The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Risks of Risk Adjustment
Abstract
Context. —Risk adjustment is essential before comparing patient outcomes across hospitals. Hospital report cards around the country use different risk adjustment methods. Objectives. —To examine the history and current practices of risk adjusting hospital death rates and consider the implications for using risk-adjusted mortality comparisons to assess quality. Data Sources and Study Selection. —This article examines severity measures used in states and regions to produce comparisons of risk-adjusted hospital death rates. Detailed results are presented from a study comparing current commercial severity measures using a single database. It included adults admitted for acute myocardial infarction (n=11 880), coronary artery bypass graft surgery (n=7765), pneumonia (n=18016), and stroke (n=9407). Logistic regressions within each condition predicted in-hospital death using severity scores. Odds ratios for in-hospital death were compared across pairs of severity measures. For each hospital, z scores compared actual and expected death rates. Results. —The severity measure called Disease Staging had the highest c statistic (which measures how well a severity measure discriminates between patients who lived and those who died) for acute myocardial infarction, 0.86; the measure called All Patient Refined Diagnosis Related Groups had the highest for coronary artery bypass graft surgery, 0.83; and the measure, MedisGroups, had the highest for pneumonia, 0.85 and stroke, 0.87. Different severity measures predicted different probabilities of death for many patients. Severity measures frequently disagreed about which hospitals had particularly low or high z scores. Agreement in identifying low- and high-mortality hospitals between severity-adjusted and unadjusted death rates was often better than agreement between severity measures. Conclusions. —Severity does not explain differences in death rates across hospitals. Different severity measures frequently produce different impressions about relative hospital performance. Severity-adjusted mortality rates alone are unlikely to isolate quality differences across hospitals.
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Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
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Defining and classifying clinical indicators for quality improvement
Mainz, J. (2003) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzg081 · Cited by 1,161
Quality Disclosure and Certification: Theory and Practice
Dranove, D., & Jin, G. Z. (2010) · Journal of Economic Literature · doi:10.1257/jel.48.4.935 · Cited by 740
The Impact of Antimicrobial Resistance on Health and Economic Outcomes
Eliopoulos, G. M., Cosgrove, S. E., & Carmeli, Y. (2003) · Clinical Infectious Diseases · doi:10.1086/375081 · Cited by 637
Lilford, R., Mohammed, M. A., Spiegelhalter, D., & Thomson, R. (2004) · The Lancet · doi:10.1016/s0140-6736(04)15901-1 · Cited by 448
Metadata
- Authors
- lezzoni, L. I.
- Venue
- JAMA: The Journal of the American Medical Association
- Publisher
- American Medical Association (AMA)
- Published
- 1997-11-19
- Volume
- 278
- Issue
- 19
- Pages
- 1600
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2150145013
- Cited by
- 408
- References
- 1
- FWCI
- 49.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- lezzoni, L. I. (1997). The Risks of Risk Adjustment. JAMA: The Journal of the American Medical Association. https://doi.org/10.1001/jama.1997.03550190064046
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