Making Patient Safety Event Data Actionable: Understanding Patient Safety Analyst Needs.

MedStar author(s):
Citation: Journal of patient safety. 17(6):e509-e514, 2021 09 01.PMID: 28787397Institution: MedStar Institute for InnovationForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Hospitals | *Patient Safety | Delivery of Health Care | HumansYear: 2021Local holdings: Available online through MWHC library: March 2005 - presentISSN:
  • 1549-8417
Name of journal: Journal of patient safetyAbstract: CONCLUSIONS: Major gaps in the analysis of patient safety report data were identified. Despite software to support reporting, many reports come from other sources. Transforming data are burdensome because of recategorization of events and integration with other data sources, processes that can be automated. Surprisingly, trend identification was mostly based on patient analyst memory, highlighting a need for new tools that better support analysts.METHODS: Semistructured interviews were conducted with 21 analysts, from 11 hospitals across 3 healthcare systems. Data were parsed into utterances and coded to extract major themes.OBJECTIVES: The increase in patient safety reporting systems has led to the challenge of effectively analyzing these data to identify and mitigate safety hazards. Patient safety analysts, who manage reports, may be ill-equipped to make sense of report data. We sought to understand the cognitive needs of patient safety analysts as they work to leverage patient safety reports to mitigate risk and improve patient care.RESULTS: From 21 interviews, 516 unique utterances were identified and categorized into the following 4 stages of data analysis: input (15.1% of utterances), transformation (14.1%), extrapolation (30%), and output (14%). Input utterances centered on the source (35.9% of inputs) and preprocessing of data. Transformation utterances centered on recategorizing patient safety events (57.5% of transformations) or integrating external data sources (42.5% of transformations). The focus of interviews was on extrapolation and trending data (56.1% of extrapolations); alarmingly, 16.1% of trend utterances explicitly mentioned a reliance on memory. The output was either a report (56.9% of outputs) or an action (43.1% of outputs).All authors: Blumenthal J, Fong A, Puthumana JS, Ratwani RMOriginally published: Journal of patient safety. , 2017 Aug 04Fiscal year: FY2022Fiscal year of original publication: FY2018Digital Object Identifier: Date added to catalog: 2017-08-23
Holdings
Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 28787397 Available 28787397

Available online through MWHC library: March 2005 - present

CONCLUSIONS: Major gaps in the analysis of patient safety report data were identified. Despite software to support reporting, many reports come from other sources. Transforming data are burdensome because of recategorization of events and integration with other data sources, processes that can be automated. Surprisingly, trend identification was mostly based on patient analyst memory, highlighting a need for new tools that better support analysts.

METHODS: Semistructured interviews were conducted with 21 analysts, from 11 hospitals across 3 healthcare systems. Data were parsed into utterances and coded to extract major themes.

OBJECTIVES: The increase in patient safety reporting systems has led to the challenge of effectively analyzing these data to identify and mitigate safety hazards. Patient safety analysts, who manage reports, may be ill-equipped to make sense of report data. We sought to understand the cognitive needs of patient safety analysts as they work to leverage patient safety reports to mitigate risk and improve patient care.

RESULTS: From 21 interviews, 516 unique utterances were identified and categorized into the following 4 stages of data analysis: input (15.1% of utterances), transformation (14.1%), extrapolation (30%), and output (14%). Input utterances centered on the source (35.9% of inputs) and preprocessing of data. Transformation utterances centered on recategorizing patient safety events (57.5% of transformations) or integrating external data sources (42.5% of transformations). The focus of interviews was on extrapolation and trending data (56.1% of extrapolations); alarmingly, 16.1% of trend utterances explicitly mentioned a reliance on memory. The output was either a report (56.9% of outputs) or an action (43.1% of outputs).

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