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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.

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Presentation on theme: "1 Predicting Pharmacy and Other Health Care Costs Arlene S. Ash, PhD Boston University School of Medicine & DxCG, Inc. Academy Health Annual Meeting San."— Presentation transcript:

1 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 2 Predicting Drug and Other Costs from Administrative Data Use various “profiles” –R x –D x –Both To predict next year’s costs –Total $ –Non-pharmacy $ –Pharmacy $

3 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 4 ICD-9-CM codes (N = 15,000+) DxGroups (N = 781) DCG/HCC Clinical Classification 184 Hierarchical Condition Categories (HCCs) D x Clinical Classification System

5 5 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

6 6 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)

7 7 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

8 8 NDC codes (n ~ 82,000+) RxGroups (n = 155) Aggregated Rx Categories (ARCs) (n = 17) R x Classification System

9 9 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

10 10 Year-1 D x and R x 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

11 11 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

12 12 Predictive Power of Models (Validated R 2 ) Predictors Total $Non-Pharm $Pharmacy $ R x 11.6%7.1%48.2% D x 14.6%11.6%22.5% R x & D x 16.8%12.4%49.3%

13 13 Validated Predictive Ratios (E/O) RxRx DxDx Rx & DxRx & Dx Asthma D x (n=38,000) 0.900.981.00 Asthma/COPD R x (84,000) 0.950.860.95 Depression D x (49,000) 0.851.01 Antidepressant R x (90,000) 0.980.820.99 Diabetes D x (33,000) 0.841.021.03 Diabetes R x (23,000) 1.010.901.03

14 14 Take Home Lessons Predicting next year’s cost is easiest for R x $, hardest for Non-R x $ Both kinds of data predict well –D x predicts other costs better –R x predicts R x $ much better than D x –Both together are extremely accurate The high predictabiity of R x $ from R x data bodes ill for the viability of the new Medicare drug insurance product


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