Effect of an electronic health record-integrated machine learning asthma risk marker on pediatrician prognostic accuracy during preschool age: a pilot randomized clinical trial.

Authors

Arthur H Owora, Bowen Jiang, Yash Shah

Published in

Scientific reports. Jun 18, 2026. Epub Jun 18, 2026.

Abstract

Early identification of children at risk for persistent asthma is challenging because preschool respiratory symptoms are heterogeneous and often overlap with transient wheezing illnesses. Machine learning-based prediction models may improve risk stratification, but few have been integrated into electronic health record (EHR) systems or evaluated for their effect on clinician decision-making. We evaluated whether access to an EHR-integrated machine learning-based Passive Digital Marker (PDM) improved pediatrician prognostic accuracy for school-age asthma and assessed the tool's usability, acceptability, and feasibility. In this pilot randomized clinical trial, practicing pediatricians in Indiana were randomized using a Solomon 4-group design with pretest and posttest assessments. Pediatricians evaluated 10 standardized pediatric patient vignettes derived from longitudinal EHR data from birth through age 3 years. Children with documented asthma diagnoses between ages 6 and 11 years were classified as cases. The PDM classified children as high or low risk for persistent school-age asthma using routinely collected clinical data. The primary outcome was clinician prognostic accuracy, defined as the proportion of correctly classified vignettes;secondary outcomes included usability, acceptability, and feasibility. Thirty-four pediatricians participated, and all completed study procedures. Clinicians with access to the PDM demonstrated higher prognostic accuracy than clinicians without access (mean [95% CI] 0.83 [0.69-0.97] vs 0.61 [0.52-0.70]; P = .008). In a mixed-effects model accounting for clustering of vignette assessments within clinicians, a significant interaction between PDM access and assessment timing was observed (b = 0.10; 95% CI 0.01-0.18; P = .02), indicating improved prognostic accuracy during posttest assessments when clinicians used the PDM. In this pilot randomized clinical trial, an EHR-integrated machine learning-based asthma risk marker improved pediatrician prognostic accuracy and demonstrated favorable usability and feasibility. These findings support further evaluation of machine learning-enabled clinical decision support tools to improve early identification of children at risk for persistent asthma.Trial registration: ClinicalTrials.gov Identifier: NCT05826561; registered April 12, 2023.

PMID:
42315888
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

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