BACKGROUND: : Although patient-reported health-related quality of life (HRQOL) is known to predict health services utilization, most risk assessment systems use provider-reported diagnoses as predictors rather than HRQOL. OBJECTIVE: : We sought to classify adult primary care patients prospectively by utilization risk based on age, gender, and HRQOL at a single clinic visit. RESEARCH DESIGN: : Patients completed the Duke Health Profile. Providers completed the Duke Severity of Illness Checklist. Diagnoses were grouped with the Ambulatory Care Groups system. Predictive coefficients for 1-year primary care charges calculated from the age, gender, and HRQOL of 728 reference patients were used to classify 474 test patients into 4 risk classes. Comparisons were made with models that used diagnoses or severity of illness as predictors. RESULTS: : The positive likelihood ratio for predicting highest risk was 2.2 for the HRQOL model, compared with 1.8 for the diagnoses model, 1.6 for the severity model, and 1.5 for age and gender alone. One-year actual primary care visits and charges increased step-wise from lowest to highest risk class. Highest risk patients were older and more likely to be women, black, or Medicaid recipients. Although the highest-risk patients represented only 18.6% of the test group, they accounted for 26.7% of the primary care clinic visits, 31.6% of the clinic charges, 34.6% of the hospital days, 35.1% of hospital charges, and 30.8% of total charges at all healthcare sites. CONCLUSION: : The HRQOL risk classification system can identify primary care patients at risk for high future health services utilization.
BACKGROUND: : Although patient-reported health-related quality of life (HRQOL) is known to predict health services utilization, most risk assessment systems use provider-reported diagnoses as predictors rather than HRQOL. OBJECTIVE: : We sought to classify adult primary care patients prospectively by utilization risk based on age, gender, and HRQOL at a single clinic visit. RESEARCH DESIGN: : Patients completed the Duke Health Profile. Providers completed the Duke Severity of Illness Checklist. Diagnoses were grouped with the Ambulatory Care Groups system. Predictive coefficients for 1-year primary care charges calculated from the age, gender, and HRQOL of 728 reference patients were used to classify 474 test patients into 4 risk classes. Comparisons were made with models that used diagnoses or severity of illness as predictors. RESULTS: : The positive likelihood ratio for predicting highest risk was 2.2 for the HRQOL model, compared with 1.8 for the diagnoses model, 1.6 for the severity model, and 1.5 for age and gender alone. One-year actual primary care visits and charges increased step-wise from lowest to highest risk class. Highest risk patients were older and more likely to be women, black, or Medicaid recipients. Although the highest-risk patients represented only 18.6% of the test group, they accounted for 26.7% of the primary care clinic visits, 31.6% of the clinic charges, 34.6% of the hospital days, 35.1% of hospital charges, and 30.8% of total charges at all healthcare sites. CONCLUSION: : The HRQOL risk classification system can identify primary care patients at risk for high future health services utilization.
Authors: Ana Maria Pah; Nicoleta Florina Buleu; Anca Tudor; Ruxandra Christodorescu; Dana Velimirovici; Stela Iurciuc; Maria Rada; Gheorghe Stoichescu-Hogea; Marius Badalica-Petrescu; Doina Georgescu; Dorina Nutiu; Mircea Iurciuc; Simona Dragan Journal: Brain Sci Date: 2020-05-22