Diabetes Mellitus, Type 2NCT04928248
Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care
A study testing Diabetes Dashboard integrated with Disease Manager App in people with Diabetes Mellitus, Type 2 and Diabetes Mellitus, run by University of Utah at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetes Mellitus, Type 2, Diabetes Mellitus
- What is being tested?
- Diabetes Dashboard integrated with Disease Manager App
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 25,915 participants
- Where
- 1 siteUnited States
- Study dates
- 23 Sep 2021to 1 Nov 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- >= 18 years old
- are being seen at a University of Utah primary care clinic
- has diabetes mellitus
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Utah Health
Salt Lake City, Utah, 84132
About this study
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.
Read more
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.
Read the detailed description
This study is a pragmatic pre-post trial of the Diabetes Dashboard integrated with the Disease Manager App. The Disease Manager App is available as a tab in the EHR and enables clinicians to confirm relevant patient parameters. A link to the Diabetes Dashboard will be available from the Disease Manager App diabetes module. In the Diabetes Dashboard, providers can select treatment goals and review likely outcomes from alternative treatment strategies through an interactive graphical user interface. In the review process, the Diabetes Dashboard enables providers and patients to compare up to three potential therapies side-by side including weight-loss in terms of a) personalized, predicted probability of achieving treatment goals; b) general potential risks, benefits, and medication costs; and c) relevant financial information specific to the patient's insurance. The personalized prediction is performed by a predictive model developed by analyzing data sets of patients with diabetes mellitus. The Disease Manager App and the Diabetes Dashboard are seamlessly integrated with the EHR using an interoperability standard known as SMART on FHIR (short for Substitutable Medical Apps Reusable Technologies on Fast Healthcare Interoperability Resources).
The study is being conducted at University of Utah primary care clinics. In all primary care clinics, providers will be provided with access to the Diabetes Dashboard integrated with the Disease Manager App. Iterative enhancements will be made to the tool if warranted based on the results of a formative evaluation during the 1-year pragmatic implementation study. Use of the tool and associated suggestions will be optional and up to the discretion of the clinician. Use of the tool will be regularly monitored, and a mixed-methods evaluation will be conducted of the tool and its impact.
The primary outcome measure will be hemoglobin A1c (HbA1c) levels, which are an important physiological marker of diabetes control. Secondary measures will include body mass index (BMI) and the cost of diabetes medications prescribed. Other measures will include usage of the tool and clinical users' opinions of the tool.
The primary study analyses will be limited to adult patients who were seen at least twice in the primary care clinics during the evaluation period for office visits with a visit diagnosis of diabetes mellitus, who are known to have diabetes mellitus (but not type-1 diabetes mellitus), who had at least one HbA1c of >= 7.5% during the evaluation period, and who are not already on maximal diabetes therapy (as defined by the use of short-acting insulin) at the start of the study. Secondary study analyses will be conducted on patient subsets, including a per protocol analysis of cases where the tool was used.
- Conditions
- Diabetes Mellitus, Type 2, Diabetes Mellitus
- Being tested
- Diabetes Dashboard integrated with Disease Manager App
- Keywords
- Digital Health Intervention, Clinical Decision Support
- Medical subject headings
- Diabetes Mellitus, Type 2, Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diabetes Dashboard integrated with Disease Manager AppOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Other: Diabetes Dashboard integrated with Disease Manager App |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
The BMI values will be estimated in a manner similar to HbA1c estimation.
Measured: Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2021
- Study start23 Sep 2021
- Primary completion22 Sep 2022
- Study completion1 Nov 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus, Type 2Studies of the same condition, open and under way.
- More trials run by University of UtahThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 25,915 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Utah
- Sponsor type
- University, hospital or other
- Collaborators
- Hitachi, Ltd.
- Responsible party
- Principal investigator, Kensaku Kawamoto, MD, PhD, MHS, University of Utah
- Investigators
- Kawamoto Kensaku, MD, PhD, MHS, Principal investigator, University of Utah
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04928248
- Sponsor's protocol ID
- IRB_00134238
- Status last verified
- Nov 2022
- First submitted
- 25 May 2021
- Last updated
- 4 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 33975376
Study at a glance
From the registry record, last updated by the sponsor on 4 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Design, Implementation and Evaluation of Scalable Decision Support for Diabetes Care
- ClinicalTrials.gov ID
- NCT04928248
- Acronym / sponsor ID
- IRB_00134238
- Conditions
- Diabetes Mellitus, Type 2, Diabetes Mellitus
- Keywords
- Digital Health Intervention, Clinical Decision Support
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 25,915 participants (actual)
- Lead sponsor
- University of UtahUniversity, hospital or other
- Collaborators
- Hitachi, Ltd.
- Study start
- 23 Sep 2021 (actual)
- Primary completion
- 22 Sep 2022 (actual)
- Study completion
- 1 Nov 2022 (actual)
- First posted
- 16 Jun 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This study is a pragmatic pre-post trial of the Diabetes Dashboard integrated with the Disease Manager App. The Disease Manager App is available as a tab in the EHR and enables clinicians to confirm relevant patient parameters. A link to the Diabetes Dashboard will be available from the Disease Manager App diabetes module. In the Diabetes Dashboard, providers can select treatment goals and review likely outcomes from alternative treatment strategies through an interactive graphical user interface. In the review process, the Diabetes Dashboard enables providers and patients to compare up to three potential therapies side-by side including weight-loss in terms of a) personalized, predicted probability of achieving treatment goals; b) general potential risks, benefits, and medication costs; and c) relevant financial information specific to the patient's insurance. The personalized prediction is performed by a predictive model developed by analyzing data sets of patients with diabetes mellitus. The Disease Manager App and the Diabetes Dashboard are seamlessly integrated with the EHR using an interoperability standard known as SMART on FHIR (short for Substitutable Medical Apps Reusable Technologies on Fast Healthcare Interoperability Resources).
The study is being conducted at University of Utah primary care clinics. In all primary care clinics, providers will be provided with access to the Diabetes Dashboard integrated with the Disease Manager App. Iterative enhancements will be made to the tool if warranted based on the results of a formative evaluation during the 1-year pragmatic implementation study. Use of the tool and associated suggestions will be optional and up to the discretion of the clinician. Use of the tool will be regularly monitored, and a mixed-methods evaluation will be conducted of the tool and its impact.
The primary outcome measure will be hemoglobin A1c (HbA1c) levels, which are an important physiological marker of diabetes control. Secondary measures will include body mass index (BMI) and the cost of diabetes medications prescribed. Other measures will include usage of the tool and clinical users' opinions of the tool.
The primary study analyses will be limited to adult patients who were seen at least twice in the primary care clinics during the evaluation period for office visits with a visit diagnosis of diabetes mellitus, who are known to have diabetes mellitus (but not type-1 diabetes mellitus), who had at least one HbA1c of >= 7.5% during the evaluation period, and who are not already on maximal diabetes therapy (as defined by the use of short-acting insulin) at the start of the study. Secondary study analyses will be conducted on patient subsets, including a per protocol analysis of cases where the tool was used.
Study design
- Primary purpose
- Treatment
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Diabetes Dashboard integrated with Disease Manager App | Experimental | Other: Diabetes Dashboard integrated with Disease Manager AppWhen 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- >= 18 years old
- are being seen at a University of Utah primary care clinic
- has diabetes mellitus
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Diabetes Dashboard integrated with Disease Manager AppThe 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. | Other | Diabetes Dashboard integrated with Disease Manager App |
Primary outcome
Change in hemoglobin A1c (HbA1c) levels
Time frame: Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period
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.
1 secondary outcome
Change in body mass index (BMI) levels
Time frame: Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Nov 2022.
- Background: PubMed 33975376Tarumi 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.
- ReImagine EHR WebsiteLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Kawamoto Kensaku, MD, PhD, MHS principal investigator
University of Utah
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Diabetes Dashboard integrated with Disease Manager App in people with Diabetes Mellitus, Type 2 and Diabetes Mellitus, run by University of Utah at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 4 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in hemoglobin A1c (HbA1c) levels (time frame: Through study completion, an average of 12 months for the intervention period and 12 months for the baseline period). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04928248, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Nov 2022.