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

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 metho...

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Autores principales: Tarumi, Shinji, Takeuchi, Wataru, Chalkidis, George, Rodriguez-Loya, Salvador, Kuwata, Junichi, Flynn, Michael, Turner, Kyle M., Sakaguchi, Farrant H., Weir, Charlene, Kramer, Heidi, Shields, David E., Warner, Phillip B., Kukhareva, Polina, Ban, Hideyuki, Kawamoto, Kensaku
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294941/
https://www.ncbi.nlm.nih.gov/pubmed/33975376
http://dx.doi.org/10.1055/s-0041-1728757
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author Tarumi, Shinji
Takeuchi, Wataru
Chalkidis, George
Rodriguez-Loya, Salvador
Kuwata, Junichi
Flynn, Michael
Turner, Kyle M.
Sakaguchi, Farrant H.
Weir, Charlene
Kramer, Heidi
Shields, David E.
Warner, Phillip B.
Kukhareva, Polina
Ban, Hideyuki
Kawamoto, Kensaku
author_facet Tarumi, Shinji
Takeuchi, Wataru
Chalkidis, George
Rodriguez-Loya, Salvador
Kuwata, Junichi
Flynn, Michael
Turner, Kyle M.
Sakaguchi, Farrant H.
Weir, Charlene
Kramer, Heidi
Shields, David E.
Warner, Phillip B.
Kukhareva, Polina
Ban, Hideyuki
Kawamoto, Kensaku
author_sort Tarumi, Shinji
collection PubMed
description 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.
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spelling pubmed-82949412021-07-23 Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus Tarumi, Shinji Takeuchi, Wataru Chalkidis, George Rodriguez-Loya, Salvador Kuwata, Junichi Flynn, Michael Turner, Kyle M. Sakaguchi, Farrant H. Weir, Charlene Kramer, Heidi Shields, David E. Warner, Phillip B. Kukhareva, Polina Ban, Hideyuki Kawamoto, Kensaku Methods Inf Med 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. Georg Thieme Verlag KG 2021-06 2021-05-11 /pmc/articles/PMC8294941/ /pubmed/33975376 http://dx.doi.org/10.1055/s-0041-1728757 Text en 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/ ) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Tarumi, Shinji
Takeuchi, Wataru
Chalkidis, George
Rodriguez-Loya, Salvador
Kuwata, Junichi
Flynn, Michael
Turner, Kyle M.
Sakaguchi, Farrant H.
Weir, Charlene
Kramer, Heidi
Shields, David E.
Warner, Phillip B.
Kukhareva, Polina
Ban, Hideyuki
Kawamoto, Kensaku
Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title_full Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title_fullStr Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title_full_unstemmed Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title_short Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus
title_sort leveraging artificial intelligence to improve chronic disease care: methods and application to pharmacotherapy decision support for type-2 diabetes mellitus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294941/
https://www.ncbi.nlm.nih.gov/pubmed/33975376
http://dx.doi.org/10.1055/s-0041-1728757
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