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
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 st...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Kerman University of Medical Sciences
2023-12-01
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| Series: | International Journal of Health Policy and Management |
| Subjects: | |
| Online Access: | https://www.ijhpm.com/article_4381_9fb98d9fce0b484a481ec69c2b8d1a1a.pdf |
| _version_ | 1797206347762106368 |
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| author | Leon Schuetze Siddharth Srivastava Naasegnibe Kuunibe Elizeus Rwezaula Abdallah Missenye Manfred Stoermer Manuela De Allegri |
| author_facet | Leon Schuetze Siddharth Srivastava Naasegnibe Kuunibe Elizeus Rwezaula Abdallah Missenye Manfred Stoermer Manuela De Allegri |
| author_sort | Leon Schuetze |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:05:35Z |
| format | Article |
| id | doaj.art-e03747bd4f5a4d539414ca2664d0a320 |
| institution | Directory Open Access Journal |
| issn | 2322-5939 |
| language | English |
| last_indexed | 2024-04-24T09:05:35Z |
| publishDate | 2023-12-01 |
| publisher | Kerman University of Medical Sciences |
| record_format | Article |
| series | International Journal of Health Policy and Management |
| spelling | doaj.art-e03747bd4f5a4d539414ca2664d0a3202024-04-15T19:04:25ZengKerman University of Medical SciencesInternational Journal of Health Policy and Management2322-59392023-12-0112Issue 11910.34172/ijhpm.2023.68964381What Factors Explain Low Adoption of Digital Technologies for Health Financing in an Insurance Setting? Novel Evidence From a Quantitative Panel Study on IMIS in TanzaniaLeon Schuetze0Siddharth Srivastava1Naasegnibe Kuunibe2Elizeus Rwezaula3Abdallah Missenye4Manfred Stoermer5Manuela De Allegri6Heidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Heidelberg, GermanySwiss Tropical and Public Health Institute (Swiss TPH), Basel, SwitzerlandHeidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Heidelberg, GermanyHealth Promotion and System Strengthening Project (HPSS), Dodoma, TanzaniaKongwa District Council, Dodoma, TanzaniaSwiss Tropical and Public Health Institute (Swiss TPH), Basel, SwitzerlandHeidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Heidelberg, GermanyBackground 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.https://www.ijhpm.com/article_4381_9fb98d9fce0b484a481ec69c2b8d1a1a.pdfhealth financinghealth insurancestrategic purchasingtanzaniadigital health interventionadoption |
| spellingShingle | Leon Schuetze Siddharth Srivastava Naasegnibe Kuunibe Elizeus Rwezaula Abdallah Missenye Manfred Stoermer Manuela De Allegri 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 International Journal of Health Policy and Management health financing health insurance strategic purchasing tanzania digital health intervention adoption |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | health financing health insurance strategic purchasing tanzania digital health intervention adoption |
| url | https://www.ijhpm.com/article_4381_9fb98d9fce0b484a481ec69c2b8d1a1a.pdf |
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