<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/NCT03244033</url>
  </required_header>
  <id_info>
    <org_study_id>2017-0555</org_study_id>
    <secondary_id>R01HS025374</secondary_id>
    <nct_id>NCT03244033</nct_id>
  </id_info>
  <brief_title>Integrating Contextual Factors Into Clinical Decision Support</brief_title>
  <official_title>Integrating Contextual Factors Into Clinical Decision Support to Reduce Contextual Error and Improve Outcomes in Ambulatory Care</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Illinois at Chicago</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Agency for Healthcare Research and Quality (AHRQ)</agency>
      <agency_class>U.S. Fed</agency_class>
    </collaborator>
    <collaborator>
      <agency>Loyola University</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>University of Illinois at Chicago</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>
      Preventing contextual errors requires heightening clinician responsiveness to clues that&#xD;
      there are contextual factors during the clinical encounter, in real time. These clues, termed&#xD;
      contextual red flags are evident in two sources: the medical record and from patients&#xD;
      directly. An effective intervention would prompt clinicians to determine whether there are&#xD;
      underlying contextual factors that could be addressed in the care plan, averting contextual&#xD;
      error. This desirable process is termed contextual probing.&#xD;
&#xD;
      While clinical decision support (CDS) has been used to provide physicians with timely&#xD;
      biomedical information at the point of care to prevent errors and promote appropriate care,&#xD;
      this technology also affords an opportunity to draw physician attention to both contextual&#xD;
      red flags and contextual factors in order to avert contextual errors. This study assesses the&#xD;
      potential of &quot;contextualized CDS&quot; to improve contextualization of care through a randomized&#xD;
      controlled intervention trial, with assessment measures of both patient health care outcomes&#xD;
      and averted costs associated with overuse and misuse of medical services. The three&#xD;
      hypotheses are that CDS:&#xD;
&#xD;
        1. Reduces contextual error: CDS tools that inform clinicians of contextual factors and&#xD;
           prompt them to explore contextual red flags should result in a reduction in contextual&#xD;
           error.&#xD;
&#xD;
        2. Improve health care outcomes: Contextualized CDS predicts improved health care outcomes&#xD;
           defined as a partial or full resolution of the contextual red flag (e.g. elevated HgB&#xD;
           A1c) after the index visit.&#xD;
&#xD;
        3. Reduces avoidable health care costs: Contextualized CDS is associated with a reduction&#xD;
           in misuse and overuse of inappropriate or unnecessary medical services.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The term patient context refers to the myriad contextual factors in patients' lives that&#xD;
      complicate the application of research evidence to patient care. For instance, the inability&#xD;
      of a patient to afford a medication for a particular condition is a contextual factor.&#xD;
      Contextual factors can be addressed when correctly identified. Substituting a low cost&#xD;
      generic for a high cost brand name medication may enable a patient to afford a medication.&#xD;
      Addressing contextual factors in a care plan is termed contextualizing care. Conversely, the&#xD;
      failure to address a contextual factor when it is feasible to so is a contextual error,&#xD;
      because it results in an inappropriate plan of care. In sum, contextual errors are medical&#xD;
      errors caused by inattention to patient context. They are common and linked to both&#xD;
      diminished health care outcomes and an increase in health care costs related to overuse and&#xD;
      misuse of medical services. These findings were determined using a validated method for&#xD;
      coding audio recorded data called Content Coding for Contextualization of Care (&quot;4C&quot;)&#xD;
      collected during the encounters by both real patients, and by unannounced standardized&#xD;
      patients (USPs) employing checklists.&#xD;
&#xD;
      Preventing contextual errors requires heightening clinician responsiveness to clues that&#xD;
      there are contextual factors during the clinical encounter, in real time. These clues, termed&#xD;
      contextual red flags are evident in two sources: the medical record and from patients&#xD;
      directly. An effective intervention would prompt clinicians to determine whether there are&#xD;
      underlying contextual factors that could be addressed in the care plan, averting contextual&#xD;
      error. This desirable process is termed contextual probing.&#xD;
&#xD;
      While clinical decision support (CDS) has been used to provide physicians with timely&#xD;
      biomedical information at the point of care to prevent errors and promote appropriate care,&#xD;
      this technology also affords an opportunity to draw physician attention to both contextual&#xD;
      red flags and contextual factors in order to avert contextual errors. This study assesses the&#xD;
      potential of &quot;contextualized CDS&quot; to improve contextualization of care through a randomized&#xD;
      controlled intervention trial, with assessment measures of both patient health care outcomes&#xD;
      and averted costs associated with overuse and misuse of medical services. The three&#xD;
      hypotheses are that CDS:&#xD;
&#xD;
        1. Reduces contextual error: CDS tools that inform clinicians of contextual factors and&#xD;
           prompt them to explore contextual red flags should result in a reduction in contextual&#xD;
           error.&#xD;
&#xD;
        2. Improve health care outcomes: Contextualized CDS predicts improved health care outcomes&#xD;
           defined as a partial or full resolution of the contextual red flag (e.g. elevated HgB&#xD;
           A1c) after the index visit.&#xD;
&#xD;
        3. Reduces avoidable health care costs: Contextualized CDS is associated with a reduction&#xD;
           in misuse and overuse of inappropriate or unnecessary medical services.&#xD;
&#xD;
      To test the hypotheses, patients who consent to participate will be randomized to usual care&#xD;
      or care enhanced with contextualized CDS. Participants will audio record their visits, and&#xD;
      the data will be coded using 4C. They will be followed several months after the index visit&#xD;
      for assessment of outcomes by blinded assessors using an established tracking method. In&#xD;
      addition, USPs presenting with cases containing complicating contextual factors that if&#xD;
      overlooked result in overuse and misuse of medical services, will be employed to assess the&#xD;
      third hypothesis, and to supplement the data obtained by observing the effects of contextual&#xD;
      alerts on the care of real patients for the first hypothesis.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">September 1, 2018</start_date>
  <completion_date type="Actual">November 12, 2021</completion_date>
  <primary_completion_date type="Actual">November 12, 2021</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>Single (Outcomes Assessor)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Resolution of Contextual Red Flags</measure>
    <time_frame>6-9 months following index visit</time_frame>
    <description>Proportion of red flags noted at index visit that have resolved</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Probing of Contextual Red Flags</measure>
    <time_frame>At index visit</time_frame>
    <description>Proportion of red flags which the examining physician probes</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Planning for Contextual Factors</measure>
    <time_frame>At index visit</time_frame>
    <description>Proportion of contextual factors identified during visit that are incorporated into care plan</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">452</enrollment>
  <condition>Medical Errors</condition>
  <condition>Decision Support Systems, Clinical</condition>
  <condition>Diagnostic Errors</condition>
  <arm_group>
    <arm_group_label>Contextual Survey + Contextual CDS</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Contextual Survey Only</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Contextual clinical decision support</intervention_name>
    <description>Incorporation of contextual data into EHR clinical decision support alerts</description>
    <arm_group_label>Contextual Survey + Contextual CDS</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Behavioral</intervention_type>
    <intervention_name>Contextual survey</intervention_name>
    <description>Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
    <arm_group_label>Contextual Survey + Contextual CDS</arm_group_label>
    <arm_group_label>Contextual Survey Only</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  English-speaking adult patients presenting to outpatient primary care clinics for&#xD;
             scheduled appointments who can be contacted in advance of their appointment and the&#xD;
             clinicians (physicians or nurse practitioners) seeing those patients at those visits.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  • Patients with emergent or unscheduled visits or who do not speak English.&#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>Saul J Weiner, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Illinois at Chicago</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>University of Illinois at Chicago</name>
      <address>
        <city>Chicago</city>
        <state>Illinois</state>
        <zip>60612</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location>
    <facility>
      <name>Loyola University Medical Center</name>
      <address>
        <city>Maywood</city>
        <state>Illinois</state>
        <zip>60153</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <verification_date>April 2022</verification_date>
  <study_first_submitted>July 31, 2017</study_first_submitted>
  <study_first_submitted_qc>August 4, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">August 9, 2017</study_first_posted>
  <results_first_submitted>February 4, 2022</results_first_submitted>
  <results_first_submitted_qc>April 12, 2022</results_first_submitted_qc>
  <results_first_posted type="Actual">January 10, 2023</results_first_posted>
  <last_update_submitted>April 12, 2022</last_update_submitted>
  <last_update_submitted_qc>April 12, 2022</last_update_submitted_qc>
  <last_update_posted type="Actual">January 10, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>University of Illinois at Chicago</investigator_affiliation>
    <investigator_full_name>Alan Schwartz</investigator_full_name>
    <investigator_title>Professor</investigator_title>
  </responsible_party>
  <keyword>context</keyword>
  <keyword>contextual error</keyword>
  <keyword>contextual care</keyword>
  <keyword>electronic medical record</keyword>
  <keyword>clinical decision support</keyword>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <provided_document_section>
    <provided_document>
      <document_type>Study Protocol and Statistical Analysis Plan</document_type>
      <document_has_protocol>Yes</document_has_protocol>
      <document_has_icf>No</document_has_icf>
      <document_has_sap>Yes</document_has_sap>
      <document_date>June 8, 2017</document_date>
      <document_url>https://ClinicalTrials.gov/ProvidedDocs/33/NCT03244033/Prot_SAP_000.pdf</document_url>
    </provided_document>
  </provided_document_section>

  <clinical_results>

    <participant_flow>
      <recruitment_details>570 patients were assessed for eligibility to participate across the clinics at the two sites. 118 declined to participate and were not randomized.</recruitment_details>
      <group_list>
        <group group_id="P1">
          <title>Contextual Survey + Contextual CDS</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
        <group group_id="P2">
          <title>Contextual Survey Only</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
      </group_list>
      <period_list>
        <period>
          <title>Overall Study</title>
          <milestone_list>
            <milestone>
              <title>STARTED</title>
              <participants_list>
                <participants group_id="P1" count="177"/>
                <participants group_id="P2" count="275"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="177"/>
                <participants group_id="P2" count="275"/>
              </participants_list>
            </milestone>
            <milestone>
              <title>NOT COMPLETED</title>
              <participants_list>
                <participants group_id="P1" count="0"/>
                <participants group_id="P2" count="0"/>
              </participants_list>
            </milestone>
          </milestone_list>
        </period>
      </period_list>
    </participant_flow>

    <baseline>
      <population>(As in participant flow diagram)</population>
      <group_list>
        <group group_id="B1">
          <title>Contextual Survey + Contextual CDS</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
        <group group_id="B2">
          <title>Contextual Survey Only</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
        <group group_id="B3">
          <title>Total</title>
          <description>Total of all reporting groups</description>
        </group>
      </group_list>
      <analyzed_list>
        <analyzed>
          <units>Participants</units>
          <scope>Overall</scope>
          <count_list>
            <count group_id="B1" value="177"/>
            <count group_id="B2" value="275"/>
            <count group_id="B3" value="452"/>
          </count_list>
        </analyzed>
      </analyzed_list>
      <measure_list>
        <measure>
          <title>Age, Customized</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <title>Adult (18 and older)</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                    <count group_id="B2" value="275"/>
                    <count group_id="B3" value="452"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="177"/>
                    <measurement group_id="B2" value="275"/>
                    <measurement group_id="B3" value="452"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Sex: Female, Male</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                    <count group_id="B2" value="275"/>
                    <count group_id="B3" value="452"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <title>Female</title>
                  <measurement_list>
                    <measurement group_id="B1" value="111"/>
                    <measurement group_id="B2" value="182"/>
                    <measurement group_id="B3" value="293"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Male</title>
                  <measurement_list>
                    <measurement group_id="B1" value="66"/>
                    <measurement group_id="B2" value="93"/>
                    <measurement group_id="B3" value="159"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Race and Ethnicity Not Collected</title>
          <population>Race and Ethnicity were not collected from any participant.</population>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="0"/>
                    <count group_id="B2" value="0"/>
                    <count group_id="B3" value="0"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B3" value="0"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Region of Enrollment</title>
          <units>participants</units>
          <param>Number</param>
          <class_list>
            <class>
              <title>United States</title>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                    <count group_id="B2" value="275"/>
                    <count group_id="B3" value="452"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <measurement_list>
                    <measurement group_id="B1" value="177"/>
                    <measurement group_id="B2" value="275"/>
                    <measurement group_id="B3" value="452"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <measure>
          <title>Clinic Site</title>
          <units>Participants</units>
          <param>Count of Participants</param>
          <class_list>
            <class>
              <analyzed_list>
                <analyzed>
                  <units>Participants</units>
                  <scope>Class</scope>
                  <count_list>
                    <count group_id="B1" value="177"/>
                    <count group_id="B2" value="275"/>
                    <count group_id="B3" value="452"/>
                  </count_list>
                </analyzed>
              </analyzed_list>
              <category_list>
                <category>
                  <title>Site 1</title>
                  <measurement_list>
                    <measurement group_id="B1" value="118"/>
                    <measurement group_id="B2" value="160"/>
                    <measurement group_id="B3" value="278"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Site 2</title>
                  <measurement_list>
                    <measurement group_id="B1" value="59"/>
                    <measurement group_id="B2" value="115"/>
                    <measurement group_id="B3" value="174"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
      </measure_list>
    </baseline>

    <outcome_list>
      <outcome>
        <type>Primary</type>
        <title>Resolution of Contextual Red Flags</title>
        <description>Proportion of red flags noted at index visit that have resolved</description>
        <time_frame>6-9 months following index visit</time_frame>
        <population>Red flags identified in visits of participants.</population>
        <group_list>
          <group group_id="O1">
            <title>Contextual Survey + Contextual CDS</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
          <group group_id="O2">
            <title>Contextual Survey Only</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
        </group_list>
        <measure>
          <title>Resolution of Contextual Red Flags</title>
          <description>Proportion of red flags noted at index visit that have resolved</description>
          <population>Red flags identified in visits of participants.</population>
          <units>Red flags</units>
          <param>Count of Units</param>
          <units_analyzed>Red flags</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
                <count group_id="O2" value="275"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>Red flags</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="362"/>
                <count group_id="O2" value="540"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Red flag improved/resolved</title>
                  <measurement_list>
                    <measurement group_id="O1" value="116"/>
                    <measurement group_id="O2" value="240"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Red flag worsened</title>
                  <measurement_list>
                    <measurement group_id="O1" value="83"/>
                    <measurement group_id="O2" value="99"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Red flag unchanged or mixed</title>
                  <measurement_list>
                    <measurement group_id="O1" value="163"/>
                    <measurement group_id="O2" value="201"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <analysis_list>
          <analysis>
            <group_id_list>
              <group_id>O1</group_id>
              <group_id>O2</group_id>
            </group_id_list>
            <groups_desc>Logistic mixed effects regression modeling likelihood of red flag improved/resolved (vs. not) during outcome period with fixed effects of intervention, site, and whether contextual factor was incorporated into care plan, and random effects of patient and provider. Red flags may be clustered in patients; patients are clustered in providers.</groups_desc>
            <non_inferiority_type>Superiority</non_inferiority_type>
            <p_value>.91</p_value>
            <method>Mixed Models Analysis</method>
            <method_desc>Adjusted for site (p&lt;.001) and for whether physician contextualized plan (AOR 2.14, p=.001), and random effects of physician and patient.</method_desc>
            <param_type>Odds Ratio (OR)</param_type>
            <param_value>.97</param_value>
            <ci_percent>96</ci_percent>
            <ci_n_sides>2-Sided</ci_n_sides>
            <ci_lower_limit>.57</ci_lower_limit>
            <ci_upper_limit>1.64</ci_upper_limit>
          </analysis>
        </analysis_list>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Probing of Contextual Red Flags</title>
        <description>Proportion of red flags which the examining physician probes</description>
        <time_frame>At index visit</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Contextual Survey + Contextual CDS</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
          <group group_id="O2">
            <title>Contextual Survey Only</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
        </group_list>
        <measure>
          <title>Probing of Contextual Red Flags</title>
          <description>Proportion of red flags which the examining physician probes</description>
          <units>Red flags</units>
          <param>Count of Units</param>
          <units_analyzed>Red flags</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
                <count group_id="O2" value="275"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>Red flags</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="362"/>
                <count group_id="O2" value="540"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Probed</title>
                  <measurement_list>
                    <measurement group_id="O1" value="215"/>
                    <measurement group_id="O2" value="271"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Not probed</title>
                  <measurement_list>
                    <measurement group_id="O1" value="147"/>
                    <measurement group_id="O2" value="269"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <analysis_list>
          <analysis>
            <group_id_list>
              <group_id>O1</group_id>
              <group_id>O2</group_id>
            </group_id_list>
            <groups_desc>Logistic mixed effects regression modeling likelihood of provider probing red flag (vs. not) during visit with fixed effects of intervention, site, \whether red flag was select on pre-visit questionnaire, and whether audiorecorder was visible to provider, and random effects of patient and provider. Red flags may be clustered in patients; patients are clustered in providers.</groups_desc>
            <non_inferiority_type>Superiority</non_inferiority_type>
            <p_value>0.02</p_value>
            <method>Mixed Models Analysis</method>
            <method_desc>Adjusted for site, source of red flag, visible/concealed recorder, and random effects of physician and patient.</method_desc>
            <param_type>Odds Ratio (OR)</param_type>
            <param_value>2.09</param_value>
            <ci_percent>95</ci_percent>
            <ci_n_sides>2-Sided</ci_n_sides>
            <ci_lower_limit>1.13</ci_lower_limit>
            <ci_upper_limit>3.86</ci_upper_limit>
            <estimate_desc>OR&gt;1 indicates greater likelihood in intervention group vs. control.</estimate_desc>
          </analysis>
        </analysis_list>
      </outcome>
      <outcome>
        <type>Secondary</type>
        <title>Planning for Contextual Factors</title>
        <description>Proportion of contextual factors identified during visit that are incorporated into care plan</description>
        <time_frame>At index visit</time_frame>
        <group_list>
          <group group_id="O1">
            <title>Contextual Survey + Contextual CDS</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
          <group group_id="O2">
            <title>Contextual Survey Only</title>
            <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
          </group>
        </group_list>
        <measure>
          <title>Planning for Contextual Factors</title>
          <description>Proportion of contextual factors identified during visit that are incorporated into care plan</description>
          <units>Contextual factors</units>
          <param>Count of Units</param>
          <units_analyzed>Contextual factors</units_analyzed>
          <analyzed_list>
            <analyzed>
              <units>Participants</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="177"/>
                <count group_id="O2" value="275"/>
              </count_list>
            </analyzed>
            <analyzed>
              <units>Contextual factors</units>
              <scope>Measure</scope>
              <count_list>
                <count group_id="O1" value="383"/>
                <count group_id="O2" value="509"/>
              </count_list>
            </analyzed>
          </analyzed_list>
          <class_list>
            <class>
              <category_list>
                <category>
                  <title>Plan contextualized for factor</title>
                  <measurement_list>
                    <measurement group_id="O1" value="221"/>
                    <measurement group_id="O2" value="255"/>
                  </measurement_list>
                </category>
                <category>
                  <title>Plan not contextualized for factor</title>
                  <measurement_list>
                    <measurement group_id="O1" value="162"/>
                    <measurement group_id="O2" value="254"/>
                  </measurement_list>
                </category>
              </category_list>
            </class>
          </class_list>
        </measure>
        <analysis_list>
          <analysis>
            <group_id_list>
              <group_id>O1</group_id>
              <group_id>O2</group_id>
            </group_id_list>
            <groups_desc>Logistic mixed effects regression modeling likelihood of provider incorporating contextual factor into care plan (vs. not) at visit with fixed effects of intervention, site, whether red flag was select on pre-visit questionnaire, whether audiorecorder was visible to provider, whether factor was identified by provider probe, whether factor was revealed by patient, and random effects of patient and provider. Red flags may be clustered in patients; patients are clustered in providers.</groups_desc>
            <non_inferiority_type>Superiority</non_inferiority_type>
            <p_value>.006</p_value>
            <method>Mixed Models Analysis</method>
            <method_desc>Adjusted for site, source of factor, recorder visible/concealed, and random effects of physician and patient.</method_desc>
            <param_type>Odds Ratio (OR)</param_type>
            <param_value>2.67</param_value>
            <ci_percent>95</ci_percent>
            <ci_n_sides>2-Sided</ci_n_sides>
            <ci_lower_limit>1.32</ci_lower_limit>
            <ci_upper_limit>5.41</ci_upper_limit>
            <estimate_desc>OR &gt; 1 indicates greater likelihood in intervention group vs. control.</estimate_desc>
          </analysis>
        </analysis_list>
      </outcome>
    </outcome_list>

    <reported_events>
      <time_frame>At index visit for each patient during the study period.</time_frame>
      <group_list>
        <group group_id="E1">
          <title>Contextual Survey + Contextual CDS</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.&#xD;
Contextual clinical decision support: Incorporation of contextual data into EHR clinical decision support alerts&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
        <group group_id="E2">
          <title>Contextual Survey Only</title>
          <description>Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.&#xD;
Contextual survey: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care</description>
        </group>
      </group_list>
      <serious_events>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, all-cause mortality</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="177"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="275"/>
              </event>
              <event>
                <sub_title>Total, serious adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="177"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="275"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </serious_events>
      <other_events>
        <frequency_threshold>5</frequency_threshold>
        <category_list>
          <category>
            <title>Total</title>
            <event_list>
              <event>
                <sub_title>Total, other adverse events</sub_title>
                <counts group_id="E1" subjects_affected="0" subjects_at_risk="177"/>
                <counts group_id="E2" subjects_affected="0" subjects_at_risk="275"/>
              </event>
            </event_list>
          </category>
        </category_list>
      </other_events>
    </reported_events>

    <certain_agreements>
      <pi_employee>Principal Investigators are NOT employed by the organization sponsoring the study.</pi_employee>
      <restrictive_agreement>There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI's rights to discuss or publish trial results after the trial is completed. </restrictive_agreement>
    </certain_agreements>
    <point_of_contact>
      <name_or_title>Dr. Saul Weiner</name_or_title>
      <organization>University of Illinois at Chicago</organization>
      <phone>312-355-1700</phone>
      <email>sweiner@uic.edu</email>
    </point_of_contact>
  </clinical_results>
</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/NCT03244033
org study id: 2017-0555
secondary id: R01HS025374
nct id: NCT03244033
lead sponsor: 
collaborator: 
collaborator: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
Preventing contextual errors requires heightening clinician responsiveness to clues that
      there are contextual factors during the clinical encounter, in real time. These clues, termed
      contextual red flags are evident in two sources: the medical record and from patients
      directly. An effective intervention would prompt clinicians to determine whether there are
      underlying contextual factors that could be addressed in the care plan, averting contextual
      error. This desirable process is termed contextual probing.

      While clinical decision support (CDS) has been used to provide physicians with timely
      biomedical information at the point of care to prevent errors and promote appropriate care,
      this technology also affords an opportunity to draw physician attention to both contextual
      red flags and contextual factors in order to avert contextual errors. This study assesses the
      potential of "contextualized CDS" to improve contextualization of care through a randomized
      controlled intervention trial, with assessment measures of both patient health care outcomes
      and averted costs associated with overuse and misuse of medical services. The three
      hypotheses are that CDS:

        1. Reduces contextual error: CDS tools that inform clinicians of contextual factors and
           prompt them to explore contextual red flags should result in a reduction in contextual
           error.

        2. Improve health care outcomes: Contextualized CDS predicts improved health care outcomes
           defined as a partial or full resolution of the contextual red flag (e.g. elevated HgB
           A1c) after the index visit.

        3. Reduces avoidable health care costs: Contextualized CDS is associated with a reduction
           in misuse and overuse of inappropriate or unnecessary medical services.
The term patient context refers to the myriad contextual factors in patients' lives that
      complicate the application of research evidence to patient care. For instance, the inability
      of a patient to afford a medication for a particular condition is a contextual factor.
      Contextual factors can be addressed when correctly identified. Substituting a low cost
      generic for a high cost brand name medication may enable a patient to afford a medication.
      Addressing contextual factors in a care plan is termed contextualizing care. Conversely, the
      failure to address a contextual factor when it is feasible to so is a contextual error,
      because it results in an inappropriate plan of care. In sum, contextual errors are medical
      errors caused by inattention to patient context. They are common and linked to both
      diminished health care outcomes and an increase in health care costs related to overuse and
      misuse of medical services. These findings were determined using a validated method for
      coding audio recorded data called Content Coding for Contextualization of Care ("4C")
      collected during the encounters by both real patients, and by unannounced standardized
      patients (USPs) employing checklists.

      Preventing contextual errors requires heightening clinician responsiveness to clues that
      there are contextual factors during the clinical encounter, in real time. These clues, termed
      contextual red flags are evident in two sources: the medical record and from patients
      directly. An effective intervention would prompt clinicians to determine whether there are
      underlying contextual factors that could be addressed in the care plan, averting contextual
      error. This desirable process is termed contextual probing.

      While clinical decision support (CDS) has been used to provide physicians with timely
      biomedical information at the point of care to prevent errors and promote appropriate care,
      this technology also affords an opportunity to draw physician attention to both contextual
      red flags and contextual factors in order to avert contextual errors. This study assesses the
      potential of "contextualized CDS" to improve contextualization of care through a randomized
      controlled intervention trial, with assessment measures of both patient health care outcomes
      and averted costs associated with overuse and misuse of medical services. The three
      hypotheses are that CDS:

        1. Reduces contextual error: CDS tools that inform clinicians of contextual factors and
           prompt them to explore contextual red flags should result in a reduction in contextual
           error.

        2. Improve health care outcomes: Contextualized CDS predicts improved health care outcomes
           defined as a partial or full resolution of the contextual red flag (e.g. elevated HgB
           A1c) after the index visit.

        3. Reduces avoidable health care costs: Contextualized CDS is associated with a reduction
           in misuse and overuse of inappropriate or unnecessary medical services.

      To test the hypotheses, patients who consent to participate will be randomized to usual care
      or care enhanced with contextualized CDS. Participants will audio record their visits, and
      the data will be coded using 4C. They will be followed several months after the index visit
      for assessment of outcomes by blinded assessors using an established tracking method. In
      addition, USPs presenting with cases containing complicating contextual factors that if
      overlooked result in overuse and misuse of medical services, will be employed to assess the
      third hypothesis, and to supplement the data obtained by observing the effects of contextual
      alerts on the care of real patients for the first hypothesis.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Health Services Research
masking: Single (Outcomes Assessor)
measure: Resolution of Contextual Red Flags
time frame: 6-9 months following index visit
description: Proportion of red flags noted at index visit that have resolved
measure: Probing of Contextual Red Flags
time frame: At index visit
description: Proportion of red flags which the examining physician probes
measure: Planning for Contextual Factors
time frame: At index visit
description: Proportion of contextual factors identified during visit that are incorporated into care plan
arm group label: Contextual Survey + Contextual CDS
arm group type: Experimental
description: Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will produce a variety of Contextual Clinical Decision Support, both passive and interruptive alerts.
arm group label: Contextual Survey Only
arm group type: Active Comparator
description: Contextual factors obtained from patients in the Contextual Survey along with contextual red flags already stored in the EHR will not be used for CDS or to produce alerts.
intervention type: Other
intervention name: Contextual clinical decision support
description: Incorporation of contextual data into EHR clinical decision support alerts
arm group label: Contextual Survey + Contextual CDS
intervention type: Behavioral
intervention name: Contextual survey
description: Patients complete a survey asking about red flags that could signal contextual factors relevant to their care
arm group label: Contextual Survey + Contextual CDS
arm group label: Contextual Survey Only
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Saul J Weiner, MD
role: Principal Investigator
affiliation: University of Illinois at Chicago
facility: 
facility: 
country: United States
responsible party type: Principal Investigator
investigator affiliation: University of Illinois at Chicago
investigator full name: Alan Schwartz
investigator title: Professor
sharing ipd: No
provided document: 
participant flow: 
baseline: 
outcome list: 
reported events: 
certain agreements: 
point of contact: 