Predicting Healthcare Costs in a Population of Veterans Affairs Beneficiaries Using Diagnosis-Based Risk Adjustment and Self-Reported Health Status

Background:Many healthcare organizations use diagnosis-based risk adjustment systems for predicting costs. Health self-report may add information not contained in a diagnosis-based system but is subject to incomplete response. Objective:The objective of this study was to evaluate the added predictive power of health self-report in combination with a diagnosis-based risk adjustment system in concurrent and prospective models of healthcare cost. Research Design:This was a cohort study using Department of Veterans Affairs (VA) administrative databases. We tested the predictive ability of the Adjusted Clinical Group (ACG) methodology and the added value of SF-36V (short form functional status for veterans) results. Linear regression models were compared using R2, mean absolute prediction error (MAPE), and predictive ratio. Subjects:Subjects were 35,337 VA beneficiaries at 8 VA medical centers during fiscal year (FY) 1998 who voluntarily completed an SF-36V survey. Measures:Outcomes were total FY 1998 and FY 1999 cost. Demographics and ACG-based Adjusted Diagnostic Groups (ADGs) with and without 8 SF-36V multiitem scales and the Physical Component Score and Mental Component Score were compared. Results:The survey response rate was 45%. Adding the 8 scales to ADGs and demographics increased the crossvalidated R2 by 0.007 in the prospective model. The 8 scales reduced the MAPE by $236 among patients in the upper 10% of FY 1999 cost. Conclusions:The limited added predictive power of health self-report to a diagnosis-based risk adjustment system should be weighed against the cost of collecting these data. Adding health self-report data may increase predictive accuracy in high-cost patients.

[1]  J. Ware.  SF-36 health survey: Manual and interpretation guide , 2003 .

[2]  Thomas F. Crossley,et al.  The reliability of self-assessed health status. , 2002, Journal of health economics.

[3]  D. Berlowitz,et al.  Evaluating Diagnosis-Based Case-Mix Measures: How Well Do They Apply to the VA Population? , 2001, Medical care.

[4]  L. Iezzoni.  Risk Adjustment for Measuring Healthcare Outcomes , 1994 .

[5]  K. E. Kilpatrick,et al.  Differences in patient characteristics between Veterans Administration and community hospitals. Implications for VA planning. , 1987, Medical care.

[6]  N. Roos,et al.  Performance of the ACG Case-Mix System in Two Canadian Provinces , 2001, Medical care.

[7]  C. Tse,et al.  Health Status and Severity of Illness as Predictors of Outcomes in Primary Care , 1995, Medical care.

[8]  N. Powe,et al.  Costs vs quality in different types of primary care settings. , 1994, JAMA.

[9]  C. McHorney,et al.  A longitudinal study of hospitalization rates for patients with chronic disease: results from the Medical Outcomes Study. , 1998, Health services research.

[10]  B Starfield,et al.  Ambulatory care groups: a categorization of diagnoses for research and management. , 1991, Health services research.

[11]  D. Berlowitz,et al.  Correlation of Risk Adjustment Measures Based on Diagnoses and Patient Self-Reported Health Status , 2000, Health Services and Outcomes Research Methodology.

[12]  Taking Health Status Into Account When Setting Capitation Rates: A Comparison of Risk-Adjustment Methods , 1996 .

[13]  Donald R. Miller,et al.  Health Status in VA Patients: Results from the Veterans Health Study , 1999, American journal of medical quality : the official journal of the American College of Medical Quality.

[14]  P. Barnett,et al.  Cost analysis in the Department of Veterans Affairs: consensus and future directions. , 1999, Medical care.

[15]  Paul A. Fishman,et al.  Risk Adjustment Using Automated Ambulatory Pharmacy Data: The RxRisk Model , 2003, Medical care.

[16]  N. Powe,et al.  "Carving out" conditions from global capitation rates: protecting high-cost patients, physicians, and health plans in a managed care environment. , 1998, American Journal of Managed Care.

[17]  L. Lamers,et al.  Risk-adjusted capitation based on the Diagnostic Cost Group Model: an empirical evaluation with health survey information. , 1999, Health services research.

[18]  Patrick Heagerty,et al.  Validation of case-mix measures derived from self-reports of diagnoses and health. , 2002, Journal of clinical epidemiology.

[19]  K W Kizer,et al.  Geographic variations in utilization rates in Veterans Affairs hospitals and clinics. , 1999, The New England journal of medicine.

[20]  Michael J. Goodman,et al.  Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. , 1996, Health services research.

[21]  L. Lamers,et al.  Multiyear diagnostic information from prior hospitalization as a risk-adjuster for capitation payments. , 1996, Medical care.

[22]  C. McHorney,et al.  The MOS 36‐Item Short‐Form Health Survey (SF‐36): II. Psychometric and Clinical Tests of Validity in Measuring Physical and Mental Health Constructs , 1993, Medical care.