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
1   Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
,
Wataru Takeuchi
1   Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
,
George Chalkidis
1   Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
,
Salvador Rodriguez-Loya
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Junichi Kuwata
3   Department of Product Design, Center for Social Innovation, Hitachi Ltd., Kokubunji, Tokyo, Japan
,
Michael Flynn
4   Departments of Internal Medicine and Pediatrics, University of Utah, Salt Lake City, Utah, United States
,
Kyle M. Turner
5   Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States
,
Farrant H. Sakaguchi
6   Department of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, United States
,
Charlene Weir
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Heidi Kramer
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
David E. Shields
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Phillip B. Warner
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Polina Kukhareva
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
,
Hideyuki Ban
1   Department of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan
,
Kensaku Kawamoto
2   Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States
› Author Affiliations
Funding This work was supported by Hitachi, Ltd.

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.

Supplementary Material



Publication History

Received: 10 December 2020

Accepted: 21 February 2021

Article published online:
11 May 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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