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Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

Author

Listed:
  • Hee Yun Seol
  • Pragya Shrestha
  • Joy Fladager Muth
  • Chung-Il Wi
  • Sunghwan Sohn
  • Euijung Ryu
  • Miguel Park
  • Kathy Ihrke
  • Sungrim Moon
  • Katherine King
  • Philip Wheeler
  • Bijan Borah
  • James Moriarty
  • Jordan Rosedahl
  • Hongfang Liu
  • Deborah B McWilliams
  • Young J Juhn

Abstract

Rationale: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. Objectives: To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). Methods: This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (

Suggested Citation

  • Hee Yun Seol & Pragya Shrestha & Joy Fladager Muth & Chung-Il Wi & Sunghwan Sohn & Euijung Ryu & Miguel Park & Kathy Ihrke & Sungrim Moon & Katherine King & Philip Wheeler & Bijan Borah & James Moriar, 2021. "Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0255261
    DOI: 10.1371/journal.pone.0255261
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    Cited by:

    1. Trudeau, Kimberlee J. & Yang, Jichen & Di, Jiaming & Lu, Yi & Kraus, David R., 2023. "Predicting successful placements for youth in child welfare with machine learning," Children and Youth Services Review, Elsevier, vol. 153(C).

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