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Triangulating case-finding tools for patient safety surveillance: a cross-sectional case study of puncture/laceration
  1. Jennifer A Taylor1,
  2. Daniel Gerwin1,
  3. Laura Morlock2,
  4. Marlene R Miller3
  1. 1Department of Environmental and Occupational Health, Drexel University School of Public Health, Philadelphia, Pennsylvania, USA
  2. 2Department of Health Policy and Management, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  3. 3Department of Pediatrics, Johns Hopkins Children's Center and Department of Health Policy and Management, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  1. Correspondence to Dr Jennifer Taylor, Department of Environmental and Occupational Health, Drexel University School of Public Health, 1505 Race Street, MS 1034, Philadelphia, PA 19102, USA; Jat65{at}drexel.edu

Abstract

Objective 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.

Data sources/study setting Retrospective hospital discharge data were collected for calendar year 2005 (n=48 418) from a large, urban medical centre in the USA.

Study design The study design was cross-sectional and used data linkage to identify the cases captured by each of four case-finding tools.

Data collection/extraction methods 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.

Results 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.

Conclusions 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.

  • Surveillance
  • patient safety
  • administrative data
  • error-reporting
  • data linkage
  • database
  • e-code
  • information tech
  • public health
  • surveillance

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Footnotes

  • Funding This research was supported (in part) by funding from the NIOSH Education and Research Center for Occupational Safety and Health at the Johns Hopkins Bloomberg School of Public Health—a doctoral training program (#T42OH00842428).

  • Competing interests None.

  • Ethics approval This study was conducted with the approval of the Johns Hopkins Bloomberg School of Public Health IRB and Johns Hopkins Hospital.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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