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Identifying and classifying opioid-related overdoses: A validation study.

Carla A Green1, Nancy A Perrin1,2, Brian Hazlehurst1

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Summary
This summary is machine-generated.

Algorithms accurately identify opioid overdoses using coded data and clinical text. NLP-enhanced algorithms improve classification of suicide attempts and substance abuse, aiding research in healthcare systems.

Keywords:
abusealgorithmsheroinmethodsopioid overdosepharmacoepidemiologysuicide

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Area of Science:

  • Health Informatics
  • Public Health
  • Data Science

Background:

  • Opioid overdose is a major public health crisis.
  • Accurate identification and classification of overdoses are crucial for intervention and research.
  • Existing methods using coded data have limitations in capturing the full scope of overdoses.

Purpose of the Study:

  • To develop and validate algorithms for identifying and classifying opioid overdoses.
  • To leverage both coded data and clinical text from electronic health records (EHRs) using natural language processing (NLP).
  • To assess algorithm performance and portability across different healthcare systems.

Main Methods:

  • Utilized data from an integrated healthcare system (Kaiser Permanente Northwest, 2008-2014).
  • Included International Classification of Diseases (ICD-9/10) codes, clinical notes, and prescription records.
  • Assessed algorithm performance (sensitivity, specificity, PPV, NPV) against medical chart review and tested portability in other systems.

Main Results:

  • Code-based algorithms showed excellent performance for opioid-related overdoses (97.2% sensitivity, 84.6% specificity) and heroin-involved overdoses (91.8% sensitivity, 99.0% specificity).
  • NLP-enhanced algorithms improved classification accuracy for suicide/suicide attempts and substance abuse-involved overdoses.
  • The opioid overdose algorithm demonstrated strong portability across different healthcare settings.

Conclusions:

  • Code-based algorithms are effective for detecting opioid-related overdoses and classifying heroin involvement.
  • NLP-enhanced algorithms offer significant improvements for classifying complex overdose types like suicides/attempts and substance abuse.
  • These validated algorithms, especially NLP-enhanced versions, are valuable tools for research in healthcare settings with NLP capabilities.