Literature DB >> 8428812

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

A J Hartz1, M S Gottlieb, E M Kuhn, A A Rimm.   

Abstract

This study assessed the relationship between the Health Care Financing Administration adjusted mortality rate for a hospital and the errors in care found by the peer review process. The three data sets used were: (1) the 1987-1988 completed reviews from 38 peer review organizations (PROs) of 4,132 hospitals and 2,035,128 patients; (2) all 1987 hospital mortality rates for Medicare patients as adjusted by HCFA for patient mix; and (3) the 1986 American Hospital Association Survey. The PRO data were used to compute the percentage of cases reviewed from each hospital confirmed by a reviewing physician to have a quality problem. The average percentage of confirmed problems was 3.73 percent with state rates ranging from 0.03 percent to 38.5 percent. The average within-state correlation between the problem rate and the adjusted mortality rate for all PROs was .19 (p < .0001), but the correlations were much higher for relatively homogeneous groups of hospitals, .42 for public hospitals and .36 for hospitals in large metropolitan statistical areas (MSAs). These results suggest that the HCFA adjusted hospital mortality rate and the PRO-confirmed problem rate are related methods to compare hospitals on the basis of quality of care. Both methods may compare quality better if used within a group of homogenous hospitals.

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Mesh:

Year:  1993        PMID: 8428812      PMCID: PMC1069912     

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


  7 in total

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Authors:  S R Eastaugh
Journal:  Hosp Health Serv Adm       Date:  1986 Nov-Dec

2.  A peer review of a peer review organization.

Authors:  S E Dippe; M M Bell; M A Wells; W Lyons; S Clester
Journal:  West J Med       Date:  1989-07

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

Authors:  R E Park; R H Brook; J Kosecoff; J Keesey; L Rubenstein; E Keeler; K L Kahn; W H Rogers; M R Chassin
Journal:  JAMA       Date:  1990-07-25       Impact factor: 56.272

4.  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

Review 5.  Outcome measurement: concepts and questions.

Authors:  K N Lohr
Journal:  Inquiry       Date:  1988       Impact factor: 1.730

6.  Accuracy of diagnostic coding for Medicare patients under the prospective-payment system.

Authors:  D C Hsia; W M Krushat; A B Fagan; J A Tebbutt; R P Kusserow
Journal:  N Engl J Med       Date:  1988-02-11       Impact factor: 91.245

7.  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

  7 in total
  10 in total

1.  HMO penetration, competition, and risk-adjusted hospital mortality.

Authors:  D B Mukamel; J Zwanziger; K J Tomaszewski
Journal:  Health Serv Res       Date:  2001-12       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

3.  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

4.  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

5.  The impact of extreme-risk cases on hospitals' risk-adjusted percutaneous coronary intervention mortality ratings.

Authors:  Matthew W Sherwood; J Matthew Brennan; Kalon K Ho; Frederick A Masoudi; John C Messenger; W Douglas Weaver; David Dai; Eric D Peterson
Journal:  JACC Cardiovasc Interv       Date:  2014-12-10       Impact factor: 11.195

6.  Mortality and length of stay in a veterans affairs hospital and private sector hospitals serving a common market.

Authors:  Gary E Rosenthal; Mary Vaughan Sarrazin; Dwain L Harper; Susan M Fuehrer
Journal:  J Gen Intern Med       Date:  2003-08       Impact factor: 5.128

7.  Insurance type and choice of hospital for coronary artery bypass graft surgery.

Authors:  M Chernew; D Scanlon; R Hayward
Journal:  Health Serv Res       Date:  1998-08       Impact factor: 3.402

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

Review 9.  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

10.  Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare.

Authors:  Richard Wilson; Steve W Goodacre; Marcin Klingbajl; Anne-Maree Kelly; Tim Rainer; Tim Coats; Vikki Holloway; Will Townend; Steve Crane
Journal:  Emerg Med J       Date:  2013-04-19       Impact factor: 2.740

  10 in total

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