Literature DB >> 8336511

How accurate are hospital discharge data for evaluating effectiveness of care?

J Green1, N Wintfeld.   

Abstract

Demand for quality of care data has led to publication of adjusted mortality rates of hospitals and physicians. Yet the accuracy of databases used for this purpose is questionable, raising the possibility that consumer-oriented profiles of providers could be misleading. A stratified random sample of discharge abstract records of Medicare-aged patients hospitalized in California were audited by the state's health data agency. The results of the audit were analyzed to determine the effect that coding errors have on expected death rates assigned to hospitals as risk-adjustment measures in the annual Medicare hospital mortality report. Discharge abstracts of Medicare-aged patients contained many errors among coded risk factors used to calculate expected death rates. Comorbidities and transfers from nursing homes were seriously underreported and 'urgency of admission' was often miscoded. Hospitals differed with respect to error rates (P < .0001); these varying levels of miscoding caused measurement errors that ranged in size from 0.2 to 2.2 expected 30-day deaths per 100 admissions (10th vs. 90th percentile). Detailed knowledge of the limitations of claims data can help analysts minimize the impact of coding error. Beyond this, quality control of data used for outcomes research needs strengthening.

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Year:  1993        PMID: 8336511     DOI: 10.1097/00005650-199308000-00005

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  41 in total

1.  Lack of gender and racial differences in surgery and mortality in hospitalized Medicare beneficiaries with bleeding peptic ulcer.

Authors:  G S Cooper; Z Yuan; G E Rosenthal; A Chak; A A Rimm
Journal:  J Gen Intern Med       Date:  1997-08       Impact factor: 5.128

2.  Mortality league tables: do they inform or mislead?

Authors:  M McKee; D Hunter
Journal:  Qual Health Care       Date:  1995-03

3.  The registration of complications in surgery: a learning curve.

Authors:  Eelco J Veen; Maryska L G Janssen-Heijnen; Loek P H Leenen; Jan A Roukema
Journal:  World J Surg       Date:  2005-03       Impact factor: 3.352

4.  Outpatient mastectomy: clinical, payer, and geographic influences.

Authors:  C Case; M Johantgen; C Steiner
Journal:  Health Serv Res       Date:  2001-10       Impact factor: 3.402

5.  The association of the number of comorbidities and complications with length of stay, hospital mortality and LOS high outlier, based on administrative data.

Authors:  Kazuaki Kuwabara; Yuichi Imanaka; Shinya Matsuda; Kiyohide Fushimi; Hideki Hashimoto; Koichi B Ishikawa; Hiromasa Horiguchi; Kenshi Hayashida; Kenji Fujimori
Journal:  Environ Health Prev Med       Date:  2008-03-29       Impact factor: 3.674

6.  Modelling of errors in databases.

Authors:  Steve Gallivan; Christina Pagel
Journal:  Health Care Manag Sci       Date:  2008-03

Review 7.  Identifying complications and low provider adherence to normative practices using administrative data.

Authors:  D H Kuykendall; C M Ashton; M L Johnson; J M Geraci
Journal:  Health Serv Res       Date:  1995-10       Impact factor: 3.402

8.  Commentary: establishing standards for the utility of administrative claims data.

Authors:  S M Retchin; D J Ballard
Journal:  Health Serv Res       Date:  1998-02       Impact factor: 3.402

9.  Fee-based care is important for access to prompt treatment of hip fractures among veterans.

Authors:  Kelly K Richardson; Peter Cram; Mary Vaughan-Sarrazin; Peter J Kaboli
Journal:  Clin Orthop Relat Res       Date:  2013-01-16       Impact factor: 4.176

10.  Acute myocardial infarction and coronary artery bypass grafting outcomes in specialty and general hospitals: analysis of state inpatient data.

Authors:  Peter Cram; Levent Bayman; Joanna Popescu; Mary S Vaughan-Sarrazin
Journal:  Health Serv Res       Date:  2009-12-04       Impact factor: 3.402

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