Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
| Autor: | Junichi Kuwata, Kensaku Kawamoto, Salvador Rodriguez-Loya, Hideyuki Ban, Kyle Turner, Michael Flynn, Takeuchi Wataru, George Chalkidis, Charlene R. Weir, Farrant Sakaguchi, Heidi Kramer, Polina V. Kukhareva, David Shields, Shinji Tarumi, Phillip B. Warner |
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| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Process management Computer science Dashboard (business) Interoperability 030209 endocrinology & metabolism Health Informatics computer.software_genre Clinical decision support system 03 medical and health sciences 0302 clinical medicine Health Information Management Artificial Intelligence Health care Electronic Health Records Humans 030212 general & internal medicine Point of care Advanced and Specialized Nursing business.industry Predictive analytics Decision Support Systems Clinical health information technology clinical decision support system Diabetes Mellitus Type 2 disease management Chronic Disease Original Article Web service business computer |
| Zdroj: | Methods of Information in Medicine |
| ISSN: | 2511-705X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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