This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (34) Decision Support for Health Professionals (1133) Artificial Intelligence (1192) Clinical Information and Decision Making (1320)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45032, first published .
An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study

An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study

An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study

Authors of this article:

Winston R Liaw1 Author Orcid Image ;   Yessenia Ramos Silva2 Author Orcid Image ;   Erica G Soltero3 Author Orcid Image ;   Alex Krist4 Author Orcid Image ;   Angela L Stotts5 Author Orcid Image
Article Authors Cited by Tweetations (2) Metrics

Winston R Liaw   1 , MPH, MD ;   Yessenia Ramos Silva   2 , BA ;   Erica G Soltero   3 , PhD ;   Alex Krist   4 , MPH, MD ;   Angela L Stotts   5 , PhD

1 Department of Health Systems and Population Health Sciences, Tilman J Fertitta Family College of Medicine, University of Houston, Houston, TX, United States

2 Rice University, Houston, TX, United States

3 USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States

4 Department of Family Medicine & Population Health, Virginia Commonwealth University School of Medicine, Richmond, VA, United States

5 Department of Family & Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States

Corresponding Author:

  • Winston R Liaw, MPH, MD
  • Department of Health Systems and Population Health Sciences
  • Tilman J Fertitta Family College of Medicine
  • University of Houston
  • 5055 Medical Circle
  • Houston, TX, 77204
  • United States
  • Phone: 1 713 743 9862
  • Email: winstonrliaw@gmail.com