N=25 control pediatric clinicians, who will receive the post test only. Each clinician will be presented with 10 randomly selected vignettes of 10 children [5 with and 5 without asthma] and asked to provide a prediction of a child's asthma risk at 6-10 years.

N=25 intervention pediatric clinicians, who will receive the post test only. Using the PDM, each clinician will be presented with 10 randomly selected vignettes of 10 children [5 with and 5 without asthma] and asked to provide a prediction of a child's asthma risk at 6-10 years.

N=25 control pediatric clinicians, who will receive the pre and post test. Each clinician will be presented with 10 randomly selected vignettes of 10 children [5 with and 5 without asthma] and asked to provide a prediction of a child's asthma risk at 6-10 years.

N=25 intervention pediatric clinicians, who will receive the pre and post test. Using the PDM, each clinician will be presented with 10 randomly selected vignettes of 10 children [5 with and 5 without asthma] and asked to provide a prediction of a child's asthma risk at 6-10 years.

A childhood asthma Passive Digital Marker (PDM) is an ML algorithm that is able to retrieve and synthesize pre-existing "passively" collected mother/child dyad prognostic data in "digital" electronic health record (EHR) to provide an objective and quantifiable "marker" of a child's risk (probability) and associated pathophysiological phenotype to inform clinician decision-making at point-of-care.

Perceived PDM acceptance will be measured using a Behavioral Intention scale (BIS).

Perceived Usability will be measured using a modified Simplified System Usability Scale (SUS).

Percent of successful study enrollment of eligible clinicians (>80%)

% correct clinician predictions at pre and post test