Evaluating diagnosis-based risk-adjustment methods in a population with spinal cord dysfunction

Published: February 1, 2004
Category: Papers
Authors: Hoenig H, Montez M, Rosen A, Wang F, Warner G
Country: United States
Language: null
Types: Care Management, Population Health
Setting: Hospital

Arch Phys Med Rehabil 85:218-226.

OBJECTIVE: To examine performance of models in predicting health care utilization for individuals with spinal cord dysfunction.DESIGN: Regression models compared 2 diagnosis-based risk-adjustment methods, the adjusted clinical groups (ACGs) and diagnostic cost groups (DCGs). To improve prediction, we added to our model: (1) spinal cord dysfunction-specific diagnostic information, (2) limitations in self-care function, and (3) both 1 and 2.

SETTING: Models were replicated in 3 populations.

PARTICIPANTS: Samples from 3 populations: (1) 40% of veterans using Veterans Health Administration services in fiscal year 1997 (FY97) (N=1,046,803), (2) veteran sample with spinal cord dysfunction identified by codes from the International Statistical Classification of Diseases, 9th Revision, Clinical Modifications (N=7666), and (3) veteran sample identified in Veterans Affairs Spinal Cord Dysfunction Registry (N=5888).

INTERVENTIONS: Not applicable.

MAIN OUTCOME MEASURES: Inpatient, outpatient, and total days of care in FY97.

RESULTS: The DCG models (R(2) range,.22-.38) performed better than ACG models (R(2) range,.04-.34) for all outcomes. Spinal cord dysfunction-specific diagnostic information improved prediction more in the ACG model than in the DCG model (R(2) range for ACG,.14-.34; R(2) range for DCG,.24-.38). Information on self-care function slightly improved performance (R(2) range increased from 0 to.04).

CONCLUSIONS: The DCG risk-adjustment models predicted health care utilization better than ACG models. ACG model prediction was improved by adding information.

PMID: 14966705

Predictive Risk Modeling,Population Markers,Care Management,Activities of Daily Living,Gender,Logistic Models,Middle Aged,Registries,United States,Veterans/statistics & numerical data

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