Evidence map›Paper›PMID 33975376›Full record

ArticleMethods of information in medicine2021

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 and 5 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Methods of information in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04928248 (Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care), which is not on this map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04928248 nacompletednot on this map

Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care

TypeinterventionalSponsorUniversity of UtahRan2021 to 2022Enrolled25,915ConditionsDiabetes Mellitus, Type 2, Diabetes MellitusArmsDiabetes Dashboard integrated with Disease Manager App
3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Shinji TarumiDepartment of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan.
Wataru TakeuchiDepartment of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan.
George ChalkidisDepartment of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan.
Salvador Rodriguez-LoyaDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Junichi KuwataDepartment of Product Design, Center for Social Innovation, Hitachi Ltd., Kokubunji, Tokyo, Japan.
Michael FlynnDepartments of Internal Medicine and Pediatrics, University of Utah, Salt Lake City, Utah, United States.
Kyle M TurnerDepartment of Pharmacotherapy, University of Utah, Salt Lake City, Utah, United States.
Farrant H SakaguchiDepartment of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, United States.
Charlene WeirDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Heidi KramerDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
David E ShieldsDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Phillip B WarnerDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Polina KukharevaDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Hideyuki BanDepartment of Media Intelligent Processing Research, Center for Technology Innovation Artificial Intelligence, Hitachi Ltd., Kokubunji, Tokyo, Japan.
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesArtificial 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.

methodsUsing 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.

resultsThe 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.

conclusionA 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.

Indexed as

Decision Support Systems, ClinicalDiabetes Mellitus, Type 2Artificial IntelligenceChronic DiseaseElectronic Health RecordsHumans

Identifiers

PMID33975376
PMCPMC8294941

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.