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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.

Elham Sagheb1, Chung-Il Wi2, Jungwon Yoon3

  • 1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn.

The Journal of Allergy and Clinical Immunology. in Practice
|November 20, 2021
PubMed
Summary

This study shows artificial intelligence using natural language processing (NLP) can accurately assess clinician adherence to asthma guidelines in electronic health records (EHRs), improving asthma care quality monitoring.

Keywords:
Adherence to asthma guidelinesAutomated chart reviewDocumentation variationNational asthma education and prev4ention programNatural language processing

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Pulmonology

Background:

  • Clinician adherence to asthma guidelines in patient care is often suboptimal.
  • Electronic health records (EHRs) are crucial for monitoring adherence, but manual chart reviews are resource-intensive.
  • Many critical care elements are embedded in unstructured EHR text, hindering automated analysis.

Purpose of the Study:

  • To demonstrate the feasibility of an AI tool utilizing natural language processing (NLP).
  • To extract key components of the 2007 National Asthma Education and Prevention Program guidelines from free-text EHRs.
  • To assess the accuracy of NLP in identifying guideline-adherent asthma care elements.

Main Methods:

  • Retrospective cross-sectional study of pediatric asthma patients (2003-2016).
  • Utilized 1,039 clinical notes from 300 patients diagnosed with asthma.
  • Developed rule-based NLP algorithms to analyze free-text EHR data for guideline-congruent care elements.

Main Results:

  • NLP algorithms achieved high performance metrics: sensitivity (0.82-1.0), specificity (0.95-1.0), PPV (0.86-1.0), and NPV (0.92-1.0) compared to manual review.
  • Medication compliance and inhaler technique assessment were identified as challenging elements due to descriptive variability.
  • The NLP tool demonstrated feasibility in extracting guideline-adherent care components from EHR free text.

Conclusions:

  • NLP technologies offer a potential solution for automated assessment of clinician adherence to asthma guidelines within EHRs.
  • This approach can serve as a valuable tool for population health management and research in asthma care quality.
  • Further multisite studies with larger sample sizes are recommended to validate the generalizability of these NLP algorithms.