DiabetesNCT02199769
Reducing Type 2 Diabetes Diagnostic Delays Using Decision Support
A study testing Clinical Decision Support in people with Diabetes and Prediabetes, run by University of Texas Southwestern Medical Center at 1 site in United States.
Trial summary
This study will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in the outpatient, adult primary care practices at UT Southwestern (two general internal medicine one family medicine and one geriatric practice). Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Diabetes, Prediabetes
- What is being tested?
- Clinical Decision Support
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 747 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Jul 2014to 1 Apr 2016
- 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 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Study Patients Included: will be those who are:
- an established patient of a study PCP;
- have no diagnosis of diabetes (encounter diagnoses, problem list, medical history);
- over 18 years of age
- have at least one RPG≥125mg/dL in the past 2 years
Who cannot join
- Study Patients Excluded: will be those who are:
- pregnant;
- under 18 years of age and
- Patients with an A1C<6.5% in the past 12 months, as this would indicate the appropriate follow-up was done
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Harnessing the Electronic Medical Record to Reduce Delays in the Diagnosis of Type 2 Diabetes: a Systems-based, Decision Support Approach
This study will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in the outpatient, adult primary care practices at UT Southwestern (two general internal medicine one family medicine and one geriatric practice). The investigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on electronic medical record (EMR) lab data to systematically identify all primary care patients with elevated random plasma glucose results (RPGs) who are at high risk of diabetes and thus in need of further testing. In a cluster-randomized trial, primary care providers will be randomized to either the intervention/DDT arm or usual care. Providers in the intervention arm will receive visit-based, EMR-enabled case identification and real-time decision support. Outcomes will be tracked at a patient level. All subjects will be followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes diagnosis, and time to diagnosis. The investigators hypothesize that the visit-based provider decision support will be superior to usual care.
Read the detailed description
The growing epidemic of type 2 diabetes affects over 8.3% of the US population and presents a major challenge to healthcare systems and public health. An additional 7 million people have undiagnosed diabetes and over 79 million have pre-diabetes, which if unrecognized and untreated can progress to full-blown diabetes. Although screening and diagnostic tests are routinely available, health systems struggle to diagnose patients with diabetes in a timely manner. In fact, clinical diagnosis lags 8-12 years behind the onset of glucose dysregulation, resulting in diagnostic delays and the presence of diabetes complications at the time of diagnosis. Among patients engaged in clinical care without a known diagnosis of diabetes, nearly all patients have random plasma glucose (RPG) data available which potentially provides valuable, early warning safety signals regarding the need for further diabetes testing. However, elevated glucose values are commonly unrecognized and over 60% of abnormal values are not followed-up with diabetes testing in a timely fashion. Opportunities exist to leverage existing data within electronic medical records (EMR) to identify patients in need of further diabetes testing and develop systems-based solutions to reduce: 1) failures in following-up abnormal glucose tests, 2) delays in diagnosing diabetes, and 3) frequency of missed diagnoses of diabetes.
This proposal will leverage the Epic EMR at the University of Texas Southwestern Medical Center (UTSW) to improve the detection and follow-up testing rates of abnormal glucose values in real-world practice.
The investigators will conduct a cluster randomized, pragmatic trial comparing the effectiveness of a clinical decision support strategy versus usual care to reduce failures in timely follow-up of abnormal RPGs.
The investigators will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in three outpatient, adult primary care practices at UTSW (two general internal medicine one family medicine and one geriatric practice). Primary care providers (PCPs) will be randomized to either the clinical decision support intervention or usual care. Providers in the clinical decision support/intervention arm will receive clinical decision support that identifies abnormal random glucose values and prompts providers to conduct diabetes screening. Outcomes will be tracked at the patient level and all subjects will be followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes diagnosis, and time to diagnosis. Data on study eligibility, patient clinical risk factors and sociodemographics, provider and visit characteristics, and outcomes will be ascertained using the comprehensive Epic EMR. The investigators hypothesize that the visit-based provider decision support will be superior to usual care.
- Conditions
- Diabetes, Prediabetes
- Being tested
- Clinical Decision Support
- Keywords
- Clinical decision support, Diabetes screening
- Medical subject headings
- Diabetes Mellitus, Prediabetic State
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Clinical Decision SupportOther
Investigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on EMR lab data to systematically identify all primary care patients with elevated RPGs who are at high risk of diabetes and in need of further diabetes testing/screening.
| Study group | Role | What they receive |
|---|---|---|
| Clinical Decision Support | Experimental | Visit-based, EMR-enabled case identification and real-time decision support to identify patients without diabetes who have a RBG>= 125mg/dL and no resulted diabetes screening. Receives: Other: Clinical Decision Support |
| Usual care | No intervention | Diabetes screening/testing and diagnosis per usual care at the discretion of the treating physician. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Resulted Diabetes Screening Test
The proportion of patients completing diabetes testing, defined by a resulted A1C or fasting plasma glucose (FPG) within 90 days of the first best practice alert (BPA) fire or the time that the alert would have fired in the control group.
Measured: 90 days
Secondary outcomes (5)
Ordered Diabetes Screening
Proportion of individuals that have diabetes screening test ordered after BPA fires or would have fired in clinical practice
Measured: 90 days
Time to diabetes testing
The time to ordered diabetes testing from the first alert fire or time when alert would have fired in usual care.
Measured: 12 months
Time to diabetes diagnosis
The time to diabetes diagnosis from first alert fire or when it would have fired in usual care.
Measured: 12 months
Pre-diabetes diagnosis
The proportion of patients diagnosed with pre-diabetes.
Measured: 90 days
Diabetes Diagnosis
proportion of patients meeting diabetes diagnostic criteria
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jul 2014 (estimated)
- Study start1 Jul 2014
- Primary completion1 Aug 2015
- Study completion1 Apr 2016
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DiabetesStudies of the same condition, open and under way.
- 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 747 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Texas Southwestern Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Michael Edward Bowen, University of Texas Southwestern Medical Center
- Investigators
- Michael E Bowen, MD, MPH, Principal investigator, University of Texas Southwestern Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02199769
- Sponsor's protocol ID
- STU 062013-058
- Status last verified
- Apr 2023
- First submitted
- 22 Jul 2014
- Last updated
- 26 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Harnessing the Electronic Medical Record to Reduce Delays in the Diagnosis of Type 2 Diabetes: a Systems-based, Decision Support Approach
- ClinicalTrials.gov ID
- NCT02199769
- Acronym / sponsor ID
- STU 062013-058
- Conditions
- Diabetes, Prediabetes
- Keywords
- Clinical decision support, Diabetes screening
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 747 participants (actual)
- Lead sponsor
- University of Texas Southwestern Medical CenterUniversity, hospital or other
- Study start
- 1 Jul 2014 (actual)
- Primary completion
- 1 Aug 2015 (actual)
- Study completion
- 1 Apr 2016 (actual)
- First posted
- 24 Jul 2014 (estimated)
- 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.
This study will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in the outpatient, adult primary care practices at UT Southwestern (two general internal medicine one family medicine and one geriatric practice). The investigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on electronic medical record (EMR) lab data to systematically identify all primary care patients with elevated random plasma glucose results (RPGs) who are at high risk of diabetes and thus in need of further testing. In a cluster-randomized trial, primary care providers will be randomized to either the intervention/DDT arm or usual care. Providers in the intervention arm will receive visit-based, EMR-enabled case identification and real-time decision support. Outcomes will be tracked at a patient level. All subjects will be followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes diagnosis, and time to diagnosis. The investigators hypothesize that the visit-based provider decision support will be superior to usual care.
Detailed description
The growing epidemic of type 2 diabetes affects over 8.3% of the US population and presents a major challenge to healthcare systems and public health. An additional 7 million people have undiagnosed diabetes and over 79 million have pre-diabetes, which if unrecognized and untreated can progress to full-blown diabetes. Although screening and diagnostic tests are routinely available, health systems struggle to diagnose patients with diabetes in a timely manner. In fact, clinical diagnosis lags 8-12 years behind the onset of glucose dysregulation, resulting in diagnostic delays and the presence of diabetes complications at the time of diagnosis. Among patients engaged in clinical care without a known diagnosis of diabetes, nearly all patients have random plasma glucose (RPG) data available which potentially provides valuable, early warning safety signals regarding the need for further diabetes testing. However, elevated glucose values are commonly unrecognized and over 60% of abnormal values are not followed-up with diabetes testing in a timely fashion. Opportunities exist to leverage existing data within electronic medical records (EMR) to identify patients in need of further diabetes testing and develop systems-based solutions to reduce: 1) failures in following-up abnormal glucose tests, 2) delays in diagnosing diabetes, and 3) frequency of missed diagnoses of diabetes.
This proposal will leverage the Epic EMR at the University of Texas Southwestern Medical Center (UTSW) to improve the detection and follow-up testing rates of abnormal glucose values in real-world practice.
The investigators will conduct a cluster randomized, pragmatic trial comparing the effectiveness of a clinical decision support strategy versus usual care to reduce failures in timely follow-up of abnormal RPGs.
The investigators will focus on the cohort of 20,000 established patients cared for by 31 attending physicians in three outpatient, adult primary care practices at UTSW (two general internal medicine one family medicine and one geriatric practice). Primary care providers (PCPs) will be randomized to either the clinical decision support intervention or usual care. Providers in the clinical decision support/intervention arm will receive clinical decision support that identifies abnormal random glucose values and prompts providers to conduct diabetes screening. Outcomes will be tracked at the patient level and all subjects will be followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes diagnosis, and time to diagnosis. Data on study eligibility, patient clinical risk factors and sociodemographics, provider and visit characteristics, and outcomes will be ascertained using the comprehensive Epic EMR. The investigators hypothesize that the visit-based provider decision support will be superior to usual care.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Clinical Decision Support | Experimental | Other: Clinical Decision SupportVisit-based, EMR-enabled case identification and real-time decision support to identify patients without diabetes who have a RBG>= 125mg/dL and no resulted diabetes screening. |
| Usual care | No intervention | –Diabetes screening/testing and diagnosis per usual care at the discretion of the treating physician. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Study Patients Included: will be those who are:
Key exclusion criteria (4 listed)
- Study Patients Excluded: will be those who are:
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 |
|---|---|---|
| Clinical Decision SupportInvestigators will develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on EMR lab data to systematically identify all primary care patients with elevated RPGs who are at high risk of diabetes and in need of further diabetes testing/screening. | Other | Clinical Decision Support |
Primary outcome
Resulted Diabetes Screening Test
Time frame: 90 days
The proportion of patients completing diabetes testing, defined by a resulted A1C or fasting plasma glucose (FPG) within 90 days of the first best practice alert (BPA) fire or the time that the alert would have fired in the control group.
5 secondary outcomes
Ordered Diabetes Screening
Time frame: 90 days
Time to diabetes testing
Time frame: 12 months
Time to diabetes diagnosis
Time frame: 12 months
Pre-diabetes diagnosis
Time frame: 90 days
Diabetes Diagnosis
Time frame: 90 days
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 26 Apr 2023.
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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.
Michael E Bowen, MD, MPH principal investigator
University of Texas Southwestern Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Clinical Decision Support in people with Diabetes and Prediabetes, run by University of Texas Southwestern Medical Center 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 5 inclusion and 4 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 26 Apr 2023. 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?
Resulted Diabetes Screening Test (time frame: 90 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 26 Apr 2023.