Literature DB >> 9096547

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

G E Rosenthal1.   

Abstract

OBJECTIVES: This study examined the consistency of hospital mortality rates across different diagnoses.
METHODS: Standardized mortality ratios for patients discharged in 1991 from US hospitals were determined via the Medicare Hospital Information Report.
RESULTS: Correlations between standardized mortality ratios for different diagnoses were relatively weak, ranging from .03 to .34. Agreement between hospital rankings (based on standardized mortality ratios), as measured by the weighted kappa statistic, was also weak.
CONCLUSIONS: The present results indicate that hospital mortality rates for individual diagnoses are weakly associated. Thus, it may not be valid to generalize conclusions about hospital performance from a single diagnosis.

Entities:  

Mesh:

Year:  1997        PMID: 9096547      PMCID: PMC1381018          DOI: 10.2105/ajph.87.3.429

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  35 in total

1.  Health benefits and health policy: public goals through private means.

Authors:  P P Cooper
Journal:  J Occup Med       Date:  1991-03

2.  Consumers see mortality data as useful tool.

Authors:  J Jensen
Journal:  Mod Healthc       Date:  1992-06-22

3.  Biased estimates of expected acute myocardial infarction mortality using MedisGroups admission severity groups.

Authors:  M S Blumberg
Journal:  JAMA       Date:  1991-06-12       Impact factor: 56.272

4.  A description and clinical assessment of the Computerized Severity Index.

Authors:  L I Iezzoni; J Daley
Journal:  QRB Qual Rev Bull       Date:  1992-02

5.  Improving intensive care: observations based on organizational case studies in nine intensive care units: a prospective, multicenter study.

Authors:  J E Zimmerman; S M Shortell; D M Rousseau; J Duffy; R R Gillies; W A Knaus; K Devers; D P Wagner; E A Draper
Journal:  Crit Care Med       Date:  1993-10       Impact factor: 7.598

6.  Differences in mortality from coronary artery bypass graft surgery at five teaching hospitals.

Authors:  S V Williams; D B Nash; N Goldfarb
Journal:  JAMA       Date:  1991-08-14       Impact factor: 56.272

7.  Validating risk-adjusted mortality as an indicator for quality of care.

Authors:  J W Thomas; J J Holloway; K E Guire
Journal:  Inquiry       Date:  1993       Impact factor: 1.730

8.  Chance, continuity, and change in hospital mortality rates. Coronary artery bypass graft patients in California hospitals, 1983 to 1989.

Authors:  H S Luft; P S Romano
Journal:  JAMA       Date:  1993-07-21       Impact factor: 56.272

9.  The relationship between adjusted hospital mortality and the results of peer review.

Authors:  A J Hartz; M S Gottlieb; E M Kuhn; A A Rimm
Journal:  Health Serv Res       Date:  1993-02       Impact factor: 3.402

10.  Evaluation of the HCFA model for the analysis of mortality following hospitalization.

Authors:  H Krakauer; R C Bailey; K J Skellan; J D Stewart; A J Hartz; E M Kuhn; A A Rimm
Journal:  Health Serv Res       Date:  1992-08       Impact factor: 3.402

View more
  16 in total

Review 1.  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

2.  Are mortality rates for different operations related?: implications for measuring the quality of noncardiac surgery.

Authors:  Justin B Dimick; Douglas O Staiger; John D Birkmeyer
Journal:  Med Care       Date:  2006-08       Impact factor: 2.983

3.  Comment: evaluating the effectiveness of hospital care.

Authors:  H Krakauer
Journal:  Am J Public Health       Date:  1997-06       Impact factor: 9.308

4.  Impact of comorbidity on outcome in kidney transplant recipients: a retrospective study in Italy.

Authors:  Fabio Fabbian; Alfredo De Giorgi; Fabio Manfredini; Nicola Lamberti; Silvia Forcellini; Alda Storari; Paola Todeschini; Massimo Gallerani; Gaetano La Manna; Dimitri P Mikhailidis; Roberto Manfredini
Journal:  Intern Emerg Med       Date:  2016-03-22       Impact factor: 3.397

5.  Improving benchmarking by using an explicit framework for the development of composite indicators: an example using pediatric quality of care.

Authors:  Jochen Profit; Katri V Typpo; Sylvia J Hysong; LeChauncy D Woodard; Michael A Kallen; Laura A Petersen
Journal:  Implement Sci       Date:  2010-02-09       Impact factor: 7.327

6.  Measuring hospital inefficiency: the effects of controlling for quality and patient burden of illness.

Authors:  Ryan L Mutter; Michael D Rosko; Herbert S Wong
Journal:  Health Serv Res       Date:  2008-09-08       Impact factor: 3.402

7.  Correlation of neonatal intensive care unit performance across multiple measures of quality of care.

Authors:  Jochen Profit; John A F Zupancic; Jeffrey B Gould; Kenneth Pietz; Marc A Kowalkowski; David Draper; Sylvia J Hysong; Laura A Petersen
Journal:  JAMA Pediatr       Date:  2013-01       Impact factor: 16.193

8.  Are diagnosis specific outcome indicators based on administrative data useful in assessing quality of hospital care?

Authors:  I Scott; D Youlden; M Coory
Journal:  Qual Saf Health Care       Date:  2004-02

9.  The Safety Attitudes Questionnaire as a tool for benchmarking safety culture in the NICU.

Authors:  Jochen Profit; Jason Etchegaray; Laura A Petersen; J Bryan Sexton; Sylvia J Hysong; Minghua Mei; Eric J Thomas
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  2012-03       Impact factor: 5.747

10.  Hospital competition, resource allocation and quality of care.

Authors:  Dana B Mukamel; Jack Zwanziger; Anil Bamezai
Journal:  BMC Health Serv Res       Date:  2002-05-27       Impact factor: 2.655

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.