Literature DB >> 2654083

Differences among hospitals in Medicare patient mortality.

M R Chassin1, R E Park, K N Lohr, J Keesey, R H Brook.   

Abstract

Using hospital discharge abstract data for fiscal year 1984 for all acute care hospitals treating Medicare patients (age greater than or equal to 65), we measured four mortality rates: inpatient deaths, deaths within 30 days after discharge, and deaths within two fixed periods following admission (30 days, and the 95th percentile length of stay for each condition). The metric of interest was the probability that a hospital would have as many deaths as it did (taking age, race, and sex into account). Differences among hospitals in inpatient death rates were large and significant (p less than .05) for 22 of 48 specific conditions studied and for all conditions together; among these 22 "high-variation" conditions, medical conditions accounted for far more deaths than did surgical conditions. We compared pairs of conditions in terms of hospital rankings by probability of observed numbers of inpatient deaths; we found relatively low correlations (Spearman correlation coefficients of 0.3 or lower) for most comparisons except between a few surgical conditions. When we compared different pairs of the four death measures on their rankings of hospitals by probabilities of the observed numbers of deaths, the correlations were moderate to high (Spearman correlation coefficients of 0.54 to 0.99). Hospitals with low probabilities of the number of observed deaths were not distributed randomly geographically; a small number of states had significantly more than their share of these hospitals (p less than .01). Information from hospital discharge abstract data is insufficient to determine the extent to which differences in severity of illness or quality of care account for this marked variability, so data on hospital death rates cannot now be used to draw inferences about quality of care. The magnitude of variability in death rates and the geographic clustering of facilities with low probabilities, however, both argue for further study of hospital death rates. These data may prove most useful as a screening mechanism to identify patterns of potentially poor quality of care. Careful choice of the mortality measure used is needed, however, to maximize the probability of identifying those hospitals, and only those hospitals, warranting more in-depth review.

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Year:  1989        PMID: 2654083      PMCID: PMC1065550     

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  19 in total

1.  The relationship between intensity and duration of medical services and outcomes for hospitalized patients.

Authors:  A B Flood; W Ewy; W R Scott; W H Forrest; B W Brown
Journal:  Med Care       Date:  1979-11       Impact factor: 2.983

2.  Adjusted hospital death rates: a potential screen for quality of medical care.

Authors:  R W Dubois; R H Brook; W H Rogers
Journal:  Am J Public Health       Date:  1987-09       Impact factor: 9.308

3.  Hospital inpatient mortality. Is it a predictor of quality?

Authors:  R W Dubois; W H Rogers; J H Moxley; D Draper; R H Brook
Journal:  N Engl J Med       Date:  1987-12-24       Impact factor: 91.245

4.  Institutional differences in Postoperative death rates. Commentary on some of the findings of the National Halothane Study.

Authors:  L E Moses; F Mosteller
Journal:  JAMA       Date:  1968-02-12       Impact factor: 56.272

5.  Relation between surgical volume and incidence of postoperative wound infection.

Authors:  B F Farber; D L Kaiser; R P Wenzel
Journal:  N Engl J Med       Date:  1981-07-23       Impact factor: 91.245

6.  Should operations be regionalized? The empirical relation between surgical volume and mortality.

Authors:  H S Luft; J P Bunker; A C Enthoven
Journal:  N Engl J Med       Date:  1979-12-20       Impact factor: 91.245

7.  Does practice make perfect? Part I: The relation between hospital volume and outcomes for selected diagnostic categories.

Authors:  A B Flood; W R Scott; W Ewy
Journal:  Med Care       Date:  1984-02       Impact factor: 2.983

8.  Multivariate discriminant analysis of the clinical and angiographic predictors of operative mortality from the Collaborative Study in Coronary Artery Surgery (CASS).

Authors:  J W Kennedy; G C Kaiser; L D Fisher; C Maynard; J K Fritz; W Myers; J G Mudd; T J Ryan; J Coggin
Journal:  J Thorac Cardiovasc Surg       Date:  1980-12       Impact factor: 5.209

9.  Hospital medical staff organization and quality of care: results for myocardial infarction and appendectomy.

Authors:  S M Shortell; J P LoGerfo
Journal:  Med Care       Date:  1981-10       Impact factor: 2.983

10.  Outcomes of surgery among the Medicare aged: mortality after surgery.

Authors:  J Lubitz; G Riley; M Newton
Journal:  Health Care Financ Rev       Date:  1985
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  20 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.  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

Review 3.  The evolving science of quality measurement for hospitals: implications for studies of competition and consolidation.

Authors:  Patrick S Romano; Ryan Mutter
Journal:  Int J Health Care Finance Econ       Date:  2004-06

4.  Practical virtue ethics: healthcare whistleblowing and portable digital technology.

Authors:  S Bolsin; T Faunce; J Oakley
Journal:  J Med Ethics       Date:  2005-10       Impact factor: 2.903

5.  The Canadian four-centre study of anaesthetic outcomes: II. Can outcomes be used to assess the quality of anaesthesia care?

Authors:  M M Cohen; P G Duncan; W D Pope; D Biehl; W A Tweed; L MacWilliam; R N Merchant
Journal:  Can J Anaesth       Date:  1992-05       Impact factor: 5.063

6.  Modeling organizational determinants of hospital mortality.

Authors:  A S al-Haider; T T Wan
Journal:  Health Serv Res       Date:  1991-08       Impact factor: 3.402

7.  Interhospital variations in admission severity-adjusted hospital mortality and morbidity.

Authors:  R C Bradbury; F E Stearns; P M Steen
Journal:  Health Serv Res       Date:  1991-10       Impact factor: 3.402

8.  Elective resection of colon cancer by high-volume surgeons is associated with decreased morbidity and mortality.

Authors:  Sebastien Drolet; Anthony R MacLean; Robert P Myers; Abdel Aziz M Shaheen; Elijah Dixon; W Donald Buie
Journal:  J Gastrointest Surg       Date:  2011-01-29       Impact factor: 3.452

9.  Weak associations between hospital mortality rates for individual diagnoses: implications for profiling hospital quality.

Authors:  G E Rosenthal
Journal:  Am J Public Health       Date:  1997-03       Impact factor: 9.308

10.  Failure to rescue in the surgical oncology population: implications for nursing and quality improvement.

Authors:  Christopher R Friese; Linda H Aiken
Journal:  Oncol Nurs Forum       Date:  2008-09       Impact factor: 2.172

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