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Enhancement of Claims Data to Improve Risk Adjustment of Hospital Mortality
Michael Pine, MD, MBA;
Harmon S. Jordan, ScD;
Anne Elixhauser, PhD;
Donald E. Fry, MD;
David C. Hoaglin, PhD;
Barbara Jones, MA;
Roger Meimban, PhD;
David Warner, MS;
Junius Gonzales, MD, MBA
JAMA. 2007;297:71-76.
Context Comparisons 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.
Objective To 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 Patients Comparison 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 Measures C 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).
Results The 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]).
Conclusions This 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.
Author Affiliations: Michael Pine and Associates Inc, Chicago, Ill (Drs Pine, Fry, and Meimban, and Ms Jones); Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, Ill (Dr Pine); Abt Associates Inc, Cambridge, Mass (Drs Jordan, Hoaglin, Gonzales, and Mr Warner); School of Medicine, Tufts University, Boston, Mass (Dr Jordan); and Agency for Healthcare Research and Quality, Rockville, Md (Dr Elixhauser).
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