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How Well Do Risk Adjustment Models Predict Utilization for Patients with a Major Functional Limitations

Warner G, Rosen A, Montez M, Rakovski C, Hoenig H. How Well Do Risk Adjustment Models Predict Utilization for Patients with a Major Functional Limitations. Paper presented at: VA HSR&D National Meeting; 2002 Feb 23; Washington, DC.


Objective: Patients with a major functional limitation may have different utilization patterns then the overall population. The analysis probe into evaluated how well diagnostic-based risk adjustment models predicted resource service use and then bolstered models with additional information in a sample of patients diagnosed with spinal cord dysfunction (SCD). Methods: This analysis used a 40% sample of veterans who used inpatient and outpatient services in FY 97 was obtained from VA databases (N = 1,046,803). The number of service days was used to measure resource utilization. Previously validated ICD-9 codes for traumatic spinal cord injury, multiple sclerosis, quadriplegia, paraplegia and other pathologies leading to possible paraplegia/quadriplegia were used to identify a subgroup of patients with SCD (N = 7,761). Weighted least squares regression models compared the baseline the performance of two risk adjustment systems (Adjusted Clinical Groups (ACG) and Diagnostic Cost Groups (DCG)). Additional markers (e.g., SCD subgroup, high risk comorbidities, and comorbidity-subgroup interaction terms) were added to basic risk adjustment models to improve model predictability. Measures of fit were calculated. These were included R-squared and root mean square errors (RMSE).Results: Comparing the baseline ACG/DCG models The DCG model risk adjustment explained more total variance and had less error for both the overall sample and for the SCD group (R-squared = .315, RMSE = 32.32 and R-squared = .221, RMSE = 57.8respectively) than the ACG model (R-squared = .232, RMSE = 34.12 and R-squared = .112, RMSE = 65.3). Additional markers minimally improved predictive ability and decreased mean errors for the overall sample (DCG model: R-squared = .317 and RMSE = 32.18 or the SCD subgroup (R-squared = .243, RMSE = 60.3). Conclusions: For people with SCD, DCG models risk adjustment predicted total resource inpatient/outpatient utilization better than the ACG model risk adjustment. Adding other markers only minimally improved the model. Given available inpatient/outpatient administrative information, the DCG modelrisk adjustment appears to be a better risk adjustment system than ACG for this group. Impact: The VA provides services for a large proportion of people with SCD, and has identified the subgroup as an important subpopulation to monitor because of their high service utilization. To ensure that adequate resources are allocated for subgroups such as these it is important to maximize the predictive accuracy of risk-adjustment methods. To further improve prediction models, additional clinical and functional status information may useful.

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