Diabetes Mellitus, Type 2 Clinical Trial
Official title:
Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care
Verified date | November 2022 |
Source | University of Utah |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
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.
Status | Completed |
Enrollment | 25915 |
Est. completion date | November 1, 2022 |
Est. primary completion date | September 22, 2022 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: 1. >= 18 years old 2. are being seen at a University of Utah primary care clinic 3. has diabetes mellitus Exclusion Criteria: 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. |
Country | Name | City | State |
---|---|---|---|
United States | University of Utah Health | Salt Lake City | Utah |
Lead Sponsor | Collaborator |
---|---|
University of Utah | Hitachi, Ltd. |
United States,
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Other | Rate of use of the Disease Manager's diabetes module | The rate of use of the Disease Manager's diabetes module will be measured. The usage will be measured through system logs and data from the enterprise data warehouse. | Through study completion, an average of 12 months for the intervention period | |
Primary | 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 | Change in body mass index (BMI) levels | The BMI values will be estimated in a manner similar to HbA1c estimation. | Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period |
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