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Analysis of Critical Incident Reports Using Natural Language Processing.

Kerstin Denecke1, Helmut Paula2

  • 1Bern University of Applied Sciences, Bern, Switzerland.

Studies in Health Technology and Informatics
|April 29, 2024
PubMed
Summary

Natural language processing (NLP) of Critical Incident Reporting System (CIRS) data reveals key patient safety risks in Swiss healthcare. Analysis identified medication errors, surgical issues, and patient handling as major concerns.

Keywords:
Critical incident reporting systemNatural language processingText analysisText mining

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Area of Science:

  • Health Informatics
  • Natural Language Processing (NLP)
  • Patient Safety

Background:

  • Critical Incident Reporting Systems (CIRS) gather employee reports on events that could harm patients.
  • Analyzing this unstructured data is crucial for understanding and mitigating healthcare risks.

Purpose of the Study:

  • To demonstrate the utility of NLP in extracting valuable insights from CIRS data.
  • To identify trends and common themes in critical incidents within Swiss healthcare.

Main Methods:

  • Analysis of terms, sentiments, and topics in the Swiss National CIRRNET database (2006-2023) using NLP.
  • Application of BERTopic modeling to group incident reports into major themes.

Main Results:

  • Ten major themes were identified, with six related to medication errors.
  • Key findings include trends in surgical errors, venous thromboembolism, falls, blood testing, COVID-19, diabetes, and pediatric care.
  • A significant portion of reports were neutral (40-50%) or negative (30-40%) in tone.

Conclusions:

  • NLP analysis of CIRS data provides valuable insights into common sources of critical incidents in Swiss healthcare.
  • Future research will explore the relationships between incident topics and sentiment.