[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"primary-care-provider\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:primary-care-provider":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,2,0,[8,41],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":4,"overallStatus":28,"whyStopped":4,"lastUpdateSubmitDate":29,"lastUpdatePostDateStruct":30,"startDateStruct":33,"completionDateStruct":35,"leadSponsor":37,"locationsCount":40},"100651750","evaluating-the-preferences-and-tradeoffs-of-ai-based-electronic-consultations-for-older-adults-in-primary-care-100651750",false,"NCT07766694","Evaluating the Preferences and Tradeoffs of AI-based Electronic Consultations for Older Adults in Primary Care","Evaluating the Safety and Appropriateness of AI-based E-Consults for Older Adults in Primary Care","PATIENTS:\n\nInclusion Criteria:\n\n* Adult, 65 years of age or older\n* Had at least two office visits at Penn Medicine in the past 12 months\n* Has an upcoming appointment in a Penn Medicine primary care setting, including family medicine, internal medicine, and geriatric medicine\n\nExclusion Criteria:\n\n* Documented Alzheimer's, dementia, or related condition\n\nCLINICIANS:\n\nInclusion Criteria:\n\n* Adult, 18 years of age or older\n* Works at Penn Medicine as one of the following: physician, nurse practitioner, or physician's assistant\n* Works in a primary care Penn Medicine setting including: family medicine, internal medicine, geriatrics medicine\n\nExclusion Criteria:\n\n* Actively in medical school, internship, or residency","ALL","65 Years",{"count":19,"type":20},220,"ESTIMATED","INTERVENTIONAL",[23],"NA","Electronic consultations, or e-consults, let a primary care doctor request medical advice from a specialist without requiring the patient to attend a separate visit. Artificial intelligence (AI) systems may be able to provide this type of advice, potentially making e-consults faster and less costly. However, whether AI-based e-consults are acceptable may depend on how patients and clinicians weigh factors such as who provides the advice, how quickly it is received, its cost, and its quality.\n\nThe purpose of this study is to examine how older adult patients and primary care clinicians weigh these factors when comparing e-consults produced by a human specialist with those produced by an AI system. In the study, participants will complete a one-time survey in which they compare sets of two hypothetical e-consults. Each e-consult will include a different combination of features, such as whether the advice comes from a human or AI, the expected wait time, the cost, and the quality of the advice. This study will estimate how much patients and clinicians value each feature in an e-consult. The findings will inform the potential future AI e-consults, so they better reflect patient and clinician preferences.",[26,27],"Primary Care Patients","Primary Care Provider","NOT_YET_RECRUITING","2026-08-09",{"date":31,"type":32},"2026-08-14","ACTUAL",{"date":34,"type":20},"2026-08",{"date":36,"type":20},"2026-12",{"name":38,"class":39},"University of Pennsylvania","OTHER",1,{"id":42,"slug":43,"hasResults":11,"nctId":44,"briefTitle":45,"officialTitle":45,"acronym":46,"eligibilityCriteria":47,"healthyVolunteers":48,"sex":16,"minAge":49,"maxAge":4,"enrollmentInfo":50,"targetDuration":4,"studyType":21,"phases":52,"briefSummary":53,"conditions":54,"keywords":4,"overallStatus":28,"whyStopped":4,"lastUpdateSubmitDate":56,"lastUpdatePostDateStruct":57,"startDateStruct":59,"completionDateStruct":61,"leadSponsor":63,"locationsCount":40},"100565062","deployment-and-evaluation-of-artificial-intelligence-software-for-electrocardiogram-analysis-and-management-in-primary-care-100565062","NCT06637293","Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care","DAISEA-ECG","Inclusion Criteria:\n\nFamily Physicians or Nurse Practitioners\n\nFamily physicians or nurse practitioners (NPs) practicing in one of the participating FMGs.\n\nFamily physicians who have given their free and informed consent. Patients\n\nAdult patients (18 years or older). Patients without follow-up in cardiology or internal medicine for cardiovascular issues (arrhythmia, heart failure, myocardial infarction, atherosclerotic coronary artery disease, valvular heart disease) or those who had a negative investigation in the past with no additional follow-up.\n\nECG\n\nAny 12-lead ECG performed with the MUSE GE 360 machine. ECG of adequate technical quality for interpretation (otherwise, it will be automatically rejected by the platform).\n\n\\-\n\nExclusion Criteria:\n\n* Family Physicians or Nurse Practitioners\n\nFamily physicians practicing exclusively in pediatrics (patients under 18 years old).\n\nFamily physicians unable to follow the project guidelines.",true,"18 Years",{"count":51,"type":20},2000,[23],"The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence.\n\nThe primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.",[27,55],"Structural Heart Disease","2025-09-18",{"date":58,"type":32},"2025-09-19",{"date":60,"type":20},"2025-10-06",{"date":62,"type":20},"2027-03",{"name":64,"class":39},"Montreal Heart Institute"]