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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Identifying patients with asthma in primary care electronic medical record systems Chart analysis-based

Nancy Xi, Rebecca Wallace, Gina Agarwal

    Canadian Family Physician Medecin De Famille Canadien
    |January 14, 2016
    PubMed
    Summary

    Accurate electronic medical record (EMR) search algorithms were developed to identify asthma patients. The best algorithm combined cumulative patient profiles and billing codes, achieving high sensitivity and specificity for improved asthma care.

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    Area of Science:

    • Health Informatics
    • Clinical Decision Support
    • Respiratory Medicine

    Background:

    • Accurate identification of patients with asthma is crucial for effective care and quality improvement.
    • Electronic Medical Records (EMRs) offer a rich data source but require effective search strategies for patient cohort identification.

    Purpose of the Study:

    • To develop and validate electronic medical record (EMR) search algorithms for precise identification of asthma patients.
    • To enable clinicians to create accurate asthma registries for quality improvement initiatives.

    Main Methods:

    • Retrospective chart analysis of 398 patients from primary care clinics.
    • Evaluation of 5 EMR fields: disease registry, cumulative patient profile, billing code, medications, and chart notes.
    • Development and testing of search algorithms using Boolean operators to combine EMR data points.

    Main Results:

    • The most accurate algorithm combined cumulative patient profiles and billing diagnostic codes for asthma.
    • This algorithm achieved a sensitivity of 90.2% and a specificity of 83.9% in identifying asthma patients.
    • High inter-reviewer concordance (κ = 0.89) was observed for physician chart review-based diagnoses.

    Conclusions:

    • Practical and accurate EMR search algorithms for identifying asthma patients have been developed.
    • These algorithms can be readily applied by clinicians to generate asthma registries.
    • The methodology is adaptable for identifying patient cohorts with other chronic diseases.