Perceived Knowledge of Health Informatics Competencies by Health Information Management Professionals

by Brooke Palkie, EdD, RHIA

Abstract

The 2009 enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act has placed unprecedented emphasis on utilizing technology to improve the quality of care and to decrease healthcare costs. To meet these goals, the healthcare field will need an increase in the number of professionals with the appropriate health informatics training and data analysis skills. Therefore, the author investigated the perceived knowledge of the emerging health informatics competencies by health information management (HIM) professionals. Expectations of analytical and health information technology skills have set the stage for HIM professionals to exert leadership in terms of health informatics.

Keywords: health information management, health informatics, health information technology

Introduction

The health information management (HIM) discipline was initiated to fulfill the need for skillful management of clinical data. Over time, HIM has been influenced by the social, economic, political, and specialized advances of technology in healthcare, evolving from just the use of technology to the demand for the use of validated data and information.¹ To meet these demands, HIM professionals are now expected to provide multifocused and decentralized services. The roles and responsibilities of HIM professionals must expand to meet the industry’s reliance on health information technology (HIT) and health informatics within the evolution of a new information science era.²

In an age that is swiftly transitioning to value-based payment and care delivery, hospitals need timely and accurate health information to support decision making.³ However, a perceived gap exists between the current state of the HIM profession and the exposure to emerging informatics competencies needed within the healthcare industry. This shortage of qualified professionals has been identified by several national surveys and publications further identifying a lack of understanding of the current healthcare data and information needs.^4–6

Background

The 2009 enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act has placed unprecedented emphasis on utilizing technology to improve health information in the areas of quality, delivery, and efficiency.⁷ These political and economic drivers are generating a new incentive to increase investment in clinical information systems. Measuring the value of HIT has previously been recognized as challenging by government, healthcare organizations, consumers, and payers alike.⁸ However, according to a HIMSS Analytics study, EMR Sophistication Correlates to Hospital Quality Data,⁹ evidence now supports that sophisticated electronic heath records (EHRs) can positively correlate to improved measures of patient outcomes. A study conducted by the Institute of Medicine¹⁰ indicates that HIT has also demonstrated the potential to improve patient safety. For these reasons, the HIM professional must remain adaptable in order to stay current with the new HIT market demands.

Innovative advances in HIT and the government’s push to employ individuals in the health informatics field have also contributed to a competitive environment. However, many healthcare organizations are still not utilizing data to their fullest potential. This situation is due to the limited knowledge of health informatics as well as a shortage of HIM professionals with the deep analytical skills needed to make effective decisions.¹¹ A report by the McKinsey Global Institute¹² predicts that the United States alone will face a shortage of 140,000 to 190,000 people with deep analytic skills by the year 2018. The continued use of HIT is the impetus for understanding the emerging roles of health informatics for HIM professionals. According to Smith et al.,¹³ little is known about the health informatics workforce. However, the health informatics workforce requires a significant expansion to support the recent national e-health agendas.¹⁴ To meet these needs, HIM professionals must be able to identify and strengthen their roles related to HIT.¹⁵

As the volume and use of data increase, clinical informatics continues to become a major contributing factor in healthcare. This study aimed to identify HIM professionals’ perceived knowledge of the emerging health informatics competencies.

Methods

The research involved the completion of a survey using the Qualtrics web-based survey software. A total of 39,578 HIM members of the American Health Information Management Association (AHIMA) received an invitation to participate in the survey via the AHIMA weekly E-Alert newsletter; 131 HIM professionals opted to begin the survey. If less than half of the survey was completed, the response was omitted from the analysis. Of the participants who started the survey, 100 participants completed half of the survey or more. Participation included acceptance of the consent form and completion of the survey. Although this study yielded a small sample size, participant responses can still be utilized to represent a sample of HIM professionals’ perceived knowledge of health informatics competencies. Since analysis of variance (ANOVA) was used with a sample size of 100, the effect size was 0.3 for all statistical results. Table 1 shows a reasonable amount of evidence to suggest that the observed significant differences are meaningful.

The survey consisted of four sets of demographic questions that included (1) length of time in the HIM field, (2) highest degree earned, (3) primary work setting, and (4) use of information technology. Additionally, the survey consisted of health informatics questions that identified degrees of competency on the basis of a Likert scale as follows: 1, No Knowledge; 2, Entry Level (Beginner/Novice); 3, Competent (Intermediate); 4, Proficient (Skilled); and 5, Expert (Reliable Source of Skills).¹⁶ The findings from the study Pointing the Way: Competencies and Curricula in Health Informatics¹⁷ have been recognized as a consistent base to identify categories of health informatics competencies.^18–22 Each category further incorporated industry roles and competencies in health informatics identified in recent publications and recommendations by the Commission on Accreditation for Health Informatics and Information Management Education²³ (see Appendix A). This model was utilized to serve as the framework for the health informatics competency environment and included (1) clinical informatics foundational competencies, (2) clinical decision-making and process improvement competencies, (3) health information system competencies, and (4) leadership and management competencies.

Results

Of the 131 participants who started the survey, 100 participants completed half of the survey or more. These 100 participants were included in the data analysis. The results include the identification of significant differences (p < .05). The results of a one-way ANOVA revealed a main effect with the health informatics competencies of information literacy (F = 4.068, p = 0.0021), characteristics and functions of information systems in healthcare (F = 2.327; p < 0.0489), policy and regulatory frameworks for health information (F = 2.688; p < 0.0259), and human resource management (F = 3.476; p < 0.0064) when comparing years of experience. For example, as shown in Table 2 and revealed by the Tukey post hoc test, respondents with 15–20 years of experience had significantly greater perceived knowledge compared to respondents with 1–3 years of experience (p < 0.005), and respondents with more than 20 years of experience had significantly greater perceived knowledge compared to respondents with 1–3 years of experience (p < 0.001).

When respondents were compared on the basis of the highest degree earned, the results of a one-way ANOVA also revealed a main effect with the health informatics competencies of structure, design, and analysis principles of health records (F = 3.594; p < 0.0165), ethical and security issues (F = 4.622; p < 0.00525), policy and regulatory frameworks of healthcare information (F = 5.874; p < 0.00101), clinical guidelines (F = 3.027; p < 0.0334), methods of workflow analysis (F = 3.926; p < 0.0109), principles of workflow reengineering (F = 4.032; p < 0.00958), quality improvement principles and practices (F = 3.372; p < 0.0217), speech recognition (F = 4.202; p < 0.0179), dealing with multiple identifiers (F = 3.231; p < 0.0259), institutional governance of clinical information systems (F = 3.104; p < 0.0303), clinical information needs analysis and system selection (F = 2.867; p < 0.0408), human resource management (F = 6.23; p < 0.000667), group management process (F = 5.744; p < 0.00119), effective communication (F = 3.077; p < 0.0313), strategic and financial planning for clinical information systems (F = 3.206; p < 0.0267), and change management (F = 4.495; p < 0.00541). The Tukey post hoc test further revealed that respondents with a master’s degree had a greater perceived knowledge than those with a bachelor’s degree (p < 0.014).

The results of a one-way ANOVA also revealed a main effect with the health informatics competencies of evidence sources (F = 3.207; p < 0.0103), data standards and data sharing (F = 3.209; p < 0.0102), messaging standards (F = 2.789; p < 0.0217), ontologies and taxonomies (F = 3.784; p < 0.00367), interoperability standards (F = 2.54; p < 0.0337), and group management process (F = 2.414; p < 0.0419) when respondents were compared on the basis of their primary work setting. Here the Tukey post hoc test revealed that respondents who work in a hospital setting had perceived less knowledge than those working in other settings (p < 0.025).

Finally, the results of a one-way ANOVA revealed a main effect of the health informatics competencies of topologies (F = 4.641; p < 0.000383); telecommunications (F = 3.557; p < 0.00329); models, theories, and practice of human-computer interaction (F = 3.054; p < 0.00914); and usability (F = 2.523; p < 0.0264) when respondents were compared on the basis of their use of information technology. The Tukey post hoc test further revealed that respondents who both use and deploy HIT have a greater perceived knowledge than those who just use HIT (p < 0.041) and those who just deploy HIT (p < 0.020).

Discussion

The perception of HIM knowledge was captured to determine if HIM professionals are proficient in the health informatics competencies currently needed in the workforce. The research demonstrates a significant association between HIM demographics and health informatics competencies. These associations can be utilized to identify where HIM professionals need further training.

Of the identified categories, the strongest predictor of HIM competency tended to be education. Respondents with a master’s degree tended to have greater knowledge of topics such as analytics than those with a bachelor’s or associate’s degree. According to Cassidy et al.,²⁴ if HIM graduates are to achieve the status that will define the future of healthcare, the HIM profession must shift its entry-level degree to a master’s degree. This initiative focuses on faculty and workforce development and supports the association between possession of a master’s degree and higher levels of competency knowledge.

The category of greater years of experience was also associated with greater knowledge in various HIM categories. However, respondents with more years of experience had less knowledge of social networks than those with fewer years of experience.

When reviewing the results of the primary work setting variable, the author expected that the hospital setting would be associated with greater knowledge of the health informatics competencies. However, the results identify that educational and other settings were associated with greater perceived knowledge than the hospital setting was.

HIT use was identified as another predictor of perceived HIM knowledge. The respondents who deployed and used HIT had greater knowledge in various categories than those who just used or just deployed HIT. For many years, HIM professionals have remained closely tied to the skill sets of information management. As the walls of the HIM departments in healthcare organizations come down and HIM professionals disperse throughout the healthcare industry, their roles and responsibilities will need to expand to meet the industry’s reliance on HIT.²⁵

Conclusion

Education is the next step in the process of increasing HIM professionals’ health informatics competencies. Educational programs must take into account the current environment in which HIM professionals will work. Additional studies of the necessary competencies should be conducted, and state and national associations should provide a roadmap of expected health informatics competencies for HIM professionals.

The results of this study imply associations between HIM professionals’ demographics and perceived health informatics competencies. These implications are consistent with studies that identified the current health informatics competencies of HIM professionals.^26–28 It is important to note that the competencies needing the most attention are those that revolve around HIT. Although HIM professionals already possess foundational skills for health informatics, additional growth in HIT competencies and deep analytical skills will be required. HIM professionals and educators alike will need to be at the forefront of this new information science era.

Brooke Palkie, EdD, RHIA, is an assistant professor in the Department of Health Informatics and Information Management at the College of St. Scholastica in Duluth, MN.

Notes

1.        Brodnik, Melanie S., and Shannon H. Houser. “Redefining the Health Information Management Scholar Role.” Perspectives in Health Information Management (2009): 1–11.

2.        Kloss, Linda. “Keynote Address.” Presented at the LaTour/Eichenwald Forum on HIIM Leadership and Innovation, Duluth, MN, June 4, 2012. Available at http://www.css.edu/Academics/School-of-Health-Sciences/Undergraduate-Areas-of-Study/Health-Information-Management/News-and-Events/LaTourEichenwald-Forum.html.

3.        HIMSS. 2012 HIMSS Leadership Survey: Senior IT Executive Results. 2012. Available at http://www.himss.org/files/HIMSSorg/content/files/2012FINAL%20Leadership%20Survey%20with%20Cover.pdf.

4.        Ibid.

5.        College of Healthcare Information Management Executives. Demand Persists for Experienced Health IT Staff. 2012. Available at http://www.cio-chime.org/chime/press/surveys/pdf/CHIME_Workforce%20_survey_report.pdf.

6.        Smith, Susan E., Lesley E. Drake, Julie-Gai B. Harris, Kay Watson, and Peter G. Pohlner. “Clinical Informatics: A Workforce Priority for 21st Century Healthcare.” Australian Health Review 35 (2011): 130–35.

7.        Shaw, Gienna. Making Data Meaningful. 2012. Available at http://www.browardhealth.org/upload/docs/Corporate/Making%20Data%20Meaningful.pdf.

8.        Thomas, Randy L. “Clinical Decision Intelligence: Improving Health Care through Information.” Healthcare Financial Management 61, no. 6 (2007): 108–9.

9.        HIMSS Analytics. EMR Sophistication Correlates to Hospital Quality Data: Comparing EMR Adoption to Care Outcomes at UHC Hospitals, Including Davies Award Winners, Using HIMSS Analytics’ EMR Adoption Model Scores. 2006. Available at http://www.himssanalytics.org/docs/uhc25.pdf.

10.      Institute of Medicine. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: National Academies Press, 2011. Available at http://www.iom.edu/Reports/2011/Health-IT-and-Patient-Safety-Building-Safer-Systems-for-Better-Care.aspx.

11.      HIMSS. 2012 HIMSS Leadership Survey: Senior IT Executive Results.

12.      Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, 2011. Available at http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation.

13.      Smith, Susan E., Lesley E. Drake, Julie-Gai B. Harris, Kay Watson, and Peter G. Pohlner. “Clinical Informatics: A Workforce Priority for 21st Century Healthcare.”

14.      Ibid.

15.      Zeng, Xiaoming, Rebecca Reynolds, and Marcia Sharp. “Redefining the Roles of Health Information Management Professionals in Health Information Technology.” Perspectives in Health Information Management (2009): 1–11.

16.      Brenner, Patricia. From Novice to Expert: Excellence and Power in Clinical Nursing Practice. Menlo Park, CA: Addison-Wesley, 1984.

17.      Covvey, H. Dominick, David Zitner, and Robert M. Bernstein. Pointing the Way: Competencies and Curricula in Health Informatics. 2001. Available at http://www.nihi.ca/nihi/ir/Pointing%20the%20Way%20MASTER%20Document%20Version%201%20Final.pdf.

18.      American Health Information Management Association (AHIMA) and American Medical Informatics Association (AMIA). Joint Work Force Task Force: Health Information Management and Informatics Core Competencies for Individuals Working with Electronic Health Records. Chicago, IL: AMIA, October 2008. 

19.      Garde, Sebastian, David Harrison, Mohammed Huque, and Evelyn J. S. Hovenga. “Building Health Informatics Skills for Health Professionals: Results from the Australian Health Informatics Skill Needs Survey.” Australian Health Review 30, no. 1 (2006): 34–45.

20.      Hersh, William, and Adam Wright. Characterizing the Health Information Technology Workforce: Analysis from the HIMSS Analytics™ Database. April 17, 2008. Available at http://www.himssanalytics.org/docs/HIT_Workforce_HIMSS_Analytics.pdf.

21.      MacNeill, Janice E., and H. Dominick Covvey. “The Development of a Model Curriculum for Applied Health Informatics.” Proceedings of the AMIA Symposium (2000): 527–31. Available at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2243833/.

22.      Mantas, John, Elske Ammenwerth, George Demiris, Arie Hasman, Reinhold Haux, William Hersh, Evelyn Hovenga, K. C. Lun, Heimar Marin, Fernando Martin-Sanchez, and Graham Wright. “Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics: First Revision.” EJBI 7, no. 2 (2011). Available at http://www.ejbi.org/img/ejbi/2011/2/Mantas_en.pdf.

23.      Commission on Accreditation for Health Informatics and Information Management Education. “Latest CAHIIM Accreditation News.” 2013. Available at http://www.cahiim.org/applyaccred_HI_grad.html.

24.      Cassidy, Bonnie S., Valerie J. M. Watzlaf, Ellen Shakespeare Karl, Rebecca B. Reynolds, Leah Grebner, Xiaoming Zeng, Vanda Crossley, Cindy Zak, Cindy Glewwe, Leslie Gordon, Samir Chatterjee, Christine Staropoli, Patt Peterson, and William J. Rudman. “Teaching the Future: An Educational Response to the AHIMA CORE Model.” Journal of AHIMA 82, no. 10 (2011): 34–38.

25.      Kloss, Linda. “Health Information Management in 2016: The HIM Industry’s Transformative Journey to Enterprise Information Management. What Does the HIM Department of the Future Look Like?” Presented at the HIMSS 2012 Annual Conference and Exposition, Las Vegas, NV, 2012.

26.      Covvey, H. Dominick, David Zitner, and Robert M. Bernstein. Pointing the Way: Competencies and Curricula in Health Informatics.

27.      Covvey, H. Dominick, Candace J. Gibson, Gail F. Crook, Kelly Abrams, Richard H. Irving, and Shirley L. Fenton. “A Time for Clarity: eHealth Human Resources-related Terminology.” Healthcare Information Management & Communications 23, no. 1 (2009): 36–43.

28. Hersh, William. “A Stimulus to Define Informatics and Health Information Technology.” BMC 9, no. 24 (2009). Available at http://www.biomedcentral.com/1472-6947/9/24.