What Factors Explain Low Adoption of Digital Technologies for Health Financing in an Insurance Setting? Novel Evidence from a Quantitative Panel Study on IMIS in Tanzania

Document Type : Original Article


1 Heidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Heidelberg, Germany

2 Swiss Tropical and Public Health Institute (Swiss TPH), Basel, Switzerland

3 University of Basel, Basel, Switzerland

4 Faculty of Integrated Development Studies, University for Development Studies, Wa, Ghana

5 Health Promotion and System Strengthening Project (HPSS), Dodoma, Tanzania

6 Chamwino District Council, Dodoma, Tanzania


Digital information management systems for health financing are implemented on the assumption that digitalization, among other things, enables strategic purchasing. However, little is known about the extent to which these systems are adopted as planned to achieve desired results. This study assesses the levels of, and the factors associated with the adoption of the Insurance Management Information System (IMIS) by healthcare providers in Tanzania.

Combining multiple data sources, we estimated IMIS adoption levels for 365 firstline health facilities in 2017 by comparing IMIS claim data (verified claims) with the number of expected claims. We defined adoption as a binary outcome capturing underreporting (verified<expected) vs. not-underreporting, using four different approaches. We used descriptive statistics and analysis of variance to examine adoption levels across facilities, districts, regions, and months. We used logistic regression to identify facility-specific factors (i.e., explanatory variables) associated with different adoption levels.
We found a median (IQR) difference of 77.8% (32.7-100) between expected and verified claims, showing a consistent pattern of underreporting across districts, regions, and months. Levels of underreporting varied across regions (ANOVA: F=7.24, p<0.001) and districts (ANOVA: F=4.65, p<0.001). Logistic regression results showed that higher service volume, share of people insured, and greater distance to district HQ were associated with a higher probability of underreporting.

Our study shows that the adoption of IMIS in Tanzania may be sub-optimal and far from policy makers’ expectations, limiting its capacity to provide the necessary information to enhance strategic purchasing in the health sector. Countries and agencies adopting digital interventions such as openIMIS to foster health financing reform are advised to closely track their implementation efforts to make sure the data they rely on is accurate. Further, our study
suggests organizational and infrastructural barriers beyond the software itself hamper effective adoption.


  1. Meessen B. The Role of Digital Strategies in Financing Health Care for Universal Health Coverage in Low- and Middle-Income Countries. Global Health: Science and Practice. 2018;6(Supplement 1):S29-S40. doi:10.9745/ghsp-d-18-00271
  2. Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health. 2018;6(11):e1196-e1252. doi:10.1016/s2214-109x(18)30386-3
  3. Honda A. What is strategic purchasing for health. Other2014.
  4. Figueras J, Robinson R, Jakubowski E. Purchasing to improve health systems performance: McGraw-Hill Education (UK); 2005.
  5. Busse R, Figueras J, Robinson R, Jakubowski E. Strategic purchasing to improve health system performance: key issues and international trends. Healthc Pap. 2007;8 Spec No:62-76.
  6. Tangcharoensathien V, Limwattananon S, Patcharanarumol W, Thammatacharee J, Jongudomsuk P, Sirilak S. Achieving universal health coverage goals in Thailand: the vital role of strategic purchasing. Health Policy and Planning. 2015;30(9):1152-1161. doi:10.1093/heapol/czu120
  7. Mathauer I, Dale E, Meessen B. Strategic purchasing for UHC: Key policy issues and questions. A summary from experts and practitioners’ discussions: World Health Organization;2017.
  8. WHO. The World Health Report 2010. World Health Organisation. 2010.
  9. Ndabarora E, Chipps JA, Uys L. Systematic review of health data quality management and best practices at community and district levels in LMIC. Information Development. 2013;30(2):103-120. doi:10.1177/0266666913477430
  10. openrbf.org. https://openrbf.org. Accessed Oct 18, 2021.
  11. openIMIS initiative. About openIMIS. https://openimis.org/about-openimis. Accessed Oct 18, 2021.
  12. Swiss TPH. What is OpenIMIS? https://www.swisstph.ch/en/about/scih/sysu/health-economics-and-financing/imis/. Accessed Jan 06, 2021.
  13. Federal Ministry for Economic Cooperation and Development. Open source software for social health insurance. 2017; http://health.bmz.de/events/In_focus/Open_source_software_for_social_health_insurance/index.html. Accessed Oct 18, 2021.
  14. Federal Ministry for Economic Cooperation and Development. openIMIS: Co-creating a global good. 2020; http://health.bmz.de/what_we_do/openimis/co-creating-a-global-good/index.html. Accessed Oct 18, 2021.
  15. Lium JT, Tjora A, Faxvaag A. No paper, but the same routines: a qualitative exploration of experiences in two Norwegian hospitals deprived of the paper based medical record. BMC Med Inform Decis Mak. 2008;8:2. doi:10.1186/1472-6947-8-2
  16. Shiferaw AM, Zegeye DT, Assefa S, Yenit MK. Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia. BMC Med Inform Decis Mak. 2017;17(1):116. doi:10.1186/s12911-017-0509-2
  17. Ludwick D, Doucette J. Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries. International Journal of Medical Informatics. 2009;78(1):22-31. doi:10.1016/j.ijmedinf.2008.06.005
  18. Fritz F, Tilahun B, Dugas M. Success criteria for electronic medical record implementations in low-resource settings: a systematic review. Journal of the American Medical Informatics Association. 2015;22(2):479-488. doi:10.1093/jamia/ocu038
  19. Jawhari B, Ludwick D, Keenan L, Zakus D, Hayward R. Benefits and challenges of EMR implementations in low resource settings: a state-of-the-art review. BMC Med Inform Decis Mak. 2016;16:116. doi:10.1186/s12911-016-0354-8
  20. Carnahan E, Ferriss E, Beylerian E, et al. Determinants of Facility-Level Use of Electronic Immunization Registries in Tanzania and Zambia: An Observational Analysis. Glob Health Sci Pract. 2020;8(3):488-504. doi:10.9745/ghsp-d-20-00134
  21. Dolan SB, Alao ME, Mwansa FD, et al. Perceptions of factors influencing the introduction and adoption of electronic immunization registries in Tanzania and Zambia: a mixed methods study. Implement Sci Commun. 2020;1:38. doi:10.1186/s43058-020-00022-8
  22. Mangone E, Riley P, Datari K. Digital Financial Services for Health – A Global Evidence Review. Rockville, MD: USAID Local Health System Sustainability Project, Abt Associates Inc.;2021.
  23. Schreiweis B, Pobiruchin M, Strotbaum V, Suleder J, Wiesner M, Bergh B. Barriers and Facilitators to the Implementation of eHealth Services: Systematic Literature Analysis. J Med Internet Res. 2019;21(11):e14197. doi:10.2196/14197
  24. Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Administration and policy in mental health. 2011;38(2):65-76. doi:10.1007/s10488-010-0319-7
  25. The United Republic of Tanzania Ministry of Health and Social Welfare. Staffing Levels for Ministry of Health and Social Welfare Departments, Health Service Facilities, Health Training Institutions and Agencies 2014-2019 (Revised). Dar Es Salaam 2014.
  26. University Consultancy Bureau (UCB) of the University of Dar Es Salaam. Household Survey Report For A Follow Up Survey For The Health Promotion And System Strengthening (HPSS): University of Dar Es Salaam;2018.
  27. Ministry of Health, Community Development, Gender, Elderly, Children (MoHCDGEC) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of Chief Government Statistician (OCGS), ICF. Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015-2016 [Dataset] TZIR7AFL.DTA. Dar es Salaam, Tanzania: MoHCDGEC, MoH, NBS, OCGS, and ICF; 2016.
  28. Kuunibe N, Lohmann J, Schleicher M, et al. Factors associated with misreporting in performance-based financing in Burkina Faso: Implications for risk-based verification. Int J Health Plann Manage. 2019. doi:10.1002/hpm.2786
  29. Leys C, Ley C, Klein O, Bernard P, Licata L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology. 2013;49(4):764-766. doi:https://doi.org/10.1016/j.jesp.2013.03.013
  30. GEODIST: Stata module to compute geographical distances [computer program]: Boston College Department of Economics; 2010.
  31. Manzi F, Schellenberg JA, Hutton G, et al. Human resources for health care delivery in Tanzania: a multifaceted problem. Hum Resour Health. 2012;10:3. doi:10.1186/1478-4491-10-3
  32. Kwesigabo G, Mwangu MA, Kakoko DC, et al. Tanzania's health system and workforce crisis. Journal of Public Health Policy. 2012;33(1):S35-S44. doi:10.1057/jphp.2012.55
  33. Gagnon M-P, Desmartis M, Labrecque M, et al. Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals. Journal of Medical Systems. 2012;36(1):241-277. doi:10.1007/s10916-010-9473-4
  34. Ndima SD, Sidat M, Give C, Ormel H, Kok MC, Taegtmeyer M. Supervision of community health workers in Mozambique: a qualitative study of factors influencing motivation and programme implementation. Human Resources for Health. 2015;13(1):63. doi:10.1186/s12960-015-0063-x
  35. Bradley S, Kamwendo F, Masanja H, et al. District health managers’ perceptions of supervision in Malawi and Tanzania. 2013;11(1):43. doi:10.1186/1478-4491-11-43
  36. Heerdegen ACS, Aikins M, Amon S, Agyemang SA, Wyss K. Managerial capacity among district health managers and its association with district performance: A comparative descriptive study of six districts in the Eastern Region of Ghana. PLOS ONE. 2020;15(1):e0227974. doi:10.1371/journal.pone.0227974
  37. Fetene N, Canavan ME, Megentta A, et al. District-level health management and health system performance. PLOS ONE. 2019;14(2):e0210624. doi:10.1371/journal.pone.0210624
  38. Blacklock C, Gonçalves Bradley DC, Mickan S, et al. Impact of Contextual Factors on the Effect of Interventions to Improve Health Worker Performance in Sub-Saharan Africa: Review of Randomised Clinical Trials. PLOS ONE. 2016;11(1):e0145206. doi:10.1371/journal.pone.0145206
  39. Van De Klundert J, Van Dongen- Van Den Broek J, Yesuf EM, Vreugdenhil J, Yimer SM. ‘We are planning to leave, all of us’—a realist study of mechanisms explaining healthcare employee turnover in rural Ethiopia. Human Resources for Health. 2018;16(1). doi:10.1186/s12960-018-0301-0
  40. Naburi H, Mujinja P, Kilewo C, et al. Job satisfaction and turnover intentions among health care staff providing services for prevention of mother-to-child transmission of HIV in Dar es Salaam, Tanzania. Human Resources for Health. 2017;15(1). doi:10.1186/s12960-017-0235-y
  41. Leonard E, de Kock I, Bam W. Barriers and facilitators to implementing evidence-based health innovations in low- and middle-income countries: A systematic literature review. Evaluation and Program Planning. 2020;82:101832. doi:https://doi.org/10.1016/j.evalprogplan.2020.101832
  42. USAID. Tanzania. Results-Based Financing - Fact Sheet. 2019; https://www.usaid.gov/sites/default/files/documents/1860/03.26.2019_Results-Based_Financing.pdf. Accessed Jan 06, 2021.
  43. Paul E, Sossouhounto N, Eclou DS. Local stakeholders’ perceptions about the introduction of performance-based financing in Benin: a case study in two health districts. International Journal of Health Policy and Management. 2014;3(4):207-214. doi:10.15171/ijhpm.2014.93
  44. Lohmann J, Wilhelm D, Kambala C, Brenner S, Muula AS, De Allegri M. ‘The money can be a motivator, to me a little, but mostly PBF just helps me to do better in my job.’ An exploration of the motivational mechanisms of performance-based financing for health workers in Malawi. Health Policy and Planning. 2018;33(2):183-191. doi:10.1093/heapol/czx156
  45. Lohmann J, Muula AS, Houlfort N, De Allegri M. How does performance-based financing affect health workers' intrinsic motivation? A Self-Determination Theory-based mixed-methods study in Malawi. Soc Sci Med. 2018;208:1-8. doi:10.1016/j.socscimed.2018.04.053
  46. The United Republic of Tanzania Ministry of health and Social Welfare. Staffing Levels for Ministry of Health and Social Welfare Departments, Health Service Facilities, Health Training Institutions and Agencies, 2014-2019 revised. Dar Es Salaam 2014.
  47. Clarke KA. The Phantom Menace: Omitted Variable Bias in Econometric Research. Conflict Management and Peace Science. 2005;22(4):341-352. doi:10.1080/07388940500339183
  48. Huang F, Blaschke S, Lucas H. Beyond pilotitis: taking digital health interventions to the national level in China and Uganda. Globalization and Health. 2017;13(1). doi:10.1186/s12992-017-0275-z
  49. Kiberu VM, Matovu JKB, Makumbi F, Kyozira C, Mukooyo E, Wanyenze RK. Strengthening district-based health reporting through the district health management information software system: the Ugandan experience. BMC Medical Informatics and Decision Making. 2014;14(1):40. doi:10.1186/1472-6947-14-40
  50. Phalkey RK, Yamamoto S, Awate P, Marx M. Challenges with the implementation of an Integrated Disease Surveillance and Response (IDSR) system: systematic review of the lessons learned. Health Policy and Planning. 2013;30(1):131-143. doi:10.1093/heapol/czt097
  51. Labrique A, Vasudevan L, Weiss W, Wilson K. Establishing Standards to Evaluate the Impact of Integrating Digital Health into Health Systems. Global Health: Science and Practice. 2018;6(Supplement 1):S5-S17. doi:10.9745/ghsp-d-18-00230
  52. Labrique AB, Wadhwani C, Williams KA, et al. Best practices in scaling digital health in low and middle income countries. Globalization and Health. 2018;14(1). doi:10.1186/s12992-018-0424-z

Articles in Press, Accepted Manuscript
Available Online from 07 January 2023
  • Receive Date: 28 October 2021
  • Revise Date: 30 October 2022
  • Accept Date: 02 January 2023
  • First Publish Date: 07 January 2023