What factors explain low adoption of digital technologies for health financing in an insurance setting?

• Verfasst von: Schütze, Leon [VerfasserIn]
• Verfasst von: Srivastava, Siddharth [VerfasserIn]
• Verfasst von: Kuunibe, Naasegnibe [VerfasserIn]
• Verfasst von: Rwezaula, Elizeus Josephat [VerfasserIn]
• Verfasst von: Missenye, Abdallah [VerfasserIn]
• Verfasst von: Störmer, Manfred [VerfasserIn]
• Verfasst von: De Allegri, Manuela [VerfasserIn]
• Titel: What factors explain low adoption of digital technologies for health financing in an insurance setting?
• Titelzusatz: Novel evidence from a quantitative panel Study on IMIS in Tanzania
• Verf.angabe: Leon Schuetze, Siddharth Srivastava, Naasegnibe Kuunibe, Elizeus Josephat Rwezaula, Abdallah Missenye, Manfred Stoermer, Manuela De Allegri
• E-Jahr: 2023
• Jahr: 13 February 2023
• Umfang: 9 S.
• Fussnoten: Gesehen am 10.07.2023
• Titel Quelle: Enthalten in: International journal of health policy and management
• Ort Quelle: Kerman : Kerman Univ. of Medical Sciences, 2013
• Jahr Quelle: 2023
• Band/Heft Quelle: 12(2023), 1, Artikel-ID 6896, Seite 1-9
• ISSN Quelle: 2322-5939
• DOI: doi:10.34172/ijhpm.2023.6896
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1852258845
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

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.