Literature DB >> 2195173

Explaining variations in hospital death rates. Randomness, severity of illness, quality of care.

R E Park1, R H Brook, J Kosecoff, J Keesey, L Rubenstein, E Keeler, K L Kahn, W H Rogers, M R Chassin.   

Abstract

We used administrative (Part A Medicare) data to identify a representative sample of 1126 patients with congestive heart failure and 1150 with acute myocardial infarction in hospitals with significant unexpectedly high inpatient, age-sex-race-disease-specific death rates ("targeted") vs all other ("untargeted") hospitals in four states. Although death rates in targeted hospitals were 5.0 to 10.9 higher per 100 admissions than in untargeted hospitals, 56% to 82% of the excess could result from purely random variation. Differences in the quality of the process of care (based on a medical record review) could not explain the remaining statistically significant differences in mortality. Comparing targeted hospitals with subsets of untargeted ones, eg, those with lower than expected death rates, did not affect this conclusion. Severity of illness explained up to 2.8 excess deaths per 100 admissions for patients with myocardial infarction. Identifying hospitals that provide poor-quality care based on administrative data and single-year death rates is unlikely; targeting based on time periods greater than 1 year may be better.

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Year:  1990        PMID: 2195173

Source DB:  PubMed          Journal:  JAMA        ISSN: 0098-7484            Impact factor:   56.272


  46 in total

1.  Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals.

Authors:  G E Rosenthal; D W Baker; D G Norris; L E Way; D L Harper; R J Snow
Journal:  Health Serv Res       Date:  2000-03       Impact factor: 3.402

2.  Hospitals in rural America.

Authors:  T C Ricketts; P E Heaphy
Journal:  West J Med       Date:  2000-12

3.  Variations in mortality rates among Canadian neonatal intensive care units: interpretation and implications.

Authors:  Jon Tyson; Kathleen Kennedy
Journal:  CMAJ       Date:  2002-01-22       Impact factor: 8.262

4.  Explaining differences in English hospital death rates using routinely collected data.

Authors:  B Jarman; S Gault; B Alves; A Hider; S Dolan; A Cook; B Hurwitz; L I Iezzoni
Journal:  BMJ       Date:  1999-06-05

5.  Using routine comparative data to assess the quality of health care: understanding and avoiding common pitfalls.

Authors:  A E Powell; H T O Davies; R G Thomson
Journal:  Qual Saf Health Care       Date:  2003-04

6.  The measurement of active errors: methodological issues.

Authors:  R J Lilford; M A Mohammed; D Braunholtz; T P Hofer
Journal:  Qual Saf Health Care       Date:  2003-12

Review 7.  A systematic review of validated methods for identifying heart failure using administrative data.

Authors:  Jane S Saczynski; Susan E Andrade; Leslie R Harrold; Jennifer Tjia; Sarah L Cutrona; Katherine S Dodd; Robert J Goldberg; Jerry H Gurwitz
Journal:  Pharmacoepidemiol Drug Saf       Date:  2012-01       Impact factor: 2.890

8.  What is the best way to estimate hospital quality outcomes? A simulation approach.

Authors:  Andrew Ryan; James Burgess; Robert Strawderman; Justin Dimick
Journal:  Health Serv Res       Date:  2012-02-21       Impact factor: 3.402

9.  Mortality league tables: do they inform or mislead?

Authors:  M McKee; D Hunter
Journal:  Qual Health Care       Date:  1995-03

10.  Routine data: a resource for clinical audit?

Authors:  M McKee
Journal:  Qual Health Care       Date:  1993-06
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