Abstract
RationaleClinical 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.ObjectivesTo 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).MethodsThis 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.MeasurementsIntervention 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.Main resultsOut 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.ConclusionsWhile 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.Trial registrationClinicalTrials.gov Identifier: NCT02865967.
Highlights
Growing deployments of Electronic Health Records (EHRs) systems have established large practice-based longitudinal patient records causing an increase in the volume of unstructured data (80%) in the currently available health care records [1]
We developed the Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted clinical decision support (CDS) tool providing 1) a high-level summary of relevant clinical information for each asthmatic patient, 2) machine-learning-based predictive analytics for future asthma exacerbation (AE) and 3) asthma management options to help clinicians make efficient and effective clinical decision-making for optimal asthma management
5 referrals to community health workers, requests for 8 skin tests, 23 spirometry, 33 asthma-specific regular visits, and 28 Asthma Control Test (ACT) update), of which were executed by asthma care team, compared to control group
Summary
Growing deployments of Electronic Health Records (EHRs) systems have established large practice-based longitudinal patient records causing an increase in the volume of unstructured data (80%) in the currently available health care records [1]. The major challenges for better asthma care during the EHRs era is the lack of efficient and effective CDS meaningfully supporting clinicians and their care teams leading to high-value asthma care improving care quality and outcomes while reducing the costs [7,8,9]. No augmented AI-assisted CDS tools for streamlining childhood asthma management are available which fully leverage technologies harnessing EHRs. While many AI algorithms have been developed [10,11,12] and even approved for Software as Medical Device (SaMD) by the FDA [12, 13], few AI algorithms have been tested and shown to have an actual improvement in health outcomes in a randomized clinical trial (RCT) [14, 15]
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