2021/05/11 by Shinji Tarumi, Wataru Takeuchi, George Chalkidis +16
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #Artificial Intelligence in Healthcare and Education #Machine Learning in Healthcare
paper · pdf · doi:10.1055/s-0041-1728757
crossref issued 2021/05/11 · crossref published 2021/05/11 · crossref published-online 2021/05/11 · openalex publication_date 2021/05/11 · crossref created 2021/05/11 · crossref published-print 2021/06/01 · crossref deposited 2024/08/30 · openalex created_date 2025/10/10 · crossref indexed 2026/07/24 · openalex updated_date 2026/08/04
OBJECTIVES: Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS: Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS: The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION: A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.