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

Authors

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 Kongwa District Council, Dodoma, Tanzania

Abstract

Background 
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.

Methods 
Combining multiple data sources, we estimated IMIS adoption levels for 365 first-line 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 (ANOVA) to examine adoption levels across facilities, districts, regions, and months. We used logistic regression to identify facility-specific factors (ie, explanatory variables) associated with different adoption levels.

Results 
We found a median (interquartile range [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 < .001) and districts (ANOVA: F = 4.65, P < .001). Logistic regression results showed that higher service volume, share of people insured, and greater distance to district headquarter were associated with a higher probability of underreporting.

Conclusion 
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.

Keywords


  1. Meessen B. The role of digital strategies in financing health care for universal health coverage in low- and middle-income countries. Glob Health Sci Pract. 2018;6(Suppl 1):S29-S40. doi: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:1016/s2214-109x(18)30386-3
  3. Honda A. What is Strategic Purchasing for Health? RESYST; 2014.
  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. doi:12927/hcpap.2007.19221
  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 Plan. 2015;30(9):1152-1161. doi:1093/heapol/czu120
  7. Mathauer I, Dale E, Meessen B. Strategic Purchasing for Universal Health Coverage: Key Policy Issues and Questions: A Summary from Expert and Practitioners’ Discussions. World Health Organization; 2017.
  8. World Health Organization (WHO). The World Health Report 2010. WHO; 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. Inf Dev. 2014;30(2):103-120. doi:1177/0266666913477430
  10. https://openrbf.org. Accessed October 18, 2021.
  11. openIMIS Initiative. About openIMIS. https://openimis.org/about-openimis. Accessed October 18, 2021.
  12. Swiss TPH. What is OpenIMIS? https://www.swisstph.ch/en/about/scih/sysu/health-economics-and-financing/imis/. Accessed January 6, 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 October 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 October 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: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:1186/s12911-017-0509-2
  17. Ludwick DA, Doucette J. Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries. Int J Med Inform. 2009;78(1):22-31. doi: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. J Am Med Inform Assoc. 2015;22(2):479-488. doi: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(1):116. doi: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: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: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:2196/14197
  24. Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65-76. doi: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;34(4):1217-1237. doi: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. J Exp Soc Psychol. 2013;49(4):764-766. doi: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:1186/1478-4491-10-3
  32. Kwesigabo G, Mwangu MA, Kakoko DC, et al. Tanzania's health system and workforce crisis. J Public Health Policy. 2012;33 Suppl 1:S35-44. doi:1057/jphp.2012.55
  33. Gagnon MP, Desmartis M, Labrecque M, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012;36(1):241-277. doi: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. Hum Resour Health. 2015;13:63. doi:1186/s12960-015-0063-x
  35. Bradley S, Kamwendo F, Masanja H, et al. District health managers' perceptions of supervision in Malawi and Tanzania. Hum Resour Health. 2013;11:43. doi: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: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: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: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. Hum Resour Health. 2018;16(1):37. doi: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. Hum Resour Health. 2017;15(1):61. doi: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. Eval Program Plann. 2020;82:101832. doi:1016/j.evalprogplan.2020.101832
  42. Tanzania. Results-Based Financing - Fact Sheet. 2019. https://www.usaid.gov/sites/default/files/documents/1860/03.26.2019_Results-Based_Financing.pdf. Accessed January 6, 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. Int J Health Policy Manag. 2014;3(4):207-214. doi: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 Plan. 2018;33(2):183-191. doi: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:1016/j.socscimed.2018.04.053
  46. Clarke KA. The phantom menace: omitted variable bias in econometric research. Confl Manag Peace Sci. 2005;22(4):341-352. doi:1080/07388940500339183
  47. Huang F, Blaschke S, Lucas H. Beyond pilotitis: taking digital health interventions to the national level in China and Uganda. Global Health. 2017;13(1):49. doi:1186/s12992-017-0275-z
  48. Kiberu VM, Matovu JK, 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 Med Inform Decis Mak. 2014;14:40. doi:1186/1472-6947-14-40
  49. 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 Plan. 2015;30(1):131-143. doi:1093/heapol/czt097
  50. Labrique A, Vasudevan L, Weiss W, Wilson K. Establishing standards to evaluate the impact of integrating digital health into health systems. Glob Health Sci Pract. 2018;6(Suppl 1):S5-S17. doi:9745/ghsp-d-18-00230
  51. Labrique AB, Wadhwani C, Williams KA, et al. Best practices in scaling digital health in low and middle income countries. Global Health. 2018;14(1):103. doi:1186/s12992-018-0424-z
  52. Schuetze L, Srivastava S, Missenye AM, Rwezaula EJ, Stoermer M, De Allegri M. Factors affecting the successful implementation of a digital intervention for health financing in a low-resource setting at scale: semistructured interview study with health care workers and management staff. J Med Internet Res. 2023;25:e38818. doi:10.2196/38818
  • Receive Date: 28 October 2021
  • Revise Date: 30 October 2022
  • Accept Date: 02 January 2023
  • First Publish Date: 07 January 2023