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

NCT04928248 | Completed

• 1 other identifier: IRB_00134238
• Study Type: interventional
• Target: 25,915
• Locations: 1 country
• Sites: 1

Brief Summary

Diabetes is a significant medical problem in the United States and across the world. Despite significant progress in understanding how to better manage diabetes, there is oftentimes still uncertainty in the optimal management strategy for a specific patient. As a result, providers and patients must often use a trial-and-error approach to identify an effective treatment regimen. The project team has previously developed a Diabetes Dashboard that summarizes relevant patient information (e.g., medication history and recent hemoglobin A1c trend). This dashboard allows a clinician to select a target hemoglobin A1c level for the patient in 3 or 6 months, then compare and contrast different options for treatment, including weight loss and the use of different medication regimens. Included in this comparison are known benefits and side effects, as well as the likely chances of achieving the treatment target given the experience of past, similar patients. The Diabetes Dashboard is already available as an optional tab in the EHR system. The project team has also previously developed the Disease Manager App for evidence-based chronic disease management and health maintenance. The Disease Manger Application is fully integrated with the EHR, and it provides care guidance via individual chronic disease modules as well as a unified module that encompasses all relevant modules for chronic diseases and health maintenance. The initial modules that have been developed are for chronic obstructive pulmonary disease, hypertension, diabetes mellitus, and health maintenance. The objective of this research is to evaluate the Diabetes Dashboard integrated with the Disease Manager App. The Intervention consists of the diabetes module of the Disease Manager App, which incorporates content from the Diabetes Dashboard for pharmacotherapy prediction and provides a link to the Diabetes Dashboard.

Conditions

Diabetes Mellitus, Type 2; Diabetes Mellitus

Keywords

Digital Health Intervention; Clinical Decision Support

Outcome Measures

Primary Outcomes (1)

• Change in hemoglobin A1c (HbA1c) levels

  Each patient's HbA1c level will be estimated for day 15 of each month, calculated as follows. If a value exists for that date, use that. Otherwise, estimate the value on that date based on the values immediately before and after that date.

  Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period

Secondary Outcomes (1)

• Change in body mass index (BMI) levels

  Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period

Other Outcomes (1)

• Rate of use of the Disease Manager's diabetes module

  Through study completion, an average of 12 months for the intervention period

Study Arms (1)

Diabetes Dashboard integrated with Disease Manager App

EXPERIMENTAL

When patients are seen in clinics in this arm, the clinical providers will have access to the intervention (EHR-integrated Diabetes Dashboard that is integrated with the diabetes module of the Disease Manager App).

Other: Diabetes Dashboard integrated with Disease Manager App

Interventions

Diabetes Dashboard integrated with Disease Manager App OTHER

The Diabetes Dashboard is available as a tab in the electronic health record (EHR) system and enables clinicians to confirm relevant patient parameters, select treatment goals, and review likely outcomes from alternative treatment strategies through an interactive graphical user interface. The Diabetes Dashboard is integrated within the diabetes module of the EHR-integrated Disease Manager App, which uses key information from the Diabetes Dashboard and provides a link to the Diabetes Dashboard.

Diabetes Dashboard integrated with Disease Manager App

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• \>= 18 years old
• are being seen at a University of Utah primary care clinic
• has diabetes mellitus

You may not qualify if:

• None.
• Note that the primary study analyses will be on a subset of these patients. See the Detailed Description subsection in the Study Description section for details.

Sponsors & Collaborators

• University of Utah: lead
• Hitachi, Ltd.: collaborator

Study Sites (1)

University of Utah Health

Salt Lake City, Utah, 84132, United States

Location

Related Publications (1)

• Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, Turner KM, Sakaguchi FH, Weir C, Kramer H, Shields DE, Warner PB, Kukhareva P, Ban H, Kawamoto K. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods Inf Med. 2021 Jun;60(S 01):e32-e43. doi: 10.1055/s-0041-1728757. Epub 2021 May 11.

  PMID: 33975376 BACKGROUND

Related Links

• ReImagine EHR Website

MeSH Terms

Conditions

Diabetes Mellitus, Type 2; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Officials

• Kawamoto Kensaku, MD, PhD, MHS

  University of Utah

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Biomedical Informatics

Study Record Dates

• First Submitted: May 25, 2021
• First Posted: June 16, 2021
• Study Start: September 23, 2021
• Primary Completion: September 22, 2022
• Study Completion: November 1, 2022
• Last Updated: November 4, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)