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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and

Peter L Elkin1,2,3, Sarah Mullin1, Jack Mardekian4

  • 1Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States.

Journal of Medical Internet Research
|November 9, 2021
PubMed
Summary

Combining natural language processing (NLP) with electronic health records (EHR) significantly improves the detection of nonvalvular atrial fibrillation (NVAF). This AI-driven approach can prevent strokes, save lives, and reduce healthcare costs.

Keywords:
CHA2DS2-VAScHAS-BLEDNVAFafibartificial intelligenceatrial fibrillationbio-surveillancebleed risknatural language processingstroke risk

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Public Health Surveillance

Background:

  • Nonvalvular atrial fibrillation (NVAF) affects millions, increasing stroke risk but often remains undiagnosed and undertreated.
  • Current diagnostic and treatment guidelines for NVAF are not fully implemented, leading to preventable adverse events.

Purpose of the Study:

  • To evaluate if combining semisupervised natural language processing (NLP) with structured electronic health record (EHR) data enhances the discovery and treatment of NVAF.
  • To assess the potential of this integrated approach to prevent strokes, reduce mortality, and lower healthcare expenditures.

Main Methods:

  • Utilized NLP to index clinical notes from 96,681 participants in the University of Buffalo faculty practice EHR.
  • Compared the identification of NVAF and related risk scores (CHA2DS2-VASc, HAS-BLED) using structured data alone versus a combination of structured and unstructured (free-text) EHR data.
  • Analyzed large-scale databases (Optum, Truven) with over 63 million participants to determine NVAF prevalence, treatment rates, and outcomes in untreated populations.

Main Results:

  • The structured-plus-unstructured method identified 3,976,056 additional true NVAF cases (P<.001), significantly improving sensitivity for NVAF detection.
  • This integrated approach demonstrated a 32.1% improvement in identifying risk factors (CHA2DS2-VASc, HAS-BLED scores) compared to structured data alone (P=.002 and P<.001).
  • Extrapolated to the US, this method could prevent an estimated 176,537 strokes, save 10,575 lives, and save over $13.5 billion annually.

Conclusions:

  • AI-informed bio-surveillance, integrating NLP with structured EHR data, substantially improves data completeness for NVAF identification.
  • This advanced method offers a powerful strategy to prevent thousands of strokes, save lives, and reduce healthcare costs.
  • The applicability of this approach extends to numerous other disorders with significant public health implications.