<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/NCT03826290</url>
  </required_header>
  <id_info>
    <org_study_id>UUtah_00109162</org_study_id>
    <nct_id>NCT03826290</nct_id>
  </id_info>
  <brief_title>Evaluation of a Dashboard for Diabetes Care Integrated With the Electronic Health Record</brief_title>
  <official_title>Evaluation of an EHR-Integrated Dashboard for Diabetes Care</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Utah</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Hitachi, Ltd.</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Utah</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Diabetes is a significant medical problem in the United States and across the world. Despite&#xD;
      significant progress in understanding how to better manage diabetes, there is oftentimes&#xD;
      still uncertainty in the optimal management strategy for a specific patient. As a result,&#xD;
      providers and patients must often use a trial-and-error approach to identify an effective&#xD;
      treatment regimen.&#xD;
&#xD;
      The objective of this research is to evaluate a diabetes dashboard integrated with the&#xD;
      electronic health record (EHR) that has been developed as a collaborative project between the&#xD;
      University of Utah and Hitachi, Ltd. This dashboard tool provides a graphical overview of the&#xD;
      patient's relevant data parameters as well as information on the impact of different&#xD;
      treatment options on previous patients with similar characteristics. The different treatment&#xD;
      options compare the predicted impact of relevant medication regimens as well as weight loss.&#xD;
      Primary care clinics are randomized to either an intervention condition where the tool is&#xD;
      available or to a control condition where the tool is not yet available. Patients' hemoglobin&#xD;
      A1c levels (a measure of diabetes control) are the main outcome variable. Other secondary&#xD;
      analyses will also be conducted. Use of the tool will be encouraged but optional. Following&#xD;
      any suggestions made in the tool will also be optional and up to the discretion of the&#xD;
      clinician.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      This study is a pragmatic clinic-randomized controlled trial of a diabetes dashboard&#xD;
      integrated with the electronic health record (EHR). The diabetes dashboard is available as a&#xD;
      tab in the EHR and enables clinicians to confirm relevant patient parameters, select&#xD;
      treatment goals, and review likely outcomes from alternative treatment strategies through an&#xD;
      interactive graphical user interface. In the review process, it enables providers and&#xD;
      patients to compare up to three potential therapies side-by side including weight-loss in&#xD;
      terms of a) personalized-predicted probability of achieving treatment goals; b) general&#xD;
      potential risks, benefits, and medication costs; and c) relevant financial information&#xD;
      specific to the patient's insurance. The personalized prediction is performed by a predictive&#xD;
      model developed by analyzing a data set of primary care clinic patients with diabetes&#xD;
      mellitus. The diabetes dashboard is seamlessly integrated with the EHR using an&#xD;
      interoperability standard known as SMART on FHIR (short for Substitutable Medical Apps&#xD;
      Reusable Technologies on Fast Healthcare Interoperability Resources).&#xD;
&#xD;
      The study is being conducted at University of Utah primary care clinics. In the intervention&#xD;
      group clinics, providers will be introduced to the tool and supported using targeted&#xD;
      implementation techniques including education feedback and tailored facilitation. Iterative&#xD;
      enhancements will be made to the tool if warranted based on the results of a formative&#xD;
      evaluation during the 1-year trial. Use of the tool and associated suggestions will be&#xD;
      optional and up to the discretion of the clinician. When patients are seen at clinics&#xD;
      randomized to the control arm, clinical providers will not have access to the tool. Following&#xD;
      introduction of the tool across intervention clinics, a 1-year trial will be conducted. Use&#xD;
      of the tool will be encouraged and supported through targeted implementation strategies. Use&#xD;
      of the tool will be regularly monitored, and a mixed-methods evaluation will be conducted of&#xD;
      the tool and its impact. The primary outcome measure will be hemoglobin A1c (HbA1c) levels,&#xD;
      which are an important physiological marker of diabetes control. Secondary measures will&#xD;
      include body mass index (BMI) and the cost of diabetes medications prescribed. Other measures&#xD;
      will include usage of the tool and clinical users' opinions of the tool.&#xD;
&#xD;
      The evaluation period will start once all intervention clinics have been educated/trained on&#xD;
      use of the tool. The primary study analyses will be limited to adult patients who were seen&#xD;
      at least twice in the intervention or control clinics during the evaluation period for office&#xD;
      visits with a visit diagnosis of diabetes mellitus, who are known to have diabetes mellitus&#xD;
      (but not type-1 diabetes mellitus), who had at least one HbA1c of &gt;= 7.5% during the&#xD;
      evaluation period, and who are not already on maximal diabetes therapy (as defined by the use&#xD;
      of short-acting insulin) at the start of the study. Secondary study analyses will be&#xD;
      conducted on patient subsets, including a per protocol analysis of cases where the tool was&#xD;
      used.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">January 1, 2019</start_date>
  <completion_date type="Actual">July 1, 2020</completion_date>
  <primary_completion_date type="Actual">January 1, 2020</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>
    <intervention_model_description>Clinic randomized trial</intervention_model_description>
    <primary_purpose>Treatment</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Per-patient change in Hemoglobin A1c (HbA1c) levels from beginning to end of evaluation period</measure>
    <time_frame>Assessed through study completion (estimated to be 1.5 years), for Day 1 and Day 365</time_frame>
    <description>Each patient's HbA1c level will be estimated for the beginning and end of the evaluation period. For each of these time points, the value at that time point will be estimated as follows. If a value exists for that date, use that. Otherwise, for the beginning date, first take the earliest value within the evaluation period (anchor value). If there are no values before the evaluation period, then use the anchor value. Otherwise, take the latest value before the evaluation period, and interpolate with the anchor value to estimate the value. For the value on the end date, first take the latest value within the evaluation period (anchor value). If there are no values after the evaluation period, then use the anchor value. Otherwise, take the earliest value after the evaluation period, and interpolate with the anchor value to estimate the value. Finally, calculate the change in values by comparing the end value to the beginning value.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Per-patient change in body mass index (BMI) from beginning to end of evaluation period</measure>
    <time_frame>Assessed through study completion (estimated to be 1.5 years), for Day 1 and Day 365</time_frame>
    <description>The beginning and end values will be estimated using the same approach used for the primary outcome measure.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Cost of diabetes medications prescribed</measure>
    <time_frame>Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365</time_frame>
    <description>The cost of diabetes medications prescribed will be estimated using National Average Drug Acquisition Costs</description>
  </secondary_outcome>
  <other_outcome>
    <measure>Use of the diabetes dashboard</measure>
    <time_frame>Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365</time_frame>
    <description>Providers' use of the diabetes dashboard will be measured. We will also seek to model patient, provider, and context predictors of tool usage.</description>
  </other_outcome>
  <other_outcome>
    <measure>User opinions of the diabetes dashboard</measure>
    <time_frame>Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365</time_frame>
    <description>User opinions of the diabetes dashboard will be solicited during the study to support the implementation and make adjustments to the dashboard as needed. Towards the end of the evaluation period, formal evaluations will be conducted of user opinions of the diabetes dashboard, including through surveys such as the System Usability Scale, user interviews, and focus groups as appropriate.</description>
  </other_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">13155</enrollment>
  <condition>Diabetes Mellitus</condition>
  <condition>Diabetes Mellitus, Type 2</condition>
  <arm_group>
    <arm_group_label>Intervention arm</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>When patients are seen in clinics in this arm, the clinical providers will have access to the intervention (EHR-integrated diabetes dashboard).</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control arm</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>When patients are seen in clinics in this arm, the clinical providers will not have access to the intervention (EHR-integrated diabetes dashboard). The providers will have access to the usual decision support tools and information sources.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>EHR-integrated diabetes dashboard</intervention_name>
    <description>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.</description>
    <arm_group_label>Intervention arm</arm_group_label>
    <other_name>Pharmacotherapy decision support system (PDSS)</other_name>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  &gt;= 18 years old&#xD;
&#xD;
          -  Are being seen at a University of Utah primary care clinic&#xD;
&#xD;
          -  Has diabetes mellitus&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  None&#xD;
&#xD;
        Note that the primary study analyses will be on a subset of these patients. See the&#xD;
        Detailed Description subsection in the Study Description section for details.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Kensaku Kawamoto, MD, PhD, MHS</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Utah</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of Utah Health</name>
      <address>
        <city>Salt Lake City</city>
        <state>Utah</state>
        <zip>84132</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <citation>American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004 Jan;27 Suppl 1:S5-S10. doi: 10.2337/diacare.27.2007.s5. No abstract available.</citation>
    <PMID>14693921</PMID>
  </reference>
  <reference>
    <citation>American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013 Apr;36(4):1033-46. doi: 10.2337/dc12-2625. Epub 2013 Mar 6.</citation>
    <PMID>23468086</PMID>
  </reference>
  <reference>
    <citation>American Diabetes Association. 7. Approaches to Glycemic Treatment. Diabetes Care. 2016 Jan;39 Suppl 1:S52-9. doi: 10.2337/dc16-S010. No abstract available.</citation>
    <PMID>26696682</PMID>
  </reference>
  <verification_date>April 2021</verification_date>
  <study_first_submitted>January 24, 2019</study_first_submitted>
  <study_first_submitted_qc>January 30, 2019</study_first_submitted_qc>
  <study_first_posted type="Actual">February 1, 2019</study_first_posted>
  <last_update_submitted>April 26, 2021</last_update_submitted>
  <last_update_submitted_qc>April 26, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">April 27, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University of Utah</investigator_affiliation>
    <investigator_full_name>Kensaku Kawamoto, MD, PhD, MHS</investigator_full_name>
    <investigator_title>Associate Professor, Department of Biomedical Informatics</investigator_title>
  </responsible_party>
  <keyword>Decision Support Systems, Clinical</keyword>
  <keyword>Machine Learning</keyword>
  <keyword>Data Visualization</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Diabetes Mellitus</mesh_term>
    <mesh_term>Diabetes Mellitus, Type 2</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- 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/NCT03826290
org study id: UUtah_00109162
nct id: NCT03826290
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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 objective of this research is to evaluate a diabetes dashboard integrated with the
      electronic health record (EHR) that has been developed as a collaborative project between the
      University of Utah and Hitachi, Ltd. This dashboard tool provides a graphical overview of the
      patient's relevant data parameters as well as information on the impact of different
      treatment options on previous patients with similar characteristics. The different treatment
      options compare the predicted impact of relevant medication regimens as well as weight loss.
      Primary care clinics are randomized to either an intervention condition where the tool is
      available or to a control condition where the tool is not yet available. Patients' hemoglobin
      A1c levels (a measure of diabetes control) are the main outcome variable. Other secondary
      analyses will also be conducted. Use of the tool will be encouraged but optional. Following
      any suggestions made in the tool will also be optional and up to the discretion of the
      clinician.
This study is a pragmatic clinic-randomized controlled trial of a diabetes dashboard
      integrated with the electronic health record (EHR). The diabetes dashboard is available as a
      tab in the EHR 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. In the review process, it 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 a data set of primary care clinic patients with diabetes
      mellitus. The diabetes dashboard is 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 the intervention
      group clinics, providers will be introduced to the tool and supported using targeted
      implementation techniques including education feedback and tailored facilitation. Iterative
      enhancements will be made to the tool if warranted based on the results of a formative
      evaluation during the 1-year trial. Use of the tool and associated suggestions will be
      optional and up to the discretion of the clinician. When patients are seen at clinics
      randomized to the control arm, clinical providers will not have access to the tool. Following
      introduction of the tool across intervention clinics, a 1-year trial will be conducted. Use
      of the tool will be encouraged and supported through targeted implementation strategies. 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 evaluation period will start once all intervention clinics have been educated/trained on
      use of the tool. The primary study analyses will be limited to adult patients who were seen
      at least twice in the intervention or control 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.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: Clinic randomized trial
primary purpose: Treatment
masking: None (Open Label)
measure: Per-patient change in Hemoglobin A1c (HbA1c) levels from beginning to end of evaluation period
time frame: Assessed through study completion (estimated to be 1.5 years), for Day 1 and Day 365
description: Each patient's HbA1c level will be estimated for the beginning and end of the evaluation period. For each of these time points, the value at that time point will be estimated as follows. If a value exists for that date, use that. Otherwise, for the beginning date, first take the earliest value within the evaluation period (anchor value). If there are no values before the evaluation period, then use the anchor value. Otherwise, take the latest value before the evaluation period, and interpolate with the anchor value to estimate the value. For the value on the end date, first take the latest value within the evaluation period (anchor value). If there are no values after the evaluation period, then use the anchor value. Otherwise, take the earliest value after the evaluation period, and interpolate with the anchor value to estimate the value. Finally, calculate the change in values by comparing the end value to the beginning value.
measure: Per-patient change in body mass index (BMI) from beginning to end of evaluation period
time frame: Assessed through study completion (estimated to be 1.5 years), for Day 1 and Day 365
description: The beginning and end values will be estimated using the same approach used for the primary outcome measure.
measure: Cost of diabetes medications prescribed
time frame: Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365
description: The cost of diabetes medications prescribed will be estimated using National Average Drug Acquisition Costs
measure: Use of the diabetes dashboard
time frame: Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365
description: Providers' use of the diabetes dashboard will be measured. We will also seek to model patient, provider, and context predictors of tool usage.
measure: User opinions of the diabetes dashboard
time frame: Assessed through study completion (estimated to be 1.5 years), for Day 1 through Day 365
description: User opinions of the diabetes dashboard will be solicited during the study to support the implementation and make adjustments to the dashboard as needed. Towards the end of the evaluation period, formal evaluations will be conducted of user opinions of the diabetes dashboard, including through surveys such as the System Usability Scale, user interviews, and focus groups as appropriate.
arm group label: Intervention arm
arm group type: Experimental
description: When patients are seen in clinics in this arm, the clinical providers will have access to the intervention (EHR-integrated diabetes dashboard).
arm group label: Control arm
arm group type: No Intervention
description: When patients are seen in clinics in this arm, the clinical providers will not have access to the intervention (EHR-integrated diabetes dashboard). The providers will have access to the usual decision support tools and information sources.
intervention type: Other
intervention name: EHR-integrated diabetes dashboard
description: 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.
arm group label: Intervention arm
other name: Pharmacotherapy decision support system (PDSS)
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Kensaku Kawamoto, MD, PhD, MHS
role: Principal Investigator
affiliation: University of Utah
facility: 
country: United States
citation: American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004 Jan;27 Suppl 1:S5-S10. doi: 10.2337/diacare.27.2007.s5. No abstract available.
PMID: 14693921
citation: American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013 Apr;36(4):1033-46. doi: 10.2337/dc12-2625. Epub 2013 Mar 6.
PMID: 23468086
citation: American Diabetes Association. 7. Approaches to Glycemic Treatment. Diabetes Care. 2016 Jan;39 Suppl 1:S52-9. doi: 10.2337/dc16-S010. No abstract available.
PMID: 26696682
responsible party type: Principal Investigator
investigator affiliation: University of Utah
investigator full name: Kensaku Kawamoto, MD, PhD, MHS
investigator title: Associate Professor, Department of Biomedical Informatics
mesh term: Diabetes Mellitus
mesh term: Diabetes Mellitus, Type 2
sharing ipd: No