Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates.

Administrative data Adverse event Bayesian adjustment Patient safety Patient safety indicators

Journal

International journal for quality in health care : journal of the International Society for Quality in Health Care
ISSN: 1464-3677
Titre abrégé: Int J Qual Health Care
Pays: England
ID NLM: 9434628

Informations de publication

Date de publication:
20 Feb 2021
Historique:
received: 17 06 2020
revised: 22 11 2020
accepted: 04 02 2021
pubmed: 6 2 2021
medline: 29 7 2021
entrez: 5 2 2021
Statut: ppublish

Résumé

Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards? 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%). 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.

Identifiants

pubmed: 33544120
pii: 6129200
doi: 10.1093/intqhc/mzab025
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Bastien 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.
Quality of Care Unit, Grenoble University Hospital, Boulevard de la Chantourne, 38043 cedex 09, Grenoble, France.
TIMC UMR 5525 CNRS, Computational and Mathematical Biology Team, Grenoble Alpes University, Boulevard de la Chantourne, Pavillon Taillefer, 38043 cedex 09, Grenoble, France.

Hude Quan (H)

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.

Jose Labarere (J)

Quality of Care Unit, Grenoble University Hospital, Boulevard de la Chantourne, 38043 cedex 09, Grenoble, France.
TIMC UMR 5525 CNRS, Computational and Mathematical Biology Team, Grenoble Alpes University, Boulevard de la Chantourne, Pavillon Taillefer, 38043 cedex 09, Grenoble, France.

Danielle 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.

Chantal M Couris (CM)

Canadian Institute for Health Information, Indicator Research and Development Team, Research and Analysis Division, 4110 Yonge Street, Suite 300, Toronto, ON M2P 2B7, Canada.

William A Ghali (WA)

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.

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