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

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.