Health Policy and Management (보건행정학회지)

Korean Academy of Health Policy and Management (한국보건행정학회)
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Prediction of Health Care Cost Using the Hierarchical Condition Category Risk Adjustment Model

위계적 질환군 위험조정모델 기반 의료비용 예측

  • Han, Ki Myoung (Department of Preventive Medicine and Public Health, Ajou University School of Medicine) ;
  • Ryu, Mi Kyung (Department of Sport and Leisure Studies, Kyonggi University College of Physical Education) ;
  • Chun, Ki Hong (Department of Preventive Medicine and Public Health, Ajou University School of Medicine)
  • 한기명 (아주대학교 의과대학 예방의학과) ;
  • 유미경 (경기대학교 체육대학 사회체육학과) ;
  • 전기홍 (아주대학교 의과대학 예방의학과)
  • Received : 2017.02.09
  • Accepted : 2017.03.31
  • Published : 2017.06.30
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Abstract

Background: This study was conducted to evaluate the performance of the Hierarchical Condition Category (HCC) model, identify potentially high-cost patients, and examine the effects of adding prior utilization to the risk model using Korean claims data. Methods: We incorporated 2 years of data from the National Health Insurance Services-National Sample Cohort. Five risk models were used to predict health expenditures: model 1 (age/sex groups), model 2 (the Center for Medicare and Medicaid Services-HCC with age/sex groups), model 3 (selected 54 HCCs with age/sex groups), model 4 (bed-days of care plus model 3), and model 5 (medication-days plus model 3). We evaluated model performance using $R^2$ at individual level, predictive positive value (PPV) of the top 5% of high-cost patients, and predictive ratio (PR) within subgroups. Results: The suitability of the model, including prior use, bed-days, and medication-days, was better than other models. $R^2$ values were 8%, 39%, 37%, 43%, and 57% with model 1, 2, 3, 4, and 5, respectively. After being removed the extreme values, the corresponding $R^2$ values were slightly improved in all models. PPVs were 16.4%, 25.2%, 25.1%, 33.8%, and 53.8%. Total expenditure was underpredicted for the highest expenditure group and overpredicted for the four other groups. PR had a tendency to decrease from younger group to older group in both female and male. Conclusion: The risk adjustment models are important in plan payment, reimbursement, profiling, and research. Combined prior use and diagnostic data are more powerful to predict health costs and to identify high-cost patients.

Keywords

References

  1. Cutler DM. Equality, efficiency, and market fundamentals: the dynamics of international medical-care reform. J Econ Lit 2002;40(3):881-906. DOI: https://doi.org/10.1257/002205102760273814.
  2. Goodson JD, Bierman AS, Fein O, Rask K, Rich EC, Selker HP. The future of capitation: the physician role in managing change in practice. J Gen Intern Med 2001;16(4):250-256. DOI: https://doi.org/10.1046/j.1525-1497.2001.016004250.x.
  3. Steinbrook R. The end of fee-for-service medicine?: proposals for payment reform in Massachusetts. N Engl J Med 2009;361(11):1036-1038. DOI: https://doi.org/10.1056/NEJMp0906556.
  4. Van de Ven WP, Ellis RP. Risk adjustment in competitive health plan markets. Handb Health Econ 2000;1(A):755-845. DOI: https://doi.org/10.1016/S1574-0064(00)80173-0.
  5. Goni MG. Risk sharing and risk adjustment strategies to deal with health plan selection and efficiency [dissertation]. Boston (MA): Boston University; 2004.
  6. Van Kleef RC, van Vliet RC, van de Ven WP. Risk equalization in The Netherlands: an empirical evaluation. Expert Rev Pharmacoecon Outcomes Res 2013;13(6):829-839. DOI: https://doi.org/10.1586/14737167.2013.842127.
  7. Van Kleef RC, van Vliet RC, van Rooijen EM. Diagnoses-based cost groups in the Dutch risk-equalization model: the effects of including outpatient diagnoses. Health Policy 2014;115(1):52-59. DOI: https://doi.org/10.1016/j.healthpol.2013.07.005.
  8. Buchner F, Goepffarth D, Wasem J. The new risk adjustment formula in Germany: implementation and first experiences. Health Policy 2013; 109(3):253-262. DOI: https://doi.org/10.1016/j.healthpol.2012.12.001.
  9. Ellis RP, Fernandez JG. Risk selection, risk adjustment and choice: concepts and lessons from the Americas. Int J Environ Res Public Health 2013;10(11):5299-5332. DOI: https://doi.org/10.3390/ijerph10115299.
  10. Winkelman R, Mehmud S. A comparative analysis of claims-based tools for health risk assessment. Schaumburg (IL): Society of Actuaries; 2007.
  11. Evans MA, Pope GC, Kautter J, Ingber MJ, Freeman S, Sekar R, et al. Evaluation of the CMS-HCC risk adjustment model. Baltimore (MD): Centers for Medicare & Medicaid Services; 2011.
  12. McGuire TG, Glazer J, Newhouse JP, Normand SL, Shi J, Sinaiko AD, et al. Integrating risk adjustment and enrollee premiums in health plan payment. J Health Econ 2013;32(6):1263-1277. DOI: https://doi.org/10.1016/j.jhealeco.2013.05.002.
  13. Yarger S, Rascati K, Lawson K, Barner J, Leslie R. Analysis of predictive value of four risk models in Medicaid recipients with chronic obstructive pulmonary disease in Texas. Clin Ther 2008;30 Spec No:1051-1057. DOI: https://doi.org/10.1016/j.clinthera.2008.06.001.
  14. Frogner BK, Anderson GF, Cohen RA, Abrams C. Incorporating new research into Medicare risk adjustment. Med Care 2011;49(3):295-300. DOI: https://doi.org/10.1097/MLR.0b013e318202839f.
  15. Chang HY, Lee WC, Weiner JP. Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's National Health Insurance claims. BMC Health Serv Res 2010;10:343. DOI: https://doi.org/10.1186/1472-6963-10-343.
  16. Monroe SM, Harkness KL. Is depression a chronic mental illness? Psychol Med 2012;42(5):899-902. DOI: https://doi.org/10.1017/S0033291711002 066.
  17. Liu CF, Sales AE, Sharp ND, Fishman P, Sloan KL, Todd-Stenberg J, et al. Case-mix adjusting performance measures in a veteran population: pharmacy- and diagnosis-based approaches. Health Serv Res 2003;38(5): 1319-1337. DOI: https://doi.org/10.1111/1475-6773.00179.
  18. Taitel M, Fensterheim L, Kirkham H, Sekula R, Duncan I. Medication days' supply, adherence, wastage, and cost among chronic patients in Medicaid. Medicare Medicaid Res Rev 2012;2(3):mmrr.002.03.a04. DOI: https://doi.org/10.5600/mmrr.002.03.a04.
  19. Chang HY, Weiner JP. An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan. BMC Med 2010;8:7. DOI: https://doi.org/10.1186/1741-7015-8-7.
  20. Hackbarth GM, Chernew M, Miller ME. Improving risk adjustment in the medicare program. In: Hackbarth GM, Chernew M, Miller ME, editors. Report to the congress: medicare and the health care delivery system. Washington (DC): Medpac; 2014. pp. 21-32.
  21. Hall MA. Risk adjustment under the Affordable Care Act: a guide for federal and state regulators. Issue Brief (Commonw Fund) 2011;7:1-12.
  22. Orueta JF, Nuno-Solinis R, Mateos M, Vergara I, Grandes G, Esnaola S. Predictive risk modelling in the Spanish population: a cross-sectional study. BMC Health Serv Res 2013;13:269. DOI: https://doi.org/10.1186/1472-6963-13-269.
  23. Ash AS, Zhao Y, Ellis RP, Schlein Kramer M. Finding future high-cost cases: comparing prior cost versus diagnosis-based methods. Health Serv Res 2001;36(6 Pt 2):194-206.
  24. Rakovski CC, Rosen AK, Wang F, Berlowitz DR. Predicting elderly at risk of increased future healthcare use: How much does diagnostic information add to prior utilization? Health Serv Outcomes Res Methodol 2002;3(3):267-277. https://doi.org/10.1023/A:1025866331616
  25. Kautter J, Pope GC, Keenan P. Affordable Care Act risk adjustment: overview, context, and challenges. Medicare Medicaid Res Rev 2014;4(3): mmrr2014-004-03-a02. DOI: https://doi.org/10.5600/mmrr.004.03.a02.