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Predicting Pharmacy and Other Health Care Costs

Predicting Pharmacy and Other Health Care Costs. Arlene S. Ash, PhD Boston University School of Medicine & DxCG, Inc. Academy Health Annual Meeting San Diego, CA June 6, 2004. Predicting Drug and Other Costs from Administrative Data. Use various “profiles” R x D x Both

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Predicting Pharmacy and Other Health Care Costs

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  1. Predicting Pharmacy and Other Health Care Costs Arlene S. Ash, PhD Boston University School of Medicine & DxCG, Inc. Academy Health Annual Meeting San Diego, CA June 6, 2004

  2. Predicting Drug and Other Costs from Administrative Data • Use various “profiles” • Rx • Dx • Both • To predict next year’s costs • Total $ • Non-pharmacy $ • Pharmacy $

  3. Data • 1998-1999 “Commercial Claims and Encounters” Medstat MarketScan • N ~ 1.3 million • Mean age: 33 yrs • Percent female: 51% • Diagnoses: ICD-9-CM codes • Pharmacy: NDC codes • Costs (incl. deductibles, copays, COB)

  4. DCG Model Structure • Diagnoses drive prediction (Risk Score, or RS) • ~15000 Diagnoses group  • 781 Disease Groups  • 184 Condition Categories (CCs) • Hierarchies imposed 184 HCCs • Model • Predicts from age, sex and (hierarchical) “CC profile” • One person can have 0, 1, 2 or many (H)CCs • Risks from HCCs add to create a summary RS

  5. Sample DCG/HCC Year-2 Prediction • Prediction • for Year 2 • $805 48 year old male • $3,512 HCC16: Diabetes w neurologic or peripheral circulatory manifestation • $1,903 HCC20: Type I Diabetes • $266 HCC24: Other endocrine/metabolic/nutritional disorders • $455 HCC43: Other musculoskeletal & connective tissue disorders • _____ • $6,941 FINAL PREDICTION (RS)

  6. Pharmacy Model Structure • 80,000+ NDC codes  155 RxGroups • Hierarchies imposed • E.g., insulin dominates oral diabetic meds • Relevant coefficients add to create a risk score for each person

  7. Rx Classification System NDC codes (n ~ 82,000+) RxGroups (n = 155) Aggregated Rx Categories (ARCs) (n = 17)

  8. Sample RxGroup Year-2 Prediction • $3,352 79-year old male • $1,332 RxGroup 23: Anticoagulants (warfarin ) • $1,314 RxGroup 42: Antianginal agents • $1,538 RxGroup 116: Oral diabetic agents • ______ • $7,536 FINAL PREDICTION

  9. Year-1 Dx and Rx Prevalence • Diagnoses • 74% have at least one valid ICD-9 code • Mean # of HCCs per person: 2.5 • Pharmacy • 66% have at least one prescription • Mean # of RxGroups per person: 2.5

  10. Year-2 Costs • Total Cost (incl., inpatient, outpatient and pharmacy) • Mean: $2,053 • CV: 386 • Non-Pharmacy Cost • Mean: $1,601 • CV: 471 • Pharmacy Cost • Mean: $452 • CV: 278

  11. Predictive Power of Models (Validated R2)

  12. Validated Predictive Ratios (E/O)

  13. Take Home Lessons • Predicting next year’s cost is easiest for Rx $, hardest for Non-Rx$ • Both kinds of data predict well • Dx predicts other costs better • Rx predicts Rx$ much better than Dx • Both together are extremely accurate • The high predictabiity of Rx$ from Rx data bodes ill for the viability of the new Medicare drug insurance product

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