Analysing low-risk patient populations allows better discrimination between high-performing and low-performing hospitals: a case study using inhospital mortality from acute myocardial infarction.
OBJECTIVE
To assess whether performance indicators based on administrative hospital data can be rendered more useful by stratifying them according to risk status of the patient.


METHODS
Retrospective analysis of 10 years of administrative hospital data for patients with acute myocardial infarction (AMI). Four risk groups defined by cross-classifying patient age (<75 years, 75+ years) against the presence or otherwise of at least one risk condition that predicted short-term AMI mortality.


METHODS
17 public hospitals in Queensland, Australia, with more than 50 AMI admissions annually.


METHODS
21,537 patients admitted through the emergency department and subsequently diagnosed as having AMI.


METHODS
Systematic variation in standardised case fatality ratios. Systematic variation is the variation across hospitals after accounting for the Poisson variation in the number of deaths at each hospital. It was obtained from an empirical-Bayes model. Case fatality ratios were standardised according to the age, sex and risk factor profile of the patient.


RESULTS
Systematic variation decreased monotonically across the four risk groups as case fatality increased (likelihood ratio test: chi(2) = 8.08, df = 1, p = 0.004). Systematic variation was largest and statistically significant (0.375; 95% CI 0.144 to 0.606) for low-risk patients (<75 years with no risk conditions; case fatality rate = 2.0%) but was smallest (0.126; 0.039 to 0.212) for high-risk patients (75+ years with at least one risk condition; case fatality rate = 24.3%).


CONCLUSIONS
Analysis of data from high-risk patients with AMI provides little opportunity to identify better-performing hospitals because there is relatively little variation across hospitals. In such patients, older age and comorbid illness are probably more important than quality of care in determining outcomes. In contrast, for low-risk patients the systematic variation was large suggesting that outcomes for such patients are more sensitive to clinical error. Analysing data for low-risk patients maximises our ability to identify best-performing hospitals and learn from their processes and structures to effect system-wide changes that will benefit all patients.
Coory
Michael
M
School of Population Health, University of Queensland, Queensland, Australia. m.coory@sph.uq.edu.au
Scott
Ian
I
D000368:Aged; D016507:Cardiology Service, Hospital / Q000592:standards*; D017052:Hospital Mortality; D006779:Hospitals, Public / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D009203:Myocardial Infarction / Q000401:mortality* / Q000628:therapy; D016017:Odds Ratio; D019982:Organizational Case Studies; D016012:Poisson Distribution; D017410:Practice Guidelines as Topic; D019984:Quality Indicators, Health Care; D011793:Queensland / Q000453:epidemiology; D018570:Risk Assessment; D012307:Risk Factors