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Diagnostic information from prior use as a risk adjuster: the Dutch experience

Diagnostic information from prior use as a risk adjuster: the Dutch experience. Leida Lamers and René van Vliet. Risk adjusters in 2001:. age * gender degree of urbanisation insurance ground * age This demographic model explains about 5% of the

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Diagnostic information from prior use as a risk adjuster: the Dutch experience

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  1. Diagnostic information from prior use as a risk adjuster:the Dutch experience Leida Lamers and René van Vliet

  2. Risk adjusters in 2001: • age * gender • degree of urbanisation • insurance ground * age This demographic model explains about 5% of the individual differences in health care expenditures. 2002: Demographic model is extended with Pharmacy-based Cost Groups (PCGs)

  3. The essence of models with diagnostic information • allocate people to a restricted number of groups • according to the diseases diagnosed during prior utilisation of health care: • diagnoses from prior hospitalizations • information on chronic conditions deduced from prior use of prescribed drugs • incorporate this information in the capitation payment model

  4. Explorative study on PCGs • Based on the ATC-codes, persons with claims for medications indicative of chronic conditions were identified. The chronic conditions of the revised Chronic Disease Score (CDS) were used (Clark et.al., 1995) • Assignment to a chronic condition based on  4 prescriptions in the base year • Allow for comorbidity • Conditions were clustered into 7 PCGs according to empirically determined similarities in future costs

  5. Predictive accuracy of PCG model: almost 10% PCGs in year t-1 to predict costs in year t • Conclusion: PCGs are good predictors of future health care expenditures • Question: Is a good predictor also a good risk adjuster for capitation payments?

  6. Requirements for ideal risk adjusters • Validity • predictive accuracy for future health care expenditures • measure the need for health care / health status • Invulnerability to manipulation • No perverse incentives • Obtainability

  7. Objective of the study:Improving the PCG model • to adjust the classification of chronic conditions to the Dutch situation • to limit the possibilities of manipulation • to overcome problems of perverse incentives

  8. New classification with 22 conditions Crohn’s + ulcerative colitis Cardiac disease Tuberculosis Rheumatologic conditions Parkinson’s disease Diabetes – High Cystic fibrosis Transplantations Malignancies HIV / AIDS Renal disease (including ESRD) Hypertension – Low Glaucoma Depression Gout Thyroid disorders Hyperlipidemia Hypertension – High Diabetes – Low Respiratory illness, Asthma Epilepsy Acid peptic disease

  9. Strategies to reduce potential gaming 1. use prescribed daily doses (not # of prescriptions) 2. do not allow for comorbidity 3. compensate only partially 4. restrict assignment to chronic conditions to persons with expenditures above a threshold 5. do not reward the chronic conditions with the lowest follow-up costs

  10. Data for 6,353,716 members of 13 sickness funds: • health care costs in 1997 en 1998 • information on drugs prescribed in 1997: per prescription: - ATC-code - amount delivered - prescribed daily doses (PDD) • demographic information: age, sex, place of residence and type of insurance

  11. R2 100 for predicting costs in 1998

  12. Strategy 2

  13. Strategy 3

  14. Strategy 4

  15. Conclusion • Assignment to chronic conditions based on  181 prescribed daily doses • Only one condition per person • 14 chronic conditions (with the highest follow-up costs) are rewarded • No need for a further clustering of conditions

  16. This ‘revised’ PCG model was implemented at January 1, 2002 with two changes: • assignment of persons to PCGs is based on  181 DDDs (not PDDs) • tuberculosis is removed

  17. Limitation of the PCG model: Persons with predictable high costs suffering from conditions and diseases that are not treated with outpatient prescribed drugs are missed. Solution: Further improvement of the capitation payment formula by the introduction of Diagnostic Cost Groups (DCGs).

  18. Current research:Adjustment of the U.S. PIP-DCG model to the Dutch situation The U.S. PIP-DCG model: • all diagnoses from ICD-9-CM are assigned to 172 clinically homogeneous groups called Dx-groups • 75 Dx-groups are excluded • the resulting 97 Dx-groups are clustered into 16 DCGs according to empirically determined similarities in future health care expenditures

  19. Criteria for including Dx-groups in the DCG model: • diagnoses must not refer to symptoms • diagnoses are for chronic conditions or diseases • diagnoses must not be vague or ambiguous • diagnoses refer to conditions or diseases with predictable high follow-up costs • diagnoses require inpatient hospital care (in general)

  20. Feasibility issues are discussed Special attention will be given to the overlap between PCGs and DCGs to avoid overcompensation Expected R2-value for the PCG + DCG model:  14% The Dutch government aims to implement DCGs in 2003

  21. Financing system for the Dutch social health insurance sector Central Fund 90 % Risk-adjusted capitation payments Income - related premium 10 % Members Sickness Funds Flat-rate premium

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