HIM150 videos

Introduction

This page will lead you to sources for finding credible information on health information management., continue below for these four (4) sections:.

• Find in-depth information in monographs (books) and find non-print material such as videos, audios, software, and multimedia .
• Find current information in magazine and journal articles and reports.
• Find federal and state rules and regulations, statutory law, and case law .

Find definitions, facts, statistics

Find in-depth information in monographs (books) and find non-print material such as videos, audios, software, and multimedia, find books and e-books with the following research databases. .

The first research database will also lead to video and audio materials.

• Bookshelf Growing collection of biomedical books that can be searched directly by typing a concept into the search box. Much of the content is accessible online. Bookshelf is provided by the U.S. National Center for Biotechnology Information (NCBI). Searches of PubMed include the material in Bookshelf.

Find current information in magazine and journal articles and reports

Search for articles & reports using the following databases or websites..

SPECIAL NOTE: Not every health database will index the key journals for health information management. Your key professional journal Journal of AHIMA and other core professional journals (for example, Joint Commission Perspectives and Hospitals and Health Networks ) may be found by searching PubMed .

• Medline Plus This link opens in a new window MedlinePlus is an online health information resource for patients and their families and friends. It is a service of the National Library of Medicine (NLM), the world's largest medical library, which is part of the National Institutes of Health (NIH). PubMed citations and abstracts include the fields of biomedicine and health, covering portions of the life sciences, behavioral sciences, chemical sciences, and bioengineering. PubMed citations often include links to the full-text article on the publishers' Web sites and/or in PubMed Central and the Bookshelf. [MEDLINE is the largest subset of PubMed. You may limit your PubMed search retrieval to MEDLINE citations by restricting your search to the MeSH controlled vocabulary or by using the Journal Categories filter called MEDLINE.]
• AHIMA publications online Web site provides access to articles and reports in "Journal of AHIMA" (including practice briefs, coding notes, plugged in, etc.) and other AHIMA publications. NOTE: Explore the "HIM Trends and Topics" tab at top
• CMS (Centers for Medicare & Medicaid Services) Web site provides statistics, reports and regulatory news (including links to rules & proposed rules in Federal Register) on Medicare, Medicaid & Child Health Insurance Programs. CMS is the federal agency that administers the Medicare, Medicaid and Child Health Insurance Programs.For regulations for different types of facilities such as Ambulatory Surgical, Home Health Agencies, etc., link to "Regulations and Guidance" and scan down to find the section "Provider Type."
• Joint Commission on Accreditation of Healthcare Organizations The Joint Commission evaluates and accredits more than 18,000 health care organizations in the United States, including hospitals, health care networks, managed care organizations, and health care organizations that provide home care, long term care, behavioral health care, laboratory, and ambulatory care services. It publishes standards, etc.

Find federal and state rules and regulations, statutory law, and case law.

Find legal information  .

To successfully find legal information, you will need to select research databases and sources appropriate for finding the legal information you need.  This means choosing a resource that provides the correct:

1. category of law ( regulatory , judicial , or statutory )

• Regulatory law = rules and regulations developed by governmental agencies and departments in order for them to carry out the laws passed by legislatures.
• Judicial law = case law, that is laws arising from decisions made in courts.
• Statutory law = laws enacted by legislatures.

2. level of government ( federal or state )

Links to these legal research tools are further below :

For Federal REGULATIONS , use the following:

• Code of Federal Regulations
• Federal Register . 
• LexisNexis Academic includes the content of both the Code of Federal Regulations and the Federal Register.  Use the "search by content type" dropdown menu to select "Federal Statutes & Regulations."
• The Centers for Medicare and Medicaid Services (CMS) website provides links to health care regulatory information published in the Federal Register and elsewhere.  On the CMS website, choose "Regulations & Guidance."

For State REGULATIONS , use the following:

• LexisNexis Academic includes the content of South Dakota Administrative Rules and South Dakota Register .  In LexisNexis use the "search by content type" dropdown menu to select "State Statutes & Regulations."
• Legislative Research Council of the South Dakota Legislature.  For rules and regulations, choose "Administrative Rules" and find rules using either the "Rules Search" or "Rules List."

For Federal CASE LAW , use:

• LexisNexis Academic . Use the "search by content type" dropdown menu to select "Federal & State Cases"

For State CASE LAW , use:

For Federal STATUTORY LAW , use: 

• LexisNexis Academic . Use the "search by content type" dropdown menu to select "Federal Statutes & Regulations." {Note: statutory laws may be found as "Public Laws" (that is, found by public law number which is a consecutive numbering system of laws during a given legislative session) or in the "U.S. Code" (that is, found in the codified laws in which laws related to a particular topic are grouped together}.

For State STATUTORY LAW , use:

• LexisNexis Academic . Use the "search by content type" dropdown menu to select "State Statutes & Regulations."
• Legislative Research Council of the South Dakota Legislature.  For statutory law, choose "Laws."
• Code of Federal Regulations (CFR) The CFR codifies (arranges numerically) the detailed rules and regulations used for carrying out the laws passed by the U. S. Congress. These regulations are devised by federal departments and agencies. So, the CFR is a comprehensive collection of all the rules and regulations in effect in the various agencies of the U.S. Government. Rules first appear in the daily publication called the Federal Register. After being finalized, they are incorporated into the CFR. Therefore, after searching the Code of Federal Regulations (CFR), search the Federal Register for any new or changed federal rules that have not yet been added to the CFR.
• Federal Register (FR) Use the Federal Register (FR) to find new federal regulations that have been proposed or that have been published in final form in the Federal Register but have not yet been added to the Code of Federal Regulations. Rules and regulations are devised by federal departments and agencies to carry out the laws passed by the U.S. Congress. FR provides the text of proposed, final, interim, and temporary rules and is published daily, Monday - Friday. Provided by the U.S. Government Printing Office.
• Legislative Research Council (South Dakota) The Legislative Research Council of the South Dakota Legislature provides administrative rules and statutory laws. For rules and regulations, choose "Administrative Rules" and find rules using either the "Rules Search" or "Rules List." For statutory law, choose "Laws."

Statistics and Other Information about Hospitals and Physicians

The following sources provide a range of factual information and statistics about hospitals and physicians

• Agency for Healthcare Research and Quality (AHRQ) Has a "Research Tools & Data" section that includes MEPS (Medical Expenditure Panel Survey), HCUP (Healthcare Cost & Utilization Project), HCUPnet ( Interactive Tool for Hospital Statistics), HIV & AIDS Costs & Use, and more. HCUPnet is a tool for identifying, tracking, analyzing, and comparing statistics on hospitals at the national, regional, and State level. AHRQ is part of the U.S. Department of Health and Human Services.
• AMA Doctor Finder This directory contains information on virtually every licensed physician in the United States (and its possessions), including more than 650,000 doctors of medicine (MD) and doctors of osteopathy or osteopathic medicine (DO). All physician credential data have been verified for accuracy and authenticated by accrediting agencies, medical schools, residency training programs, licensing and certifying boards, and other data sources.
• American Board of Medical Specialties This directory may be used to verify the certification status of any physician certified by one or more of the 24 member boards of the American Board of Medical Specialties (ABMS). Click on "Is Your Doctor Certified."
• HRET: Health Research & Educational Trust Provides information about hospitals, health care agencies, quality/cost/disparities, data analysis, payment reform, and much more.
• National Center for Health Statistics (NCHS) NCHS is the primary Federal organization responsible for the collection, analyses, and dissemination of health statistics. The intent of this site is to provide users access to the health information that NCHS produces.

PRINT SOURCES IN LIBRARY:

• AHA Hospital Statistics Call Number: RA981.A2 A6234 Publication Date: annual This is a compilation of data compiled from AHA Annual Survey of Hospitals and is a print book in the Mundt Library. Note: Distance students may request the Library to copy and email specific sections.
• << Previous: How to Use this Guide
• Next: Research medical/disease topics >>
• Last Updated: Apr 10, 2024 2:49 PM
• URL: https://library.dsu.edu/him

• Weill Cornell Medicine

MS in Health Informatics

Transforming healthcare through data and information technology.

Our MS in Health Informatics program is focused on the application of information technology , social and behavioral science, and data science in healthcare delivery. We study, develop, and improve health care information technologies. To apply these information technologies effectively, we also study human and organizational behavior.  

Core Training

Our core curriculum covers three domains:

• Information technology and data science:   Students study research and visualization methods, artificial intelligence , data management, and informatics standards and technology infrastructure.
• Health and health care:   Students learn about domestic and global healthcare, with the opportunity to immerse themselves in the daily life of a hospital .
• Human and organizational behavior:   Students cover human factors, human-computer interaction, and diffusion of innovation to learn how to position information systems for success.

Our program offers innovative key informatics skills blended with healthcare system knowledge. Students study state of the art topics in health informatics such as artificial intelligence , natural language processing, data management, and consumer informatics, culminating in a hands-on capstone project with clients from our industry partners worldwide .  

Collaboration is at the core of our program, with students and faculty from a range of fields, working with many collaborating NYC (New York City) institutes and around the world. This diversity creates a unique learning environment. To get a sense of our culture, please take a look at ours Admissions Information .  

Our alumni hold positions in data and policy analysis, health information technology, process improvement, consulting, and more at healthcare institutions and startups. Many alumni pursue advanced doctoral studies.  

Students can complete the health informatics curriculum in 12 months , we have an option for part-time students as well .  

Research Projects

Prerequisites for Admission

Information sessions, alumni outcomes, program director.

Jose Florez-Arango, MD, PhD, MS

HI 1 Year Student - Recommended Curriculum Progression

Students are recommended to follow the schedule below in order to ensure eligibility for graduation. The Education Team will monitor progression, but it is ultimately the student’s responsibility to track their progression to ensure they meet graduation requirements. Course offerings and course availability are subject to change. Health Informatics students must take 27 credits of the required courses, and 9 credits of electives (optional courses).

Note that each student must take a statistics placement test prior to the fall term beginning to determine whether the student is waived from taking Intro to Biostatistics with STATA Lab (HBDS 5001). If the student does not pass the statistics placement test, they must enroll in HBDS 5001 in the fall term.

Students take 12 required credits, with the option of 1 or 2 electives

Statistics Placement Test - Optional

Introduction to health informatics (hinf 5001) - required.

Course Director: Marianne Sharko MD, MS 3 credits

Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.

Research Methods in Health Informatics (HINF 5004) - Required

Course Director: Yunyu Xiao, PhD 3 credits

Informatics innovations have their desired impact only when they have high quality, are highly usable, are integrated into their organizational setting, and are widely adopted and used. That makes it critical for informatics students to understand not only how informatics innovations work, but also the users and settings in which they are used. Students will learn methods and models for: measuring data and system quality; assessing and predicting technology adoption (what makes technology sticky?); improving humancomputer interaction via human factors engineering; understanding organizational and systemic challenges in the real world; influencing patients’ health behavior and decisions; and assessing quality, safety, and cost outcomes using health services research study designs. In this mixed methods course, students will gain experience using both quantitative and qualitative methods.

Artificial Intelligence in Medicine I (HINF 5012) - Required

Course Director: Fei Wang, PhD 3 credits

Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.

Master’s Project I and Professional Development (HCPR 6010) - Required

Course Director: Faculty 2 credit

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of 12 the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Clinical Medicine for Informaticians (HINF 5024) - Elective

Course Director: Mark Weiner, MD 3 credits

In addition to technical, programming and analytical skills, healthcare informaticians and data scientists need clinical domain expertise to understand and interpret real world data and analytical findings and to communicate effectively with healthcare practitioners and investigators.  This course is designed to equip informaticians with a foundational understanding of key concepts in clinical medicine, especially as they relate to the collection, application and interpretation of real world data toward clinical phenotypes and predictive analytics.   Students will learn the fundamentals of the cardiovascular, gastrointestinal, respiratory, hematological, endocrine, neurological, musculoskeletal, psychiatric, and renal systems and how diseases in these body systems are reflected in subjective and objective measures collected through patient reports, clinical observations, laboratory tests and ancillary studies.  Students will understand the clinicians approach to ordering tests to evaluate for the presence of disease.  They will also learn about the variety and classification of pharmacological therapies, the context and rationale for starting and stopping medications, and their intended and unintended effects on body systems.  Students will also learn how the physical and social environment in which patients live may impact the recognition and severity of illness, as well as  the timing, approach and outcomes of care. Students will be introduced to differentiated care in the management of different patient specialties, including pediatrics and geriatrics.

Healthcare Organization and Delivery (HPEC 5002) - Elective

Course Director: Lisa Kern MD, MPH 3 credits

The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will not summarize healthcare; rather, they will analyze healthcare – through themes such as people, time, money, communication, uncertainty, and others. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.

Introduction to Biostatistics with STATA Lab (HBDS 5001)* - Elective w/ Placement Test

Introduction to Biostatistics with STATA Lab Course Director: Arindam RoyChoudhury, PhD 4 credits

An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will be accompanied with a Stata lab to explore, visualize and perform statistical analysis with data. Lectures and discussions will focus on the following: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; the development of statistical methods for analyzing data; and development of mathematical models used to relate a response variable to explanatory or descriptive variables.

Biostatistics I with R Lab Course Director: Xi Kathy Zhou, PhD 4 credits

This course provides an introduction to important topics in biostatistical concepts and reasoning. Specific topics include tools for describing central tendency and variability in data, probability distributions, sampling distributions, estimation, and hypothesis testing. Assignments will involve computation using the R programming language.

Spring Term 

Clinical informatics (hinf 5011) - required.

Course Director: Sameer Malhotra, M.B.B.S., M.A. 3 credits

Prerequisite: Introduction to Health Informatics Clinical information systems such as electronic health records are central to modern healthcare. This course introduces students to the complex infrastructure of clinical information systems, technologies used to improve healthcare quality and safety (including clinical decision support and electronic ordering), and policies surrounding health information technology.

Health Data Management (HINF 5018) - Required

Course Director: Yiye Zhang, PhD 3 credits

Database systems are central to most organizations’ information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems – understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications – is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that are most often used in healthcare organizations and is the primary focus of this course. An overview of the non-relational database structure will also be given using Python programming language to provide a fuller picture of the current data management landscape. Further, to provide students with opportunities to apply the knowledge they learn from the lectures, various homework assignments, lab assignments, an exam, and a database implementation project will be given.

Health Information Standards & Interoperability (HINF 5020) - Required

Course Director: Jyoti Pathak, PhD 3 credits

In modern healthcare. exchange of clinical data across multiple stakeholders — between healthcare organizations, between providers and patients, and among agencies and governmental entities — is pivotal. Health information standards provide the “backbone” to achieve uniform data interoperability and exchange across multiple heterogeneous systems. This course will introduce existing and emerging clinical data modeling, terminology and knowledge representation standards.

Master’s Project II (HCPR 6020) - Required

Course Director: Faculty 3 credits

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation.

Artificial Intelligence in Medicine II (HINF 5025) - Elective

Prerequisite: Artificial Intelligence in Medicine I This class will teach students more advanced topics on AI in medicine. It requires students to have taken the AI in medicine I class. The contents of the class cover generalizability of AI models, computational fairness, model interpretation and explanation, privacy and security, federated learning, multi-modal learning, generative AI, causal inference, target trial emulation. The students will be asked to do a final project with teams based on the contents taught in the class, and python programming will be needed for doing the project.

Natural Language Processing (HINF 5016) - Elective

This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.

Summer Term

Students take 3 required credits, with the option of 1 or 2 electives

Master’s Project III (HCPR 6030) - Required

This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.

Health Behavior and Consumer Informatics (HINF 5017) - Elective

Consumer health informatics (CHI) is the study of consumer information needs and technologies that provide consumers with the information they need to be more engaged in self-care and healthcare. This introductory CHI course will present an overview of theories of health and information behavior; key concepts and terminology; and main application domains. We will explore how health behavior theories 8 provide a framework for explaining consumers’ health behaviors and how CHI tools that are built with a theoretical foundation can promote health behavior change. The course will cover CHI applications in major application domains including electronic patient portals, mobile health (mHealth), and telehealth. Students will learn how to assess end-user needs and technological practices of potential users who experience health information and technological disparities. Students will also learn how to design for endusers, evaluate CHI applications and research.

Implementation Science and AI Ethics (HINF 5023) - Elective

Course Director:  Marianne Sharko MD, MS 3 credits

This course will provide an overview of implementation science and introduce issues surrounding ethics in the use of artificial intelligence (AI) in healthcare. It will explore the challenges in the safe and effective implementation of predictive models, large language models and generative AI in healthcare. It will identify ethical issues surrounding the use of AI in healthcare through the lens of the medical ethical principles of autonomy, beneficence, nonmaleficence and justice and will provide a framework for evaluating the ethics of AI generated tools from the perspective of multiple stakeholders, including patients, providers, health systems and payors. Students will examine predictive models created to assist in healthcare management, understand the challenges in their effective and appropriate implementation, and appreciate the potential for unintended consequences and safety risks. We will explore the need to develop clinical decision support tools that are guided by the principles of fairness, appropriateness, validity, effectiveness, and safety (FAVES). We will discuss the importance of informaticists and providers as advocates for seeking transparency in predictive algorithms, and utilizing measures of reliability, validity, and effectiveness in their outcomes. We will address the importance of advocating for equity in accessibility and the need to address bias in the development of AI-generated clinical decision tools.

We will introduce implementation science, frameworks and theories, including Diffusion of Innovation, RE-AIM and PRISM. We will include projects to provide practical experience in the process of implementation that will highlight research methods, measures, and potential barriers and facilitators. We will invite experts in the field to provide guest lectures and to lead student workshops within their areas of expertise. The learning style is mostly student-driven, using “flipped classroom”, participatory exercises, teamwork, workshops and presentations. After the midterm, students will work to define a project, analyze related literature, and give a presentation in the final week.

PHS Internship Course (HCPR 5040) - Elective

1-3 credits

*Contingent upon results of the statistics placement test

Admissions Information

Academic Calendar

About Our Faculty

Request More Information

Population Health Sciences 402 E. 67th St. New York, NY 10065 Phone: (646) 962-8001

Student Spotlights from this Program

In 2020, Taylor Dang began medical school with plans to become a naturopathic physician. She was passionate about healthcare and was actively involved in political advocacy centered around health justice. However, as one of few students of color in her cohort, Taylor felt isolated at medical school. She made the call to withdraw and identify another path in healthcare. Taylor is now a graduate of the  MS in Health Informatics   program  at Weill Co rnell Medicine (WC M ) , where she has found a way to pursue advocacy through the lens of healthcare systems and information.     

Dayoung Kim became interested in artificial intelligence (AI) while completing her undergraduate degree in computer science at the EWHA Womans University in South Korea.  She worked in a bioinformatics and natural language processing (NLP) lab there, which shaped her decision to pursue higher education and further her knowledge of AI. She is now an alumna of the  MS in Health Informatics program at Weill Cornell Medicine (WCM )  and  works as a machine learning engineer at Boeing.   

Maya discovered the  MS in Health Informatics  program while in Austria, where she taught English at a secondary school for two years. While there, she volunteered to tutor Syrian refugees in English, and at welcome centers to help Ukrainian refugees who fled the country when the war began. The experiences were jarring but propelled her to want to do more for others on a global scale.    

View all Student Spotlights

Health Information Management Research Guide

• Find Articles
• Find Organizations
• Find Statistics

Web Resources for HIM Studies

Health information websites, evaluating health information on the web.

• Citing, Writing, & Research Tips

Presented here are assorted HIM-related web resources you can explore according to interest.  Note that these resources are categorized using the Baccalaureate Level HIM Curriculum Domains developed by the AHIMA Foundation's Council for Excellence in Education.

Listed below are selected health information websites that can be helpful to browse when you are looking for general and consumer-oriented medical information.  Most of these sites are comprehensive "gateway" portals that provide access to a broad range of resources .

Health Information Portals:

• Centers for Disease Control and Prevention
• See also: NIH Directory of 27 Research Institutes and Centers
• National Library of Medicine (NLM): Home Page
• National Library of Medicine (NLM): MedlinePlus (Patient/consumer-focused portal)
• The Cleveland Clinic Disease Management Project — Online Medical Guide
• The Mayo Clinic Patient Care and Health Information Portal
• The Merck Manuals Online (Professional and consumer versions)
• Top Health Websites for Consumers and Patients (Medical Library Association selections)
• MedicineNet.com (for consumers)
• Medscape.com (for healthcare professionals and consumers)
• WebMD.com (for consumers)

Drug Information:

• Drug Information from the National Library of Medicine
• Drugs, Herbs and Supplements (NLM MedlinePlus consumer gateway)

Patient Safety:

• AHRQ Patient Safety Network
• National Patient Safety Foundation

Tests/Lab Work:

• Lab Tests Online (from American Association for Clinical Chemistry)
• ClinicalTrials.gov

Evaluating Health Information:  Using Trusted Resources (National Cancer Institute)

KEY Evaluation Questions to Ask

• WHO is the author and/or sponsoring organization?  Are they reputable?
• WHAT qualifications or expertise does the author have?
• WHY was the website created?  What is its purpose and who is the intended audience?
• WHEN was the information last updated? Is it current?
• WHERE can you verify the information presented? Are references provided?
• HOW reliable is the information?  Is it trustworthy?

COVID-19 and Misinformation

• List of Covid-19 Misinformation Resources
• << Previous: Find Statistics
• Next: Citing, Writing, & Research Tips >>
• Last Updated: Feb 3, 2024 2:34 PM
• URL: https://sdmesa.libguides.com/him

This paper is in the following e-collection/theme issue:

Published on 13.8.2024 in Vol 26 (2024)

Effect of a Mobile Health–Based Remote Interaction Management Intervention on the Quality of Life and Self-Management Behavior of Patients With Low Anterior Resection Syndrome: Randomized Controlled Trial

Authors of this article:

Original Paper

• Peng Zhou 1, 2 * , MNS   ; 
• Hui Li 3 * , MSN   ; 
• Xueying Pang 4 * , MSN   ; 
• Ting Wang 4 , MSN   ; 
• Yan Wang 2 , MSN   ; 
• Hongye He 4 , MSN   ; 
• Dongmei Zhuang 2 , MSN   ; 
• Furong Zhu 2 , MNS   ; 
• Rui Zhu 1 , MSN   ; 
• Shaohua Hu 1 , PhD  

1 Department of Nursing, the First Affiliated Hospital of Anhui Medical University, Hefei, China

2 School of Nursing, Anhui Medical University, Hefei, China

3 College of Traditional Chinese Medicine, Bozhou University, Bozhou, China

4 Department of Gastrointestinal Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei, China

*these authors contributed equally

Corresponding Author:

Shaohua Hu, PhD

Department of Nursing

the First Affiliated Hospital of Anhui Medical University

218 Jixi Road

Hefei, 230009

Phone: 86 62922005

Email: [email protected]

Background: People who undergo sphincter-preserving surgery have high rates of anorectal functional disturbances, known as low anterior resection syndrome (LARS). LARS negatively affects patients’ quality of life (QoL) and increases their need for self-management behaviors. Therefore, approaches to enhance self-management behavior and QoL are vital.

Objective: This study aims to assess the effectiveness of a remote digital management intervention designed to enhance the QoL and self-management behavior of patients with LARS.

Methods: From July 2022 to May 2023, we conducted a single-blinded randomized controlled trial and recruited 120 patients with LARS in a tertiary hospital in Hefei, China. All patients were randomly assigned to the intervention group (using the “e-bowel safety” applet and monthly motivational interviewing) or the control group (usual care and an information booklet). Our team provided a 3-month intervention and followed up with all patients for an additional 3 months. The primary outcome was patient QoL measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30. The secondary outcomes were evaluated using the Bowel Symptoms Self-Management Behaviors Questionnaire, LARS score, and Perceived Social Support Scale. Data collection occurred at study enrollment, the end of the 3-month intervention, and the 3-month follow-up. Generalized estimating equations were used to analyze changes in all outcome variables.

Results: In the end, 111 patients completed the study. In the intervention group, 5 patients withdrew; 4 patients withdrew in the control group. Patients in the intervention group had significantly larger improvements in the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 total score (mean difference 11.51; 95% CI 10.68-12.35; Cohen d =1.73) and Bowel Symptoms Self-Management Behaviors Questionnaire total score (mean difference 8.80; 95% CI 8.28-9.32; Cohen d =1.94) than those in the control group. This improvement effect remained stable at 3-month follow-up (mean difference 14.47; 95% CI 13.65-15.30; Cohen d =1.58 and mean difference 8.85; 95% CI 8.25-9.42; Cohen d =2.23). The LARS score total score had significantly larger decreases after intervention (mean difference –3.28; 95% CI –4.03 to –2.54; Cohen d =–0.39) and at 3-month follow-up (mean difference –6.69; 95% CI –7.45 to –5.93; Cohen d =–0.69). The Perceived Social Support Scale total score had significantly larger improvements after intervention (mean difference 0.47; 95% CI 0.22-0.71; Cohen d =1.81).

Conclusions: Our preliminary findings suggest that the mobile health–based remote interaction management intervention significantly enhanced the self-management behaviors and QoL of patients with LARS, and the effect was sustained. Mobile health–based remote interventions become an effective method to improve health outcomes for many patients with LARS.

Trial Registration: Chinese Clinical Trial Registry ChiCTR2200061317; https://tinyurl.com/tmmvpq3

Introduction

The Global Cancer Statistics 2020 showed that colorectal cancer ranks third in incidence of malignant tumors and second in cause of death worldwide [ 1 ]. Colorectal cancer incidence is also on the rise in China, with rectal cancer accounting for 60% of cases and middle and lower rectal cancers being the most common [ 2 ]. With the advancement of medical technology, optimal management of middle and lower rectal cancers increasingly favors sphincter-preserving surgery (SPS) [ 3 ]. This operation preserves anal function and avoids the inconvenience and pressure caused by permanent colostomy [ 4 ]. However, 70%-90% of patients after SPS struggle with long-term anorectal functional disturbances called low anterior resection syndrome (LARS) [ 5 , 6 ].

The presence of LARS has a severe adverse effect on the quality of life (QoL) of patients [ 7 ]. Postoperative LARS induces a spectrum of adverse physical and psychological effects in patients; for example, up to 50% of patients with LARS report toilet dependence during rehabilitation [ 8 , 9 ], 36% of patients experience pain, and approximately 13% of patients report high psychological distress [ 10 , 11 ]. Furthermore, LARS can restrict a patient’s social life, leading to further impact on their QoL [ 12 ]. Recently, longitudinal studies have found that patients’ QoL is still affected by LARS even 15 years after surgery [ 13 ]. Research has shown that patients can improve their QoL through methods, such as pelvic floor muscle exercises and dietary adjustments during home care; however, the effectiveness of these methods is limited by patients’ lack of knowledge of LARS and rehabilitation guidance [ 14 , 15 ].

Owing to the frequent occurrence of LARS in patients post discharge, patients must have a high level of self-management behavior [ 16 ]. However, in China, the majority of patients have a passive response to LARS, and their self-management behavior is at a low level [ 17 ]. Enhancing self-management awareness and providing information on supportive care can improve the self-management behavior of patients with LARS [ 18 ]. Research has demonstrated that motivational interviewing (MI) enhances self-management awareness and supports behavioral change [ 19 ].

Therefore, to improve patients’ QoL and self-management behaviors, providing supportive care information to patients is crucial. A qualitative exploration of patients with LARS’s perspectives on information needs revealed that timely symptom management measures are critical during home-based rehabilitation [ 20 ]. However, it is difficult to maintain continuity and instantaneity with existing management measures [ 21 , 22 ]. Owing to current advances in mobile technology, mobile health (mHealth) has been widely considered a means of patient health management, which can improve the effects of symptoms and assist patients in timely access to the required information [ 23 , 24 ].

To date, remote follow-up tools for patients with LARS have yielded promising results [ 25 ]. For patients with LARS, mHealth-based remote interventions may become an effective method to assist them in improving symptoms. However, mHealth intervention measures constructed for patients with LARS are rare. Most studies have only completed the development and pilot research of remote intervention programs, leading to insufficient data on the effectiveness of remote interventions in improving patient health outcomes [ 26 , 27 ]. WeChat (Tencent Corp) is China’s most frequently used instant messaging and social media application [ 28 ]. Evidence suggests that WeChat-based mHealth interventions effectively improve health outcomes in various health conditions [ 29 , 30 ].

This study aimed to assess the effectiveness of a remote digital management intervention designed for patients with LARS. The effectiveness of the intervention measure is determined by improvement in QoL, self-management behaviors, gastrointestinal symptoms, and social support. We hypothesized that the remote digital management intervention can effectively improve the health outcomes of patients with LARS.

Study Design

This study was conducted from July 15, 2022, to March 15, 2023, in Hefei, China. Our team provided a 3-month intervention and followed up with all patients for an additional 3 months. The intervention group used the “e-bowel safety” applet and received monthly MI. The control group received the usual care and was provided with a handbook containing information related to LARS. The CONSORT (Consolidated Standards of Reporting Trials) checklist is in Multimedia Appendix 1 .

Ethical Considerations

This randomized controlled trial (RCT) was approved by the ethics committee of the First Affiliated Hospital of Anhui Medical University (PJ2022-07-53) and registered on the Chinese Clinical Trial Registry (ChiCTR2200061317). All data were identified with a code number to ensure the confidentiality of the subjects’ data. No compensation was provided to participants.

Participants

The patients were recruited from a tertiary hospital in Hefei, Anhui Province, China. Patients were eligible to participate in our study if they met the following criteria: age older than 18 years, a diagnosis of rectal cancer, underwent SPS, LARS scores ≥21, ostomy closure surgery performed at least 3 months prior, the ability to read and write text, and proficiency in using WeChat. Patients with chronic gastrointestinal conditions, prior or current mental health disorders, cognitive impairments, communication disorders, or those who have participated in other clinical studies are ineligible for participation in this research. When patients meeting the recruitment criteria appeared in the hospital database, the system sent recruitment information to these patients with the approval of doctors not directly involved in the research design.

In this study, the sample size was determined based on the QoL. Previous research has shown that the QoL for patients with rectal cancer is 77±19 [ 31 ]. In an RCT using the EORTC QLQ-C30, a difference of 10 points is considered clinically significant [ 32 ]. With a two-sided test level of 0.05 and 80% test efficacy, each group requires a sample size of 45. Accounting for a 20% dropout rate, 112 patients are needed.

Intervention

Our previous study provided a comprehensive description of the intervention protocol [ 33 ]. The patients in the intervention group used the “e-bowel safety” applet for 3 months. They were required to check in on the applet daily and record their daily gastrointestinal symptoms. Our “e-bowel safety” applet comprises 4 main sections: a rehabilitation plan, LARS knowledge, web-based consultation, and patient stories. The rehabilitation plan module involves the collaborative development of home dietary and exercise plans by patients and researchers. The applet features intelligent reminders to monitor daily plan completion and provide prompts. After completing the rehabilitation plan, patients must fill out a daily health diary, and researchers dynamically adjust the rehabilitation plan based on patients’ feedback and physical condition. The LARS knowledge module offers evidence-based information on LARS and symptom management strategies. The web-based consultation module provides patients with an opportunity to interact with health care professionals, offering personalized guidance and feedback. The patient stories module allows patients to share symptom management experiences or engage with other patients, with all published content subject to researcher approval. Additionally, an incentive system has been designed to encourage participation. For instance, patients earn points by sharing personal stories or comments, which can later be exchanged for rewards after accumulating a certain number of points.

Moreover, our team members conducted monthly MIs with patients. MIs were led by 4 researchers with expertise in health coaching and disease management, including 1 clinical psychologist (Shangxin Zhang) and 3 registered nurses (TW, HH, and Ling Fang). The researchers engage with patients via WeChat for 30-60 minutes per call. The aim of MIs is to assist patients in setting rehabilitation goals, reinforcing self-management awareness, and promoting health behavior changes. The content of MIs is based on the interview guide determined by the research team, which guides the conversation from the initial session to explore the participant’s motivation to identify the facilitating factors and barriers to achieving their health goals. The interview guide is outlined in Multimedia Appendix 2 .

Patients in the control group received the usual care and were provided with a handbook containing information related to LARS. At the same time, our team members followed up with patients, using the same timing and frequency as the MI intervention group.

Randomization and Masking

This study was a single-blind, two-arm RCT. After obtaining consent from eligible patients, assistants who were not involved in the study randomly assigned them to the intervention and control groups at a 1:1 ratio. The randomization process was performed by the assistants and anonymized envelopes were used with block randomization, including block sizes randomly varying between 4 (2:2) and 6 (3:3). The research assistants (Ping Ni and Ai Wang) who collected the data were unaware of the patient assignments throughout the study. Patients used the QR codes provided by the research team to access the “e-bowel safety” applet, effectively reducing contamination between the 2 groups. Patients were blinded to their group assignments throughout the entire research process.

Quality Control and Participant Retention

Several strategies were used to ensure quality control and participant retention. Our “e-bowel safety” applet can monitor patients’ plan execution and provide reminders, which ensures the daily plans are followed strictly by patients. Before the formal intervention, we conducted a pilot experiment and gathered participant feedback to enhance our plan. The specific results are included in Multimedia Appendix 3 . Furthermore, patients received consistent guidance from our research assistants (Ping Ni and Ai Wang) when they had questions about the questionnaire content. Before the start of the study, all research assistants must undergo training and assessment on the use of all questionnaires by research team members. Only research assistants who pass the assessment can participate in data collection. Additionally, team members regularly check the progress of research assistants’ work to ensure that they are following the questionnaire collection process, identifying issues promptly, and making corrections.

Outcome Measures

The patients’ demographic and clinical information were obtained from the hospital database. Data were collected from patients using scales for their QoL, social support, self-management behaviors, and LARS scores at different time periods (0, 3, and 6 months). The research assistants (Ping Ni and Ai Wang) who collected the data assisted patients in completing questionnaires over the phone or through direct personal interaction.

Primary Outcome: QoL

The EORTC-QLQ-C30 (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30) was used to measure QoL. This questionnaire comprises 30 items divided into 15 dimensions, including 1 dimension for QoL, 5 dimensions for functionality, 3 dimensions for symptoms, and 6 dimensions for additional symptoms. All dimension scores were linearly transformed to a scale of 0-100 points. Elevated scores on the 5 functionality dimensions and the QoL dimension were linked to improved functional status, whereas the reverse pattern was observed for the symptom dimensions and additional symptom dimensions. The Cronbach α coefficient ranged from 0.764 to 0.809 [ 34 ].

Secondary Outcome

Self-management.

The self-management behavior of patients was assessed by the Bowel Symptoms Self-Management Behaviors Questionnaire (BSSBQ). This questionnaire comprises 24 items divided into 5 functional scales, with each item scored on a scale of 0 (never) to 7 (always). Higher scores indicate better bowel symptom self-management behavior. The Cronbach α coefficient was 0.81 [ 17 ].

Bowel Function

The LARS score consists of 5 items, with a total score ranging from 0 to 42. Patients’ gastrointestinal symptoms are classified into no LARS, minor LARS, and major LARS based on the total score. The LARS score is a validated instrument for assessing bowel symptoms. The Cronbach α coefficient was 0.767 [ 35 ].

Social Support

The Perceived Social Support Scale (PSSS) consists of 12 items, with each item scored on a scale of 1 (extreme disagreement) to 7 (strong consent). The total scores ranged from 12 to 84. The higher the score, the stronger the perceived social support by the patient. This scale is widely used to assess the level of social support among patients in China. The Cronbach α coefficient of this scale was 0.899 [ 36 ].

Feasibility

The feasibility of intervention was assessed through the completion status of MI sessions and the adherence to health diary entries. The 3-month intervention corresponds to 3 MI sessions and 84 days of health diary entries.

Statistical Methods

All data were analyzed using SPSS Statistics (version 23.0; IBM Corp). An intention-to-treat analysis was performed in this study. We used the last observed values of the patients to replace missing data. Chi-square analysis was used to analyze the remaining demographic characteristics, and a 2-tailed independent sample t test was used to analyze the age and tumor height. Descriptive data were computed, including means with SD, medians with ranges, and frequencies with proportions where appropriate. The statistical significance was established at P <.05 (2-tailed test). Generalized estimating equations were used to analyze changes in QoL, self-management behaviors, LARS, and social support scores at different time points. The calculation of effect sizes was performed using Cohen d for the mean differences at various time periods.

Participant Characteristics

Initially, 60 patients were recruited in the control and intervention groups. During the study, 9 patients dropped out (dropout rate 7.5%). In the intervention group, 5 patients withdrew from the study, including 2 patients who received a reostomy because of an anastomotic fistula and 3 patients whose condition worsened. In the control group, 4 patients dropped out, including 2 patients whose condition worsened and 2 patients who refused to continue the intervention because of the side effects of chemotherapy. No statistically significant differences were observed between the patients who dropped out and those who completed all evaluations ( P =.17). Figure 1 shows the CONSORT flowchart of this study. Table 1 demonstrates no statistically significant differences in the demographics and clinical information between the control and intervention groups at baseline.

a LAR: low anterior resection.

b TaTME: transanal total mesorectal excision.

c EORTC-QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

d BSSBQ: Bowel Symptoms Self-Management Behaviors Questionnaire.

e LARS: Low anterior resection syndrome.

f PSSS: Perceived Social Support Scale.

Main Evaluation Indexes

Table 2 shows that the patients’ QoL improved for both groups. Patients in the intervention group demonstrated greater improvements in the EORTC-QLQ-C30 total score than those in the control group after intervention (mean difference 11.51; 95% CI 10.68-12.35; Cohen d =1.73). Furthermore, this improvement effect remained stable at 3-month follow-up (mean difference 14.47; 95% CI 13.65-15.30; Cohen d =1.58). Table 3 shows that the EORTC-QLQ-C30 total score in both groups exhibited a trend of change over the 6-month period ( P <.001). Differences were observed between the 2 groups and the interaction between group and time. A subgroup analysis was conducted on patients receiving preoperative chemotherapy versus postoperative chemotherapy. Among the 49 patients in the intervention group and 51 in the control group undergoing postoperative chemotherapy, a nominally significant improvement in the change from baseline in the EORTC-QLQ-C30 total score at 3 months was observed compared to the control group (difference of 4.42; P <.001). However, this effect was not seen in patients receiving preoperative chemotherapy. The specific results are included in Multimedia Appendix 4 .

a GEE: Generalized estimating equations.

b Difference in mean change from baseline to endpoint between the groups.

d Baseline.

e N/A: Not applicable.

f After the intervention.

g 3-month follow-up.

h BSSBQ: Bowel Symptoms Self-Management Behavior Questionnaire.

i LARS: Low anterior resection syndrome score.

j PSSS: Perceived Social Support Scale.

a EORTC-QLQ-C30: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30.

b BSSBQ: Bowel Symptoms Self-Management Behaviors Questionnaire.

c LARS: Low anterior resection syndrome.

d PSSS: Perceived Social Support Scale.

Secondary Evaluation Indexes

Table 2 shows that the patients’ self-management behavior was enhanced for both groups. The BSSBQ total score had significantly larger improvements after intervention (mean difference 8.80; 95% CI 8.28-9.32; Cohen d =1.94) and at 3-month follow-up (mean difference 8.85; 95% CI 8.25-9.42; Cohen d =2.23) between groups. The BSSBQ total score showed statistically significant time effects ( P <.001; Table 3 ).

The LARS score total score had significantly larger decreases after intervention (mean difference –3.28; 95% CI –4.03 to –2.54; Cohen d =–0.39) and at 3-month follow-up (mean difference –6.69; 95% CI –7.45 to –5.93; Cohen d =–0.69). Table 3 shows that the LARS score total score in both groups exhibited a trend of change over the 6-month period. The intergroup effect exhibits homogeneity ( P =.05).

The PSSS total score had significantly larger improvements after intervention (mean difference 0.47; 95% CI 0.22-0.71; Cohen d =1.81); however, the improvement in this effect did not persist at 3-month follow-up (mean difference 0.23; 95% CI –0.20 to 0.45; P =.07; Table 2 ). Table 3 shows that the PSSS total score in both groups exhibited a trend of change over the 6-month period.

Among the 55 patients who completed the intervention, 45 patients completed 3 MI sessions on time, 7 patients postponed 1 MI session because of scheduling conflicts, and 3 patients only completed 2 MI sessions. The mean number of attended MI sessions was 2.95 (SD 0.23). Additionally, 40 patients completed 84 health diary entries, while the remaining 11 patients did not submit completed entries or fulfill the required entries. The mean number of days of health diary entries was 82.87 (SD 3.15). We invited patients from the intervention group to complete a survey to evaluate their perceptions of the intervention's usability. In the end, 49 people completed the survey. The specific results are included in Appendix 5.

Principal Findings

To the best of our knowledge, the “e-bowel safety” applet is the first mobile app developed for patients with LARS in China. This study offers a valuable reference point for future initiatives in mHealth interventions for patients with LARS. A mHealth-based intervention was found to be feasible and effective in helping patients with LARS relieve bowel dysfunction, improve their self-management behavior, and improve their QoL compared to usual care.

This study found that the EORTC-QLQ-C30 total score of the intervention group increased significantly more than that of the control group after the intervention, indicating that the mHealth-based remote interaction could improve the QoL of patients with LARS. These results can be attributed to multiple factors. First, uncontrollable changes in intestinal function, concerns about prognosis, and fear of the future make patients with LARS feel uncertain [ 37 ]. A sense of uncertainty influences a patient’s QoL [ 38 ]. Patients using the “e-bowel safety” applet can provide timely feedback on their problems to the medical staff and obtain solutions, which can effectively reduce the uncertainty of patients during home rehabilitation. Second, decreased bowel dysfunction severity positively affected the QoL [ 39 ]. Third, peer support reportedly enhances cancer adaptation and QoL [ 40 ]. The patients’ stories module offers a channel for communication and emotional support among patients with LARS. In this section, patients can share their experiences related to disease management or self-management and receive responses from their peers through comments.

As expected, the BSSBQ total score in the intervention group after the intervention was significantly higher than that in the control group. The findings supported our hypothesis that health-based remote interaction can enhance the self-management behavior of patients with LARS. After the intervention, the results of enhanced self-management behavior were consistent with a previous face-to-face 6-month self-management program study for LARS, which may indicate that mHealth-based remote interaction may yield intervention effects on self-management behavior similar to those observed in face-to-face interventions [ 41 ]. However, a more significant effect was observed at 3-month follow-up. This may be because monthly motivational interviews help patients adopt positive health behaviors and improve their self-management awareness [ 42 ]. Moreover, current web-based self-management information on LARS is overly intricate for patients, and the information fails to meet the patient’s needs [ 43 ]. The strength of our “e-bowel safety” applet is the credibility of the information provided and medical consultation from experts, which can meet the information needs of patients. Finally, our team members created an individualized self-management plan for each participant in the intervention group and reminded them to follow the plans on the applet, which ensured that the patients developed good habits.

Consistent with previous studies [ 41 ], this study found that the intervention group demonstrated a more significant decline in the LARS score than the control group. The LARS score also showed significant time effects, indicating that the patient’s bowel dysfunction changed significantly during the 6-month period. This may be because our team members guided patients in rehabilitation exercises and diet adjustments, which have been proven effective in improving bowel dysfunction [ 44 - 46 ]. Meanwhile, the severity of bowel dysfunction decreased over time [ 13 ].

Unlike those of previous studies, our findings indicated that mHealth-based remote interaction management intervention could improve the social support levels in the short term; however, sustaining a stable long-term effect on social support was not realized [ 47 ]. The patients in the study might have used the “e-bowel safety” applet only for 3 months, and the impact of the intervention on social support may not yield a residual advantage at 3-month follow-up. Furthermore, most patients’ physical and social functions gradually stabilized at 6 months. Our “e-bowel safety” applet focuses on intensive support for symptom management and lacks support knowledge for patients when symptoms plateau, which should be refined in future studies to achieve long-term effects.

In this study, MI was used to stimulate behavioral change and maintenance. The dual intervention of mHealth and MI promotes effective engagement and motivation for health behavior changes. Nearly all the patients (55/60) successfully completed the 3-month intervention and the follow-up during the intervention process, signifying that the mHealth-based remote interaction management intervention is feasible and acceptable. In addition, none of the patients in the intervention group experienced adverse consequences caused by the intervention, indicating that the intervention was safe.

Limitations

This study has some limitations. First, this study enrolled patients from a tertiary hospital in China, which restricts the generalizability of our results. In the future, we will recruit patients from more hospitals to confirm our research findings. Second, patients were subjected to a limited 3-month follow-up period, thereby restricting our assessment of the enduring effects of the mHealth-based remote interaction management intervention on self-management behavior and QoL. Finally, patients were required to use WeChat and smartphones, which presents the potential for selection bias.

Conclusions

The mHealth-based remote interaction management intervention effectively enhanced the self-management behavior and QoL of patients with LARS, and the impact remained consistent during the 3-month follow-up. Bowel dysfunction also significantly improved throughout the entire research process. This study suggests that mHealth intervention could provide an effective and new option for many patients with LARS. Multicenter studies are necessary to establish the generalizability and effectiveness of these interventions.

Acknowledgments

This work was supported by the 2021 Anhui Higher Education Institutions Provincial Quality Engineering Project (grant 2021jyxm0718) and the Scientific Research and Cultivation project of the School of Nursing, Anhui Medical University (grant hlqm12023055).

Conflicts of Interest

None declared.

CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth) checklist (version 1.6.1).

The interview guide of motivational interviewing.

Results of pilot experiment.

The results of subgroup analysis.

Comments and attitudes towards intervention of intervention group.

• Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cao W, Chen H, Yu Y, Li N, Chen W. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J (Engl). 2021;134(7):783-791. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zheng K, Hu Q, Yu G, Zhou L, Yao Y, Zhou Y, et al. Trends of sphincter-preserving surgeries for low lying rectal cancer: a 20-year experience in China. Front Oncol. 2022;12:996866. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Langenfeld SJ, Davis BR, Vogel JD, Davids JS, Temple LKF, Cologne KG, et al. The American society of colon and rectal surgeons clinical practice guidelines for the management of rectal cancer 2023 supplement. Dis Colon Rectum. 2024;67(1):18-31. [ CrossRef ] [ Medline ]
• Pappou EP, Temple LK, Patil S, Smith JJ, Wei IH, Nash GM, et al. Quality of life and function after rectal cancer surgery with and without sphincter preservation. Front Oncol. 2022;12:944843. [ CrossRef ] [ Medline ]
• Keane C, Fearnhead NS, Bordeianou LG, Christensen P, Basany EE, Laurberg S, et al. International consensus definition of low anterior resection syndrome. Dis Colon Rectum. 2020;63(3):274-284. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pieniowski EHA, Nordenvall C, Palmer G, Johar A, Tumlin Ekelund S, Lagergren P, et al. Prevalence of low anterior resection syndrome and impact on quality of life after rectal cancer surgery: population-based study. BJS Open. 2020;4(5):935-942. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sturiale A, Martellucci J, Zurli L, Vaccaro C, Brusciano L, Limongelli P, et al. Long-term functional follow-up after anterior rectal resection for cancer. Int J Colorectal Dis. 2017;32(1):83-88. [ CrossRef ] [ Medline ]
• van der Heijden JAG, Thomas G, Caers F, van Dijk WA, Slooter GD, Maaskant-Braat AJG. What you should know about the low anterior resection syndrome—clinical recommendations from a patient perspective. Eur J Surg Oncol. 2018;44(9):1331-1337. [ CrossRef ] [ Medline ]
• Qaderi SM, van der Heijden JAG, Verhoeven RHA, de Wilt JHW, Custers JAE, PLCRC study group. Trajectories of health-related quality of life and psychological distress in patients with colorectal cancer: a population-based study. Eur J Cancer. 2021;158:144-155. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moon J, Garfinkle R, Zelkowitz P, Dell'Aniello S, Vasilevsky C, Brassard P, et al. Incidence and factors associated with mental health disorders in patients with rectal cancer post-restorative proctectomy. Dis Colon Rectum. 2023;66(9):1203-1211. [ CrossRef ] [ Medline ]
• Bohlok A, Mercier C, Bouazza F, Galdon MG, Moretti L, Donckier V, et al. The burden of low anterior resection syndrome on quality of life in patients with mid or low rectal cancer. Support Care Cancer. 2020;28(3):1199-1206. [ CrossRef ] [ Medline ]
• Pieniowski EHA, Palmer GJ, Juul T, Lagergren P, Johar A, Emmertsen KJ, et al. Low anterior resection syndrome and quality of life after sphincter-sparing rectal cancer surgery: a long-term longitudinal follow-up. Dis Colon Rectum. 2019;62(1):14-20. [ CrossRef ] [ Medline ]
• Eyl RE, Xie K, Koch-Gallenkamp L, Brenner H, Arndt V. Quality of life and physical activity in long-term (≥5 years post-diagnosis) colorectal cancer survivors—systematic review. Health Qual Life Outcomes. 2018;16(1):112. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun V, Wendel CS, Demark-Wahnefried W, Grant M, McMullen CK, Bulkley JE, et al. Diet and behavior modifications by long-term rectal cancer survivors to manage bowel dysfunction-associated symptoms. Nutr Cancer. 2019;71(1):89-99. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lai X, Wong FKY, Ching SSY. Review of bowel dysfunction of rectal cancer patients during the first five years after sphincter-preserving surgery: a population in need of nursing attention. Eur J Oncol Nurs. 2013;17(5):681-692. [ CrossRef ] [ Medline ]
• Hou X, Pang D, Lu Q, Yang P, Jin S. Bowel dysfunction and self-management for bowel symptoms after sphincter-preserving surgery: a cross-sectional survey of Chinese rectal cancer patients. Cancer Nurs. 2017;40(4):E9-E16. [ CrossRef ] [ Medline ]
• Pape E, Pattyn P, Van Hecke A, Somers N, Van de Putte D, Ceelen W, et al. Impact of low anterior resection syndrome (LARS) on the quality of life and treatment options of LARS—a cross sectional study. Eur J Oncol Nurs. 2021;50:101878. [ CrossRef ] [ Medline ]
• Frost H, Campbell P, Maxwell M, O'Carroll RE, Dombrowski SU, Williams B, et al. Effectiveness of motivational interviewing on adult behaviour change in health and social care settings: a systematic review of reviews. PLoS One. 2018;13(10):e0204890. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pape E, Decoene E, Debrauwere M, Van Nieuwenhove Y, Pattyn P, Feryn T, et al. Information and counselling needs of patients with major low anterior resection syndrome: a qualitative study. J Clin Nurs. 2023;32(7-8):1240-1250. [ CrossRef ] [ Medline ]
• Bazzell A, Madsen LT, Dains J. Clinical management of bowel dysfunction after low anterior resection for rectal cancer. J Adv Pract Oncol. 2016;7(6):618-629. [ FREE Full text ] [ Medline ]
• Sakr A, Sauri F, Alessa M, Zakarnah E, Alawfi H, Torky R, et al. Assessment and management of low anterior resection syndrome after sphincter preserving surgery for rectal cancer. Chin Med J (Engl). 2020;133(15):1824-1833. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Keutzer L, Wicha SG, Simonsson US. Mobile health apps for improvement of tuberculosis treatment: descriptive review. JMIR Mhealth Uhealth. 2020;8(4):e17246. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Saeidzadeh S, Kamalumpundi V, Chi N, Nair R, Gilbertson-White S. Web and mobile-based symptom management interventions for physical symptoms of people with advanced cancer: a systematic review and meta-analysis. Palliat Med. 2021;35(6):1020-1038. [ CrossRef ] [ Medline ]
• Liu F, Guo P, Su X, Cui M, Jiang J, Wang S, et al. A novel remote follow-up tool based on an instant messaging/social media app for the management of patients with low anterior resection syndrome: pilot prospective self-control study. JMIR Mhealth Uhealth. 2021;9(3):e22647. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moon J, Monton O, Smith A, Garfinkle R, Zhao K, Zelkowitz P, et al. Interactive online informational and peer support application for patients with low anterior resection syndrome: patient survey and protocol for a multicentre randomized controlled trial. Colorectal Dis. 2021;23(5):1248-1257. [ CrossRef ] [ Medline ]
• Olivia M, Allister S, Jeongyoon M, Marie D, Richard G, Carol-Ann V, et al. An online educational and supportive care application for rectal cancer survivors with low anterior resection syndrome: a mixed methods pilot study. Colorectal Dis. 2023;25(9):1812-1820. [ CrossRef ] [ Medline ]
• Montag C, Becker B, Gan C. The multipurpose application WeChat: a review on recent research. Front Psychol. 2018;9:2247. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bao Y, Wang C, Xu H, Lai Y, Yan Y, Ma Y, et al. Effects of an mHealth intervention for pulmonary tuberculosis self-management based on the integrated theory of health behavior change: randomized controlled trial. JMIR Public Health Surveill. 2022;8(7):e34277. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guo Y, Hong YA, Cai W, Li L, Hao Y, Qiao J, et al. Effect of a WeChat-based intervention (Run4Love) on depressive symptoms among people living with HIV in China: a randomized controlled trial. J Med Internet Res. 2020;22(2):e16715. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Battersby NJ, Juul T, Christensen P, Janjua AZ, Branagan G, Emmertsen KJ, et al. Predicting the risk of bowel-related quality-of-life impairment after restorative resection for rectal cancer: a multicenter cross-sectional study. Dis Colon Rectum. 2016;59(4):270-280. [ CrossRef ] [ Medline ]
• Cocks K, King MT, Velikova G, Martyn St-James M, Fayers PM, Brown JM. Evidence-based guidelines for determination of sample size and interpretation of the European organisation for the research and treatment of cancer quality of life questionnaire core 30. J Clin Oncol. 2011;29(1):89-96. [ CrossRef ] [ Medline ]
• Li H, Zhou P, Pang X, Wang T, Yin D, Fu M, et al. Mobile health-based remote interaction management intervention for patients with low anterior resection syndrome: study protocol for a randomised controlled trial. BMJ Open. 2022;12(12):e066046. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al. The European organization for research and treatment of cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85(5):365-376. [ CrossRef ] [ Medline ]
• Emmertsen KJ, Laurberg S. Low anterior resection syndrome score: development and validation of a symptom-based scoring system for bowel dysfunction after low anterior resection for rectal cancer. Ann Surg. 2012;255(5):922-928. [ CrossRef ] [ Medline ]
• Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA. Psychometric characteristics of the multidimensional scale of perceived social support. J Pers Assess. 1990;55(3-4):610-617. [ CrossRef ] [ Medline ]
• Yin L, Fan L, Tan R, Yang G, Jiang F, Zhang C, et al. Bowel symptoms and self-care strategies of survivors in the process of restoration after low anterior resection of rectal cancer. BMC Surg. 2018;18(1):35. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Giammanco MD, Gitto L. Coping, uncertainty and health-related quality of life as determinants of anxiety and depression on a sample of hospitalized cardiac patients in Southern Italy. Qual Life Res. 2016;25(11):2941-2956. [ CrossRef ] [ Medline ]
• Emmertsen K, Laurberg S, Rectal Cancer Function Study Group. Impact of bowel dysfunction on quality of life after sphincter-preserving resection for rectal cancer. Br J Surg. 2013;100(10):1377-1387. [ CrossRef ] [ Medline ]
• Brodar KE, Carlisle V, Tang PY, Fisher EB. Identification and characterization of peer support for cancer prevention and care: a practice review. J Cancer Educ. 2022;37(3):645-654. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li C, Li Z, Lu Q, Zhou Y, Qin X, Wu A, et al. The effectiveness of a self-management program of bowel dysfunction in patients with mid and low rectal cancer after sphincter-preserving surgery: a pilot randomized controlled trial. Cancer Nurs. 2023;46(1):67-76. [ CrossRef ] [ Medline ]
• Seven M, Reid A, Abban S, Madziar C, Faro JM. Motivational interviewing interventions aiming to improve health behaviors among cancer survivors: a systematic scoping review. J Cancer Surviv. 2023;17(3):795-804. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lightner A. Assessing the readability, quality and accuracy of online health information for patients with low anterior resection syndrome following surgery for rectal cancer, by Garfinkle et al. Colorectal Dis. 2019;21(5):500. [ CrossRef ]
• Yuanyuan W, Shiyin H, Lei H, Ding D. Pelvic floor muscle exercises alleviate symptoms and improve mental health and rectal function in patients with low anterior resection syndrome. Front Oncol. 2023;13:1168807. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun V, Crane TE, Slack SD, Yung A, Wright S, Sentovich S, et al. Rationale, development, and design of the altering intake, managing symptoms (AIMS) dietary intervention for bowel dysfunction in rectal cancer survivors. Contemp Clin Trials. 2018;68:61-66. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sun V, Crane TE, Freylersythe S, Slack SD, Yung A, Krouse RS, et al. Altering intake and managing symptoms: feasibility of a diet modification intervention for post-treatment bowel dysfunction in rectal cancer. Clin J Oncol Nurs. 2022;26(3):283-292. [ CrossRef ] [ Medline ]
• Zhu J, Ebert L, Liu X, Wei D, Chan SW. Mobile breast cancer e-Support program for Chinese women with breast cancer undergoing chemotherapy (part 2): multicenter randomized controlled trial. JMIR Mhealth Uhealth. 2018;6(4):e104. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 24.10.23; peer-reviewed by V Sun, C Thomson; comments to author 13.03.24; revised version received 07.05.24; accepted 03.06.24; published 13.08.24.

©Peng Zhou, Hui Li, Xueying Pang, Ting Wang, Yan Wang, Hongye He, Dongmei Zhuang, Furong Zhu, Rui Zhu, Shaohua Hu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Associate in Applied Science in Health Information Management

Why major in health information management.

Students completing this associate degree will be able to:

• Successfully utilize technology for the management of health care information
• Demonstrate accurate use CPT and ICD-10 coding
• Provide information to administration using computer skills of preparing datasheet, analysis and presentation graphics

Health information technology is a fast-growing occupation in the U.S. today. The HIM professional has a thorough knowledge of medical office procedures including:

• Health insurance filing
• Medical coding
• Regulations

The curriculum for this associate’s degree includes medical coding. Medical coding professionals stand in the crossroads of healthcare technology which is an important component of the healthcare delivery system.

Discover careers and salaries for this program

• Careers In Health Information Management

HIM graduates are prepared to use health information technology to maintain, compile, and report health information data for reimbursement, facility planning, risk management, quality assessment and research, and code clinical data using appropriate classification systems and analyze health records according to the current standards.

Graduates document patient care and facilitate delivery of health care services. They are aware of all standards and requirements that apply to the medical record, as well as the legal significance of the patient file.

Courses in Health Information Management

A student with a medical coding certificate may transfer coursework towards this Heath Information Management degree. This degree includes a 240-hour internship in which students will engage in supervised “on the job training.” Internship sites may include physician’s offices, medical centers or insurance offices.

First Semester

HIM 110       Human Anatomy & Disease  (3) HIM 150       Technical Medical Terminology  (3) HIM 156       Introduction to Health Insurance  (3) HIM 255       Management of Elec. Health Records  (3) HIM 257       Procedure & Diagnosis Coding I  (3)

Second Semester

HIM 200 Advanced Medical Terminology    (3) HIM 251       Medical Office Procedures    (3) HIM 252       Pharmacology Terminology   (3) HIM 254       Law, Liability, and Medical Ethics  (3) HIM 258       Procedure & Diagnosis Coding II  (3)

HIM 259       Proced & Diagnosis Coding III   (3) HIM 261       Seminar     (1) HIM 265      Internship  (3)

Third Semester

CS 100          Introduction to Computers   (3) COMM 105  Essentials of English    (3) BE 180          Business Communications    (3) PHIL 100      Logic      (3)

Fourth Semester

SPEC 175     Intercultural Communication   (3) BE 100          Work Environment Orientation   (2) HIM 249       Management of Health Info   (3) PSYC 101    Intro to Psychology  or SOC 101       Principles of Sociology   (3)

• Why Health Information Management
• Featured Courses

Let’s Get Started!

" * " indicates required fields

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Health and Human Services News

Stuffed animals get checkups at 2nd Teddy Bear Hospital

Dozens of stuffed animals are now healthier after visiting the Teddy Bear Hospital at Black Hawk College. The second annual event brought 57 children to the East Campus to get […]

BHC Practical Nursing program ranked #2 in Illinois

Congratulations to the Practical Nursing program for being named the #2 Ranked Practical Nursing Program in Illinois by PracticalNursing.org! Black Hawk College received an overall score of 97.11 out of […]

Stuffed animals get checkups at Teddy Bear Hospital

Dozens of stuffed animals are now healthier after visiting the Teddy Bear Hospital at Black Hawk College. The first annual event brought 55 children to the East Campus to get […]

Jodi Becker Department Chair

Quad-Cities Campus Academic Services Center Health Sciences Center, Room 313 309-796-5379

Related Programs

College Credit Certificate in Basic Nurse Assistant Training Program

College Credit Certificate in Medical Assisting

College Credit Certificate in Medical Coding Specialist

• Open access
• Published: 07 August 2024

Management training programs in healthcare: effectiveness factors, challenges and outcomes

• Lucia Giovanelli 1 ,
• Federico Rotondo 2 &
• Nicoletta Fadda 1  

BMC Health Services Research volume  24 , Article number:  904 ( 2024 ) Cite this article

120 Accesses

Metrics details

Different professionals working in healthcare organizations (e.g., physicians, veterinarians, pharmacists, biologists, engineers, etc.) must be able to properly manage scarce resources to meet increasingly complex needs and demands. Due to the lack of specific courses in curricular university education, particularly in the field of medicine, management training programs have become an essential element in preparing health professionals to cope with global challenges. This study aims to examine factors influencing the effectiveness of management training programs and their outcomes in healthcare settings, at middle-management level, in general and by different groups of participants: physicians and non-physicians, participants with or without management positions.

A survey was used for gathering information from a purposive sample of professionals in the healthcare field attending management training programs in Italy. Factor analysis, a set of ordinal logistic regressions and an unpaired two-sample t-test were used for data elaboration.

The findings show the importance of diversity of pedagogical approaches and tools and debate, and class homogeneity, as effectiveness factors. Lower competencies held before the training programs and problems of dialogue and discussion during the course are conducive to innovative practice introduction. Interpersonal and career outcomes are greater for those holding management positions.

Conclusions

The study reveals four profiles of participants with different gaps and needs. Training programs should be tailored based on participants’ profiles, in terms of pedagogical approaches and tools, and preserve class homogeneity in terms of professional backgrounds and management levels to facilitate constructive dialogue and solution finding approach.

Peer Review reports

Several healthcare systems worldwide have identified management training as a precondition for developing appropriate strategies to address global challenges such as, on one hand, poor health service outcomes in front of increased health expenditure, particularly for pharmaceuticals, personnel shortages and low productivity, and on the other hand in terms of unbalanced quality and equal access to healthcare across the population [ 1 ]. The sustainability of health systems itself seems to be associated with the presence of leaders, at all levels of health organizations, who are able to correctly manage scarce resources to meet increasingly complex health needs and demands, at the same time motivating health personnel under an increasing amount of stress and steering their behaviors towards the system’s goals, in order to drive the transition towards more decentralized, interorganizational and patient-centered care models [ 2 ].

Recently, professional training as an activity aimed at increasing learning of new capabilities (reskilling) and improving existing ones (upskilling) during the lifetime of individuals (lifelong learning) has been identified by the European Commission as one of the seven flagship programs to be developed in the National Recovery and Resilience Plans (NRRP) to support the achievement of European Union’s goals, such as green and digital transitions, innovation, economic and social inclusion and occupation [ 3 ]. As a consequence, many member states have implemented training programs to face current and future challenges in health, which often represents a core mission in their NRRPs.

The increased importance of developing management training programs is also related to the rigidity and focalization of university degree courses in medicine, which do not provide physicians with the basic tools for fulfilling managerial roles [ 4 ]. Furthermore, taking on these roles does not automatically mean filling existing gaps in management capabilities and skills [ 5 ]. Several studies have demonstrated that, in the health setting, management competencies are influenced by positions and management levels as well as by organization and system’s features [ 6 , 7 ]. Hence, training programs aimed at increasing management competencies cannot be developed without considering these differences.

To date, few studies have focused on investigating management training programs in healthcare [ 8 ]. In particular, much more investigation is required on methods, contents, processes and challenges determining the effectiveness of training programs addressed to health managers by taking into account different environments, positions and management levels [ 1 ]. A gap also exists in the assessment of management training programs’ outcomes [ 9 ]. This study aims to examine factors influencing the effectiveness and outcomes of management training, at the middle-management level, in healthcare. It intends to answer the following research questions: which factors influence the management training process? Which relationships exist between management competencies held before the program, factors of effectiveness, critical issues encountered, and results achieved or prefigured at the end of the program? Are there differences, in terms of factors of effectiveness, challenges and outcomes, between the following groups of management training programs’ participants: physicians and non-physicians, participants with or without management positions?

Management training in healthcare

Currently, there is a wide debate about the added value of management to health organizations [ 10 ] and thus about the importance of spreading management competencies within health organizations to improve their performance. Through a systematic review, Lega et al. [ 11 ] highlighted four approaches to examine the impact of management on healthcare performance, focusing on management practices, managers’ characteristics, engagement of professionals in performance management and organizational features and management styles.

Although findings have not always been univocal, several studies suggest a positive relationship between management competencies and practices and outcomes in healthcare organizations, both from a clinical and financial point of view [ 12 ]. Among others, Vainieri et al. [ 13 ] found, in the Italian setting, a positive association between top management’s competencies and organizational performance, assessed through a multidimensional perspective. This study also reveals the mediating effect of information sharing, in terms of strategy, results and organization structure, in the relationship between managerial competencies and performance.

The key role of management competencies clearly emerges for health executives, who have to turn system policies into a vision, and then articulate it into effective strategies and actions within their organizations to steer and engage professionals [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, health systems are increasingly complex and continually changing across contexts and health service levels. This means the role of health executives is evolving as well and identifying the capacities they need to address current and emerging issues becomes more difficult. For instance, a literature review conducted by Figueroa et al. [ 20 ] sheds light on priorities and challenges for health leadership at three structural levels: macro context (international and national), meso context (organizations) and micro context (individual healthcare managers).

Doctor-managers are requested to carry both clinical tasks and tasks related to budgeting, goal setting and performance evaluation. As a consequence, a growing stream of research has speculated whether managers with a clinical background actually affect healthcare performance outcomes, but studies have produced inconclusive findings. In relation to this topic, Sarto and Veronesi [ 21 ] carried out a literature review showing a generally positive impact of clinical leadership on different types of outcome measures, with only a few studies reporting negative impacts on financial and social performance. Morandi et al. [ 22 ] focused on doctor-managers who have become middle managers and investigated the potential bias in performance appraisal due to the mismatch between self-reported and official performance data. At the individual level, the role played by managerial behavior, training, engagement, and perceived organizational support was analyzed. Among others indications they suggested that training programs should be revised to reduce bias in performance appraisal. Tasi et al. [ 23 ] conducted a cross-sectional analysis of the 115 largest U.S. hospitals, divided into physician-led and non-physician-led, which revealed that physician-led hospital systems have higher quality ratings across all specialities and more inpatient days per hospital bed than non-physician-led hospitals. No differences between the groups were found in total revenue and profit margins. The main implication of their study is that hospital systems may benefit from the presence of physician leadership to improve the quality and efficiency of care delivered to patients as long as education and training are able to adequately prepare them. The main issue, as also observed by others [ 4 , 24 ], is that university education in medicine still includes little focus on aspects such as collaborative management, communication and coordination, and leadership skills. Such a circumstance motivates the call for further training. Regarding the implementation of training programs, Liang et al. [ 1 ] have recently shown how it is hindered, among others, by a lack of sufficient knowledge about needed competencies and existing gaps. Their analysis, which focuses on senior managers from three categories in Chinese hospitals, shows that before commencing the programs senior managers had not acquired adequate management competencies either through formal or informal training. It is worth noticing that significant differences exist between hospital categories and management levels. For this reason, they recommend using a systemic approach to design training programs, which considers different hospital types, management levels and positions. Yarbrough et al. [ 6 ] examined how competence training worked in healthcare organizations and the competencies needed for leaders at different points of their careers at various organizational levels. They carried out a cross-sectional survey of 492 US hospital executives, whose most significant result was that competence training is effective in healthcare organizations.

Walston and Khaliq [ 25 ], from a survey of 2,001 hospital CEOs across the US concluded that the greatest contribution of continuing education is to keep CEOs updated on technological and market changes that impact their current job responsibilities. Conversely, it does not seem to be valued for career or succession planning. About the methods of continuing education, an increasing use of some internet-based tools was found. Walston et al. [ 26 ] identified the factors affecting continuing education, finding, among others, that CEOs from for-profit and larger hospitals tend to take less continuing education, whereas senior managers' commitment to continuing education is influenced by region, gender, the CEO's personal continuing education hours and the focus on change.

Furthermore, the principles that inspire modern healthcare models, such as dehospitalization, horizontal coordination and patient-centeredness, imply the increased importance of middle managers, within single structures but also along clinical pathways and projects, to create and sustain high performances [ 27 , 28 , 29 ].

Whaley and Gillis [ 8 ] investigated the development of training programs aimed at increasing managerial competencies and leadership of middle managers, both from clinical and nonclinical backgrounds, in the US context. By adopting the top managers’ perspective, they found a widespread difficulty in aligning training needs and program contents. A 360° assessment of the competencies of Australian middle-level health service managers from two public hospitals was then conducted by Liang et al. [ 7 ] to identify managerial competence levels and training and development needs. The assessment found competence gaps and confirmed that managerial strengths and weaknesses varied across management groups from different organizations. In general, several studies have shown that leading at various organizational levels, in healthcare, does not necessarily require the same levels and types of competencies.

Liang et al. [ 30 ] explored the core competencies required for middle to senior-level managers in Victorian public hospitals. By adopting mixed methods, they confirmed six core competencies and provided guidance to the development of the competence-based educational approach for training the current and future management workforce. Liang et al. [ 31 ] then focused on the poorly investigated area of community health services, which are one of the main solutions to reducing the increasing demand for hospital care in general, and, in particular, in the reforms of the Australian health system. Their study advanced the understanding of the key competencies required by senior and mid-level managers for effective and efficient community health service delivery. A following cross-sectional study by AbuDagga et al. [ 32 ] highlighted that some community health services, such as home healthcare and hospice agencies, also need specific cultural competence training to be effective, in terms of reducing health disparities.

Using both qualitative and quantitative methods, Liang et al. [ 33 ] developed a management competence framework. Such a framework was then validated on a sample of 117 senior and middle managers working in two public hospitals and five community services in Victoria, Australia [ 34 ]. Fanelli et al. [ 35 ] used mixed methods to identify the following specific managerial competencies, which healthcare professionals perceive as crucial to improve their performance: quality evaluation based on outcomes, enhancement of professional competencies, programming based on process management, project cost assessment, informal communication style and participatory leadership.

Loh [ 5 ], through a qualitative analysis conducted in Australian hospitals, examined the motivation behind the choice of medically trained managers to undertake postgraduate management training. Interesting results stemming from the analysis include the fact that doctors often move into management positions without first undertaking training, but also that clinical experience alone does not lead to required management competencies. It is also interesting to remark that effective postgraduate management training for doctors requires a combination of theory and practice, and that doctors choose to undertake training mostly to gain credibility.

Ravaghi et al. [ 36 ] conducted a literature review to assess the evidence on the effectiveness of different types of training and educational programs delivered to hospital managers. The analysis identifies a set of aspects that are impacted by training programs. Training programs focus on technical, interpersonal and conceptual skills, and positive effects are mainly reported for technical skills. Numerous challenges are involved in designing and delivering training programs, including lack of time, difficulty in employing competencies in the workplace, also due to position instability, continuous changes in the health system environment, and lack of support by policymakers. One of the more common flaws concerns the fact that managers are mainly trained as individuals, but they work in teams. The implications of the study are that increased investments and large-scale planning are required to develop the knowledge and competencies of hospital managers. Another shortage concerns the outcome measurement of training programs, which is a usually neglected issue in the literature [ 9 ]. It also emerges that the training programs performing best are specific, structured and comprehensive.

Kakemam and Liang [ 2 ] conducted a literature review to shed light on the methods used to assess management competencies, and, thus, professional development needs in healthcare. Their analysis confirms that most studies focus on middle and senior managers and demonstrate great variability in methods and processes of assessment. As a consequence, they elaborate a framework to guide the design and implementation of management competence studies in different contexts and countries.

In the end, the literature has long pointed out that developing and strengthening the competencies and skills of health managers represent a core goal for increasing the efficiency and effectiveness of health systems, and management training is crucial for achieving such a goal [ 37 ]. The reasons can be summarized as follows: university education has scarcely been able to provide physicians and, in general, health operators, with adequate, or at least basic, managerial competencies and skills; over time, professionals have been involved in increasingly complex and rapidly changing working environments, requiring increased management responsibilities as well as new competencies and skills; in many settings, for instance in Italy, delays in the enforcement of law requiring the attendance of specific management training courses to take up a leadership position, hindered the acquisition of new competencies and the improvement of existing ones by those already managing health organizations, structures and services.

For the purposes of this study, management competencies refer to the possession and ability to use skills and tools for service organization and service planning, control and evaluation, evidence-informed decision-making and human resource management in the healthcare field.

Management training in the Italian National Health System

The reform of the Italian National Health System (INHS), implemented by Legislative Decree No. 502/1992 and inspired by neo-managerial theories, introduced the role of the general manager and assigned new responsibilities to managers.

However, the inadequate performance achieved in the first years of the application of the reform highlighted the cultural gap that made the normative adoption of managerial approach and tools unproductive on the operational level. Legislation evolved accordingly, and in order to hold management positions, management training became mandatory. Decree-Law No. 583/1996 (converted into Law No. 4/1997) provided that the requirements and criteria for access to the top management level were to be determined. Therefore, Presidential Decree No. 484/1997 determined these requirements and also the requirements and criteria to access the middle-management level of INHS’ healthcare authorities. This regulation also imposed the acquisition of a specific management training certificate, dictated rules concerning the duration, contents, and teaching methods of management training courses issuing this certificate, and indicated the requirements for attendance. Immediately afterwards, Legislative Decree No. 229/1999 amended the discipline of medical management and health professions and promoted continuous training in healthcare. It also regulated management training, which became an essential requirement for the appointments of health directors and directors of complex structures in the healthcare authorities, for the categories of physicians, dentists, veterinarians, pharmacists, biologists, chemists, physicists and psychologists.

The second pillar of the INHS reform was the regionalization of the INHS. Therefore, the Regions had to organize the courses to achieve management training certificates on the basis of specific agreements with the State, which regulated the contents, the methodology, the duration and the procedures for obtaining certification. The State-Regions Conference approved the first interregional agreement on management training in July 2003, whereas the State-Regions Agreement of 16 May 2019 regulated the training courses. The mandatory contents of the management training outlined the skills and behaviors expected from general managers and other top management key players (Health Director, Administrative Director and Social and Health Director), but also for all middle managers.

A survey was used to gather information from a purposive sample of professionals in the healthcare field taking part in management training programs. In particular, a structured questionnaire was submitted to 140 participants enrolled in two management programs organized by an Italian university: a second-level specializing master course and a training program carried out in collaboration with the Region. The programs awarded participants the title needed to be appointed as a director of a ward or administrative unit in a public healthcare organization, and share the same scientific committee, teaching staff, administrative staff and venue. The respondents’ profile is shown in Table  1 .

It is worth pointing out that the teaching staff is characterized by diversity: teachers have different educational and professional backgrounds, are practitioners or academics, and come from different Italian regions.

The questionnaire was submitted and completed in presence and online between November 2022 and February 2023. All participants decided to take part in the analysis spontaneously and gave their consent, being granted total anonymity.

The questionnaire, which was developed for this study and based on the literature, consisted of 64 questions shared in the following five sections: participant profile (10 items), management competencies held by participants before the training program (4 items), effectiveness factors of the training program (23 items), challenges to effectiveness (10 items), and outcomes of the training program (17 items) (an English language version of the questionnaire is attached to this paper as a supplementary file). In particular, the second section aimed to shed light on the participants’ situation regarding management competencies held before the start of the training program and how they were acquired; the third section aimed to collect participants’ opinions regarding how the program was conducted and the factors influencing its effectiveness; the fourth section aimed to collect participants’ opinions regarding the main obstacles encountered during the program; and the fifth section aimed to reveal the main outcomes of the program in terms of knowledge, skills, practices and career.

Except for those of the first section, which collected personal information, all the items of the next four categories – management competencies, effectiveness factors, challenges and outcome — were measured through a 5-point Likert scale. To ensure that the content of the questionnaire was appropriate, clear and relevant, a pre-testing was conducted in October 2022 by asking four academics and four practitioners, both physicians and not, with and without management positions, to fill it out. The aim was to understand whether the questionnaire really addressed the information needs behind the study and was easily and correctly understood by respondents. Therefore, the four individuals involved in the pre-testing were asked to fill it out simultaneously but independently, and at the end of the compilation, a focus group that included them and the three authors was used to collect their opinions and suggestions. After this phase, the following changes were made: in the ‘Participant profile’ section, ‘Veterinary medicine’ was added to the fields accounting for the ‘Educational background’ (item 3); in Sect. 2, it was decided to modify the explanation given to ‘basic management competencies’ and align it to what required by Presidential Decree No. 484/1997; in Sect. 3, item 25 was added to catch a missing aspect that respondents considered important, and brackets were added to the description of items 15, 16 and 29 to clarify the concepts of mixed and homogenous class and pedagogical approaches and tools; in Sect. 4, in the description of item 40, the words ‘find the energy required’ were added to avoid confusion with items 38 and 39, whereas brackets were added to items 41 and 45 to provide more explanation; in Sect. 5, brackets were added to the description of item 51 to increase clarity, and the last item was divided into two (now items 63 and 64) to distinguish the training program’s impact on career at different times.

With reference to the methods, first, a factor analysis based on the principal component method was conducted within each section of the questionnaire (except for the first again), in order to reduce the number of variables and shed light on the factors influencing the management training process. Bartlett's sphericity test and the Kaiser–Meyer–Olkin (KMO) value were performed to assess sampling adequacy, whereas factors were extracted following the Kaiser criterion, i.e., eigenvalues greater than unity, and total variance explained. The rotation method used was the Varimax method with Kaiser normalization, except for the second section (i.e., management competencies held by participants before the training program) that), which did not require rotation since a single factor emerged from the analysis. Bartlett's sphericity test was statistically significant ( p  < 0.001) in all sections, KMO values were all greater than 0.65 (average value 0.765), and the total variances explained were all greater than 65% (average value of approximately 70.89%), which are acceptable values for such analysis.

Second, a set of ordinal logistic regressions were performed to assess the relationships existing between management competencies held before the start of the course, effectiveness factors, challenges, and outcomes of the training program.

The factors that emerged from the factor analysis were used as independent variables, whereas some significant outcome items accounting for different performance aspects were selected as dependent variables: improved management competencies, innovation practices, professional relationships, and career prospects. Ordered logit regressions were used because the dependent variables (outcomes) were measured on ordinal scales. Some control variables for the respondent profiles were included in the regression models: age, gender, educational background, management position, and working in the healthcare field.

With the aim of understanding which explanatory variables could exert an influence, a backward elimination method was used, adopting a threshold level of significance values below 0.20 ( p  < 0.20). Table 4 shows the results of regressions with independent variables obtained following the criterion mentioned above. All four models respected the null hypothesis, which means that the proportional odds assumption behind the ordered logit regressions had not been rejected ( p  > 0.05). Third and last, an unpaired two-sample t-test was used to examine the differences between groups of participants in the management training programs selected based on two criteria: physicians and non-physicians, and participants with or without management positions.

First, descriptive statistics is useful for understanding the aspects participants considered the most and least important by category. This can be done by focusing on the items of the four sections of the questionnaire (except for the first one depicting participant profiles) that were given the highest and lowest scores at the sample level and by different groups of participants (physicians and non-physicians, participants with or without management positions). Table 2 summarizes the mean values and standard deviations by group of these higher and lower scores. Focusing on management competencies, all groups reported having mainly acquired them through professional experience, except for non-physicians who attributed major significance to postgraduate training programs, with a mean value of 3.05 out of 5. All groups agreed on the poor role of university education in providing management competencies, with mean values for the sample and all four groups below 2.5. It is worth noting that this item exhibits the lowest value for physicians (1.67) and the highest for non-physicians (2.37). In addition, physicians are the group attributing the lowest values to postgraduate education and professional experience for acquiring management competencies. In reference to factors of effectiveness, all groups also agree on the necessity of mixing theoretical and practical lessons during the training program with mean values of well above 4.5, whereas exclusive use of self-assessment is generally viewed as the most ineffective practice, except for non-physician, who attribute the lowest value to remote lessons (mean 1.82). Among the challenges, the whole sample and physicians and participants without management positions see the lack of financial support from their organization as the main problem (mean 4.10), while non-physicians and participants with management positions believe this is represented by a lack of time, with mean values, respectively, of 3.75 and 4. All agree that dialogue and discussion during the course have been the least relevant of the problems, with mean values below 1.5. Outcomes show generally high values, as revealed by the fact that the lowest values exhibit mean values around 3.5. It is worth noting that an increased understanding of the healthcare systems has been the main benefit gained from the program, with mean values equal to or higher than 4.50. The lowest positive impact is attributed by all attendees to improved relationships with superiors and top management, with mean values between 3.44 and 3.74, with the exception of participants without management positions who mention improved career prospects.

To shed light on the factors influencing the management training process, the findings of the factor analyses conducted by category are reported. Starting from the management competencies held before the training program, the following single factor was extracted from the four items, named and interpreted as follows:

Basic management competencies, which measures the level of management competencies acquired by participants through higher education, post-graduate training and professional experience.

The effectiveness factors are then grouped into six factors, named and explained as follows:

Diversity and debate, which aggregates five items assessing the importance of diversity in participants’ and teachers’ educational and professional backgrounds and pedagogical approaches and tools, as well as level of participant engagement and discussion during lessons and in carrying out the project work required to complete the program.

Specialization, which includes three items accounting for a robust knowledge of healthcare systems by focusing on teachers’ profiles and lessons’ theoretical approaches.

Lessons in presence, which groups three items explaining that in-presence lessons increase learning outcomes and discussion among participants.

Final self-assessment, made up of three items asserting that learning outcomes should be assessed by participants themselves at the end of the course.

Written intermediate assessment, composed of two items explaining that mid-terms assessment should only be written.

Homogeneous class, which is made up of a single component accounting for participants’ similarity in terms of professional backgrounds and management levels, tasks and responsibilities.

The challenges are aggregated into the following four factors:

Lack of time, which includes three items reporting scarce time and energy for lessons and study.

Problems of dialogue and discussion, which groups three items focusing on difficulties in relating to and debating with other participants and teachers.

Low support from organization, which is made up of two items reporting poor financial support and low value given to the initiative from participants’ own organizations.

Organizational issues, which aggregates two items demonstrating scarce flexibility and collaboration by superiors and colleagues of participants’ own organizations and unfamiliarity to study.

Table 3 shows the component matrix with saturation coefficients and factors obtained for the management competencies held before the training program (unrotated), effectiveness factors (rotated), and challenges (rotated).

A set of ordinal logistic regressions was performed to examine the relationships between management competencies held before the start of the course, effectiveness factors, challenges and outcomes of the training program. The results, shown in Table  4 , are articulated into four models, one for each selected outcome. In relation to model 1, the factors ‘diversity and debate’ ( p  < 0.001), ‘written intermediate assessment’ ( p  < 0.05) and ‘homogeneous class’ ( p  < 0.001) have a significant positive impact on the improvement of management competencies, which is also increased by low values attributed to ‘problems of dialogue and discussion’ ( p  < 0.01). In model 2, the change of professional practices in light of lessons learned during the program, selected as an innovation outcome, is then positively affected by ‘diversity and debate’ ( p  < 0.001), ‘homogeneous class’ ( p  < 0.05) and ‘organizational issues’ ( p  < 0.01), while it was negatively influenced by a high value of ‘basic management competencies’ held before the course ( p  < 0.05). Regarding model 3, ‘Diversity and debate’ ( p  < 0.001) and ‘homogeneous class’ ( p  < 0.01) have a significant positive effect on the improvement of professional relationships as well, whereas the same is negatively affected by ‘lessons in presence’ ( p  < 0.05). Finally, concerning model 4, the outcome career prospects benefit from ‘diversity and debate’ ( p  < 0.05) and ‘homogeneous class’ ( p  < 0.01), since both factors exert a positive effect. ‘Low support from organization’ negatively influences career prospects ( p  < 0.001). Table 4 also shows that the LR test of proportionality of odds across the response categories cannot be rejected (all four p  > 0.05).

Finally, it is worth noting that none of the control variables reflecting the respondent profiles (age, gender, management position, working in the healthcare field, and educational background) was found to be statistically significant. These variables are not reported in Table  4 because regression models were obtained following a backward elimination method, as explained in the method section.

In the end, the t-test reveals significant differences between physicians and non-physicians, as well as between participants with or without management positions. Table 5 shows only figures of t-test statistically significant with regards to competencies held before the attendance of the course, the factors of effectiveness, challenges of the training program, and outcomes achieved. In the first comparison, non-physicians show higher management competencies at the start of the program, with a mean value of 0.31, while physicians suffer from less support from their own organization with a mean value of 0.13 compared to -0.18, the mean value of the non-physicians. Concerning the second comparison, participants with management positions have higher management competencies at the start of the program (0.19 versus -0.13) and suffer more from lack of time, with higher mean values compared to participants without managerial positions, respectively 0.23 and -0.16. For what concerns the factors related to the effectiveness of the training program, participants with management positions exhibit a lower mean value in relation to written mid-term assessments, -0.24 versus 0.17, reported by participants with management positions. Differently, the final self-assessment at the end of the program is higher for participants with management positions, 0.24 compared to -0.17, the mean value of the participants without management positions. This latter category feels more the problem of low support from their organizations, with a mean value of 0.16 compared to -0.23, and is slightly less motivated by possible career improvement, with a mean value of 3.31 compared to 3.73 reported by participants with management positions.

The results stemming from the different analyses are now considered and interpreted in the light of the extant literature. Personal characteristics such as gender and age, differently from what was found by Walston et al. [ 26 ] for executives’ continuing education, and professional characteristics such as seniority and working in public or private sectors, do not seem to affect participation in management training programs.

The findings clearly show the outstanding importance of ‘diversity and debate’ and ‘class homogeneity’ as factors of effectiveness, since they positively impact all outcomes: competencies, innovation, professional relationships and career. These factors capture two key aspects complementing each other: on the one hand, participants and teachers’ different backgrounds provide the class with a wider pool of resources and expertise, whereas the use of pedagogical tools fostering discussion enriches the educational experience and stimulates creativity. On the other hand, due to the high level of professionalism in the setting, sharing common management levels means similar tasks and responsibilities, as well as facing similar problems. Consequently, speaking the same language leads to deeper knowledge and effective technical solutions.

In relation to the improvement of management competencies, it also emerges the critical role of a good class atmosphere, that is, the absence of problems of dialogue and discussion. ‘Diversity and debate’ and ‘class homogeneity’, as explained before, seem to contribute to this, since they enhance freedom of expression and fair confrontation, leading to improved learning outcomes. It is interesting to notice that the problems of dialogue and discussion turned out to be the least relevant challenge across the sample.

Two interesting points come from the factors affecting innovation. First, it seems that lower competencies before the training programs lead to the development of more innovative practices. The reason is that holding fewer basic competencies means a greater scope for action once new capabilities are learned: the spirit of openness is conducive to breaking down routines, and innovative practices hindered by a lack of knowledge and tools can thus be introduced. The reason is that holding fewer basic competencies means greater scope for action once new capabilities are learned: the spirit of openness is conducive to breaking down routines, and innovative practices hindered by a lack of knowledge and tools can thus be introduced. This extends the findings of previous studies since the employment of competencies in the workplace is influenced by the starting competence equipment of professionals [ 36 ], and those showing gaps have more room to recover, also in terms of motivation to change, that is, understanding the importance of meeting current and future challenges [ 26 ]. Second, more innovative practices are introduced by participants perceiving more organizational issues. This may reveal, on the one side, a stronger individual motivation towards professional growth of participants who suffer from lack of flexibility and collaboration from their own superiors and colleagues. In this regard, poor tolerance, flexibility and permissions in their workplace act as a stimulus to innovation, which can be viewed as a way of challenging the status quo. On the other side, in line with the above-mentioned concept, this confirms that unfamiliarity with the study increases the innovative potential of participants. Since this study reveals that physicians are neither adequately educated from a management point of view nor incentivized to attend post-graduation training programs, it points out how important is extending continuing education to all health professional categories [ 25 , 26 ].

The topic of competencies held by different categories needs more attention. The study reveals that physicians and participants without management positions start the program with less basic competencies. At the sample level, higher education is viewed as the most ineffective tool to provide such competencies, whereas professional experience is seen as the best way to gather them. Actually, non-physicians give the highest value to postgraduate education, which suggests they are those more interested or incentivized to take part in continuing education. Although holding managerial positions does not automatically mean having higher competencies [ 5 ], it is evident that such a professional experience contributes to filling existing gaps. Physicians stand out as the category for which university education, postgraduate education and professional experience exert the lowest impact on management competence improvement. Considering the relationship between competence held before the course and innovation, as described above, engaging physicians in training programs, even more if they do not have management responsibilities, has a major impact on health organizations’ development prospects. The findings also point out that effective management training requires a combination of theory and practice for all categories of professionals, not just for physicians, as observed by Loh [ 5 ].

The main outcome, in general and for all participant categories, is an increased understanding of how healthcare systems work, which anticipates increased competencies. This confirms the importance of knowledge on the healthcare environment [ 31 ], and clarifies the order of aspects impacted by training programs as reported by Ravaghi et al. [ 36 ]: first conceptual, then technical, and finally interpersonal. However, interpersonal outcomes are by far greater for those holding management positions, which extends the findings by Liang et al. [ 31 ]. In particular, participants already managing units report the greatest impacts in terms of ability to understand colleagues’ problems, improvement of professional relationships and collaboration with colleagues from other units. Obviously, participants with management positions, more than others, feel the lack of collaborative and communication skills, which represents one of the main flaws of university education in the field of medicine [ 4 ] and is also often neglected in management training [ 36 ]. This also confirms that different management levels show specific competence requirements and education needs [ 6 , 7 ]. 

It is then important to discuss the negative effect of lessons in presence on the improvement of professional relationships. At first glance, it may sound strange, but its real meaning emerges from a comprehensive interpretation of all the findings. First, it does not mean that remote lessons are more effective, as revealed by the fact that they, as a factor of effectiveness, are attributed very low values and, for all categories of participants, lower values than those attributed to lessons in presence and hybrid lessons. Non-physicians, in particular, attribute them the lowest value at all. At most, remote lessons are viewed as convenient rather than effective. The negative influence of lessons in presence can be explained by the fact that a specific category, i.e., those with management positions, rate this aspect much more important than other participants and, as reported above, find much more benefits in terms of improved relationships from management training. Participants with management positions, due to their tasks and responsibilities, suffer more than others from lack of time to be devoted to course participation. For them, as for the category of non-physicians, lack of time represents the main challenge to effectively attending the course. In the literature, such a problem is well considered, and lack of time is also viewed as a challenge to apply the skills learned during the course [ 36 ]. Considering that class discussion and homogeneity contribute to fostering relationships, a comprehensive reading of the findings reveals that due to workload, participants with management positions see particularly convenient and still effective remote lessons. Furthermore, if the class is formed by participants sharing similar professional backgrounds and management levels, debate is not precluded and interpersonal relationships improved as a consequence. From the observation of single items, it can be concluded that participants with management positions and in general those with higher basic management competencies at the start of the program, prefer more flexible and leaner training programs: intermediate assessment through conversation, self-assessment at the end of the course, more concentrated scheduled lessons and greater use of remote lessons.

Differently from what was found by Walston and Khaliq [ 25 ], the findings highlight that participants with management positions value the impact of management training on career prospects positively. These participants are also those more supported by their own organizations. Conversely, the lack of support, especially in terms of inadequate funds devoted to these initiatives, strongly affects physicians and participants without management positions, which clarifies what this challenge is about and who is mainly affected by it [ 36 ]. Low incentives mean having attended fewer training programs in the past, which, together with less management experience, explains why they have developed less competencies. Among the outcomes of the training program, the little attention paid by organizations is also testified by the lowest values attributed by all categories, except for participants without management positions, to the improvement of relationships with superiors and top management.

In general, the study contributes to a better understanding of the outcomes of management training programs in healthcare and their determinants [ 9 ]. In particular, it sheds light on gaps and education needs [ 1 ] by category of health professionals [ 2 ]. The research findings have major implications for practice, which can be drawn after identifying the four profiles of participants revealed by the study. All profiles share common characteristics, such as value given to debate, diversity of pedagogical approaches and tools and class homogeneity, rather than the need for a deeper comprehension of healthcare systems. However, they present characteristics that determine specific issues and education gaps, which are summarized as follows:

Physicians without management positions: low competencies at the start of the program and scarce incentives for attending the course from their own organization;

Physicians with management positions: they partially compensate for competence gaps through professional experience, suffer from lack of time, and are motivated by the chance to improve their career prospects;

Non-physicians without management positions: they partially fill competence gaps through postgraduate education, suffer from lack of time, and have scarce incentives for attending the course from their own organization;

Non-physicians with management positions: they partially bridge competence gaps through postgraduate education and professional experience, are the most affected by a lack of time, and are motivated by the chance to improve their career prospects.

Recommendations are outlined for different levels of action:

For policymakers, it is suggested to strengthen the ability of higher education courses in medicine and related fields to advance the understanding of healthcare systems’ structure and operation, as well as their current and future challenges. Such a new approach in the design curricula should then have as a main goal the provision of adequate management competencies.

For healthcare organizations, it is suggested to incentivize the acquisition of management competencies by all categories of professionals through postgraduate education and training programs. This means supporting them from both financial and organizational point of view, for instance, in terms of more flexible working conditions. Special attention should be paid to physicians who, even without executive roles, manage resources and directly impact the organization's effectiveness and efficiency levels through their day-by-day activity, and are the players holding the greatest innovative potential within the organization. Concerning the executives, especially in the current changing context of healthcare systems, much higher attention should be paid to fostering interpersonal skills, in terms of communication and cooperation.

For those designing training programs, it is suggested to tailor courses on the basis of participants’ profiles, using different pedagogical approaches and tools, for instance, in terms of teacher composition, lesson delivery methods and learning assessment methods, while preserving class homogeneity in terms of professional backgrounds and management levels to facilitate constructive dialogue and solution finding approaches. Designing ad hoc training programs would give the possibility to meet the needs of participants from an organizational point of view as well as, for instance, in terms of program length and lesson concentration.

Limitations

This study has some limitations, which pave the way for future research. First, it is context-specific by country, since it is carried out within the INHS, which mandatorily requires health professionals to attend management training programs to hold certain positions. It is then context-specific by training program, since it focuses on management training programs providing participants with the title to be appointed as a director of a ward or administrative unit in a public healthcare organization. This determines the kind of management competencies included in the study, which are those mandatorily required for such a middle-management category. Therefore, there is a need to extend research and test these findings on different types of management training programs, participants and countries. Second, this study is based on a survey of participants’ perceptions, which causes two kinds of unavoidable issues: although based on the literature and pre-tested, the questionnaire could not be able to measure what it intends to or capture detailed and nuanced insights from respondents, and responses may be affected by biases due to reactive effects. Third, a backward elimination method was adopted to select variables in model building. Providing a balance between simplicity and fit of models, this variable selection technique is not consequences-free. Despite advantages such as starting the process with all variables included, removing the least important early, and leaving the most important in, it also has some disadvantages. The major is that once a variable is deleted from the model, it is not included anymore, although it may become significant later [ 38 ]. For these reasons, it is intended to reinforce research with new data sources, such as teachers’ perspectives and official assessments, and different variable selection strategies. A combination of qualitative and quantitative methods for data elaboration could then be used to deepen the analysis of the relationships between motivations, effectiveness factors and outcomes. Furthermore, since the investigation of competence development, acquisition of new competencies and the transfer of acquired competencies was beyond the purpose of this study, a longitudinal approach will be used to collect data from participants attending future training programs to track changes and identify patterns.

Availability of data and materials

An English-language version of the questionnaire used in this study is attached to this paper as a supplementary file. The raw data collected via the questionnaire are not publicly available due to privacy and other restrictions. However, datasets generated and analyzed during the current study may be available from the corresponding author upon reasonable request.

Abbreviations

Italian National Health System

Kaiser–Meyer–Olkin

National Recovery and Resilience Plan

Liang Z, Howard PF, Wang J, Xu M, Zhao M. Developing senior hospital managers: does ‘one size fit all’? – evidence from the evolving Chinese health system. BMC Health Serv Res. 2020;20(281):1–14. https://doi.org/10.1186/s12913-020-05116-6 .

Article   Google Scholar  

Kakemam E, Liang Z. Guidance for management competency identification and development in the health context: a systematic scoping review. BMC Health Serv Res. 2023;23(421):1–13. https://doi.org/10.1186/s12913-023-09404-9 .

European Commission. Annual Sustainable Growth Strategy. 2020. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0575&from=en

Blakeney EAR, Ali HN, Summerside N. Sustaining improvements in relational coordination following team training and practice change: a longitudinal analysis. Health Care Manag Rev. 2021;46(4):349–57. https://doi.org/10.1097/HMR.0000000000000288 .

Loh E. How and why medically-trained managers undertake postgraduate management training - a qualitative study from Victoria. J Health Organ Manag. 2015;29(4):438–54. https://doi.org/10.1108/jhom-10-2013-0233 .

Article   PubMed   Google Scholar  

Yarbrough LA, Stowe M, Haefner J. Competency assessment and development among health-care leaders: results of a cross-sectional survey. Health Serv Manag Res. 2012;25(2):78–86. https://doi.org/10.1258/hsmr.2012.012012 .

Liang Z, Blackstock FC, Howard PF, Briggs DS, Leggat SG, Wollersheim D, Edvardsson D, Rahman A. An evidence-based approach to understanding the competency development needs of the health service management workforce in Australia. BMC Health Serv Res. 2018;18(976):1–12. https://doi.org/10.1186/s12913-018-3760-z .

Whaley A, Gillis WE. Leadership development programs for health care middle managers: an exploration of the top management team member perspective. Health Care Manag Rev. 2018;43(1):79–89. https://doi.org/10.1097/HMR.0000000000000131 .

Campbell C, Lomperis A, Gillespie K, Arrington B. Competency-based healthcare management education: the Saint Louise University experience. J Health Adm Educ. 2006;23:135–68.

PubMed   Google Scholar  

Issel ML. Value Added of Management to Health Care Organizations. Health Care Manag Rev. 2020;45(2):95. https://doi.org/10.1097/HMR.0000000000000280 .

Lega F, Prenestini A, Spurgeon P. Is management essential to improving the performance and sustainability of health care systems and organizations? a systematic review and a roadmap for future studies. Value Health. 2013;16(1 Suppl.):S46–51. https://doi.org/10.1016/j.jval.2012.10.004 .

Bloom N, Propper C, Seiler S, Van Reenen J. Management practices in hospitals. Health, Econometrics and Data Group (HEDG) working papers 09/23, HEDG, c/o department of economics, University of York. 2009.

Vainieri M, Ferrè F, Giacomelli G, Nuti S. Explaining performance in health care: how and when top management competencies make the difference. Health Care Manag Rev. 2019;44(4):306–17. https://doi.org/10.1097/HMR.0000000000000164 .

Del Vecchio M, Carbone C. Stabilità dei Direttori Generali nelle aziende sanitarie. In: Anessi Pessina E, Cantù E, editors. Rapporto OASI 2002 L’aziendalizzazione della sanità in Italia. Milano, Italy: Egea; 2002. p. 268–301.

Google Scholar  

McAlearney AS. Leadership development in healthcare: a qualitative study. J Organ Behav. 2006;27:967–82.

McAlearney AS. Using leadership development pro- grams to improve quality and efficiency in healthcare. J Healthcare Manag. 2008;53:319–31.

McAlearney AS. Executive leadership development in U.S. health systems. J Healthcare Manag. 2010;55:207–22.

McAlearney AS, Fisher D, Heiser K, Robbins D, Kelleher K. Developing effective physician leaders: changing cultures and transforming organizations. Hosp Top. 2005;83(2):11–8.

Thompson JM, Kim TH. A profile of hospitals with leadership development programs. Health Care Manag. 2013;32(2):179–88. https://doi.org/10.1097/HCM.0b013e31828ef677 .

Figueroa C, Harrison R, Chauhan A, Meyer L. Priorities and challenges for health leadership and workforce management globally: a rapid review. BMC Health Serv Res. 2019;19(239):1–11. https://doi.org/10.1186/s12913-019-4080-7 .

Sarto F, Veronesi G. Clinical leadership and hospital performance: assessing the evidence base. BMC Health Serv Res. 2016;16(169):85–109. https://doi.org/10.1186/s12913-016-1395-5 .

Morandi F, Angelozzi D, Di Vincenzo F. Individual and job-related determinants of bias in performance appraisal: the case of middle management in health care organizations. Health Care Manag Rev. 2021;46(4):299–307. https://doi.org/10.1097/HMR.0000000000000268 .

Tasi MC, Keswani A, Bozic KJ. Does physician leadership affect hospital quality, operational efficiency, and financial performance? Health Care Manag Rev. 2019;44(3):256–62. https://doi.org/10.1097/hmr.0000000000000173 .

Hopkins J, Fassiotto M, Ku MC. Designing a physician leadership development program based on effective models of physician education. Health Care Manag Rev. 2018;43(4):293–302. https://doi.org/10.1097/HMR.0000000000000146 .

Walston SL, Khaliq AA. The importance and use of continuing education: findings of a national survey of hospital executives. J Health Admin Educ. 2010;27(2):113–25.

Walston SL, Chou AF, Khaliq AA. Factors affecting the continuing education of hospital CEOs and their senior managers. J Healthcare Manag. 2010;55(6):413–27. https://doi.org/10.1097/00115514-201011000-00008 .

Garman AN, McAlearney AS, Harrison MI, Song PH, McHugh M. High-performance work systems in health- care management, part 1: development of an evidence-informed model. Health Care Manag Rev. 2011;36(3):201–13. https://doi.org/10.1097/HMR.0b013e318201d1bf .

MacDavitt K, Chou S, Stone P. Organizational climate and healthcare outcomes. Joint Comm J Qual Patient Saf. 2007;33(S11):45–56. https://doi.org/10.1016/s1553-7250(07)33112-7 .

Singer SJ, Hayes J, Cooper JB, Vogt JW, Sales M, Aristidou A, Gray GC, Kiang MV, Meyer GS. A case for safety leadership training of hospital manager. Health Care Manag Rev. 2011;36(2):188–200. https://doi.org/10.1097/HMR.0b013e318208cd1d .

Liang Z, Leggat SG, Howard PF, Lee K. What makes a hospital manager competent at the middle and senior levels? Aust Health Rev. 2013;37(5):566–73. https://doi.org/10.1071/AH12004 .

Liang Z, Howard PF, Koh L, Leggat SG. Competency requirements for middle and senior managers in community health services. Aust J Prim Health. 2013;19(3):256–63. https://doi.org/10.1071/PY12041 .

AbuDagga A, Weech-Maldonado R, Tian F. Organizational characteristics associated with the provision of cultural competency training in home and hospice care agencies. Health Care Manag Rev. 2018;43(4):328–37. https://doi.org/10.1097/HMR.0000000000000144 .

Liang Z, Howard PF, Leggat SG, Bartram T. Development and validation of health service management competencies. J Health Organ Manag. 2018;32(2):157–75. https://doi.org/10.1108/JHOM-06-2017-0120 . (Epub 2018 Feb 8).

Howard PF, Liang Z, Leggat SG, Karimi L. Validation of a management competency assessment tool for health service managers. J Health Organ Manag. 2018;32(1):113–34. https://doi.org/10.1108/JHOM-08-2017-0223 .

Fanelli S, Lanza G, Enna C, Zangrandi A. Managerial competences in public organisations: the healthcare professionals’ perspective. BMC Health Serv Res. 2020;20(303):1–9. https://doi.org/10.1186/s12913-020-05179-5 .

Ravaghi H, Beyranvand T, Mannion R, Alijanzadeh M, Aryankhesal A, Belorgeot VD. Effectiveness of training and educational programs for hospital managers: a systematic review. Health Serv Manag Res. 2020;34(2):1–14. https://doi.org/10.1177/0951484820971460 .

Woltring C, Constantine W, Schwarte L. Does leadership training make a difference? J Public Health Manag Prac. 2003;9(2):103–22.

Chowdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Comm Health. 2020;8(1):1–7. https://doi.org/10.1136/fmch-2019-000262 .

Download references

Acknowledgements

Not applicable.

DM 737/2021 risorse 2022–2023. Funded by the European Union - NextGenerationEU.

Author information

Authors and affiliations.

Department of Economics and Business, University of Sassari (Italy), Via Muroni 25, Sassari, 07100, Italy

Lucia Giovanelli & Nicoletta Fadda

Department of Humanities and Social Sciences, University of Sassari (Italy), Via Roma 151, 07100, Sassari, Italy

Federico Rotondo

You can also search for this author in PubMed   Google Scholar

Contributions

Although all the authors have made substantial contributions to the design and drafting of the manuscript: LG and FR conceptualized the study, FR and NF conducted the analysis and investigation and wrote the original draft; LG, FR and NF reviewed and edited the original draft, and LG supervised the whole process. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Federico Rotondo .

Ethics declarations

Ethics approval and consent to participate.

The research involved human participants. All authors certify that participants decided to take part in the analysis voluntarily and provided informed consent to participate. Participants were granted total anonymity and were adequately informed of the aims, methods, institutional affiliations of the researchers and any other relevant aspects of the study. In line with the Helsinki Declaration and the Italian legislation (acknowledgement of EU Regulation no. 536/2014 on January 31st, 2022 and Ministerial Decree of November 30th, 2021), ethical approval by a committee was not required since the study was non-medical and non-interventional.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Giovanelli, L., Rotondo, F. & Fadda, N. Management training programs in healthcare: effectiveness factors, challenges and outcomes. BMC Health Serv Res 24 , 904 (2024). https://doi.org/10.1186/s12913-024-11229-z

Download citation

Received : 15 January 2024

Accepted : 20 June 2024

Published : 07 August 2024

DOI : https://doi.org/10.1186/s12913-024-11229-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Management training programs
• Healthcare professionals
• Factors of effectiveness

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Discover CALS

See how our current work and research is bringing new thinking and new solutions to some of today's biggest challenges.

• Agriculture
• Applied Economics
• Climate Change
• Communication
• Environment
• Global Development
• Health + Nutrition

New tools and data for improving individual cow and herd profitability

• Animal Science

In this webinar Cornell Animal Science Professor Julio Giordano will discuss how data from precision livestock farming technologies can be used to learn about and improve the profitability of individual cows and herds. MyCow$, a novel tool to estimate individual and group level profitability automatically and in real time, will be presented and examples of its use will be provided. He will also discuss recent research on the effects of health and reproductive management on cow profitability. Also, learn how automated health monitoring programs and targeted reproductive management can help increase profitability of herds and subgroups of cows.

• Julio Giordano, Professor, Animal Science

Date & Time

August 21, 2024 12:00 pm - 1:00 pm

More information about this event.

Contact Information

Kathy Barrett

• kfb3 [at] cornell.edu

Departments

Related Events

We openly share valuable knowledge.

Sign up for more insights, discoveries and solutions.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Yearb Med Inform
• v.28(1); 2019 Aug

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management

Mary h. stanfill.

1 United Audit Systems, Inc., Cincinnati, OH, USA

David T. Marc

2 The College of St. Scholastica, Department of Health Informatics and Information Management, Duluth, MN, USA

Objective: This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals. Methods: A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email.

Results: The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education.

Discussion: HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional’s ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use.

Conclusions: AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.

1 Introduction

Health information technology has greatly impacted the health information management (HIM) profession. HIM professionals are part of the allied health team and they support efforts to ensure the availability, accuracy, integrity, and security of healthcare data. The digitizing of healthcare data has greatly impacted the responsibilities and work of HIM professionals requiring many to take on more technical roles related to the collection, storage, and use of healthcare data.

The digitizing of healthcare data, as well as advancements in computer processing and data storage, has also enabled the development of advanced algorithms in the form of Artificial Intelligence (AI). As of 2011, the U.S. Agency for Healthcare Research and Quality (AHRQ) had compiled over 17,000 algorithms and computer programs for healthcare evaluation, treatment, and administration 1 . In a recent white paper on AI in Radiology, the Canadian Association of Radiologists stated “In the next 5 years, Canadian radiologists will see more competent AI applications incorporated into PACS workflows, especially for laborious tasks prone to human error such as detection of lung nodules on x-rays or bone metastases on CT.” 2 .

Multiple factors are driving the development of AI in healthcare. In the United States (U.S.), legislative pressures are mounting to keep pace with other countries regarding AI developments 3 . There are financial pressures on the healthcare industry globally, with increasing demands due to growing and aging population. The industry needs labor-saving technology and techniques to better understand the health of the population while managing the health of a greater number of people and saving money 4 . AI, whether or not it eliminates the need for a person to fill a job, can make the workforce more efficient 5 – 9 . Accenture estimates that “key clinical health AI applications” can create $150 billion in annual savings for the U.S. healthcare economy by 2026 10 . Even if a fraction of that figure is realized, that is a powerful incentive for adopting AI solutions.

Beyond economic concerns, an additional driver of AI technology is the sheer volume of healthcare data. Healthcare is experiencing an information boom. “The rapid expansion of scientific knowledge and pace of technological development have resulted in an overwhelming sea of data that is difficult to decipher and apply.” 11 . Physicians are drowning in data that requires ever more sophisticated interpretation, yet are still expected to perform efficiently. The promise that AI “augments decision making by clinicians by uncovering clinically relevant information hidden in a massive amount of data” 5 is extremely enticing, particularly now, when there are clinician shortages worldwide. The needs-based shortage of healthcare workers globally is estimated at approximately 17.4 million 12 . According to the Canadian Association of Radiologists, “...there is evidence that AI can improve the performance of clinicians and that both clinicians and AI working together are better than either alone” 2 . Indeed, AI technology is necessary to achieve the goal of “precision medicine”. Precision medicine is an emerging medical model where medical decisions and treatments are tailored to the patient. “Precision medicine presupposes the availability of massive computing power and algorithms that can learn by themselves at an unprecedented rate” 5 .

Predictions on when healthcare will experience widespread deployment of disruptive AI applications vary widely. Though AI is developing rapidly, and there are current and imminent uses of AI in healthcare, it is still largely immature. According to witnesses who testified before the U.S. Subcommittee on Information Technology of the House Committee on Oversight and Government Reform at a series of hearings on AI held in 2018, “narrow” AI, i.e. systems focused on specific tasks, is commonly used today but more general systems that can work across multiple tasks are underdeveloped 13 . However, given the pace of development, the timeline for AI in healthcare is years, not decades 14 .

Presuming AI will eventually be widespread and affordable, there are implications for the management of healthcare data and information in an AI-enabled world which can greatly impact the HIM profession. The purpose of this paper is to describe the results of a literature review and the findings from interviews with key HIM leaders. The paper explores the relationship of the HIM profession and AI, focusing on the following key aspects: 1) Changes in HIM practices for specific HIM use cases, including automated medical coding and management of AI-based information; 2) Changes in management of healthcare data and the need for evolving data practices and data governance; 3) Legal, ethical, and regulatory data challenges; and 4) Changes in the HIM workforce, including foreshadowing new roles and skills that are required. The conclusion presents steps the HIM profession can take now to help advance the development of reliable AI applications and to respond to their use in healthcare.

2 Changing Health Information Management Practices

A core responsibility of the HIM profession is ensuring the right information is provided to the right people to enable quality patient care 15 . Increased adoption of AI-enabled applications and more sophisticated use of AI systems by healthcare providers at the point of care have significant implications for HIM practices. These include practical implications both for common HIM processes, such as medical coding, as well as more generally the core HIM responsibility to manage health data and information. This section explores the impact of AI systems on HIM practices for the following use cases:

• Automated medical coding;
• AI-based diagnosis specificity;
• AI-based early detection information.

Each use case includes examples of the anticipated use of AI, discusses the associated impact to current HIM processes and practices, and explores new opportunities and challenges to adapt HIM practices.

2.1 Automated Medical Coding

A systematic literature review of published studies evaluating the performance of automated coding and classification systems indicated that automated coding systems have been in use since at least the mid 1990’s 16 . Computer-assisted coding (CAC) is the term that refers to the automated generation of medical codes reported on healthcare claims that are derived from clinical documentation. CAC applications have been available since the early 2000’s 17 with adoption rates increasing markedly in recent years. According to a report available through Research and Markets, the global market for CAC software is projected to reach $4.75 billion by 2022 at a compound annual growth rate of11.5% 18 . North America is seeing the largest growth followed by Europe, Asia-Pacific, and the rest of the world.

CAC applications use natural language processing (NLP) to read and interpret clinical documentation in patient health records and suggest applicable diagnosis and procedure codes. Typically, a person reviews the suggested codes to determine the final code selection. This computer-assisted approach to the medical coding process is becoming more common and has been credited with measurable gains in coder productivity 19 , 20 . However, productivity impacts vary widely, depending on the specific deployment. Some studies reported a drop in productivity when medical coders were forced to validate, and frequently eliminate, a large number of suggested codes. Still, a Cleveland Clinic study found that CAC increased their coder productivity by over 20% without reducing quality when suggested codes were reviewed and edited by a medical coder 19 . The referenced Cleveland Clinic study found that CAC alone, without the intervention of a credentialed coder, however had a lower recall and precision rate.

Adoption of CAC requires reengineering the medical coding workflow to fully integrate the CAC tool in the process and gain optimal efficiency 21 . Early adopters of CAC in the U.S. reported that CAC had “. improved medical coding workflows, increased medical coding accuracy, and balanced medical coding resources to focus on more volume and complex cases” 22 . Not all hospitals however have experienced these benefits 23 . Some implementations have failed entirely. Effective implementation of a CAC application requires interfaces to work properly so the application can read all documents relevant for coding. In addition clinical documents must comply with a consistent format dictated by the CAC vendor 24 . And where CAC has been most effective, a new role has emerged to fine tune the rules and train the system to adapt as the code sets and reporting requirements change.

As the technology advances, and machine learning techniques improve the capabilities of CAC tools, the medical coding workflow will further evolve. A WinterGreen market shares research report released in 2017 stated that as much as 88% of medical coding in physician offices for billing purposes could occur automatically without human review 25 . This report requires independent validation and more research is needed on the accuracy of these systems to rely on them, but advancements in CAC are poised to further augment the medical coding process. Medical coding is a significant responsibility of many HIM professionals currently and this role will continue to evolve.

There are significant opportunities for medical coding professionals as CAC advances to increase coding efficiency. The fully automated coding workflow requires reengineering and a focus on data quality, which medical coders, with their intimate knowledge of the code sets and reporting requirements, are uniquely qualified to address. In addition to assigning or validating codes on complex cases, medical coders could also focus on validating aberrant coded data patterns across large groups of cases. For example, a medical coder has the knowledge to question the use of a code for an acute phase of a condition repeatedly for a patient, when the more likely data pattern would be the acute code followed by codes for the chronic phase or sequela. This code-specific pattern recognition is key in validating accurate reporting for risk-scoring payment methodologies for example. Clearly, HIM professionals’ ability to identify data patterns to enhance business intelligence or improve compliance with code reporting requirements will be an important skill as automation advances.

2.2 Diagnosis Specificity

AI systems are expected to assist healthcare providers with diagnosis accuracy and specificity. Medical specialties that utilize images for diagnosis (e.g. radiology, pathology, dermatology, ophthalmology) are particularly amendable to AI-aided diagnoses. AI machine learning (ML) is very good at detecting anomalies in images, for example it has been proven effective in detecting lung nodules on a radiologic image 2 , 6 , 9 and congenital cataract as well as diabetic retinopathy on ocular image data 6 , 26 . The sensitivity and specificity of deep learning algorithms, in detecting diabetic retinopathy through retinal fundus photographs, for example, are both over 90%, which is “competitive against experienced physicians in the accuracy for classifying both normal and disease cases” 6 . An algorithm that can identify skin cancer by analyzing images of skin lesions has also performed as well as board-certified dermatologists 26 , 27 . It has been suggested that what might take an experienced radiologist 30 years of radiology-pathology correlation to master may only take an AI system hours or days to analyze and learn in the future 28 .

Code reporting guidelines for using diagnostic test results to add specificity to a diagnosis code vary by country. As AI systems become more adept and are proven reliable in visual diagnosis, the need for physicians to read images may become less necessary, perhaps done only by exception. This change in responsibilities could result in either a decrease in code specificity or less consistency of international diagnosis code data, depending on a country’s code reporting guidelines and how the guidelines are adjusted to account for AI. For example, currently in the U.S., “code assignment is based on the documentation by patient’s provider (i.e., the physician or other qualified healthcare practitioner legally accountable for establishing the patient’s diagnosis)” 29 . U.S. guidelines specifically state that clinically significant “laboratory, x-ray, pathologic, and other diagnostic results” can be used for coding only if the test has been “interpreted by a physician” 29 . In the U.K., the NHS National Clinical Coding Standards, while less explicit than U.S. guidelines, also imply that a physician has to interpret diagnostic test results 30 . In contrast, the Canadian Coding Standards are much more amendable to AI development. Canadian medical coders are directed to use diagnostic results “when they clearly add specificity in identifying the appropriate diagnosis code for conditions documented in the physician/primary care provider notes” 31 . In Canada, there is no specific requirement that the test itself has to be interpreted by a physician. Based on this varying guidance, in the instance where a physician has documented a diagnosis, additional specificity of that diagnosis in images interpreted by an AI system alone (without a physician over-read) would be lost in diagnosis data in the U.S. and possibly the U.K., whereas specificity would not necessarily be lost in Canada.

Medical coding and reporting guidelines and standards will need to be adjusted to account for AI applications. There are multiple points to consider including whether reporting of diagnosis specificity using diagnostic test results should vary depending on the AI application itself. Some method is needed to demonstrate that the AI application meets the same degree of accuracy as physicians. For example, reporting guidelines might depend on whether the AI application is approved or credentialed in some manner. Reporting specificity based on AI results might also depend on whether the AI application is employing supervised verses unsupervised ML techniques. Unsupervised ML is well known for feature extraction, whereas supervised ML, which goes through a training process to determine the best outputs, is more suitable for predictive modeling and is generally considered to provide more clinically relevant results 6 . Thus, the type of AI and how the AI application is used in the clinical workflow (e.g. whether AI-generated interpretations are validated or certified as equally accurate compared to physicians) could potentially be factors in determining future reporting requirements for diagnosis code specificity.

2.3 Early Detection Information

AI systems are expected to assist healthcare providers with early detection of likely or impending conditions, allowing for faster intervention. ML algorithms are proving effective in making inferences about specific health risks and predicting health events. For example, neural network algorithms have proven effective in detecting strokes. Input variables analyzed by the algorithm include stroke-related symptoms such as paresthesia of the arm or leg, acute confusion, vision alteration, problems with mobility, etc. This input data is analyzed to determine the probability of stroke 6 . There are other examples of healthcare data being used to detect and predict future events including hospital readmissions, sepsis, and surgical complications 32 – 34 .

Coding guidelines and standards for reporting suspected or impending conditions also vary from one country to the next. In the U.S., coders are directed to report a condition that remains “suspected and/or impending” at the time of discharge as if it existed or was established for a hospital inpatient admission, but not to code it on an outpatient encounter 29 . For outpatient cases the condition is coded to the highest degree of certainty 29 . Similarly, NHS National Coding Standards instructions are to code the diagnosis being “treated or investigated” and an example is given of a “probable myocardial infarction” reported with the code for an acute unspecified myocardial infarction 30 . According to the Canadian Coding Standards however, impending or threatened conditions are coded only when indexed as such in the Canadian version of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD) ICD-10-CA. In addition, unconfirmed diagnoses in Canada are reported with a specific “Q prefix” to denote the uncertainty associated with the code 31 . This variability and the inability in some countries to qualify reported diagnoses as unconfirmed or uncertain is concerning. Consider for example, if an AI system triggers an alert for suspected sepsis on a patient and the healthcare team takes immediate action, thus intervening and preventing severe sepsis, the coding and reporting of this circumstance may be missed, or inconsistently reported at best. Coding guidelines and standards will need to be revised to capture this sequence of events and support AI developments in early detection of likely or impending conditions. This has broad implications and will require an interdisciplinary team to address the issue fully, including standards developers and members of the healthcare team as well as HIM professionals.

One solution is to capture qualifiers to diagnoses. If the functionality was built into Electronic Health Records (EHRs), the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard framework could potentially be leveraged to qualify diagnoses 35 . For example, the FHIR code system verification status defines codes as provisional, differential, confirmed, and refuted 35 . A status could potentially be added to reflect AI as the source for a condition or diagnosis. Alternatively, diagnosis qualifiers could also be addressed by the clinical terminology or classification system itself, which is demonstrated in

SNOMED CT. Prefixes, such as Canada’s Q prefix, could be defined and appended to ICD codes. Perhaps ICD-11 extension codes could be defined to characterize the degree of certainty of a condition (e.g. unconfirmed, impending) or identify the source for the diagnosis (e.g. clinician, AI system, patient). Again, there are multiple factors to consider. Use of a status, prefix or extension to a code would require some mechanism to ensure it remains linked with the base code. Otherwise data validity would be a major concern. This is the case for example when an “impending” stroke is identified as an actual stroke because the “impending” qualifier was lost. Implications for insurance coverage or payment policy have also to be considered. As the industry continues to refine what is deemed clinically relevant data/information, medical coding standards and guidelines will need to align with such data standards.

3 Changing Data Management Practices

Increased adoption of AI-enabled applications, and more sophisticated use of these AI applications by healthcare providers at the point of care, holds practical implications for managing the data. HIM professionals have an opportunity to help develop, implement, and manage the policies and procedures related to governing healthcare data, as well as to support the development, deployment, and assessment of AI models to ensure that the technology can be trusted to improve care and support greater efficiency.

New and more varied data types are generated by AI-enabled applications affecting data practices and data governance. Today, healthcare data is almost entirely encounter-based. Healthcare data is collected during an encounter with specific interaction with a care provider. However, healthcare data also includes streams of data collected remotely and automatically from multiple data sources. As the Internet of Things (IoT) expands further into healthcare, it is necessary to develop infrastructures to support the proliferation and use of these data streams. IoT is a connection of physical objects with network connectivity that are used to collect and exchange data. ’In IoT, Things’ refers to a device which is connected to the Internet and transfers the device information to other devices. “The future’s data will not be collected solely within the health care setting. The proliferation of mobile sensors will allow physicians of the future to monitor, interpret, and respond to additional streams of biomedical data collected remotely and automatically” 7 . Such applications have been in development for several years. More than five years ago, a blood pressure cuff that connects to a smartphone, and transmits data to a care provider was already available 36 . Devices are also available that measure glucose levels, provide electrocardiogram readings, or even collect measures of people’s cognition and emotional health 37 . As wearable sensors improve, they will increasingly allow specific health parameters to be tracked constantly and discreetly. They may replace commonly worn items such as a watch, may be worn under regular clothing, or even built into “smart” clothing 38 . These types of devices would conceivably transmit data back to a healthcare provider, potentially directly into an EHR, which presents numerous challenges. It will be critical to track the source of this data as the accuracy, value, and clinical significance may be uncertain. In addition, today’s data practices are entirely oriented toward an episode of care. In AI-enabled healthcare, the underlying organizing schema for health data needs to shift from dates of service to the patient. It may require completely different data architecture to collect, store, process, validate, interpret, and potentially retrieve non-episodic ongoing streams of patient-specific data.

Manogaran and colleagues 39 proposed a framework to support the collection, transfer, and storage of data from multiple data streams. They emphasized that the security of data must occur at numerous stages including during the collection of data from devices, the transfer of data between devices, the storage of data, and during the application and use of the data. Additionally, how the data is received from various streams and integrated into a single system poses a challenge. Data streams may include structured, semi-structured, or unstructured data and for integration to occur there is a need for standardization. Initiatives such as International Standard for Metadata Registries (ISO/IEC 11179) aim to support what is referred to as ’semantic interoperability’ between data that may be expressed differently across devices and technologies 40 . Semantic interoperability is intended to support the unambiguous exchange of data. One method for standardization is to create globally unique cross-reference identifiers for data elements that are semantically equivalent using extensible Markup Language (XML) standards, even though the data elements may have different names 40 . The Open Data Element Framework (O-DEF) was developed by The Open Group and can support the categorization, naming, and indexing of data using a controlled vocabulary that associates data elements with structured unique identifiers so that equivalencies and similarities between data can be easily determined 41 . These identifiers can be the basis of an indexing schema where a data element from one device can be integrated with a data element from another device because they both share the same equivalent content evidenced by the same structured unique identifier. O-DEF works well for collaborating enterprises, but may not serve the purpose of integrating data from disparate systems and organizations. Alternatively, other frameworks such as those from the World Wide Web Consortium (W3C) that focus on data integration of web-based data like RDF (Resource Description Framework), OWL (Ontology Web Language), and SKOS (Simple Knowledge Organization System) may be more useful 42 . Data integration challenges will require an interdisciplinary team to address the issue. HIM professionals can seek to examine how existing information models can be leveraged within an organization to support a data governance framework that accommodates multiple data streams. The utilization of existing vocabularies may serve to accelerate the collection and use of data from non-episodic sources.

An additional challenge is the need for quality healthcare data. ML techniques require substantial amounts of data to ensure algorithms work accurately and are applied appropriately to their targeted goals. “ML algorithms are highly data hungry, often requiring millions of observations to reach acceptable performance levels” 14 . Thus researchers and developers need access to large sets of health data from thousands of patients. The reliability of an AI application is dependent upon the quality of the data that was used to develop and train it. “At its core, AI is reliant upon data. If the data itself is incomplete, biased, or skewed in some other fashion, the AI system is at risk of being inaccurate” 13 . However, it’s widely recognized in the U.S. that data in EHRs and claims databases need “careful curation and processing before they are usable” 14 . Healthcare data are highly heterogeneous, ambiguous, noisy, and incomplete 26 . Data curation (i.e., managing data to make it more useful) requires significant financial investment and without investing resources to support data curation the healthcare industry risks producing ML models based on factually inaccurate data 8 . The adoption of data governance principles can help organizations ensure that the people, processes, and systems involved in AI initiatives are held accountable for ethical use and deployment, the process is transparent, the result has integrity, the information is protected, the approach is compliant with organizational and legal practices, the technology is available, the method of AI development is retained, and when appropriate the healthcare data is disposed of properly 43 . These principles can help support the use of AI models that minimize the risk to patients, providers, developers, and healthcare organizations.

Evolving data governance principles are necessary and must be a priority for all healthcare organizations. Developing clear, consistent, and standardized policies and procedures for creating and managing current and emerging sources of data is a key enabler to development of AI applications. Data sources can include EHR data, lab data, imaging data, claims data, various types of master data (e.g., enterprise master patient index), patient-generated data, and metadata as well as a real-time streaming data from medical devices. Several issues need to be managed, such as data sparsity, redundancy, and missing values 26 . Data governance, including data modeling, data standards and definitions, data mapping, data auditing, data quality controls, and data quality management, must keep pace with evolving data types and data uses. For example, data quality management in healthcare organizations today focuses on assuring data is fit for use for the organization’s business operations, decision-making and planning. More focus is needed on detecting, assessing, and fixing data defects in a systematic way. Data governance has never been a higher priority in healthcare as it “empowers users to trust the predictions of analytics models in their decision-making because there is certainty that the data and algorithms can be trusted” 44 .

As advances in AI enable precision medicine, HIM professionals will need to develop practices to enable precision HIM. Treating all healthcare data and information the same will no longer be practical or efficient in an era of big data. More robust data analytics and processes need to be established to identify data patterns and trends and address data outliers. “Precision medicine attempts to ensure that the right treatment is delivered to the right patient at the right time by taking into account several aspects of patient’s data, including variability in molecular traits, environment, EHRs and lifestyle” 26 . Precision HIM attempts to ensure the right data and information is delivered to the right person at the right time by taking into account the data source and the people, processes, and technology that interface with that data to ensure it is used and reused appropriately.

4 Legal, Ethical, and Regulatory Data Challenges

The use of healthcare data to develop AI applications has introduced substantial legal, ethical, and regulatory challenges. Patient privacy is a key concern affecting how AI is developed and tested. Development of AI applications may require updates to privacy and confidentiality laws and regulations, which vary widely. In the U.K., protection of health information centers on obtaining explicit consent from the patient in order to share information with any third party that is not in a direct care relationship with the patient. Researchers must apply to the Health Research Authority’s Confidentiality Advisory Group (CAG) for approval to access confidential patient information without patients’ consent 45 . In the U.S., government regulation is less strict. Privacy and confidentiality of protected health information are addressed in the Health Insurance Portability and Accountability Act (HIPAA). HIPAA provides data privacy and security provisions for safeguarding medical information and allows for sharing protected health information without patient consent specifically for the purposes of “treatment, payment and operations” 46 . How the U.K. or U.S. approaches will be interpreted on cases related to data sharing for AI development is largely undetermined. The U.K. consent requirement, and the definition of a “direct care relationship,” was challenged in 2017 in a published case study. The case study alleged that a technology company, Google DeepMind, did not have a direct patient care relationship with every patient included in the data shared and thus “held data on millions of Royal Free patients and former patients since November 2015, with neither consent, nor research approval” 47 . This case study underscores the need to examine current privacy laws and regulations to determine how they may apply to AI applications. The U.S. Subcommittee on Information Technology recommends that federal agencies conduct such a review and, where necessary, update existing regulations to account for the addition of AI 13 . HIM professionals are involved with developing and implementing organizational policies regarding privacy and security of health information, training staff, and ensuring compliance. Therefore, with the access and use of health information for the development and deployment of AI models, HIM professionals should explore current privacy practices considering how they might apply to AI applications and how they might be amended to account for AI technology.

In addition to data privacy and protection, another looming legal issue is liability and accountability for the use of AI applications. Questions on who is ultimately liable for patient care decisions based on, or aided by, an AI application are yet to be answered. Should healthcare providers be held fully responsible for decisions suggested by algorithms they cannot understand? Will physicians use a system they cannot understand? Can the developer be held responsible? The problem is complicated since the reasoning in an AI application is difficult, often too complex to understand 48 . AI applications evolve and change constantly in unforeseeable ways as they are “learning” from data 9 . Though mechanisms to ensure AI applications are safe and effective are still being formulated, prevailing approaches include an expectation that algorithms can be inspected. “Each algorithm should be able to explain its output” 13 . To advance deployment and acceptance of AI applications, developers will need to be able to produce the algorithm for inspection, support why the algorithm works, and ensure the application can meet expected outcomes in testing or certification procedures. Product master data, which includes data about the components that make up the product, may include information on the algorithm deployed. In the future, individual patient health information may include the algorithm that was applied to the patient’s data in order to validate or authenticate healthcare decisions. In addition, there may be a need to audit AI events for reporting purposes. HIM professionals can establish the necessary data governance principles that must be adopted for AI applications to be implemented successfully within healthcare organizations.

Another aspect that deserves attention is the need to balance the financial incentive to make processes more efficient with the ethical and legal uses of health information. For example, the financial motivators to adopt CAC for the sole purpose of coding a higher level of care must be tempered by ethical considerations. HIM professionals involved in the clinical coding process can greatly impact the amount of funding provided to a healthcare organization. Hoyle 49 and Shepherd 50 argued that HIM professionals are positioned as advocates for the ethical use of technology and data. HIM professionals must urge healthcare organizations to consider the ethical frameworks and practice guides not just deemed appropriate for health information professionals, but also for CAC and AI technologies. These activities will provide support for the HIM professionals in healthcare organization to go about the business of “providing the clinical truth in their coding and resisting the perverse incentives” 50 . Therefore, with the access and use of health information for the development and deployment of AI models, HIM professionals should be involved to ensure policies and procedures are being developed, amended accordingly, and followed to account for the influence of AI technology. Although HIM professionals are just beginning to work with AI technology, there has already been notable impacts on the HIM workforce.

5 Response of the Health Information Management Workforce

Healthcare technology has greatly impacted the way care is approached and delivered. The digitizing of healthcare data has supported efforts to automate processes that were previously done manually. These processes have inevitably impacted the healthcare workforce, including the HIM profession. There is a greater need for employees that have technical skills to better collect, manage, and use healthcare data. Sandefer and colleagues 51 evaluated data from a workforce survey that yielded responses from 6,475 healthcare professionals that were largely from HIM. The survey asked respondents to rate the percentage of their time they spent on current tasks and how much they anticipate they will spend on these tasks 10 years in the future. The findings of the study suggested that many HIM professionals spent significant time on diagnostic and procedural coding and records processing, but they expected these tasks to decline the most in the future while leadership, teaching, and informatics tasks are expected to increase. Historically, the HIM profession has focused on medical records and coding. However, the profession has evolved into more diverse roles and continues to change with technological advances. Today, many HIM professionals find themselves in diverse roles related to healthcare leadership, teaching, technology, compliance, quality, and informatics 51 , 52 .

In 2018, Sandefer 53 evaluated data from a workforce survey of 274 senior-level professionals within clinical (e.g., hospitals, clinics) and non-clinical (e.g., software vendors, consulting firms) organizations. The goal of the survey was to identify the needed job skills, competencies, and education required by HIM professionals to meet future workforce needs. Seventy-two percent of clinical respondents reported that at least half of coding functions will be automated, and 50 % reported that more than half of the coding functions will be automated in the near future. The paper suggests that the application of natural language processing combined with the quality of voice to text translation will support improvements in extracting meaning from unstructured data, which will greatly revolutionize the healthcare industry.

Automation is also expected to impact the HIM workforce beyond just influencing how diagnostic and procedural coding is approached. Data analytics has been more prolific across the profession. More professionals are moving into roles to evaluate data related to financial, operational, and clinical performance 54 . HIM professionals are becoming more involved with developing solutions for healthcare organizations to better manage and use data. For instance, HIM professionals are actively participating in the development of policies, procedures, and best practices to ensure data are being used ethically and abiding by the required laws when research or data reporting is being adopted 55 . However, in the future, HIM professionals are going to need to be more involved in developing similar policies and procedures to accommodate AI developments. To date, there is very little attention on the needs for data governance to support AI. Without having a workforce to support AI data governance, there will likely be barriers to widespread adoption and use. For instance, past efforts to implement ICU mortality risk scores have been met with reluctance due to a lack of trust in the technology, despite the obvious benefits the technology may serve 56 . By engaging more stakeholders in the development of the technology, including HIM professionals, a culture of acceptance may be achieved by adopting principles of data governance that offer enterprise-wide technology support.

The evolving use of healthcare data for AI applications is already impacting the roles and responsibilities of HIM professionals. HIM professionals are findings themselves in more leadership roles that govern healthcare data and technology, and more technical roles that involve the access and use of healthcare data for reporting and evaluation purposes 51 . With some tasks being automated, there will likely be continuing opportunity for HIM professionals to take on more tasks that focus on the data collection, validation, analysis, and overall the ethical use of that data. HIM professionals who currently find themselves working in medical coding who embrace automated coding have an opportunity to transition into a role that focuses on data validation to improve the quality of healthcare data. However, to emerge into these roles, these professionals will need technical training related to methods and tools for data storage, acquisition, and analytics. With advancements in technology, many professions are realizing the need for greater competence in computational thinking skills to better translate data into abstract concepts and understand data-based reasoning 57 . Although exact details on how AI technologies will impact the future of HIM are not yet known, current workforce studies suggest that HIM professionals are going to continue to work in more technical roles and will therefore support AI developments and use.

6 Conclusion

AI has and will continue to impact the way decisions are made in healthcare. For example, decisions are influenced by ML algorithms that support the prediction of future events, or the use of clinical decision support systems that aid in the detection of anomalies in diagnostic images. The decisions that HIM professionals make are also being impacted. For instance, CAC has supplemented a medical coder’s role in selecting diagnostic and procedural codes for healthcare claims. The promise that AI can support a more efficient decision-making process with greater accuracy is certainly a promise worth exploring. HIM professionals should participate in efforts to align medical coding standards and guidelines with evolving data types and standards. In addition, as AI technologies present new and varied types of source data, HIM professionals have an opportunity to influence the development of mechanisms to collect and integrate emerging data types, including non-episodic ongoing streams of patient data and algorithms in product master data for example. The adoption of data standards and vocabularies that support semantic interoperability is part of the solution to the data integration challenge 40 and one that HIM professionals should participate in evaluating and testing.

HIM professionals should also participate in developing the data governance framework within healthcare organizations to establish mechanisms to collect emerging data types from various sources, manage the policies and procedures related to the access and use of data, and develop methods to validate the reliability and impact of AI technology. This includes considering how evolving data structures impact the use and reuse of data and the related policy implications (e.g., data reporting requirements, payment policy). It also includes for example ensuring data governance practices include product master data (e.g., data about the algorithms deployed) to support efforts to audit, inspect, or certify AI applications. These endeavors will require HIM professionals to have the technical knowledge to analyze and monitor AI tools and the necessary technical skills related to collecting and managing healthcare data in AI-enabled healthcare. To acquire such technical skills, HIM professionals may need to seek additional education or training.

There are significant data management practices as well as laws and regulations surrounding the use of healthcare data that have the potential to either impede or enable development of AI applications. HIM professionals can support future AI developments today by increasing data validation efforts and beginning to evaluate relevant policies and processes. HIM professionals should analyze coded data patterns and establish processes to validate coded data across large groups of cases. HIM professionals must focus on detecting, assessing, and fixing data defects in a systematic way in order to improve the quality of current healthcare data that is being used to develop AI applications. Other examples of steps HIM professionals can take now include ensuring the proper laws and regulations are being followed (e.g., ensuring only authorized personnel and technology accesses clinical data), beginning to explore current privacy practices in light of how they may apply to AI applications, and establishing collaborative relationships with data standards developers and informaticists involved in developing AI applications.

Although there is an emphasis on creating policies and procedures to accommodate AI technology, HIM professionals will also find that there are emerging opportunities for careers related to the greater adoption of AI. HIM professionals are well situated to proactively manage and monitor data governance, data sets, and data models related to the implementation and use of AI. AI technologies are not intended to replace healthcare workers, but individuals who are able to adapt to new workflows and processes may replace those who cannot. There are wonderful opportunities for career moves and advancements for those who continue to increase their knowledge of data analytical methods and tools.

The future of AI holds the promise of a more effective and efficient healthcare system built on a strong foundation of reliable and accurate data. HIM professionals manage and support the entire continuum of healthcare data from the collection of the data to the use and disposition of that data. AI technology will continue to evolve and so will the role that HIM professionals would play to support this technology. The challenge for HIM professionals is to identify leading practices to achieve precision HIM and develop practice standards for the management of healthcare data and information in an AI-enabled world.

• Environment
• Science & Technology
• Business & Industry
• Health & Public Welfare
• Topics (CFR Indexing Terms)
• Public Inspection
• Presidential Documents
• Document Search
• Advanced Document Search
• Public Inspection Search
• Reader Aids Home
• Office of the Federal Register Announcements
• Using FederalRegister.Gov
• Understanding the Federal Register
• Recent Site Updates
• Federal Register & CFR Statistics
• Videos & Tutorials
• Developer Resources
• Government Policy and OFR Procedures
• Congressional Review
• My Clipboard
• My Comments
• My Subscriptions
• Sign In / Sign Up
• Site Feedback
• Search the Federal Register

This site displays a prototype of a “Web 2.0” version of the daily Federal Register. It is not an official legal edition of the Federal Register, and does not replace the official print version or the official electronic version on GPO’s govinfo.gov.

The documents posted on this site are XML renditions of published Federal Register documents. Each document posted on the site includes a link to the corresponding official PDF file on govinfo.gov. This prototype edition of the daily Federal Register on FederalRegister.gov will remain an unofficial informational resource until the Administrative Committee of the Federal Register (ACFR) issues a regulation granting it official legal status. For complete information about, and access to, our official publications and services, go to About the Federal Register on NARA's archives.gov.

The OFR/GPO partnership is committed to presenting accurate and reliable regulatory information on FederalRegister.gov with the objective of establishing the XML-based Federal Register as an ACFR-sanctioned publication in the future. While every effort has been made to ensure that the material on FederalRegister.gov is accurately displayed, consistent with the official SGML-based PDF version on govinfo.gov, those relying on it for legal research should verify their results against an official edition of the Federal Register. Until the ACFR grants it official status, the XML rendition of the daily Federal Register on FederalRegister.gov does not provide legal notice to the public or judicial notice to the courts.

Design Updates: As part of our ongoing effort to make FederalRegister.gov more accessible and easier to use we've enlarged the space available to the document content and moved all document related data into the utility bar on the left of the document. Read more in our feature announcement .

Submission for Office of Management and Budget Review; Behavioral Interventions To Advance Self-Sufficiency-Next Generation (BIAS-NG) (Office of Management and Budget #0970-0502)

A Notice by the Children and Families Administration on 08/14/2024

This document has been published in the Federal Register . Use the PDF linked in the document sidebar for the official electronic format.

• Document Details Published Content - Document Details Agencies Department of Health and Human Services Administration for Children and Families Document Citation 89 FR 66115 Document Number 2024-18065 Document Type Notice Pages 66115-66117 (3 pages) Publication Date 08/14/2024 Published Content - Document Details
• View printed version (PDF)
• Document Dates Published Content - Document Dates Dates Text 8/31/2025.) Under this overarching clearance, ACF collects data as part of rapid cycle testing and evaluation, to inform the design of interventions informed by behavioral science and to better understand the mechanisms and effects of such interventions. Interventions have been and will continue to be developed in the program area domains of Temporary Assistance for Needy Families (TANF), child welfare, and Early Head Start/Head Start (EHS/ HS). This revision would also allow for collection of data in the child care program area, and would extend the approval of the overarching generic. These interventions are intended to improve outcomes for participants in these programs. Published Content - Document Dates

This table of contents is a navigational tool, processed from the headings within the legal text of Federal Register documents. This repetition of headings to form internal navigation links has no substantive legal effect.

SUPPLEMENTARY INFORMATION:

This feature is not available for this document.

Additional information is not currently available for this document.

• Sharing Enhanced Content - Sharing Shorter Document URL https://www.federalregister.gov/d/2024-18065 Email Email this document to a friend Enhanced Content - Sharing
• Print this document

Document page views are updated periodically throughout the day and are cumulative counts for this document. Counts are subject to sampling, reprocessing and revision (up or down) throughout the day.

This document is also available in the following formats:

More information and documentation can be found in our developer tools pages .

This PDF is the current document as it appeared on Public Inspection on 08/13/2024 at 8:45 am.

It was viewed 0 times while on Public Inspection.

If you are using public inspection listings for legal research, you should verify the contents of the documents against a final, official edition of the Federal Register. Only official editions of the Federal Register provide legal notice of publication to the public and judicial notice to the courts under 44 U.S.C. 1503 & 1507 . Learn more here .

Document headings vary by document type but may contain the following:

• the agency or agencies that issued and signed a document
• the number of the CFR title and the number of each part the document amends, proposes to amend, or is directly related to
• the agency docket number / agency internal file number
• the RIN which identifies each regulatory action listed in the Unified Agenda of Federal Regulatory and Deregulatory Actions

See the Document Drafting Handbook for more details.

Department of Health and Human Services

Administration for children and families.

Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.

Request for public comments.

The Office of Planning, Research, and Evaluation (OPRE) in the Administration for Children and Families (ACF), U.S. Department of Health and Human Services (HHS) requests Office of Management and Budget (OMB) approval to modify and extend the approval of the ACF Behavioral Interventions to Advance ( print page 66116) Self-Sufficiency-Next Generation (BIAS-NG) Project Overarching Generic (OMB #: 0970-0502; Expiration date: 8/31/2025.) Under this overarching clearance, ACF collects data as part of rapid cycle testing and evaluation, to inform the design of interventions informed by behavioral science and to better understand the mechanisms and effects of such interventions. Interventions have been and will continue to be developed in the program area domains of Temporary Assistance for Needy Families (TANF), child welfare, and Early Head Start/Head Start (EHS/HS). This revision would also allow for collection of data in the child care program area, and would extend the approval of the overarching generic. These interventions are intended to improve outcomes for participants in these programs.

Comments due within 30 days of publication. OMB must make a decision about the collection of information between 30 and 60 days after publication of this document in the Federal Register . Therefore, a comment is best assured of having its full effect if OMB receives it within 30 days of publication.

Written comments and recommendations for the proposed information collection should be sent within 30 days of publication of this notice to www.reginfo.gov/​public/​do/​PRAMain . Find this particular information collection by selecting “Currently under 30-day Review—Open for Public Comments” or by using the search function. You can also obtain copies of the proposed collection of information by emailing [email protected] . Identify all requests by the title of the information collection.

Description: OPRE is conducting the BIAS-NG project, which uses behavioral insights to design and test interventions intended to improve the efficiency, operations, and efficacy of human services programs. The BIAS-NG project is applying and testing behavioral insights to ACF programs including TANF, child welfare, and EHS/HS, and intends to expand these efforts to child care. This notice is a request for comments on ACF's proposal to revise and extend a previously approved collection, which included data collection to design and test interventions in the TANF, child welfare, and EHS/HS domains. Under the approved pilot generic clearance, OPRE has already conducted work with seven sites to conduct seven tests, and is planning to continue to work with at least one additional site, conducting one or more tests of behavioral interventions for a total of nine tests of behavioral interventions. All approved information collection activities can be found here: https://www.reginfo.gov/​public/​do/​PRAICList?​ref_​nbr=​202206-0970-002 .

In addition to extending approval, this approval would also allow OPRE to conduct tests in the newly added program area of child care. The design and testing of BIAS-NG interventions is rapid and, to the extent possible, iterative. Each specific intervention is designed in consultation with agency leaders and launched as quickly as possible. To maximize the likelihood that the intervention produces measurable, significant, and positive effects on outcomes of interest, rapid cycle evaluation techniques will be employed in which proximate outcomes will be measured to allow the research team to more quickly iterate and adjust the intervention design, informing subsequent tests. Due to the rapid and iterative nature of this work, OPRE sought and received approval for an overarching generic clearance to conduct this research. Following standard OMB requirements for generic clearances, once instruments subject to PRA are tailored to a specific site and the site's intervention, OPRE submits an individual generic information collection request under this umbrella clearance. Each request includes the individual instrument(s), a justification specific to the individual information collection, a description of the proposed intervention, and any supplementary documents. Each specific information collection includes up to two submissions—one submission for the formative stage research and another submission for any further data collection requiring burden during the testing phase. The type of information to be collected and the uses of the information is described in the supporting statements, found here: https://www.reginfo.gov/​public/​do/​PRAViewDocument?​ref_​nbr=​202206-0970-002 .

Respondents: (1) Program Administrators, (2) Program Staff, and (3) Program Clients.

Estimated Total Annual Burden Hours: 2,014.

Authority: 42 U.S.C. 1310 .

Mary C. Jones,

ACF/OPRE Certifying Officer.

[ FR Doc. 2024-18065 Filed 8-13-24; 8:45 am]

BILLING CODE 4184-07-P

• Executive Orders

Reader Aids

Information.

• About This Site
• Legal Status
• Accessibility
• No Fear Act
• Continuity Information