[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100635505":3},{"organization":4,"outcomesModule":7,"designInfo":26,"detailedDescription":25,"studyPopulation":25,"armGroups":32,"interventions":39,"overallOfficials":44,"centralContacts":48,"locations":57,"responsibleParty":67,"collaborators":70,"id":76,"slug":77,"hasResults":78,"nctId":79,"briefTitle":80,"officialTitle":81,"acronym":82,"eligibilityCriteria":83,"healthyVolunteers":78,"sex":84,"minAge":85,"maxAge":25,"enrollmentInfo":86,"targetDuration":25,"studyType":89,"phases":90,"briefSummary":91,"conditions":92,"keywords":25,"overallStatus":94,"whyStopped":25,"lastUpdateSubmitDate":95,"lastUpdatePostDateStruct":96,"startDateStruct":99,"completionDateStruct":101,"leadSponsor":103,"locationsCount":104},{"fullName":5,"class":6},"University of Pennsylvania","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":25,"otherOutcomes":25},[9,13,16,19,21,23],{"measure":10,"description":11,"timeFrame":12},"Feasibility of embedding the AI CDSS into a primary care visit","Participants will complete a Feasibility of Intervention Measure (FIM) via feedback survey following the primary care visit. Responses are measured on a 5-point Likert scale.","From enrollment to the end of the study interview, up to two weeks",{"measure":10,"description":14,"timeFrame":15},"Feasibility will also be assessed through brief survey questions asking whether participants experienced any barriers to using INTERLACE, whether it was useful during the visit, and how long it was used (all measured on a 7-point Likert scale), as well as open-ended questions in a semi-structured interview.","From enrollment until the end of the study interview, up to two weeks",{"measure":17,"description":18,"timeFrame":15},"Acceptability of embedding the AI CDSS into a primary care visit","Participants will complete the Acceptability of Intervention Measure (AIM) via feedback survey after the primary care visit. Responses to the AIM are measured on a 5-point Likert scale.",{"measure":17,"description":20,"timeFrame":15},"Participants will also complete a modified version of the Technology Acceptance model (TAM). Responses to the TAM are measured on a 7-point Likert scale.",{"measure":17,"description":22,"timeFrame":15},"Participants will complete a Net Promoter Score assessing the likelihood that they would recommend the AI CDSS to others at the end of the survey. This will be measured on a 10-point scale.",{"measure":17,"description":24,"timeFrame":15},"Acceptability will also be assessed through brief survey questions assessing whether INTERLACE caused distress (measured on a 7 point Likert scale), as well as open-ended questions in a semi-structured interview.",null,{"allocation":27,"interventionModel":28,"interventionModelDescription":25,"primaryPurpose":29,"observationalModel":25,"timePerspective":25,"maskingInfo":30},"NA","SINGLE_GROUP","DIAGNOSTIC",{"masking":31,"maskingDescription":25,"whoMasked":25},"NONE",[33],{"label":34,"type":35,"description":36,"interventionNames":37},"Use of INTERLACE tool in primary care visit","EXPERIMENTAL","All participants will be asked to use INTERLACE in their primary care visit. The tool will be presented on an handheld electronic device and may be used by patients, caregivers, and primary clinicians at each visit. First, the patient will input their current symptoms into INTERLACE. Then, using these symptoms, latest vital signs, and the patient's medical history, INTERLACE will make suggestions for diagnoses and tests. Patients, caregivers, and clinicians can view and discuss these suggestions together to arrive at a potential diagnosis.",[38],"Other: Artificial intelligence-based clinical decision support tool for diagnostic support",[40],{"type":6,"name":41,"description":42,"armGroupLabels":43,"otherNames":25},"Artificial intelligence-based clinical decision support tool for diagnostic support","INTERLACE is an artificial intelligence-based clinical decision support tool. It uses a patient's medical history, vital signs, and current symptoms to make suggestions for diagnoses and tests. These suggestions can be considered and discussed amongst patients, caregivers, and clinicians during primary care visits to help find a good diagnosis for the patient's symptoms.",[34],[45],{"name":46,"affiliation":5,"role":47},"Gary E Weissman, MD, MSPH","PRINCIPAL_INVESTIGATOR",[49,54],{"name":50,"role":51,"phone":52,"phoneExt":25,"email":53},"Alyssa M Sliwa, BA","CONTACT","215-746-3080","alyssa.sliwa@pennmedicine.upenn.edu",{"name":55,"role":51,"phone":25,"phoneExt":25,"email":56},"Nicholas Bishop, BA","nicholas.bishop@pennmedicine.upenn.edu",[58],{"facility":5,"status":25,"city":59,"state":60,"zip":61,"country":62,"countryCode":63,"cosmosGeoPoint":25,"geoPoint":25,"contacts":64},"Philadephia","Pennsylvania","19104","United States","US",[65],{"name":66,"role":51,"phone":52,"phoneExt":25,"email":53},"Clinical Research Coordinator",{"type":47,"investigatorFullName":68,"investigatorTitle":69,"investigatorAffiliation":5,"oldNameTitle":25,"oldOrganization":25},"Gary Weissman","Assistant Professor of Medicine and Informatics",[71,74],{"name":72,"class":73},"Penn Artificial Intelligence and Technology (PennAITech) Collaboratory for Healthy Aging","UNKNOWN",{"name":75,"class":73},"National Academy of Medicine (NAM)","100635505","evaluating-an-artificial-intelligence-based-diagnostic-support-tool-for-older-adults-in-primary-care-100635505",false,"NCT07553559","Evaluating an Artificial Intelligence-Based Diagnostic Support Tool for Older Adults in Primary Care","Advancing Diagnostic Excellence For Older Adults Through Collective Intelligence And Imitation Learning","INTERLACE","Inclusion Criteria:\n\nClinicians\n\n1. Work in the University of Pennsylvania Health System\n2. Work in a primary care setting (i.e., Internal Medicine, Geriatric Medicine, Family Medicine, and Penn Primary Care)\n3. Actively treat adult patients who are 65 years old or older\n\nPatients\n\n1. 65 years of age or older\n2. Have an upcoming encounter with a participating primary care clinician\n3. Indicate a new or worsening health concern that they wish to discuss at the upcoming encounter\n\nCaregivers\n\n1. 18 years old or older\n2. Accompanying the participating patient during the encounter with the AI CDSS\n3. Identified by the patient as a caregiver\n\nExclusion Criteria:\n\nAn individual who meets any of the following criteria will be excluded from participation in this study:\n\n1. Under the age of 18 years old\n2. Unable to provide informed consent in the opinion of the investigator\n3. Has a \"Research Do Not Contact\" status in the electronic health record","ALL","65 Years",{"count":87,"type":88},40,"ESTIMATED","INTERVENTIONAL",[27],"Older adults commonly experience diagnostic errors that may lead to direct harms and increased healthcare costs. Older adults are especially at risk because of higher rates of comorbidity burden, medical complexity, frailty, and cognitive impairment. An artificial intelligence (AI) clinical decision support system (CDSS) offer a promising approach to promote diagnostic excellence for older adults.\n\nThe purpose of this study is to assess the acceptability and feasibility of a new AI CDSS for older adults in primary care. The goal of this AI CDSS is to provide diagnostic support during primary care visits (i.e., help make timely and accurate diagnoses) and support communication amongst patients, doctors, and caregivers about the patient's health.\n\nIn this study, participants will use the AI CDSS in a primary care visit and review its suggestions for diagnoses and tests. Afterwards, they will complete a feedback survey and interview where they share their thoughts about and experience using the AI CDSS.",[93],"Diagnostic Support","NOT_YET_RECRUITING","2026-09-21",{"date":97,"type":98},"2026-09-23","ACTUAL",{"date":100,"type":88},"2026-10",{"date":102,"type":88},"2027-01",{"name":5,"class":6},1]