<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT02199769</url>
  </required_header>
  <id_info>
    <org_study_id>STU 062013-058</org_study_id>
    <nct_id>NCT02199769</nct_id>
  </id_info>
  <brief_title>Reducing Type 2 Diabetes Diagnostic Delays Using Decision Support</brief_title>
  <official_title>Harnessing the Electronic Medical Record to Reduce Delays in the Diagnosis of Type 2 Diabetes: a Systems-based, Decision Support Approach</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Texas Southwestern Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Texas Southwestern Medical Center</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
  </oversight_info>
  <brief_summary>
    <textblock>
      This study will focus on the cohort of 20,000 established patients cared for by 31 attending&#xD;
      physicians in the outpatient, adult primary care practices at UT Southwestern (two general&#xD;
      internal medicine one family medicine and one geriatric practice). The investigators will&#xD;
      develop and implement an automated Diabetes Detection Tool (DDT) that does data mining on&#xD;
      electronic medical record (EMR) lab data to systematically identify all primary care patients&#xD;
      with elevated random plasma glucose results (RPGs) who are at high risk of diabetes and thus&#xD;
      in need of further testing. In a cluster-randomized trial, primary care providers will be&#xD;
      randomized to either the intervention/DDT arm or usual care. Providers in the intervention&#xD;
      arm will receive visit-based, EMR-enabled case identification and real-time decision support.&#xD;
      Outcomes will be tracked at a patient level. All subjects will be followed for 12 months to&#xD;
      assess rates of follow-up diabetes testing, time to testing, rates of subsequent diabetes&#xD;
      diagnosis, and time to diagnosis. The investigators hypothesize that the visit-based provider&#xD;
      decision support will be superior to usual care.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The growing epidemic of type 2 diabetes affects over 8.3% of the US population and presents a&#xD;
      major challenge to healthcare systems and public health. An additional 7 million people have&#xD;
      undiagnosed diabetes and over 79 million have pre-diabetes, which if unrecognized and&#xD;
      untreated can progress to full-blown diabetes. Although screening and diagnostic tests are&#xD;
      routinely available, health systems struggle to diagnose patients with diabetes in a timely&#xD;
      manner. In fact, clinical diagnosis lags 8-12 years behind the onset of glucose&#xD;
      dysregulation, resulting in diagnostic delays and the presence of diabetes complications at&#xD;
      the time of diagnosis. Among patients engaged in clinical care without a known diagnosis of&#xD;
      diabetes, nearly all patients have random plasma glucose (RPG) data available which&#xD;
      potentially provides valuable, early warning safety signals regarding the need for further&#xD;
      diabetes testing. However, elevated glucose values are commonly unrecognized and over 60% of&#xD;
      abnormal values are not followed-up with diabetes testing in a timely fashion. Opportunities&#xD;
      exist to leverage existing data within electronic medical records (EMR) to identify patients&#xD;
      in need of further diabetes testing and develop systems-based solutions to reduce: 1)&#xD;
      failures in following-up abnormal glucose tests, 2) delays in diagnosing diabetes, and 3)&#xD;
      frequency of missed diagnoses of diabetes.&#xD;
&#xD;
      This proposal will leverage the Epic EMR at the University of Texas Southwestern Medical&#xD;
      Center (UTSW) to improve the detection and follow-up testing rates of abnormal glucose values&#xD;
      in real-world practice.&#xD;
&#xD;
      The investigators will conduct a cluster randomized, pragmatic trial comparing the&#xD;
      effectiveness of a clinical decision support strategy versus usual care to reduce failures in&#xD;
      timely follow-up of abnormal RPGs.&#xD;
&#xD;
      The investigators will focus on the cohort of 20,000 established patients cared for by 31&#xD;
      attending physicians in three outpatient, adult primary care practices at UTSW (two general&#xD;
      internal medicine one family medicine and one geriatric practice). Primary care providers&#xD;
      (PCPs) will be randomized to either the clinical decision support intervention or usual care.&#xD;
      Providers in the clinical decision support/intervention arm will receive clinical decision&#xD;
      support that identifies abnormal random glucose values and prompts providers to conduct&#xD;
      diabetes screening. Outcomes will be tracked at the patient level and all subjects will be&#xD;
      followed for 12 months to assess rates of follow-up diabetes testing, time to testing, rates&#xD;
      of subsequent diabetes diagnosis, and time to diagnosis. Data on study eligibility, patient&#xD;
      clinical risk factors and sociodemographics, provider and visit characteristics, and outcomes&#xD;
      will be ascertained using the comprehensive Epic EMR. The investigators hypothesize that the&#xD;
      visit-based provider decision support will be superior to usual care.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">July 1, 2014</start_date>
  <completion_date type="Actual">April 1, 2016</completion_date>
  <primary_completion_date type="Actual">August 1, 2015</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Resulted Diabetes Screening Test</measure>
    <time_frame>90 days</time_frame>
    <description>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.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Ordered Diabetes Screening</measure>
    <time_frame>90 days</time_frame>
    <description>Proportion of individuals that have diabetes screening test ordered after BPA fires or would have fired in clinical practice</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time to diabetes testing</measure>
    <time_frame>12 months</time_frame>
    <description>The time to ordered diabetes testing from the first alert fire or time when alert would have fired in usual care.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Time to diabetes diagnosis</measure>
    <time_frame>12 months</time_frame>
    <description>The time to diabetes diagnosis from first alert fire or when it would have fired in usual care.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Pre-diabetes diagnosis</measure>
    <time_frame>90 days</time_frame>
    <description>The proportion of patients diagnosed with pre-diabetes.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Diabetes Diagnosis</measure>
    <time_frame>90 days</time_frame>
    <description>proportion of patients meeting diabetes diagnostic criteria</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">747</enrollment>
  <condition>Diabetes</condition>
  <condition>Prediabetes</condition>
  <arm_group>
    <arm_group_label>Clinical Decision Support</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Visit-based, EMR-enabled case identification and real-time decision support to identify patients without diabetes who have a RBG&gt;= 125mg/dL and no resulted diabetes screening.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Usual care</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Diabetes screening/testing and diagnosis per usual care at the discretion of the treating physician.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Clinical Decision Support</intervention_name>
    <description>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.</description>
    <arm_group_label>Clinical Decision Support</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Study Patients Included: will be those who are:&#xD;
&#xD;
               1. an established patient of a study PCP;&#xD;
&#xD;
               2. have no diagnosis of diabetes (encounter diagnoses, problem list, medical&#xD;
                  history);&#xD;
&#xD;
               3. over 18 years of age&#xD;
&#xD;
               4. have at least one RPG≥125mg/dL in the past 2 years&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Study Patients Excluded: will be those who are:&#xD;
&#xD;
               1. pregnant;&#xD;
&#xD;
               2. under 18 years of age and&#xD;
&#xD;
               3. Patients with an A1C&lt;6.5% in the past 12 months, as this would indicate the&#xD;
                  appropriate follow-up was done&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>100 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Michael E Bowen, MD, MPH</last_name>
    <role>Principal Investigator</role>
    <affiliation>UT Southwestern Medical Center</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>UT Southwestern Medical Center</name>
      <address>
        <city>Dallas</city>
        <state>Texas</state>
        <zip>75390</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>April 2023</verification_date>
  <study_first_submitted>July 22, 2014</study_first_submitted>
  <study_first_submitted_qc>July 23, 2014</study_first_submitted_qc>
  <study_first_posted type="Estimate">July 24, 2014</study_first_posted>
  <last_update_submitted>April 24, 2023</last_update_submitted>
  <last_update_submitted_qc>April 24, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">April 26, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University of Texas Southwestern Medical Center</investigator_affiliation>
    <investigator_full_name>Michael Edward Bowen</investigator_full_name>
    <investigator_title>Assistant Professor</investigator_title>
  </responsible_party>
  <keyword>Clinical decision support</keyword>
  <keyword>Diabetes screening</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes Mellitus</mesh_term>
  </condition_browse>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT02199769
org study id: STU 062013-058
nct id: NCT02199769
lead sponsor: 
has dmc: No
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.
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.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
masking: None (Open Label)
measure: Resulted Diabetes Screening Test
time frame: 90 days
description: 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.
measure: Ordered Diabetes Screening
time frame: 90 days
description: Proportion of individuals that have diabetes screening test ordered after BPA fires or would have fired in clinical practice
measure: Time to diabetes testing
time frame: 12 months
description: The time to ordered diabetes testing from the first alert fire or time when alert would have fired in usual care.
measure: Time to diabetes diagnosis
time frame: 12 months
description: The time to diabetes diagnosis from first alert fire or when it would have fired in usual care.
measure: Pre-diabetes diagnosis
time frame: 90 days
description: The proportion of patients diagnosed with pre-diabetes.
measure: Diabetes Diagnosis
time frame: 90 days
description: proportion of patients meeting diabetes diagnostic criteria
arm group label: Clinical Decision Support
arm group type: Experimental
description: 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.
arm group label: Usual care
arm group type: No Intervention
description: Diabetes screening/testing and diagnosis per usual care at the discretion of the treating physician.
intervention type: Other
intervention name: Clinical Decision Support
description: 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.
arm group label: Clinical Decision Support
criteria: 
gender: All
minimum age: 18 Years
maximum age: 100 Years
healthy volunteers: No
last name: Michael E Bowen, MD, MPH
role: Principal Investigator
affiliation: UT Southwestern Medical Center
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: University of Texas Southwestern Medical Center
investigator full name: Michael Edward Bowen
investigator title: Assistant Professor
mesh term: Diabetes Mellitus