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Triangulating case-finding tools for patient safety surveillance: a cross-sectional case study of puncture/laceration
Journal article   Open access   Peer reviewed

Triangulating case-finding tools for patient safety surveillance: a cross-sectional case study of puncture/laceration

Jennifer A Taylor, Daniel Gerwin, Laura Morlock and Marlene R Miller
Injury prevention, v 17(6), pp 388-393
Dec 2011
DOI: https://doi.org/10.1136/ip.2010.029108
PMID: 21546524
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1136/ip.2010.029108View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open

Abstract

Cross-Sectional Studies Data Collection - standards Humans Middle Aged Quality Indicators, Health Care Male Clinical Coding - methods Medical Errors - statistics & numerical data Safety Management - methods Wounds, Penetrating - epidemiology Data Collection - methods Quality Assurance, Health Care Clinical Coding - standards Hospitals Adult Female Retrospective Studies Lacerations - epidemiology Population Surveillance - methods Patient Safety
To evaluate the need for triangulating case-finding tools in patient safety surveillance. This study applied four case-finding tools to error-associated patient safety events to identify and characterise the spectrum of events captured by these tools, using puncture or laceration as an example for in-depth analysis. Retrospective hospital discharge data were collected for calendar year 2005 (n=48,418) from a large, urban medical centre in the USA. The study design was cross-sectional and used data linkage to identify the cases captured by each of four case-finding tools. Three case-finding tools (International Classification of Diseases external (E) and nature (N) of injury codes, Patient Safety Indicators (PSI)) were applied to the administrative discharge data to identify potential patient safety events. The fourth tool was Patient Safety Net, a web-based voluntary patient safety event reporting system. The degree of mutual exclusion among detection methods was substantial. For example, when linking puncture or laceration on unique identifiers, out of 447 potential events, 118 were identical between PSI and E-codes, 152 were identical between N-codes and E-codes and 188 were identical between PSI and N-codes. Only 100 events that were identified by PSI, E-codes and N-codes were identical. Triangulation of multiple tools through data linkage captures potential patient safety events most comprehensively. Existing detection tools target patient safety domains differently, and consequently capture different occurrences, necessitating the integration of data from a combination of tools to fully estimate the total burden.

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2 citations in Web of Science
3 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
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