Journal Article

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

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

2021

Overview

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. 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). 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 (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. ClinicalTrials.gov Identifier: NCT02865967.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adolescent / Algorithms / Artificial Intelligence / Asthma / Asthma - therapy / Asthma in children / Care and treatment / Child / Child, Preschool / Childhood / Children / Childrens health / Clinical trials / Computer and Information Sciences / Decision making / Decision support systems / Decision Support Systems, Clinical / Electronic Health Records / Electronic medical records / Female / Health care delivery / Health risks / Humans / Informatics / Information management / Intervention / Learning algorithms / Machine learning / Male / Management / Medical practice software / Medical records / Medicine / Medicine and Health Sciences / Pediatric research / Pediatrics / Physical Sciences / Population / Primary care / Randomization / Research and Analysis Methods / Testing