Universal Health Insurance Coverage in Estonia: Case Study

1. Universal Health Insurance Coverage in Estonia: Case Study • Country context: • Population 1.3 million • Minister of Social Affairs • Administratively 15 counties • ALE at birth~72 years • IMR ~ 7 • OPD 5,8 amb.visits • 0.2 hospital admissions per capita • From 115 hospitals in 1993 to 19 today • ALOS 8,7 days in 2002 – to 6-7 now • PCH –about 800 family doctors

2. Health Care Expenditure in 20006,0 % GDP - 241 EURO per person

3. Finance - from budget to earmarked tax in 1992 • Health Insurance Law in force since 1992 • Financed: 13 % earmarked health insurance tax on salaries paid by employers • Collected by Tax Agency from 1999 • Coverage: 93% of population • Administration by Health Insurance Fund (EHIF)

4. Earmarked health insurance tax- stability in revenue in Estonia

5. Solidarity in Estonian health insurance: contributions

6. EHIF budget 2002 - 315 mill EURO

7. EHIF budget for health services 2002: 200 million Euros

8. Primary Care from 1998 - separation from specialists and more revenues to primary level • Private family practitioners • 1500 - 2300 registered people/ FP • Overall need 810 FP / 562 in 2001 • Health insurance financing • weighted capitation • fee-for-service • basic allowance • bonuses • Gatekeeping for referrals to specialist care

9. Paying hospitals in Estonia:moving towards needs based planning and case-mix systems Toomas Palu Sr. Health Specialist, World Bank Ex-Member of Management Board Estonian Health Insurance Fund (EHIF)

10. Specialist Care • Hospitals mostly publicly owned, working under private law • Foundations • Joint-stock companies • EHIF contracts with centers and hospitals • Payment mostly fee-for-service to the limit of the contract – now DRGs • Block-contracts have evolved into very detailed contracts, specifying patient numbers and case-costs per speciality

11. Needs assessment and contract planning • Analyze service utilisation variation among 7 population pools as proxies for need

13. Needs assessment and contract planning in EHIF • Analyze service utilisation variation among 7 population pools as a proxy for need • Utilisation of data warehouse concept • Data warehouse is an IT tool for regular extraction of data from the EHIF database • Verify analysis with GPs to separate supplyinduced demand from medical need as much as possible • Plan cost-and-volume contracts according to needs assessment results

14. Needs assessment: aggregate population based utilisation rates

15. Neurology: small area variations in service use

16. Obstetrics and gynaecology: small area variation in service use

17. Monitoring hospital waiting times • Standard waiting times reporting introduced in 2002 • reported by hospitals, breakdown by specialty and by EHIF population pool • standardised for population (per 100 000 people) • some action must be taken if waiting times are longer than 6 months

19. Prioritizing queues • Application of prioritization protocols • joint replacement and cataract surgery queues • evaluate need, e.g. • - physical impairment (visual aquity, functional mobility) • - pain • - ability to work, give care to dependents, live independently • protocols based on New Zealand experience • People with higher needs needs are treated faster

20. Priority criteria for major joint replacement • Pain 40% • appearance and severity • Functional activity 20% • walking and other function limitations • Movement and deformity 20% • pain on examination and other abnormal finding • Other factors 20% • Multiple joint involvement and ability to work, give care to dependents of live independently

21. Waiting list – major joint replacement Score

22. Conclusions on needs assessment • Utilisation and queues are notperfect indicators for needs assessment • depend also on supply and other factors • To improve assessment, initial results are reviewed by family physicians (“corporate view”) • More thorough research is needed into different population based determinants of morbidity and mortality as well into analysis of cost-efficiency for the best health service response to needs • Adjustments in regional allocation formula possible

23. Case based hospital financing: introduction of NordDRGs in 2003 • Nordic DRGs (Diagnosis Related Groups) • Used by Nordic countries • 498 DRGs • Based on ICD-10 and NOMESCO-NCSP classification • Why NordDRGs? • Number of cases too small to develop Estonian own unique system • Currently available data fits the best with the NordDRG locic, e.g. AR-DRG (Australia) requires detailed information on co-morbidities • Nordic countries are near-by, technical support available

24. Estonian DRG TOP 10 based on case cost* % of DRG Name Cost (EEK) cost 373 Normal delivery without complicatoins 2,95% 45 231 195 430 Psychoses 2,88% 44 184 304 105 Procedures with heart valves w/out catheterisation 2,80% 42 870 424 483 Tracheostomy, expt face, neck and lower jaw diagnoses 2,69% 41 219 090 112 Percutaneous cardio-vascular diagnoses 2,65% 40 598 849 Hip and femur procedures, patient age > 17, without 211 1,88% 28 778 315 complications 39 Procedures on lens, w/ or w/out vitrectomy 1,64% 25 149 968 140 Stenocardia ( angina pectoris ) 1,64% 25 071 615 14 Certain cererbrovascular diseases, expt transitory ishaemia 1,52% 23 264 651 Procedures on uterus and ovaries, nonmalignant tumors 359 1,52% 23 195 885 without complications Total 1 533 194 564 * Preliminary results

25. Estonian DRG TOP 10 based on number of cases* % of Number DRG Name cases of cases Other factors influencing health status 467 4,53% 14 398 Abortion by cervical dilatation, currettage, aspiration currettage or 381 3,41% 10 831 hysterectomy Normal delivery without complications 373 3,13% 9 950 High blood pressure 134 1,77% 5 629 Slight skin disorders w/out complications 284 1,70% 5 383 Psychoses 430 1,68% 5 348 Intraocular procedures, expt iris, retina, lens 42 1,62% 5 157 Medical back problems 243 1,62% 5 143 Otitis media, age 0-17, w/out complications 70B 1,59% 5 047 Dilatation and curretage, conisation, nonmalignant tumour 364 1,57% 5 000 Kokku 317 526 * Preliminary results

26. Example of cost-volume contract

27. Reimbursing Pharmaceuticals: Estonian case Toomas Palu Sr. Health Specialist, World Bank Ex-Executive, Estonian Health Insurance Fund

28. EHIF Expenditures on Health Services, Pharmaceuticals and Sickness Benefits

29. Expenditures on pharmaceuticals

30. Average prescription cost

31. Number of prescriptions

32. EHIF pharmaceutical expenditure projections

33. Pharmaceuticals´ reimbursement problems • Estonia has very liberal pharmaceuticals pricing policy • Prices determined by producers • Wholesale and retail margins allowed by state guarantee profits • Retail sector interested in selling expensive drugs • Estonia has liberal pharmaceuticals reimbursement policy • All prescriptions are reimbursed for at least 50% • EHIF compensates regardless of price (cost of just brandname among c/v drugs is EEK 100-150 mill annually) • Adding new drugs to reimbursement list without considerations on expenditures and cost-effectiveness • Doctors and patients are not sensitive to price, make decisions regardless of price • Large practice variations among doctors • Why?

34. Pharmaceuticals´ reimbursement problems (II) • Generic substitution not regulated • doctors do not have to prescribe generic compound names • pharmacists - no right nor obligation for generic substitution • Need to increase prescription volumes • chronic patients do not take drugs regularly

35. EHIF strategy to control pharmaceutical expenditure growths • Limit reimbursement • Reference prices – based only on active ingredient • Price setting through price agreements • Analysis of doctors prescription behaviour and feedback • Possible pharmaceuticals budget holding - No • Agreements with doctors about priorities and proportions - No • Review of wholesale and retail margins • Capping pharmaceuticals reimbursement budget to 20% of that for health services - unrealistic

36. EHIF expenditures on cardio-vascular drugs in2001 – EEK 267 M

37. EHIF expenditure and expected impact of reference prices; 2001 data

38. Drug prescription behaviour feedback to doctors • Goals • analyse prescription behaviour variations • influence behaviour through peer pressure • variations should decrease • started March 2001 • Period - quarterly

39. Variation analysis of prescription practices by family doctors. 2001 I kvartal IV kvartal

40. Cardiologist prescriptions for high blood pressure, cost per person in 2001 (n=148)

41. Antibiotics’ prescriptions by family doctors with influenza diagnosis. January 2001.

42. Purchasing and Quality • EHIF commissions guidelines and medical audits • Contracts mandate using approved guidelines • Benchmarking on utilization and quality indicators – monopsonic purchasr • Standard quality reporting • Monitoring of waiting time and population perception