Literature DB >> 2403601

The importance of severity of illness in assessing hospital mortality.

J Green1, N Wintfeld, P Sharkey, L J Passman.   

Abstract

Each year, the Health Care Financing Administration (HCFA) releases a report comparing hospital mortality rates with predicted rates. Some argue that the HCFA's prediction model does not adequately account for patient severity. We tested this hypothesis by comparing the HCFA's model (replicated as closely as we could) to a second that added a severity measure (the Stage of Principal Diagnosis at Admission, a subscale of the Severity of Illness Index). In our simulation, the HCFA's model had very limited capacity to predict mortality (average R2, 2.5%). Patients grouped according to admission severity had markedly different mortality rates, which the HCFA's model's predictions could not differentiate. The HCFA model also failed to predict large differences in mortality between hospitals with low- and high-severity admissions. Adding severity to the HCFA's model yielded more than an eightfold increase in the R2, to 21.5%, and reduced instances of higher than expected hospital mortality to chance levels. These findings suggest that the HCFA's mortality release needs to be made much more sensitive to admission severity before it can be used to make valid inferences about the quality or effectiveness of hospital care.

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Year:  1990        PMID: 2403601

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


  34 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

3.  Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model.

Authors:  Chee-Fah Chong; Yu-Chuan Li; Tzong-Luen Wang; Hang Chang
Journal:  AMIA Annu Symp Proc       Date:  2003

Review 4.  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

5.  Routine data: a resource for clinical audit?

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

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.  Quality assurance and audit: lessons from North America.

Authors:  M H Liang; P Fortin
Journal:  Ann Rheum Dis       Date:  1991-07       Impact factor: 19.103

8.  Effect of correcting outcome data for case mix: an example from stroke medicine.

Authors:  R J Davenport; M S Dennis; C P Warlow
Journal:  BMJ       Date:  1996-06-15

9.  Factors affecting postoperative morbidity and mortality in isolated coronary artery bypass graft surgery.

Authors:  Abbasali Karimi; Hossein Ahmadi; Saeed Davoodi; Namvar Movahedi; Mehrab Marzban; Kyomars Abbasi; Abbas Salehi Omran; Saeed Sadeghian; Parin Yazdanifard; Seyed Hesameddin Abbasi; Nader Fallah
Journal:  Surg Today       Date:  2008-09-27       Impact factor: 2.549

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

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