Powered by: Blackwell Publishing

HRET - Health Research & Educational Trust

HSR - Health Services Research

Impacting Health Practice and Policy Through State-of-the-Art Research and Thinking

Our Next Issue

June 2019
Coming Soon! Read



Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes

Objective: To assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices.

Data Sources: Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005–2009.

Study Design: Conducted treebased classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as those used for Centers for Medicare and Medicaid Services (CMS) outcome models.

Principal Findings: Diagnosis codes as predictors increased predictive accuracy 5.6 percent (95% CI: 4.5–6.9 percent) relative to CMS condition categories for heart failure 30day mortality. Most other outcomes exhibited statistically significant accuracy gains and facility ranking shifts. Sensitivity analysis showed improvements even when predictors were limited to only the diagnoses included in CMS models.

Conclusions: Discretizing patient severity information beyond the levels of traditional comorbidity indices improves patient outcome predictions and substantially shifts facility rankings.

back to top | back to article index | access/purchase full article

Copyright© 2018, Health Research & Educational Trust. All rights reserved. Content Disclaimer
Health Research & Educational Trust, 155 North Wacker, 4th Floor Chicago, IL 60606 (312) 422.2600