Document Type : Original Article
Heidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Heidelberg, Germany
Swiss Tropical and Public Health Institute (Swiss TPH), Basel, Switzerland
University of Basel, Basel, Switzerland
Faculty of Integrated Development Studies, University for Development Studies, Wa, Ghana
Health Promotion and System Strengthening Project (HPSS), Dodoma, Tanzania
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.