Literature DB >> 22167323

Determining population based mortality risk in the Department of Veterans Affairs.

Theodore Stefos1, Laura Lehner, Marta Render, Eileen Moran, Peter Almenoff.   

Abstract

We develop a patient level hierarchical regression model using administrative claims data to assess mortality outcomes for a national VA population. This model, which complements more traditional process driven performance measures, includes demographic variables and disease specific measures of risk classified by Diagnostic Cost Groups (DCGs). Results indicate some ability to discriminate survivors and non-survivors with an area under the Receiver Operating Characteristic Curve (C-statistic) of .86. Observed to expected mortality ranges from .86 to 1.12 across predicted mortality deciles while Risk Standardized Mortality Rates (RSMRs) range from .76 to 1.29 across 145 VA hospitals. Further research is necessary to understand mortality variation which persists even after adjusting for case mix differences. Future work is also necessary to examine the role of personal behaviors on patient outcomes and the potential impact on population survival rates from changes in treatment policy and infrastructure investment.

Entities:  

Mesh:

Year:  2011        PMID: 22167323     DOI: 10.1007/s10729-011-9189-0

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  25 in total

1.  Accuracy of risk-adjusted mortality rate as a measure of hospital quality of care.

Authors:  J W Thomas; T P Hofer
Journal:  Med Care       Date:  1999-01       Impact factor: 2.983

2.  Comparing mortality and time until death for medicare HMO and FFS beneficiaries.

Authors:  M L Maciejewski; B Dowd; K T Call; R Feldman
Journal:  Health Serv Res       Date:  2001-02       Impact factor: 3.402

3.  What is value in health care?

Authors:  Michael E Porter
Journal:  N Engl J Med       Date:  2010-12-08       Impact factor: 91.245

4.  The inverse relationship between mortality rates and performance in the Hospital Quality Alliance measures.

Authors:  Ashish K Jha; E John Orav; Zhonghe Li; Arnold M Epstein
Journal:  Health Aff (Millwood)       Date:  2007 Jul-Aug       Impact factor: 6.301

5.  Variations in inpatient mortality among hospitals in different system types, 1995 to 2000.

Authors:  Askar S Chukmaitov; Gloria J Bazzoli; David W Harless; Robert E Hurley; Kelly J Devers; Mei Zhao
Journal:  Med Care       Date:  2009-04       Impact factor: 2.983

6.  Mortality after noncardiac surgery: prediction from administrative versus clinical data.

Authors:  Howard S Gordon; Michael L Johnson; Nelda P Wray; Nancy J Petersen; William G Henderson; Shukri F Khuri; Jane M Geraci
Journal:  Med Care       Date:  2005-02       Impact factor: 2.983

7.  Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.

Authors:  Marta L Render; H Myra Kim; James Deddens; Siva Sivaganesin; Deborah E Welsh; Karen Bickel; Ron Freyberg; Stephen Timmons; Joseph Johnston; Alfred F Connors; Douglas Wagner; Timothy P Hofer
Journal:  Crit Care Med       Date:  2005-05       Impact factor: 7.598

8.  Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.

Authors:  Gabriel J Escobar; John D Greene; Peter Scheirer; Marla N Gardner; David Draper; Patricia Kipnis
Journal:  Med Care       Date:  2008-03       Impact factor: 2.983

9.  An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction.

Authors:  Harlan M Krumholz; Yun Wang; Jennifer A Mattera; Yongfei Wang; Lein Fang Han; Melvin J Ingber; Sheila Roman; Sharon-Lise T Normand
Journal:  Circulation       Date:  2006-03-20       Impact factor: 29.690

10.  Diagnosis-based risk adjustment for Medicare capitation payments.

Authors:  R P Ellis; G C Pope; L Iezzoni; J Z Ayanian; D W Bates; H Burstin; A S Ash
Journal:  Health Care Financ Rev       Date:  1996
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  1 in total

1.  Hospital-level variation in the development of persistent critical illness.

Authors:  Elizabeth M Viglianti; Sean M Bagshaw; Rinaldo Bellomo; Joanne McPeake; Xiao Qing Wang; Sarah Seelye; Theodore J Iwashyna
Journal:  Intensive Care Med       Date:  2020-06-04       Impact factor: 17.440

  1 in total

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