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

ABSTRACT:

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 tree-based 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 30-day mortality. Most other outcomes exh

SUBMITTER: DeCenso B 

PROVIDER: S-EPMC5867142 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature