Identifying Future High-Healthcare Users

Springer Science and Business Media LLC - Tập 13 - Trang 117-127 - 2012
Amy K. Rosen1,2, Fei Wang1,2, Maria E. Montez1, Carter C. Rakovski3, Dan R. Berlowitz1,2, Jaime C. Lucove4
1Center for Health Quality, Outcomes, and Economic Research, Bedford VAMC (152), Bedford, USA
2Department of Health Services, Boston University School of Public Health, Boston, USA
3Bentley College, Academic Technology Center, Waltham, USA
4School of Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, USA

Tóm tắt

Diagnosis-based risk-adjustment measures are increasingly being promoted as disease management tools. We compared the ability of several types of predictive models to identify future high-risk older people likely to benefit from disease management. Veterans Health Administration (VHA) data were used to identify veterans ≥65 years of age who used healthcare services during fiscal years (FY) 1997 and 1998 and who remained alive through FY 1997. This yielded a development sample of 412 679 individuals and a validation sample of 207 294. Prospective risk-adjustment models were fitted and tested using Adjusted Clinical Groups (ACGs), Diagnostic Cost Groups (DCGs), a prior-utilization model (prior), and combined models (prior + ACGs and prior + DCGs). Prespecified high use in FY 1998 was defined as ≥92 days of care (top 2.2%) for an individual (i.e. the number of days during the year in which an individual received inpatient or outpatient healthcare services). We developed a second outcome, defined as ≥164 days of care (top 1.0%), to explore whether changing the criterion for high risk would affect the number of misclassifications. The diagnosis-based models performed better than the prior model in identifying a subgroup of future high-cost individuals with high disease burden and chronic diseases appropriate for disease management. The combined models performed best at correctly classifying those without high use in the prospective year. The utility for efficiently identifying high-risk cases appeared limited because of the high number of individuals misclassified as future high-risk cases by all the models. Changing the criterion for high risk generally decreased the number of patients misclassified. There was little agreement between the models regarding who was identified as high risk. Health plans should be aware that different risk-adjustment measures may select dissimilar groups of individuals for disease management. Although diagnosis-based measures show potential as predictive modeling tools, combining a diagnosis-based measure with prior-utilization model may yield the best results.

Tài liệu tham khảo

Grosel C, Hamilton M, Koyano J, et al., editors. The institute for the future. Health & health care 2010: the forecast, the challenge. San Francisco (CA): Jossey-Bass, 2000 Coleman JR. Integrated case management: the 21st century challenge for HMO case managers. Part I. Case Manager 1999; 10: 28–34 Lynch JP, Forman SA, Graff S, et al. High-risk population health management: achieving improved patient outcomes and near-term financial results. Am J Manag Care 2000; 6: 781–91 Weiner JP, Abrams C, Forrest CB, et al. The Johns Hopkins University ACG casemix adjustment system: software documentation and application manual. Version 6.0, section 2. Baltimore (MD): The Johns Hopkins University, 2003, 9–32 Starfield B, Weiner JP, Mumford L, et al. Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res 1991; 26: 53–74 Weiner JP, Starfield BH, Steinwachs DM, et al. Development and application of a population-oriented measure of ambulatory care case-mix. Med Care 1991; 29: 452–72 Meenan RT, O’Keefe-Rosetti MC, Hornbrook MC, et al. The sensitivity and specificity of forecasting high-cost users of medical care. Med Care 1999; 37: 815–23 Ash A, Zhao Y, Ellis RP, et al. Finding future high-cost cases: comparing prior cost versus diagnosis-based methods. Health Serv Res 2001; 36 (6 Pt II): 194–206 Meenan RT, Goodman MJ, Fishman PA, et al. Using risk-adjustment models to identify high-cost risks. Med Care 2003; 41(11): 1301–12 Zhao Y, Ash A, Haughton J, et al. Identifying high-cost cases through predictive modeling. Dis Manage Health Outcomes 2003; 11(6): 389–97 Patient Classification Work Group. Houston Health Services Research and Development (HSR&D). Management Science Group (MSG) and Allocation Resource Center (ARC). VA-DCG Plus — National Leadership Board, 2002 Lamoreaux J. The organizational structure for medical information management in the Department of Veterans Affairs: an overview of major health care databases. Med Care 1996; 34(3 Suppl.): MS31–44 Rosen AK, Loveland SM, Anderson JJ, et al. Evaluating diagnosis-based case-mix measures: how well do they apply to the VA population? Med Care 2001; 39: 692–704 Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe populations and predict costs. Health Care Financ Rev 2000; 21: 7–28 Weiner JP, Starfield BH, Lieberman R. The Johns Hopkins Ambulatory Care Groups (ACGs): a case-mix system for UR, QA, and capitation adjustment. HMO Pract 1992; 6: 13–9 Weiner JP, Dobson A, Maxwell SL, et al. Risk-adjusted Medicare capitation rates using ambulatory and inpatient diagnoses. Health Care Financ Rev 1996; 17: 77–99 Ash A, Porell F, Gruenberg L, et al. Adjusting Medicare capitation payments using prior hospitalization. Health Care Financ Rev 1989; 10(4): 17–29 Ellis R, Ash A. Refinements to the diagnostic cost group model. Inquiry 1995; 32(4): 1–12 Ellis RP, Pope GC, Iezzoni L, et al. Diagnosis-based risk adjustment for Medicare capitation payments. Health Care Financ Rev 1996; 17(3): 101–28 Pope GC, Ellis RP, Ash AS, et al. Principal inpatient diagnostic cost group models for Medicare risk adjustment. Health Care Financ Rev 2000; 21(3): 93–118 Rosen AK, Loveland SA, Rakovski CC, et al. Do different case-mix measures affect assessments of provider efficiency? Lessons from the Department of Veterans Affairs. J Ambul Care Manage 2003; 26(3): 229–42 SAS Institute Inc. SAS Software. Version 8.0. Cary (NC): SAS Institute Inc., 2000 Iezzoni LI, editor. Risk adjustment for measuring health outcomes. 3rd ed. Chicago (IL): Health Administration Press, 2003 Boult L, Boult C, Pirie P, et al. Test-retest reliability of a questionnaire that identifies elders at risk for hospital admission. J Am Geriatr Soc 1994; 42: 707–11 Pacala JT, Boult C, Boult L. Predictive validity of a questionnaire that identifies older persons at risk for hospital admission. J Am Geriatr Soc 1995; 43: 374–7 Sidorov J, Shull R. “My patients are sicker”: using the PRA risk survey for case finding and examining primary care utilization patterns in a Medicare-risk MCO. Am J Manag Care 2002; 8: 569–75 Rakovski C, Rosen AK, Loveland S, et al. Evaluation of diagnosis-based risk adjustment among specific subgroups: can existing adjusters be improved by simple modifications? Health Serv and Outcomes Res Methodol 2002; 3: 57–73 Reuben DB, Keeler E, Seeman TE, et al. Identification of risk for high hospital utilization: cost-effectiveness of four strategies and performance across subgroups. J Am Geriatr Soc 2003; 51: 615–20 Rosen AK, Rakovski C, Loveland S, et al. Profiling resource use: do different outcomes affect assessments of provider efficiency? Am J Manag Care 2002; 8: 1105–15 Kronick R, Gilmer T, Dreyfus T, et al. Improving health-based payment for Medicaid beneficiaries: CDPS. Health Care Financ Rev 2000; 21: 29–64 Hannan EL, Kilburn H, Lindsey M, et al. Clinical versus administrative databases for CABG surgery: does it matter? Med Care 1992; 30: 892–907 US Department of Veterans Affairs. Data verification and validation [online]. Available at URL: http://www.va.gov/cfo/pubs/acct99/datavalidation_1.html [Accessed 2005 Feb 11] Kashner TM. Agreement between administrative files and written medical records: a case of the Department of Veterans Affairs. Med Care 1998; 36: 1324–36