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Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates.
Boussat, Bastien; Quan, Hude; Labarere, Jose; Southern, Danielle; Couris, Chantal M; Ghali, William A.
  • Boussat B; Department of Community Health Sciences, Cumming School of Medicine, O'Brien Institute for Public Health, University of Calgary, TRW Building, 3280 Hospital Drive NW, Calgary, AB T2N 1N4, Canada.
  • Quan H; Quality of Care Unit, Grenoble University Hospital, Boulevard de la Chantourne, 38043 cedex 09, Grenoble, France.
  • Labarere J; TIMC UMR 5525 CNRS, Computational and Mathematical Biology Team, Grenoble Alpes University, Boulevard de la Chantourne, Pavillon Taillefer, 38043 cedex 09, Grenoble, France.
  • Southern D; Department of Community Health Sciences, Cumming School of Medicine, O'Brien Institute for Public Health, University of Calgary, TRW Building, 3280 Hospital Drive NW, Calgary, AB T2N 1N4, Canada.
  • Couris CM; Quality of Care Unit, Grenoble University Hospital, Boulevard de la Chantourne, 38043 cedex 09, Grenoble, France.
  • Ghali WA; TIMC UMR 5525 CNRS, Computational and Mathematical Biology Team, Grenoble Alpes University, Boulevard de la Chantourne, Pavillon Taillefer, 38043 cedex 09, Grenoble, France.
Int J Qual Health Care ; 33(1)2021 Feb 20.
Article en En | MEDLINE | ID: mdl-33544120
ABSTRACT
QUESTION Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards?

FINDINGS:

Applying a methodological framework on simulated PSI report card data, we compare the adjusted PSI rates of three hospitals with variable quality of data and coding. This framework combines (i) a measure of PSI rates using existing algorithms; (ii) a medical record review on a small random sample of charts to produce a measure of hospital-specific data validity and (iii) a simple Bayesian calculation to derive estimated true PSI rates. For example, the estimated true PSI rate, for a theoretical hospital with a moderately good quality of coding, could be three times as high as the measured rate (for example, 1.4% rather than 0.5%). For a theoretical hospital with relatively poor quality of coding, the difference could be 50-fold (for example, 5.0% rather than 0.1%). MEANING Combining a medical chart review on a limited number of medical charts at the hospital level creates an approach to producing health system report cards with estimates of true hospital-level adverse event rates.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Indicadores de Calidad de la Atención de Salud / Hospitales Tipo de estudio: Prognostic_studies Límite: Humans País como asunto: America do norte Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Indicadores de Calidad de la Atención de Salud / Hospitales Tipo de estudio: Prognostic_studies Límite: Humans País como asunto: America do norte Idioma: En Año: 2021 Tipo del documento: Article