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Comparative analysis of competency coverage within accredited master’s in health informatics programs in the East African region

Comparative analysis of competency coverage within accredited master’s in health informatics... Abstract Objective As master of science in health informatics (MSc HI) programs emerge in developing countries, quality assurance of these programs is essential. This article describes a comprehensive comparative analysis of competencies covered by accredited MSc HI programs in the East African common labor and educational zone. Materials and Methods Two reviewers independently reviewed curricula from 7 of 8 accredited MSc HI university programs. The reviewers extracted covered competencies, coding these based on a template that contained 73 competencies derived from competencies recommended by the International Medical Informatics Association, plus additional unique competencies contained within the MSc HI programs. Descriptive statistics were used to summarize the structure and completion requirements of each MSc HI program. Jaccard similarity coefficient was used to compare similarities in competency coverage between universities. Results The total number of courses within the MSc HI degree programs ranged from 8 to 22, with 35 to 180 credit hours. Cohen’s kappa for coding competencies was 0.738. The difference in competency coverage was statistically significant across the 7 institutions (P = .012), with covered competencies across institutions ranging from 32 (43.8%) to 49 (67.1%) of 73. Only 4 (19%) of 21 university pairs met a cutoff of over 70% similarity in shared competencies. Discussion Significant variations observed in competency coverage within MSc HI degree programs could limit mobility of student, faculty, and labor. Conclusions Comparative analysis of MSc HI degree programs across 7 universities in East Africa revealed significant differences in the competencies that were covered. graduate education, health informatics, competency-based education, developing countries INTRODUCTION Low- and middle-income countries (LMICs) have over the last decade seen an exponential increase in adoption of digital health solutions. In sub-Saharan Africa (SSA), numerous countries have rolled out nationally endorsed electronic health record (EHR) systems within their public health sectors.1 Additional patient-level systems, such as laboratory information systems, pharmacy information systems, and mobile health applications, have also been widely adopted. Beyond systems for direct patient care, data aggregation systems (eg, the District Health Information System) and public health information systems (eg, electronic disease surveillance systems) are now widely used.2,3 These countries also have ongoing initiatives to implement national-level digital health enterprise architectures and to support health information exchanges.4 Further, multiple institutions are engaging in data analytics and research projects with the goal of leveraging health data for decision making and knowledge discovery. Despite increasing use of digital technologies to support the health system, many countries remain unprepared to systematically train the workforce required to manage the digital systems. A robust health informatics ecosystem requires local- and institutional-level developers, systems administrators, implementation leads and other health informatics (HI) specialists. Based on the roles, these individuals need to be adequately proficient in developing, implementing, supporting or evaluating the digital health systems. However, developing the required human capacity cannot be achieved without deliberate and concerted capacity-building approaches. The World Health Organization advises its member states to draw up long-term strategic plans for developing and implementing digital health services,5 and SSA countries are starting to heed this call. As an example, Kenya and Uganda now have national eHealth policies and strategies that include significant capacity-building components.6,7 Capacity building in health informatics often takes the forms of in-service training and preservice training. In-service training involves training of employees to familiarize them with various aspects of digital health solutions relevant to their work, and this training approach often forms a large part of many Ministries of Health and donor-supported capacity-building initiatives. However, the important role of preservice training, through degree and certificate programs is increasingly being recognized, especially in helping develop a highly trained cadre of HI leaders and professionals. To this end, a number of formal HI degree training programs are emerging across higher education institutions in SSA. Within the field of HI, bachelor’s-level degree programs tend to focus mostly on providing practical training to students around various applications. More advanced degrees, such as master’s and doctoral degree levels, provide both a theoretical foundation for HI along with other practical skills and analytical approaches, and implement novel methods for solving challenging problems across the entire spectrum of HI.8 A majority of the degree programs in HI that currently exist in SSA are at the master of science (MSc) level. As a multidisciplinary field at the intersection of health and information sciences, MSc HI programs are often hosted in different types of schools or departments within universities. As an example, it is not uncommon to find one MSc HI program hosted within a School of Public Health, while another MSc HI program in a different institution hosted in the School of Information Science. With varying areas of expertise of the hosting units, a significant risk exists for the various MSc HI programs to have vastly different courses and competencies covered in each program. This can pose a risk for student and faculty mobility across institutions. Further, wide variability in competencies can threaten the credibility of an MSc HI degree, especially if employers are not as familiar with the field. To date, no critical assessments exist that compare competencies covered by the various MSc HI programs in the SSA region. Early identification of similarities and differences between programs can help inform whether core competency standards are met, and whether there is need to set minimum standards and harmonize competencies for the various existing and emerging MSc HI programs. In this article, we present a comprehensive comparative analysis of the curricula and competencies contained within accredited MSc HI programs in the East African region of SSA. The goal of this evaluation is to shed light on similarities and differences between programs in a shared economic and academic zone, and to inform whether there is need for curricula harmonization. MATERIALS AND METHODS Study setting This study was conducted at institutions within the East African Community (EAC), which is a regional intergovernmental organization composed of Burundi, Kenya, Rwanda, South Sudan, Tanzania, and Uganda. 9 Ethiopia, Somalia, and South Sudan participate in the community as observer countries. Countries in the EAC belong in a common labor and educational zone, which guides free mobility of students, faculty, workforce, and services. The EAC is home to approximately 300 universities, with over a million students enrolled in tertiary educational institutions. Higher education and research in the EAC are developed and coordinated by the Inter-University Council for East Africa (IUCEA), which oversees 127 public and private universities.10 At the time of this evaluation, 8 institutions within the IUCEA (inclusive of Ethiopia) were accredited to offer MSc HI programs in the East African region. Study population All 8 accredited MSc HI programs were eligible to participate in the study, through review of their most current MSc HI curricula. The University of Rwanda was excluded from the study, as its curriculum was undergoing significant revision at the time of this evaluation. The remaining 7 institutions included Muhimbili University (Tanzania), Mekelle University (Ethiopia), University of Gondar (Ethiopia), Kenyatta University (Kenya), University of Dar es Salaam (Tanzania), Moi University (Kenya), and Makerere University (Tanzania). Each institution provided its detailed curriculum that was in current use for their MSc HI program. Study design We conducted a cross-sectional study in 2019 and 2020 involving review of curricula from the 7 accredited MSc HI programs in East Africa. The curriculum for each university contained details of competencies to be covered in each course. The curriculum of each study institution was evaluated against a comprehensive list of 73 competencies comprising (1) 40 recommended competencies by the International Medical Informatics Association (IMIA) (Table 1, domains 1-3),8 (2) 8 optional competencies per IMIA guidelines (Table 1, domain 4),8 and (3) an 25 additional unique competencies that were not part of the IMIA recommendations but were found in 1 or more of the reviewed curricula (Table 1 and Supplementary Appendix A). Details of the rigorous process used to derive the additional competencies are outlined in Were et al.11 Table 1. Competency domains contained in the evaluation template. Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domains 1-4 are derived from the International Medical Informatics Association Education Recommendations.8 BMHI: Biomedical and Health Informatics. a Domain 5 is detailed in Were et al.11 Open in new tab Table 1. Competency domains contained in the evaluation template. Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domains 1-4 are derived from the International Medical Informatics Association Education Recommendations.8 BMHI: Biomedical and Health Informatics. a Domain 5 is detailed in Were et al.11 Open in new tab Data collection Two independent reviewers (P.B. and B.E.B.) with formal MSc HI training and over 2 years of experience working in the HI space conducted independent reviews of each curriculum. Reviewers first performed a qualitative descriptive assessment on each program’s structure and completion requirements. They then used the coding template (Supplementary Appendix A) to extract competencies contained within each course contained in each curriculum as detailed in Were et al.11 For each course, the reviewer read through the course content and coded with a “yes” or “no” against every competency outlined in the coding instrument (Supplementary Appendix A). Given that each course covers only a few competencies relative to the full set of competencies in the coding template, the resulting ratings per course within the template had much higher numbers of competencies rated as “no” as opposed to “yes.” After completing independent evaluations, the 2 reviewers compared assessments and reached consensus when there were coding disagreements in regards to competencies contained in a course. If consensus on a competency for a course was not reached by the 2 reviewers, a third-party reviewer (M.C.W.) with expertise in HI was available to moderate the discussion in order to reach a final consensus. Thesis and Practicum courses were excluded from the review, as the competencies involved were deemed highly variable based on the project each student undertook. Data analysis Summary of MSc HI programs Descriptive statistics were used to summarize general characteristics of the various MSc HI programs. When this information was not available from the curriculum, the program was contacted directly by the study team and asked to provide this information. Assessed descriptive variables included year of curriculum development, program start year, degree duration, full- or part-time status, program hosting unit, number of courses within the curriculum, number of credits, practicum and thesis requirements, and the admission criteria used. Interrater reliability between reviewers on coding of competencies within courses Interrater agreement on coding of competencies by the 2 reviewers was measured as Cohen's kappa on domains 1 to 4. Cohen's kappa was calculated with the R “psych” package,12 and the 95% confidence interval was estimated with the Fleiss, Cohen, and Everitt method (R Foundation for Statistical Computing, Vienna, Austria, version 3.6.2).13 As the additional domain 5 competencies were determined through consensus by the 2 reviewers, this domain was not included in the Cohen’s kappa analysis. As detailed in Were et al., 11 Cohen’s kappa for the 2 reviewers was 0.738 (95% confidence interval, 0.713-0.764). The reviewers reached consensus on all ratings after discussions, without having to involve the third reviewer for arbitration. Comparative analysis of the competencies in MSc HI programs in East Africa, with a focus on similarities and differences between curricula for the various institutions Descriptive statistics were used to determine competency coverage by institution and across institutions. We fit a multivariable logistic regression model with the outcome being coverage (yes/no) for each competency for each institution and predictors being the institution, the competency domain, and a random effect for each competency. A likelihood ratio test from this model was used to test whether coverage differed between institutions. To compare competency coverage similarities between institutions, a Jaccard similarity coefficient was used.14 The Jaccard similarity coefficient is calculated by one-to-one comparisons of competencies across university pairs. It is a percentage of the number of competencies covered by both universities (numerator) over the total number of competencies covered by either one of the 2 universities (denominator). Comparison of a university against itself always gives a Jaccard coefficient of 1 (100%). RESULTS Overall summary of master’s programs Table 2 provides descriptive data on the various MSc HI programs. The number of courses required to complete each MSc HI program ranged from 8 to 22, and the number of credited hours required for each degree program ranged 35 and 180. The departments in which the MSc HI programs were quite variable, among them being Medicine, Public Health, Computer Science and Engineering, and Health Informatics. All programs required a thesis, and 6 of 7 required a practicum. A majority of the programs (6 of 7) admitted students with either health or information sciences background, whereas one of the institutions (#7) only admitted candidates with a health sciences background. Table 2. Descriptive characteristics of master of science in health informatics programs across the East African community. University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 Open in new tab Table 2. Descriptive characteristics of master of science in health informatics programs across the East African community. University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 Open in new tab Competency coverage across institutions The institution with the highest competencies coverage within its curriculum covered 49 (66.2%) of the 73 identified total competencies across the institutions, while the institution with the lowest coverage covered 32 (43.8%) competencies (Table 3 and Supplementary Appendix B). Only 19 (26.0%) of the competencies were covered within curricula of all the 7 evaluated institutions (Figure 1). These competencies included 1.5, 1.6, 1.8, 1.11, 1.12, 1.14, 1.16, 1.19, 2.5, 2.7, 3.2, 3.4, 3.6, 3.8, 3.9, 3.10, 3.11, 3.13 and 5.22 (Supplementary Appendix B). Three (4.1%) of the competencies were not covered in any of the evaluated curricula, and included patient-matching approaches (3.7), medical chemo-informatics (4.4) and medical nano-informatics (4.6) (Figure 1 and Supplementary Appendix B). The difference in competency coverage was statistically significant across the 7 institutions (P = .012). Figure 1. Open in new tabDownload slide Competency coverage across institutions. Figure 1. Open in new tabDownload slide Competency coverage across institutions. Table 3. Competency coverage by institution. Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Open in new tab Table 3. Competency coverage by institution. Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Open in new tab Comparison of competence coverage between institutions Figure 2 shows similarities in competency coverage when institutional pairs are compared. The 2 institutions with the highest Jaccard similarity shared 77% (n = 41 of 53) total competencies between them. The 2 with the lowest Jaccard similarity shared only 43% (n = 21 of 49) of the total competencies between them. If a cutoff of 70% is used to determine degree of similar competencies across programs, only 4 (19%) university pairs out of 21 would meet this threshold. A similarity cutoff of 60% would only be satisfied by 11 (53.3%) university pairs. Similarity thresholds are typically used by accrediting bodies to determine minimum shared competencies across degree programs, while allowing room for institutions to still distinguish themselves. Figure 2. Open in new tabDownload slide Comparison of competency coverage between institutions. Figure 2. Open in new tabDownload slide Comparison of competency coverage between institutions. DISCUSSION This comparative analysis of competencies within accredited MSc HI program curricula in East Africa reveals significant variations in competency coverage across the evaluated institutions. Only 19% of the university pairs had competency similarity that exceeded 70%. Further, the number of courses and credits offered per degree program were also highly variable. The consequence of such wide variability in covered competencies include (1) limitations on student mobility across institutions, with difficulty in transferring credits and taking courses across universities, (2) limitations in faculty mobility across institutions, and (3) limitations in labor mobility, with employers unable to easily determine what core skillsets to expect from any MSc HI graduate. This is a significant concern especially for institutions that belong in a common economic and educational zone underpinned by the “The EAC Common Market Protocol and the Regional Qualifications Framework.”15 The Common Market Protocol emphasizes the “desire to facilitate free movement of persons and labor” and “mutual recognition of academic and professional qualifications.”15 The variability observed in the MSc HI programs thus goes against a fundamental goal of the EAC. It should be noted that the observed differences in competency coverage are not unique to LMICs, as observations are also seen across programs in developed countries.16–19 The differences in program content likely reflect a number of things. This difference could simply be a reflection of the breadth of subject matter supported by the field, and the diversity in primary areas of focus by the hosting units for evaluated MSc HI programs within universities. As an example, programs hosted within information sciences departments would tend to have curricula that overemphasize information sciences competencies, while those hosted within health sciences units would emphasize health-related competencies. There is a possibility that the variabilities in competency coverage reflects differences in understanding of what core competencies make up an MSc HI degree program. As an example, an earlier evaluation of only the 40 core competencies approved by IMIA showed wide variability in coverage of these competencies within the East African programs (62.5%-97.5%).11 However, the fact that these programs include another 25 competencies not contained in the IMIA recommendations underscores that comparisons in competency coverage cannot be limited to only those recommended by IMIA, hence the approach to use all 73 competencies in this study. The observed significant differences in competency coverage between universities highlights the need for curricula harmonization of the MSc HI programs, to ensure that each program adequately covers the same set of agreed minimum competencies for the region. A regionalized harmonized set of core competencies in MSc HI can be developed leveraging already defined competency recommendations, 8,15 as well as emerging core competency recommendations and revised knowledge base from IMIA.20 However, these minimum competencies also need to recognize that there are numerous social, cultural, political, infrastructural, and implementation differences that impact the application of HI in LMICs, and wholesome adoption of international competencies would likely not meet the unique needs for each country or region. Region-specific competencies should be agreed on and incorporated into the minimum competency standards. Numerous examples of development of minimum competency standards for degree programs exist and can serve as models for MSc HI programs.21,22 Curriculum harmonization of the programs in the East African region is practically feasible through the Credit Accumulation and Transfer mechanism overseen by the IUCEA. The IUCEA is mandated to “maintain comparable academic standards in higher education recognized regionally and internationally.”15 The IUCEA has already undertaken harmonization and benchmarking of various programs, and this can be emulated by the MSc HI programs in the region. This study has several limitations. It was conducted in institutions within a single region, and the findings might not necessarily translate to other regions. Determination of competency coverage relied purely on the content within the curricula, but this does not necessarily translate to the content of the courses taught. Exact competencies covered by thesis and practicum were not included, but as applications of HI, these courses might have contained competencies that were not coded by institution. Further, determination of competency coverage involved some degree of judgment by the reviewers. Both reviewers were from one of the reviewed programs, and this could have introduced some bias in ratings. However, a moderate kappa and consensus in which there were differences provide confidence in the determination of competency coverage. As the next step, our team will embark on working with the IUCEA and the institutions in the region to develop a common set of minimum competency requirements as “Benchmarks for MSc HI” for the East Africa region. This will be done in close collaboration with Ministries of Health and the private and public sectors, and with professional health informatics bodies such as the Pan-African Health Informatics Association and the IMIA. The benchmarks will facilitate transfer of credits across institutions, and mobility of students and faculty. It would also likely impact quality of MSc HI programs offered, with ability to have shareable educational content developed for use across institutions to help ameliorate faculty shortages. CONCLUSION MSc HI programs at the common economic and educational zone of East Africa have curricula with limited similarity in competency coverage. This evaluation highlights the need for critical comparative competency evaluations in emerging MSc HI programs, especially for those programs within similar labor and educational zones. Further, strong considerations should be given to setting common minimum competency standards when large differences exist within programs. Comparative analysis of MSc HI degree programs across 7 universities in East Africa revealed significant differences in covered competencies. FUNDING This work was made possible by the support of the American people through the U.S. Agency for International Development (grant number 7200AA18CA00019). The contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Agency for International Development or the U.S. government. AUTHOR CONTRIBUTIONS MCW contributed to conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, validation, and writing (original draft; review and editing). WG contributed to data curation, formal analysis, funding acquisition, methodology, visualization, and writing (review and editing). PB contributed to data curation, resources, and writing (original draft; review and editing). BEB contributed to data curation, resources, and writing (original draft; review and editing). AY contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). LP contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). DL contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). HJL contributed to data curation, resources, and writing (review and editing). YK contributed to conceptualization, funding acquisition, project administration, supervision, and writing (review and editing). BES contributed to data curation, formal analysis, investigation, methodology, project administration, supervision, validation, and writing (review and editing). SUPPLEMENTARY MATERIAL Supplementary material is available at Journal of the American Medical Informatics Association online. ACKNOWLEDGMENTS The authors thank the participating universities for sharing their curricula with us and responding to our requests for clarification. They also thank the Vanderbilt Institute for Global Health and Moi University Institute of Biomedical Informatics for logistical support during the study. CONFLICT OF INTEREST STATEMENT All authors report no competing interests to declare. DATA AVAILABILITY STATEMENT The data underlying this article will be shared on reasonable request to the corresponding author. References 1 World Health Organization . Global Diffusion of eHealth: Making Universal Health Coverage Achievable . Geneva; Switzerland : World Health Organization ; 2016 . https://www.who.int/goe/publications/global_diffusion/en/. Accessed January 23, 2021. Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 2 Dehnavieh R , Haghdoost A, Khosravi A, et al. The District Health Information System (DHIS2): A literature review and meta-synthesis of its strengths and operational challenges based on the experiences of 11 countries . Health Inf Manag 2019 ; 48 ( 2 ): 62 – 75 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 3 Fall IS , Rajatonirina S, Yahaya AA, et al. Integrated Disease Surveillance and Response (IDSR) strategy: current status, challenges and perspectives for the future in Africa . BMJ Global Health 2019 ; 4 ( 4 ): e001427 . Google Scholar Crossref Search ADS PubMed WorldCat 4 Akhlaq A , McKinstry B, Muhammad KB, Sheikh A. Barriers and facilitators to health information exchange in low- and middle-income country settings: a systematic review . Health Policy Plan 2016 ; 31 ( 9 ): 1310 – 25 . Google Scholar Crossref Search ADS PubMed WorldCat 5 World Health Organization . Global Strategy on Digital Health 2020-2025 . Geneva; Switzerland : World Health Organization ; 2019 . https://www.who.int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf. Accessed January 23, 2021. Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 6 Republic of Kenya Ministry of Health . Kenya National eHealth Policy 2016-2030. Towards attainment of the highest standard of health through adoption and use of ICT. https://health.eac.int/file-download/download/public/86. Accessed January 23, 2021 . 7 Republic of Uganda Ministry of Health . Uganda National eHealth Policy. 2016 . http://library.health.go.ug/download/file/fid/517. Accessed January 23, 2021. 8 Mantas J , Ammenwerth E, Demiris G, et al. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics . First Rev Methods Inf Med 2010 ; 49 ( 2 ): 105 – 20 . Google Scholar OpenURL Placeholder Text WorldCat 9 East African Community . Quick Facts about EAC. 2019 . https://www.eac.int/eac-quick-facts. Accessed January 23, 2021. 10 Trines S. Bologna-type harmonization in Africa: an overview of the common higher education area of the East African community . World Education News and Reviews . 2018 . https://wenr.wes.org/2018/12/common-higher-education-area-chea-of-the-east-african-community. Accessed January 23, 2021. Google Scholar OpenURL Placeholder Text WorldCat 11 Were MC , Gong W, Balirwa P, et al. Coverage of IMIA-recommended competencies by masters in health informatics degree programs in East Africa . Int J Med Inform 2020 ; 143 : 104265 . Google Scholar Crossref Search ADS PubMed WorldCat 12 Revelle W. osych: procedures for personality and psychological research. Version 1.8.12. https://CRAN.R-project.org/package=psych. Accessed January 23, 2021. 13 R Core Team ( 2013 ). R: A language and environment for statistical computing R Foundation for Statistical Computing ., Vienna, Austria . http://www.R-project.org. Accessed Januarty 23, 2021. Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 14 Ivchenko GI , Honov SA. On the Jaccard similarity test . J Math Sci 1998 ; 88 : 789 – 94 . Google Scholar Crossref Search ADS WorldCat 15 Inter-University Council of East Africa . Developing a regional qualifications framework for higher education in East Africa. https://www.iucea.org/index.php?option=com_content&view=article&id=317&itemid=279. Accessed January 23, 2021 . 16 Kampov-Polevoi J , Hemminger BM. A curricula-based comparison of biomedical and health informatics programs in the USA . J Am Med Inform Assoc 2011 ; 18 ( 2 ): 195 – 202 . Google Scholar Crossref Search ADS PubMed WorldCat 17 Hart MD. Informatics competency and development within the US nursing population workforce: a systematic literature review . Comput Inform Nurs 2008 ; 26 ( 6 ): 320 – 9 ; quiz 330 – 1 . Google Scholar Crossref Search ADS PubMed WorldCat 18 Ashrafi N , Kuilboer J-P, Joshi C, Ran I, Pande P. Health informatics in the classroom: An empirical study to investigate higher education's response to healthcare transformation . J Inf Syst Educ 2014 ; 25 ( 4 ): 305 – 16 . Google Scholar OpenURL Placeholder Text WorldCat 19 Wholey DR , LaVenture M, Rajamani S, Kreiger R, Hedberg C, Kenyon C. Developing workforce capacity in public health informatics: core competencies and curriculum design . Front Public Health 2018 ; 6 : 124 . Google Scholar OpenURL Placeholder Text WorldCat 20 Wright G. The development of the IMIA knowledge base . South African J Inf Manag 2011 ; 13 ( 1 ): 1 – 5 . Google Scholar OpenURL Placeholder Text WorldCat 21 Kulikowski CA , Shortliffe EH, Currie LM, et al. AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline . J Am Med Inform Assoc 2012 ; 19 ( 6 ): 931 – 8 . Google Scholar Crossref Search ADS PubMed WorldCat 22 Monsen KA , Bush RA, Jones J, Manos EL, Skiba DJ, Johnson SB. Alignment of American association of colleges of nursing graduate-level nursing informatics competencies with American medical informatics association health informatics core competencies . Comput Inform Nurs 2019 ; 37 ( 8 ): 396 – 404 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Comparative analysis of competency coverage within accredited master’s in health informatics programs in the East African region

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Oxford University Press
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Copyright © 2021 American Medical Informatics Association
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1067-5027
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1527-974X
DOI
10.1093/jamia/ocab075
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Abstract

Abstract Objective As master of science in health informatics (MSc HI) programs emerge in developing countries, quality assurance of these programs is essential. This article describes a comprehensive comparative analysis of competencies covered by accredited MSc HI programs in the East African common labor and educational zone. Materials and Methods Two reviewers independently reviewed curricula from 7 of 8 accredited MSc HI university programs. The reviewers extracted covered competencies, coding these based on a template that contained 73 competencies derived from competencies recommended by the International Medical Informatics Association, plus additional unique competencies contained within the MSc HI programs. Descriptive statistics were used to summarize the structure and completion requirements of each MSc HI program. Jaccard similarity coefficient was used to compare similarities in competency coverage between universities. Results The total number of courses within the MSc HI degree programs ranged from 8 to 22, with 35 to 180 credit hours. Cohen’s kappa for coding competencies was 0.738. The difference in competency coverage was statistically significant across the 7 institutions (P = .012), with covered competencies across institutions ranging from 32 (43.8%) to 49 (67.1%) of 73. Only 4 (19%) of 21 university pairs met a cutoff of over 70% similarity in shared competencies. Discussion Significant variations observed in competency coverage within MSc HI degree programs could limit mobility of student, faculty, and labor. Conclusions Comparative analysis of MSc HI degree programs across 7 universities in East Africa revealed significant differences in the competencies that were covered. graduate education, health informatics, competency-based education, developing countries INTRODUCTION Low- and middle-income countries (LMICs) have over the last decade seen an exponential increase in adoption of digital health solutions. In sub-Saharan Africa (SSA), numerous countries have rolled out nationally endorsed electronic health record (EHR) systems within their public health sectors.1 Additional patient-level systems, such as laboratory information systems, pharmacy information systems, and mobile health applications, have also been widely adopted. Beyond systems for direct patient care, data aggregation systems (eg, the District Health Information System) and public health information systems (eg, electronic disease surveillance systems) are now widely used.2,3 These countries also have ongoing initiatives to implement national-level digital health enterprise architectures and to support health information exchanges.4 Further, multiple institutions are engaging in data analytics and research projects with the goal of leveraging health data for decision making and knowledge discovery. Despite increasing use of digital technologies to support the health system, many countries remain unprepared to systematically train the workforce required to manage the digital systems. A robust health informatics ecosystem requires local- and institutional-level developers, systems administrators, implementation leads and other health informatics (HI) specialists. Based on the roles, these individuals need to be adequately proficient in developing, implementing, supporting or evaluating the digital health systems. However, developing the required human capacity cannot be achieved without deliberate and concerted capacity-building approaches. The World Health Organization advises its member states to draw up long-term strategic plans for developing and implementing digital health services,5 and SSA countries are starting to heed this call. As an example, Kenya and Uganda now have national eHealth policies and strategies that include significant capacity-building components.6,7 Capacity building in health informatics often takes the forms of in-service training and preservice training. In-service training involves training of employees to familiarize them with various aspects of digital health solutions relevant to their work, and this training approach often forms a large part of many Ministries of Health and donor-supported capacity-building initiatives. However, the important role of preservice training, through degree and certificate programs is increasingly being recognized, especially in helping develop a highly trained cadre of HI leaders and professionals. To this end, a number of formal HI degree training programs are emerging across higher education institutions in SSA. Within the field of HI, bachelor’s-level degree programs tend to focus mostly on providing practical training to students around various applications. More advanced degrees, such as master’s and doctoral degree levels, provide both a theoretical foundation for HI along with other practical skills and analytical approaches, and implement novel methods for solving challenging problems across the entire spectrum of HI.8 A majority of the degree programs in HI that currently exist in SSA are at the master of science (MSc) level. As a multidisciplinary field at the intersection of health and information sciences, MSc HI programs are often hosted in different types of schools or departments within universities. As an example, it is not uncommon to find one MSc HI program hosted within a School of Public Health, while another MSc HI program in a different institution hosted in the School of Information Science. With varying areas of expertise of the hosting units, a significant risk exists for the various MSc HI programs to have vastly different courses and competencies covered in each program. This can pose a risk for student and faculty mobility across institutions. Further, wide variability in competencies can threaten the credibility of an MSc HI degree, especially if employers are not as familiar with the field. To date, no critical assessments exist that compare competencies covered by the various MSc HI programs in the SSA region. Early identification of similarities and differences between programs can help inform whether core competency standards are met, and whether there is need to set minimum standards and harmonize competencies for the various existing and emerging MSc HI programs. In this article, we present a comprehensive comparative analysis of the curricula and competencies contained within accredited MSc HI programs in the East African region of SSA. The goal of this evaluation is to shed light on similarities and differences between programs in a shared economic and academic zone, and to inform whether there is need for curricula harmonization. MATERIALS AND METHODS Study setting This study was conducted at institutions within the East African Community (EAC), which is a regional intergovernmental organization composed of Burundi, Kenya, Rwanda, South Sudan, Tanzania, and Uganda. 9 Ethiopia, Somalia, and South Sudan participate in the community as observer countries. Countries in the EAC belong in a common labor and educational zone, which guides free mobility of students, faculty, workforce, and services. The EAC is home to approximately 300 universities, with over a million students enrolled in tertiary educational institutions. Higher education and research in the EAC are developed and coordinated by the Inter-University Council for East Africa (IUCEA), which oversees 127 public and private universities.10 At the time of this evaluation, 8 institutions within the IUCEA (inclusive of Ethiopia) were accredited to offer MSc HI programs in the East African region. Study population All 8 accredited MSc HI programs were eligible to participate in the study, through review of their most current MSc HI curricula. The University of Rwanda was excluded from the study, as its curriculum was undergoing significant revision at the time of this evaluation. The remaining 7 institutions included Muhimbili University (Tanzania), Mekelle University (Ethiopia), University of Gondar (Ethiopia), Kenyatta University (Kenya), University of Dar es Salaam (Tanzania), Moi University (Kenya), and Makerere University (Tanzania). Each institution provided its detailed curriculum that was in current use for their MSc HI program. Study design We conducted a cross-sectional study in 2019 and 2020 involving review of curricula from the 7 accredited MSc HI programs in East Africa. The curriculum for each university contained details of competencies to be covered in each course. The curriculum of each study institution was evaluated against a comprehensive list of 73 competencies comprising (1) 40 recommended competencies by the International Medical Informatics Association (IMIA) (Table 1, domains 1-3),8 (2) 8 optional competencies per IMIA guidelines (Table 1, domain 4),8 and (3) an 25 additional unique competencies that were not part of the IMIA recommendations but were found in 1 or more of the reviewed curricula (Table 1 and Supplementary Appendix A). Details of the rigorous process used to derive the additional competencies are outlined in Were et al.11 Table 1. Competency domains contained in the evaluation template. Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domains 1-4 are derived from the International Medical Informatics Association Education Recommendations.8 BMHI: Biomedical and Health Informatics. a Domain 5 is detailed in Were et al.11 Open in new tab Table 1. Competency domains contained in the evaluation template. Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domain . Description . Competencies . 1 BMHI Core Knowledge and Skills 19 2 Medicine, Health and Biosciences, Health System Organization 7 3 Informatics/Computer Science, Mathematics, Biometry 14 4 Optional Modules in BMHI and from Related Fields 8 5 Additional competencies not in domains 1-4a 25 Domains 1-4 are derived from the International Medical Informatics Association Education Recommendations.8 BMHI: Biomedical and Health Informatics. a Domain 5 is detailed in Were et al.11 Open in new tab Data collection Two independent reviewers (P.B. and B.E.B.) with formal MSc HI training and over 2 years of experience working in the HI space conducted independent reviews of each curriculum. Reviewers first performed a qualitative descriptive assessment on each program’s structure and completion requirements. They then used the coding template (Supplementary Appendix A) to extract competencies contained within each course contained in each curriculum as detailed in Were et al.11 For each course, the reviewer read through the course content and coded with a “yes” or “no” against every competency outlined in the coding instrument (Supplementary Appendix A). Given that each course covers only a few competencies relative to the full set of competencies in the coding template, the resulting ratings per course within the template had much higher numbers of competencies rated as “no” as opposed to “yes.” After completing independent evaluations, the 2 reviewers compared assessments and reached consensus when there were coding disagreements in regards to competencies contained in a course. If consensus on a competency for a course was not reached by the 2 reviewers, a third-party reviewer (M.C.W.) with expertise in HI was available to moderate the discussion in order to reach a final consensus. Thesis and Practicum courses were excluded from the review, as the competencies involved were deemed highly variable based on the project each student undertook. Data analysis Summary of MSc HI programs Descriptive statistics were used to summarize general characteristics of the various MSc HI programs. When this information was not available from the curriculum, the program was contacted directly by the study team and asked to provide this information. Assessed descriptive variables included year of curriculum development, program start year, degree duration, full- or part-time status, program hosting unit, number of courses within the curriculum, number of credits, practicum and thesis requirements, and the admission criteria used. Interrater reliability between reviewers on coding of competencies within courses Interrater agreement on coding of competencies by the 2 reviewers was measured as Cohen's kappa on domains 1 to 4. Cohen's kappa was calculated with the R “psych” package,12 and the 95% confidence interval was estimated with the Fleiss, Cohen, and Everitt method (R Foundation for Statistical Computing, Vienna, Austria, version 3.6.2).13 As the additional domain 5 competencies were determined through consensus by the 2 reviewers, this domain was not included in the Cohen’s kappa analysis. As detailed in Were et al., 11 Cohen’s kappa for the 2 reviewers was 0.738 (95% confidence interval, 0.713-0.764). The reviewers reached consensus on all ratings after discussions, without having to involve the third reviewer for arbitration. Comparative analysis of the competencies in MSc HI programs in East Africa, with a focus on similarities and differences between curricula for the various institutions Descriptive statistics were used to determine competency coverage by institution and across institutions. We fit a multivariable logistic regression model with the outcome being coverage (yes/no) for each competency for each institution and predictors being the institution, the competency domain, and a random effect for each competency. A likelihood ratio test from this model was used to test whether coverage differed between institutions. To compare competency coverage similarities between institutions, a Jaccard similarity coefficient was used.14 The Jaccard similarity coefficient is calculated by one-to-one comparisons of competencies across university pairs. It is a percentage of the number of competencies covered by both universities (numerator) over the total number of competencies covered by either one of the 2 universities (denominator). Comparison of a university against itself always gives a Jaccard coefficient of 1 (100%). RESULTS Overall summary of master’s programs Table 2 provides descriptive data on the various MSc HI programs. The number of courses required to complete each MSc HI program ranged from 8 to 22, and the number of credited hours required for each degree program ranged 35 and 180. The departments in which the MSc HI programs were quite variable, among them being Medicine, Public Health, Computer Science and Engineering, and Health Informatics. All programs required a thesis, and 6 of 7 required a practicum. A majority of the programs (6 of 7) admitted students with either health or information sciences background, whereas one of the institutions (#7) only admitted candidates with a health sciences background. Table 2. Descriptive characteristics of master of science in health informatics programs across the East African community. University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 Open in new tab Table 2. Descriptive characteristics of master of science in health informatics programs across the East African community. University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 University . 1 . 2 . 3 . 4 . 5 . 6 . 7 . Program name Master of science in health informatics Master of health informatics Master of science in biostatistics and health informatics Master of science in health informatics Master of public health in health informatics Master of science in health information management Master of science in health information management Duration 2 y 2 y 2 y 2 y 2 y 2-4 y 2-3 y Part time vs full time Full time Full time Full time Part time Full time Both full and part time Part time Number of courses 22 23 13 23 16 10 8 Number of credit hours required 57 80 35 180 45 10 180 Hosting unit Medicine School of Public Health College of Health Sciences Department of Computer Science and Engineering Department of Health Informatics Department of Health Management and Informatics School of Public Health and Social Sciences Practicum Yes Yes Yes Yes Yes Yes No Thesis Yes Yes Yes Yes Yes Yes Yes Program start year 2015 2016 2010 2007 2007 2009 2015 Year current curriculum started 2014 2015 2011 2016 2018 2009 2018 Open in new tab Competency coverage across institutions The institution with the highest competencies coverage within its curriculum covered 49 (66.2%) of the 73 identified total competencies across the institutions, while the institution with the lowest coverage covered 32 (43.8%) competencies (Table 3 and Supplementary Appendix B). Only 19 (26.0%) of the competencies were covered within curricula of all the 7 evaluated institutions (Figure 1). These competencies included 1.5, 1.6, 1.8, 1.11, 1.12, 1.14, 1.16, 1.19, 2.5, 2.7, 3.2, 3.4, 3.6, 3.8, 3.9, 3.10, 3.11, 3.13 and 5.22 (Supplementary Appendix B). Three (4.1%) of the competencies were not covered in any of the evaluated curricula, and included patient-matching approaches (3.7), medical chemo-informatics (4.4) and medical nano-informatics (4.6) (Figure 1 and Supplementary Appendix B). The difference in competency coverage was statistically significant across the 7 institutions (P = .012). Figure 1. Open in new tabDownload slide Competency coverage across institutions. Figure 1. Open in new tabDownload slide Competency coverage across institutions. Table 3. Competency coverage by institution. Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Open in new tab Table 3. Competency coverage by institution. Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Institution . Competencies . Percent . 1 49 67.1 2 45 61.6 3 45 61.6 4 44 60.2 5 38 52.1 6 38 52.1 7 32 43.8 Open in new tab Comparison of competence coverage between institutions Figure 2 shows similarities in competency coverage when institutional pairs are compared. The 2 institutions with the highest Jaccard similarity shared 77% (n = 41 of 53) total competencies between them. The 2 with the lowest Jaccard similarity shared only 43% (n = 21 of 49) of the total competencies between them. If a cutoff of 70% is used to determine degree of similar competencies across programs, only 4 (19%) university pairs out of 21 would meet this threshold. A similarity cutoff of 60% would only be satisfied by 11 (53.3%) university pairs. Similarity thresholds are typically used by accrediting bodies to determine minimum shared competencies across degree programs, while allowing room for institutions to still distinguish themselves. Figure 2. Open in new tabDownload slide Comparison of competency coverage between institutions. Figure 2. Open in new tabDownload slide Comparison of competency coverage between institutions. DISCUSSION This comparative analysis of competencies within accredited MSc HI program curricula in East Africa reveals significant variations in competency coverage across the evaluated institutions. Only 19% of the university pairs had competency similarity that exceeded 70%. Further, the number of courses and credits offered per degree program were also highly variable. The consequence of such wide variability in covered competencies include (1) limitations on student mobility across institutions, with difficulty in transferring credits and taking courses across universities, (2) limitations in faculty mobility across institutions, and (3) limitations in labor mobility, with employers unable to easily determine what core skillsets to expect from any MSc HI graduate. This is a significant concern especially for institutions that belong in a common economic and educational zone underpinned by the “The EAC Common Market Protocol and the Regional Qualifications Framework.”15 The Common Market Protocol emphasizes the “desire to facilitate free movement of persons and labor” and “mutual recognition of academic and professional qualifications.”15 The variability observed in the MSc HI programs thus goes against a fundamental goal of the EAC. It should be noted that the observed differences in competency coverage are not unique to LMICs, as observations are also seen across programs in developed countries.16–19 The differences in program content likely reflect a number of things. This difference could simply be a reflection of the breadth of subject matter supported by the field, and the diversity in primary areas of focus by the hosting units for evaluated MSc HI programs within universities. As an example, programs hosted within information sciences departments would tend to have curricula that overemphasize information sciences competencies, while those hosted within health sciences units would emphasize health-related competencies. There is a possibility that the variabilities in competency coverage reflects differences in understanding of what core competencies make up an MSc HI degree program. As an example, an earlier evaluation of only the 40 core competencies approved by IMIA showed wide variability in coverage of these competencies within the East African programs (62.5%-97.5%).11 However, the fact that these programs include another 25 competencies not contained in the IMIA recommendations underscores that comparisons in competency coverage cannot be limited to only those recommended by IMIA, hence the approach to use all 73 competencies in this study. The observed significant differences in competency coverage between universities highlights the need for curricula harmonization of the MSc HI programs, to ensure that each program adequately covers the same set of agreed minimum competencies for the region. A regionalized harmonized set of core competencies in MSc HI can be developed leveraging already defined competency recommendations, 8,15 as well as emerging core competency recommendations and revised knowledge base from IMIA.20 However, these minimum competencies also need to recognize that there are numerous social, cultural, political, infrastructural, and implementation differences that impact the application of HI in LMICs, and wholesome adoption of international competencies would likely not meet the unique needs for each country or region. Region-specific competencies should be agreed on and incorporated into the minimum competency standards. Numerous examples of development of minimum competency standards for degree programs exist and can serve as models for MSc HI programs.21,22 Curriculum harmonization of the programs in the East African region is practically feasible through the Credit Accumulation and Transfer mechanism overseen by the IUCEA. The IUCEA is mandated to “maintain comparable academic standards in higher education recognized regionally and internationally.”15 The IUCEA has already undertaken harmonization and benchmarking of various programs, and this can be emulated by the MSc HI programs in the region. This study has several limitations. It was conducted in institutions within a single region, and the findings might not necessarily translate to other regions. Determination of competency coverage relied purely on the content within the curricula, but this does not necessarily translate to the content of the courses taught. Exact competencies covered by thesis and practicum were not included, but as applications of HI, these courses might have contained competencies that were not coded by institution. Further, determination of competency coverage involved some degree of judgment by the reviewers. Both reviewers were from one of the reviewed programs, and this could have introduced some bias in ratings. However, a moderate kappa and consensus in which there were differences provide confidence in the determination of competency coverage. As the next step, our team will embark on working with the IUCEA and the institutions in the region to develop a common set of minimum competency requirements as “Benchmarks for MSc HI” for the East Africa region. This will be done in close collaboration with Ministries of Health and the private and public sectors, and with professional health informatics bodies such as the Pan-African Health Informatics Association and the IMIA. The benchmarks will facilitate transfer of credits across institutions, and mobility of students and faculty. It would also likely impact quality of MSc HI programs offered, with ability to have shareable educational content developed for use across institutions to help ameliorate faculty shortages. CONCLUSION MSc HI programs at the common economic and educational zone of East Africa have curricula with limited similarity in competency coverage. This evaluation highlights the need for critical comparative competency evaluations in emerging MSc HI programs, especially for those programs within similar labor and educational zones. Further, strong considerations should be given to setting common minimum competency standards when large differences exist within programs. Comparative analysis of MSc HI degree programs across 7 universities in East Africa revealed significant differences in covered competencies. FUNDING This work was made possible by the support of the American people through the U.S. Agency for International Development (grant number 7200AA18CA00019). The contents are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Agency for International Development or the U.S. government. AUTHOR CONTRIBUTIONS MCW contributed to conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, validation, and writing (original draft; review and editing). WG contributed to data curation, formal analysis, funding acquisition, methodology, visualization, and writing (review and editing). PB contributed to data curation, resources, and writing (original draft; review and editing). BEB contributed to data curation, resources, and writing (original draft; review and editing). AY contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). LP contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). DL contributed to data curation, formal analysis, project administration, supervision, validation, and writing (review and editing). HJL contributed to data curation, resources, and writing (review and editing). YK contributed to conceptualization, funding acquisition, project administration, supervision, and writing (review and editing). BES contributed to data curation, formal analysis, investigation, methodology, project administration, supervision, validation, and writing (review and editing). SUPPLEMENTARY MATERIAL Supplementary material is available at Journal of the American Medical Informatics Association online. ACKNOWLEDGMENTS The authors thank the participating universities for sharing their curricula with us and responding to our requests for clarification. They also thank the Vanderbilt Institute for Global Health and Moi University Institute of Biomedical Informatics for logistical support during the study. CONFLICT OF INTEREST STATEMENT All authors report no competing interests to declare. 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Journal of the American Medical Informatics Association – Oxford University Press

Published: Jun 21, 2021

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