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Related Concept Videos

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Enhancing Pressure Injury Surveillance Using Natural Language Processing.

Carly E Milliren1, Al Ozonoff, Kerri A Fournier1

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|December 26, 2023
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Summary
This summary is machine-generated.

Natural language processing (NLP) can effectively identify underreported hospital-acquired pressure injuries (HAPI) using nursing notes. This feasible surveillance method offers high yield for detecting HAPI events in pediatric care.

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Area of Science:

  • Medical Informatics
  • Clinical Nursing
  • Natural Language Processing

Background:

  • Hospital-acquired pressure injuries (HAPI) are a significant concern in patient care.
  • Accurate identification and reporting of HAPI are crucial for quality improvement and patient safety.
  • Traditional methods may underreport HAPI events, necessitating innovative surveillance approaches.

Purpose of the Study:

  • To assess the feasibility of using nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.
  • To develop and validate a natural language processing (NLP) system for HAPI detection in pediatric care.

Main Methods:

  • A natural language processing-assisted manual review process was established for data extraction from nursing notes.
  • Multiple models, including a tri-gram classifier, logistic regression, and a random forest model, were developed and compared.
  • The final model was trained and validated on a large corpus of nursing notes from inpatient and intensive care units.

Main Results:

  • The random forest model demonstrated high sensitivity (95%) and good accuracy (78.7%) in identifying likely HAPI events.
  • A total of 264 notes indicating potential HAPI were identified, representing an incidence of 11.9 per 1000 discharges.
  • The interrater agreement for HAPI identification was high (κ = 0.67).

Conclusions:

  • Natural language processing-based surveillance using nursing handoff notes is a feasible and high-yield method for identifying HAPI.
  • This approach can improve the accuracy of HAPI reporting and support patient safety initiatives.
  • The study highlights the potential of NLP in enhancing clinical surveillance for adverse events.