Literature DB >> 7655814

Using severity-adjusted stroke mortality rates to judge hospitals.

L I Iezzoni1, M Shwartz, A S Ash, J S Hughes, J Daley, Y D Mackiernan.   

Abstract

Mortality rates are commonly used to judge hospital performance. In comparing death rates across hospitals, it is important to control for differences in patient severity. Various severity tools are now actively marketed in the United States. This study asked whether one would identify different hospitals as having higher- or lower-than-expected death rates using different severity measures. We applied 11 widely-used severity measures to the same database containing 9407 medically-treated stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Unadjusted hospital mortality rates ranged from 0 to 24.4%. For 27 hospitals, observed mortality rates differed significantly from expected rates when judged by one or more, but not all 11, severity methods. The agreement between pairs of severity methods for identifying the worst 10% or best 50% of hospitals was fair to good. Efforts to evaluate hospital performance based on severity-adjusted, in-hospital death rates for stroke patients are likely to be sensitive to how severity is measured.

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Year:  1995        PMID: 7655814     DOI: 10.1093/intqhc/7.2.81

Source DB:  PubMed          Journal:  Int J Qual Health Care        ISSN: 1353-4505            Impact factor:   2.038


  6 in total

1.  Preoperative risk factors and surgical complexity are more predictive of costs than postoperative complications: a case study using the National Surgical Quality Improvement Program (NSQIP) database.

Authors:  Daniel L Davenport; William G Henderson; Shukri F Khuri; Robert M Mentzer
Journal:  Ann Surg       Date:  2005-10       Impact factor: 12.969

2.  Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method.

Authors:  L I Iezzoni; A S Ash; M Shwartz; J Daley; J S Hughes; Y D Mackiernan
Journal:  Am J Public Health       Date:  1996-10       Impact factor: 9.308

3.  Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?

Authors:  P S Romano; B K Chan
Journal:  Health Serv Res       Date:  2000-03       Impact factor: 3.402

4.  Derivation and validation of a risk adjustment model for predicting seven day mortality in emergency medical admissions: mixed prospective and retrospective cohort study.

Authors:  Steve Goodacre; Richard Wilson; Neil Shephard; Jon Nicholl
Journal:  BMJ       Date:  2012-05-01

Review 5.  What is the empirical evidence that hospitals with higher-risk adjusted mortality rates provide poorer quality care? A systematic review of the literature.

Authors:  David W Pitches; Mohammed A Mohammed; Richard J Lilford
Journal:  BMC Health Serv Res       Date:  2007-06-20       Impact factor: 2.655

6.  Rural-Urban Disparities in Intracerebral Hemorrhage Mortality in the USA: Preliminary Findings from the National Inpatient Sample.

Authors:  Fadar Oliver Otite; Emmanuel Oladele Akano; Emmanuel Akintoye; Priyank Khandelwal; Amer M Malik; Seemant Chaturvedi; Jonathan Rosand
Journal:  Neurocrit Care       Date:  2020-06       Impact factor: 3.210

  6 in total

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