xml: <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/NCT06026098</url>
    </required_header>
    <id_info>
      <org_study_id>23-1262</org_study_id>
      <nct_id>NCT06026098</nct_id>
    </id_info>
    <brief_title>Evaluation of Impact of AI Assistance on Workload Associated w Preparation of Rare Tumor Case Repts</brief_title>
    <official_title>A Prospective Evaluation of Impact of AI Assistance on Workload Associated With Preparation of Rare Tumor Case Reports</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>UNC Lineberger Comprehensive Cancer Center</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>UNC Lineberger Comprehensive Cancer Center</source>
    <oversight_info>
      <has_dmc>Yes</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>
        The goal of this study is to explore cognitive burden perceptions among physicians in&#xD;
        relation to case report writing. Furthermore, this study evaluates the use of artificial&#xD;
        intelligence (AI) assistance as a tool to reduce cognitive burden among providers preparing&#xD;
        and submitting case reports. If an AI-tool is helpful in this setting, it may potentially&#xD;
        help increase reporting of rare medical events and thereby improve the evidence base for care&#xD;
        of these patient populations. This study will occur at a single time point which is expected&#xD;
        to last approximately 2 hours. This session will include reviewing two rare tumor cases and&#xD;
        then writing a clinical vignette with and without AI assistance.&#xD;
      </textblock>
    </brief_summary>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">September 2023</start_date>
    <completion_date type="Anticipated">August 2024</completion_date>
    <primary_completion_date type="Anticipated">August 2024</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>Non-Randomized</allocation>
      <intervention_model>Crossover Assignment</intervention_model>
      <primary_purpose>Other</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Subjective Cognitive workload (CWL)</measure>
      <time_frame>Baseline to 2 hours</time_frame>
      <description>Subjective Cognitive workload (CWL) will be measured while interviews using The National Aeronautics and Space Administration task load (NASA TLX) index is a tool for measuring and conducting a subjective mental workload (MWL) assessment.&#xD;
  The NASA-TLX is a subjective measure of CWL and is used across many disciplines. The NASA-TLX considers six dimensions-Mental, Physical, and Temporal Demands, Frustration, Effort, and Performance. NASA-TLX scores ≥55 have been associated with reduced performance in numerous settings. NASA-TLX is considered to be the most used subjective measure of CWL. The validated two-stage process with participants performing 15 separate pair-wise comparisons between 6 dimensions of NASA-TLX will be used. A workload score will be marked from low and high for each dimension.&#xD;
  Score values from 0 -100. The Interpretation Score of NASA TLX for Low 0-9, Medium 10-29, Somewhat high 30-49, High 50-79, and Very high 80-100</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Perceived cognitive load</measure>
      <time_frame>Baseline to 2 hours</time_frame>
      <description>The perceived cognitive load will be measured using System Usability Scale (SUS).&#xD;
  SUS is a validated post-test questionnaire that measures user satisfaction. Further, studies confirm that SUS is predictive of the impacts of changes to the user interface on usability when multiple changes to a single product were made over a large number of iterations.&#xD;
  SUS is composed of 10 questions- five positive and five negative statements, each having a five-point scale that ranges from strongly disagree to strongly agree. SUS provides a score (range, 0-100) based on the participant's rating of 10 statements regarding usability with higher scores indicating greater satisfaction with usability.</description>
    </primary_outcome>
    <number_of_arms>2</number_of_arms>
    <enrollment type="Anticipated">10</enrollment>
    <condition>Cognitive Burden</condition>
    <arm_group>
      <arm_group_label>Case Report with Artificial Intelligence</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Medical students, resident physicians, or attending physicians report rare cases with using Artificial Intelligence.</description>
    </arm_group>
    <arm_group>
      <arm_group_label>Case Report without Artificial Intelligence</arm_group_label>
      <arm_group_type>No Intervention</arm_group_type>
      <description>Medical students, resident physicians, or attending physicians report rare cases without using Artificial Intelligence.</description>
    </arm_group>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>Artificial Intelligence</intervention_name>
      <description>Participants will use the Artificial Intelligence assistance tool for case reports.</description>
      <arm_group_label>Case Report with Artificial Intelligence</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
          The subject is physician, medical student, or postdoctoral student.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
          -&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Shivani Sud</last_name>
      <role>Principal Investigator</role>
      <affiliation>UNC Lineberger Comprehensive Cancer Center</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Shivani Sud</last_name>
      <phone>984-987-1072</phone>
      <email>shivani.sud@unchealth.unc.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Victoria Xu</last_name>
      <phone>984-974-8744</phone>
      <email>victoria_xu@med.unc.edu</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Lineberger Comprehensive Cancer Center</name>
        <address>
          <city>Chapel Hill</city>
          <state>North Carolina</state>
          <zip>27514</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Shivani Sud</last_name>
        <phone>984-987-1072</phone>
        <email>shivani.sud@unchealth.unc.edu</email>
      </contact>
      <contact_backup>
        <last_name>Victoria Xu</last_name>
        <phone>984-974-8744</phone>
        <email>victoria_xu@med.unc.edu</email>
      </contact_backup>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <link>
      <url>http://unclineberger.org/patientcare/clinical-trials/clinical-trials</url>
      <description>University of North Carolina Lineberger Comprehensive Cancer Center Clinical Trials</description>
    </link>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>August 29, 2023</study_first_submitted>
    <study_first_submitted_qc>August 29, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">September 6, 2023</study_first_posted>
    <last_update_submitted>August 29, 2023</last_update_submitted>
    <last_update_submitted_qc>August 29, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">September 6, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Sponsor</responsible_party_type>
    </responsible_party>
    <keyword>artificial intelligence</keyword>
    <keyword>case report</keyword>
    <patient_data>
      <sharing_ipd>No</sharing_ipd>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: 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/NCT06026098
  org study id: 23-1262
  nct id: NCT06026098
  lead sponsor: 
  has dmc: Yes
  is fda regulated drug: No
  is fda regulated device: No
  The goal of this study is to explore cognitive burden perceptions among physicians in
        relation to case report writing. Furthermore, this study evaluates the use of artificial
        intelligence (AI) assistance as a tool to reduce cognitive burden among providers preparing
        and submitting case reports. If an AI-tool is helpful in this setting, it may potentially
        help increase reporting of rare medical events and thereby improve the evidence base for care
        of these patient populations. This study will occur at a single time point which is expected
        to last approximately 2 hours. This session will include reviewing two rare tumor cases and
        then writing a clinical vignette with and without AI assistance.
  allocation: Non-Randomized
  intervention model: Crossover Assignment
  primary purpose: Other
  masking: None (Open Label)
  measure: Subjective Cognitive workload (CWL)
  time frame: Baseline to 2 hours
  description: Subjective Cognitive workload (CWL) will be measured while interviews using The National Aeronautics and Space Administration task load (NASA TLX) index is a tool for measuring and conducting a subjective mental workload (MWL) assessment.
  The NASA-TLX is a subjective measure of CWL and is used across many disciplines. The NASA-TLX considers six dimensions-Mental, Physical, and Temporal Demands, Frustration, Effort, and Performance. NASA-TLX scores ≥55 have been associated with reduced performance in numerous settings. NASA-TLX is considered to be the most used subjective measure of CWL. The validated two-stage process with participants performing 15 separate pair-wise comparisons between 6 dimensions of NASA-TLX will be used. A workload score will be marked from low and high for each dimension.
  Score values from 0 -100. The Interpretation Score of NASA TLX for Low 0-9, Medium 10-29, Somewhat high 30-49, High 50-79, and Very high 80-100
  measure: Perceived cognitive load
  time frame: Baseline to 2 hours
  description: The perceived cognitive load will be measured using System Usability Scale (SUS).
  SUS is a validated post-test questionnaire that measures user satisfaction. Further, studies confirm that SUS is predictive of the impacts of changes to the user interface on usability when multiple changes to a single product were made over a large number of iterations.
  SUS is composed of 10 questions- five positive and five negative statements, each having a five-point scale that ranges from strongly disagree to strongly agree. SUS provides a score (range, 0-100) based on the participant's rating of 10 statements regarding usability with higher scores indicating greater satisfaction with usability.
  arm group label: Case Report with Artificial Intelligence
  arm group type: Experimental
  description: Medical students, resident physicians, or attending physicians report rare cases with using Artificial Intelligence.
  arm group label: Case Report without Artificial Intelligence
  arm group type: No Intervention
  description: Medical students, resident physicians, or attending physicians report rare cases without using Artificial Intelligence.
  intervention type: Behavioral
  intervention name: Artificial Intelligence
  description: Participants will use the Artificial Intelligence assistance tool for case reports.
  arm group label: Case Report with Artificial Intelligence
  criteria: 
  gender: All
  minimum age: 18 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Shivani Sud
  role: Principal Investigator
  affiliation: UNC Lineberger Comprehensive Cancer Center
  last name: Shivani Sud
  phone: 984-987-1072
  email: shivani.sud@unchealth.unc.edu
  last name: Victoria Xu
  phone: 984-974-8744
  email: victoria_xu@med.unc.edu
  facility: 
  contact: 
  contact backup: 
  country: United States
  url: http://unclineberger.org/patientcare/clinical-trials/clinical-trials
  description: University of North Carolina Lineberger Comprehensive Cancer Center Clinical Trials
  responsible party type: Sponsor
  sharing ipd: No