Enhancement of Claims Data to Improve Risk Adjustment of Hospital Mortality

TLDR

Risk‑adjusted hospital performance is a key component of public reporting, pay‑for‑performance programs, and quality improvement initiatives. The study aimed to evaluate how adding present‑on‑admission codes and numerical laboratory data to administrative claims affects risk‑adjusted inpatient mortality rates. Researchers compared risk‑adjustment models built from administrative data, POA codes, laboratory values, and additional clinical information for 188 Pennsylvania hospitals, using c statistics to assess discriminatory power across five conditions and three procedures. Inclusion of POA codes and laboratory values raised the c statistic from 0.79 to 0.84–0.86, and further clinical data only modestly increased it to 0.88, showing limited added benefit beyond the initial enhancements.

Abstract

ContextComparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality.ObjectiveTo assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data.Design, Setting, and PatientsComparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy.Main Outcome MeasuresC statistics as a measure of the discriminatory power of alternative risk-adjustment models (administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures).ResultsThe mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations (mean [SD] c statistic of 0.84 [0.01] and 0.86 [0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records (mean [SD] c statistic of 0.88 [0.01]).ConclusionsThis study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.