Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

Shinji Tarumi; Wataru Takeuchi; George Chalkidis; Salvador Rodriguez-Loya; Junichi Kuwata; Michael Flynn; Kyle M. Turner; Farrant H. Sakaguchi; Charlene Weir; Heidi Kramer; David E. Shields; Phillip B. Warner; Polina Kukhareva; Hideyuki Ban; Kensaku Kawamoto

doi:10.1055/s-0041-1728757

Methods of Information in Medicine, Table of Contents

CC BY-NC-ND 4.0 · Methods Inf Med 2021; 60(S 01): e32-e43
DOI: 10.1055/s-0041-1728757

Original Article

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus

Shinji Tarumi

¹Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan

,

Wataru Takeuchi

¹Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan

,

George Chalkidis

¹Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan

,

Salvador Rodriguez-Loya

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

Junichi Kuwata

³Department of Product Design, Center for Social Innovation, Hitachi Ltd., Kokubunji, Tokyo, Japan

,

Michael Flynn

⁴Departments of Internal Medicine and Pediatrics, University of Utah, Salt Lake City, Utah, United States

,

Kyle M. Turner

⁵Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States

,

Farrant H. Sakaguchi

⁶Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, United States

,

Charlene Weir

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

Heidi Kramer

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

David E. Shields

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

Phillip B. Warner

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

Polina Kukhareva

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

,

Hideyuki Ban

¹Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan

,

Kensaku Kawamoto

²Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States

› Author Affiliations

Abstract

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.

Keywords

artificial intelligence - clinical decision support system - health information technology - disease management - chronic disease

Full Text

References

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Supplementary Material

Supplementary Material