ICT-based tools for evaluating impact of health insurance on health care delivery Frank Verbeke Vrije Universiteit Brussel Department of Biostatistics and Medical Informatics

Outline The need for care provider – health insurer data merging User centered software tools for data quality enhancement Case study in Rwanda: the relationship between insurance status, mortality and care consumption at the University Teaching Hospital of Kigali

1. The need for care provider – health insurer data merging Data merging for linking inputs to outputs and outcomes – Inputs Labor: quantitative & qualitative  care provider Capital: beds, budget-funding, user-fees, insurer-fees  care provider & insurer Materials: drugs, consumables, vacciness  care provider – Outputs Health activities: information management (e.g. correct health coverage plans application), case load, length of stay, procedure load  care provider & insurer Health outputs: completed treatments, morbidity load (proxy)  care provider – Outcomes Nosocomial morbidity, mortality, physiologic measures, patient satisfaction, patient health status, education, research  care provider

1. The need for care provider – health insurer data merging (2) Information standardization – International classifications of clinical conditions (ICD-10, ICPC-2, DSM-4, SNOMED...) Many local and ad-hoc coding systems in the field Limited expertise in clinical coding – National or regional nomenclatures for health services, drugs and products (CTP, ATC, LOINC...) Nonexistent in most sub-Saharan countries

2. User centered software tools for data quality enhancement Health insurance related functional needs – Care providers Receive & apply patients’ health insurance status Receive & apply detailed health coverage plans from insurers – Insurers Manage affiliates & dependents (ID-cards, biometrics) Receive & manage reïmbursement claims respecting applicable coverage plans Receive & manage structured health services data

2. User centered software tools for data quality enhancement Reporting & analysis needs – Insurance performance measurement Care consumption profiling (patients, care providers & employers) – Insurance impact measurement Cost – output evaluation Cost – outcome evaluation – Real-time budget control based on care consumption Open source care provider tool used in Rwanda, Burundi, RDC, Mali & Ivory Coast: OpenClinic Open source health insurance management tool used in Rwanda & Burundi: OpenInsurance

3. Case study in Rwanda: CHUK Objective: evaluate health insurance impact on care consumption and in-hospital mortality Materials ( period): – documented admissions for top-11 DRGs – ICD-10 & ICPC-2 coded primary & secundary discharge diagnoses – invoiced health services with DRG-weighed reimbursement data – DRG-weighed mortality data

3. Case study in Rwanda: CHUK - care consumption DRG-group ɛi ɛi ɛu ɛu Δ ɛ 1. Traumatology & Burns39,9627,84+43,53% 2. Cancer33,1329,51+12,29% 3. Diabetes37,5427,31+37,49% 4. Tuberculosis40,2530,32+32,75% 5. HIV/AIDS35,6827,53+29,63% 6. Cardiovascular diseases32,7822,65+44,72% 7. Pneumonia29,1121,74+33,89% 8. Digestive diseases38,2426,53+44,15% 9. Malaria29,5023,32+26,50% 10. Genital-urinary diseases35,6729,01+22,95% 11. Pregnancy related problems33,1329,74+11,39% All DRGs 35,34 n=14313 SD=61,12 28,08 n=1512 SD=42,56 +25,87% P<0.001

3. Case study in Rwanda: CHUK - mortality DRG-group InsuredUninsured Δmortality Signifi- Cance AliveDeceasedAliveDeceased 1. Traumatology & Burns ,64%203125,58%-52,70%p=0,0293 * 2. Cancer ,58%391426,42%-44,81%p=0,0286 * 3. Diabetes ,14%27925,00%-55,44%p=0,0289 * 4. Tuberculosis ,05%25619,35%+3,61%NS * 5. HIV/AIDS ,40%281230,00%-5,34%NS * 6. Cardiovascular diseases ,51%572025,97%-40,28%p=0,024 * 7. Pneumonia ,79%441018,52%-52,51%p=0,0479 * 8. Digestive diseases ,49% ,92%-46,24%p=0,0085 * 9. Malaria ,42%5035,66%-21,98%NS * 10. Genital-urinary diseases ,12% ,03%-44,50%p=0,033 * 11. Pregnancy related problems ,32%64191,38%-4,60%NS * All DRGs ,11% ,80%-19,14%p=0,019 **