Literature DB >> 3057250

Interpreting hospital mortality data. The role of clinical risk adjustment.

S F Jencks1, J Daley, D Draper, N Thomas, G Lenhart, J Walker.   

Abstract

This study uses national Medicare data as well as data that were abstracted to calibrate the Medicare Mortality Predictor System to assess the usefulness of a risk adjustment system in interpreting hospital mortality rates. The majority of variation in annual hospital death rates for the four conditions studied (stroke, pneumonia, myocardial infarction, and congestive heart failure) is chance variability that results from the relatively small numbers of patients treated in most hospitals in a year. For hospitals in the highest and lowest quartiles of observed death rates, the difference between observed rates and those predicted by the Medicare Mortality Predictor System is not quite on third smaller than the difference between observed rates and unadjusted national rates. Risk adjustment methods do not show whether the unexplained difference in mortality rates results from differences in effectiveness of care or unmeasured differences in patient risk at the time of admission. Risk-adjusted mortality rates, therefore, should be supplemented by review of the actual care rendered before conclusions are drawn regarding effectiveness of care.

Entities:  

Mesh:

Year:  1988        PMID: 3057250

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


  30 in total

1.  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

2.  Characteristics associated with clinician diagnosis of aspiration pneumonia: a descriptive study of afflicted patients and their outcomes.

Authors:  Michael J Lanspa; Paula Peyrani; Timothy Wiemken; Emily L Wilson; Julio A Ramirez; Nathan C Dean
Journal:  J Hosp Med       Date:  2014-11-01       Impact factor: 2.960

3.  Can readmission rates be used as an outcome indicator?

Authors:  R Milne; A Clarke
Journal:  BMJ       Date:  1990-11-17

4.  Routine data: a resource for clinical audit?

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

5.  Quality of care: 1. What is quality and how can it be measured? Health Services Research Group.

Authors: 
Journal:  CMAJ       Date:  1992-06-15       Impact factor: 8.262

6.  Admission and mid-stay MedisGroups scores as predictors of death within 30 days of hospital admission.

Authors:  L I Iezzoni; A S Ash; G Coffman; M A Moskowitz
Journal:  Am J Public Health       Date:  1991-01       Impact factor: 9.308

7.  Health-related quality of life: an indicator of quality of care?

Authors:  H F Treurniet; M L Essink-Bot; J P Mackenbach; P J van der Maas
Journal:  Qual Life Res       Date:  1997-05       Impact factor: 4.147

8.  Comparison of a disease-specific and a generic severity of illness measure for patients with community-acquired pneumonia.

Authors:  M J Fine; B H Hanusa; J R Lave; D E Singer; R A Stone; L A Weissfeld; C M Coley; T J Marrie; W N Kapoor
Journal:  J Gen Intern Med       Date:  1995-07       Impact factor: 5.128

9.  Validation of the Infectious Disease Society of America/American Thoracic Society 2007 guidelines for severe community-acquired pneumonia.

Authors:  Samuel M Brown; Barbara E Jones; Al R Jephson; Nathan C Dean
Journal:  Crit Care Med       Date:  2009-12       Impact factor: 7.598

10.  Mortality in a public and a private hospital compared: the severity of antecedent disorders in Medicare patients.

Authors:  R Burns; L O Nichols; M J Graney; W B Applegate
Journal:  Am J Public Health       Date:  1993-07       Impact factor: 9.308

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.