[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"clinical-decision-support\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:clinical-decision-support":27},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,7,0,[8,54,80,108,144,172,198],{"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":30,"overallStatus":41,"whyStopped":4,"lastUpdateSubmitDate":42,"lastUpdatePostDateStruct":43,"startDateStruct":46,"completionDateStruct":48,"leadSponsor":50,"locationsCount":53},"100642808","anchor-validation-trial-in-high-risk-multidisciplinary-care-100642808",false,"NCT07597499","ANCHOR Validation Trial in High-Risk Multidisciplinary Care","ANCHOR (Auditable Navigation of Clinical Hazards With Oversight and Reasoning) Multicenter Randomized Validation Study: A Pragmatic Three-Arm (1:1:1) Patient-Level Randomized Controlled Trial of a Structural Verification Layer for AI-Assisted High-Risk Multidisciplinary Care Across Three U.S. States","INCLUSION CRITERIA:\n\n1. Age 18 years or older.\n2. Attributed to a participating Waymark provider (academic medical center, community-hospital network, federally qualified health center, or independent physician practice in Ohio, Washington, or Virginia; full TIN-consolidated list deposited at the Open Science Framework).\n3. Meets high-risk multidisciplinary criteria (combined claims-based and clinical: 2 or more emergency-department visits or 1 or more hospitalization in the prior 12 months, 5 or more active medications, 2 or more active specialist relationships, 2 or more chronic conditions, or claims-based equivalents).\n4. Encounter occurs in one of the three Waymark service modalities: high-risk primary care, specialty care coordination, or real-time telemedicine urgent care.\n5. English-language clinical documentation.\n6. Encounter requires clinical reasoning (not administrative-only).\n\nEXCLUSION CRITERIA:\n\n1. Pediatric (age less than 18 years).\n2. Hospice or palliative-care-exclusive care plan.\n3. Active psychiatric crisis routed to crisis line.\n4. Encounter is administrative only.\n5. Pharmacy-only encounter that does not surface a clinical decision to the supervising physician.\n6. Encounter where the supervising physician is the principal investigator.\n7. Patient enrolled in a competing AI-safety study within the prior 90 days.","ALL","18 Years",{"count":19,"type":20},240,"ESTIMATED","INTERVENTIONAL",[23],"NA","This pre-registered, pragmatic, three-arm (1:1:1) patient-level randomized controlled trial with mixed-effects analysis at the encounter level tests two questions in real high-risk multidisciplinary clinical encounters at the Waymark clinically integrated network across three U.S. states (Ohio, Washington, Virginia): (1) does adding ANCHOR - a clinical AI structural verification layer - to a Gemini 3.1 Pro-assisted supervising-physician workflow reduce the rate of clinically meaningful safety failures, compared with the same Gemini 3.1 Pro-assisted workflow without ANCHOR? (2) does the Gemini 3.1 Pro-assisted workflow itself reduce the same safety endpoint compared with unassisted standard care in which the supervising physician writes their own SOAP assessment\u002Fplan from a blank template?\n\nANCHOR is a single-call structural verification layer combining a Logical Neural Network (Riegel et al. 2020) certificate, six specialist agents, and concept-decomposed output with PMID citation provenance. ANCHOR is physician-facing only and is used by supervising physicians, not by the multidisciplinary clinical team they oversee.\n\nThe trial randomizes 240 patients 1:1:1 across the Waymark clinically integrated network over a 12-week active-enrolment window (80 per arm). Eligible patients are adults (age 18+) identified as high-risk by combined claims-based and clinical criteria. Eligible encounters span three integrated Waymark service modalities: high-risk primary care, specialty care coordination, and real-time telemedicine urgent care. The primary endpoint is a per-encounter binary composite: any of (a) failure to mention a do-not-miss diagnosis, (b) under-triage, (c) contraindicated medication recommendation, (d) failure to recommend escalation when clinically warranted; adjudicated by a blinded panel of 3 board-certified physicians with majority-of-three scoring. The primary contrast is Arm 3 (LLM+ANCHOR) versus Arm 2 (LLM with safety prompt), isolating ANCHOR's marginal contribution over a deployment-equivalent LLM safety stack. The pre-specified secondary contrast is Arm 2 versus Arm 1.\n\nThe trial is sized to the operational ceiling of the Waymark integrated-network workflow across the three states (240 enrollees over 12 weeks). At realistic effect sizes derived from the retrospective evaluation, the trial is underpowered for definitive efficacy declaration on either pairwise contrast and is reported as an initial deployment-feasibility validation cohort with effect estimates and 95 percent confidence intervals; full power calculations are pre-registered in the Statistical Analysis Plan.\n\nSingle-blind outcome adjudication: 3 adjudicators score only the supervising physician's final clinical decision, so all three arms produce adjudication packets in identical format and arm allocation is structurally invisible. Statisticians remain blinded until database lock. A full waiver of informed consent is requested per 45 CFR 46.116(f)(3) with a companion HIPAA waiver of authorization under 45 CFR 164.512(i)(2)(ii). The study is registered on the Open Science Framework prior to first enrollment and reported under CONSORT-AI 2020.",[26,27,28,29],"High-Risk Multidisciplinary Care","Clinical Decision Support","Artificial Intelligence-Assisted Care","Telemedicine",[31,32,33,34,35,36,37,38,39,40],"clinical decision support","large language model","artificial intelligence","verification layer","logical neural network","care management","telemedicine","patient safety","algorithmic fairness","CONSORT-AI","RECRUITING","2026-06-04",{"date":44,"type":45},"2026-06-09","ACTUAL",{"date":47,"type":45},"2026-05-15",{"date":49,"type":20},"2026-12",{"name":51,"class":52},"Waymark","INDUSTRY",1,{"id":55,"slug":56,"hasResults":11,"nctId":57,"briefTitle":58,"officialTitle":58,"acronym":4,"eligibilityCriteria":59,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":60,"targetDuration":4,"studyType":21,"phases":62,"briefSummary":63,"conditions":64,"keywords":66,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":47,"lastUpdatePostDateStruct":70,"startDateStruct":72,"completionDateStruct":74,"leadSponsor":76,"locationsCount":79},"100637733","adaptive-decision-support-for-addiction-treatment-adapt-serial-randomized-testing-for-usability-round-2-100637733","NCT07602218","Adaptive Decision Support for Addiction Treatment (ADAPT) Serial Randomized Testing for Usability, Round 2","Inclusion Criteria:\n\n* Emergency department patient\n* 18 years of age or older\n* Moderate to severe opioid use disorder\n\nExclusion Criteria:\n\n* Under 18 years of age\n* Pregnant\n* Currently receiving medication for opioid use disorder",{"count":61,"type":20},300,[23],"This study is stage 2, round 2 of a larger study which refines and optimizes the EMBED clinical decision support (CDS); see NCT03658642 to increase number of ED physicians following standard of care for the administration of buprenorphine to appropriate patients with opioid use disorder.",[65,27],"Opioid Use Disorder",[67,68],"opioid use disorder","Clinical decision support","NOT_YET_RECRUITING",{"date":71,"type":45},"2026-05-22",{"date":73,"type":20},"2026-05-21",{"date":75,"type":20},"2026-08",{"name":77,"class":78},"Yale University","OTHER",3,{"id":81,"slug":82,"hasResults":11,"nctId":83,"briefTitle":84,"officialTitle":85,"acronym":4,"eligibilityCriteria":86,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":87,"enrollmentInfo":88,"targetDuration":4,"studyType":21,"phases":90,"briefSummary":91,"conditions":92,"keywords":94,"overallStatus":41,"whyStopped":4,"lastUpdateSubmitDate":99,"lastUpdatePostDateStruct":100,"startDateStruct":102,"completionDateStruct":104,"leadSponsor":106,"locationsCount":53},"100566383","closing-the-gaps-guideline-adherence-prevention-and-surveillance-in-hereditary-cancer-100566383","NCT06654466","Closing the GAPS: Guideline Adherence, Prevention and Surveillance in Hereditary Cancer","Enhancing Information Management for Young Adults After Genetic Cancer Risk Testing","Inclusion Criteria:\n\n* Ages 18-49 years, inclusive\n* previous cancer genetic testing with a finding of a pathogenic or likely pathogenic variant resulting in an increased risk of cancer warranting clinical management.\n* English-speaking and -reading\n* Receiving care at Dana Farber Cancer Institute\n* Not in active cancer therapy at the time of approach\n\nExclusion Criteria:\n\n* Age \\\u003C18 or \\>49 years\n* Has not had genetic testing for hereditary cancer syndromes or has been tested but no pathogenic or likely pathogenic variant was identified.\n* Non-English speaking and reading\n* Not receiving care at Dana Farber Cancer Institute\n* Active cancer with therapy in progress","49 Years",{"count":89,"type":20},100,[23],"The goal of this clinical trial is to see if a software platform can improve cancer screening in young adults with genetic risk for cancer.\n\nThe trial will also help improve the software platform (Nest). The main questions it aims to answer are:\n\n* Do Nest users know more about their cancer risks and recommended care than non-users?\n* Do Nest users have less psychological distress than non-users?\n* Do Nest users share cancer risks with family and other doctors more than non-users?\n* Are Nest users more likely than non-users to have up-to-date care plans?\n\nResearchers will compare Nest users to non-users to see if the Nest users are more likely to do recommended cancer screening.\n\nParticipants will:\n\n* Have a genetic counseling or follow up visit\n* Take a post-visit survey\n* Intervention arm only: use the Nest Patient Navigator\n* Complete screening and follow-up care recommended by doctors",[93,27],"Hereditary Cancer Syndromes",[93,95,96,97,27,98],"Cancer Surveillance","Cancer Prevention","Guideline Adherence","Adolescent and Young Adult Cancer","2026-02-23",{"date":101,"type":45},"2026-02-25",{"date":103,"type":45},"2026-02-10",{"date":105,"type":20},"2027-09",{"name":107,"class":52},"Nest Genomics",{"id":109,"slug":110,"hasResults":11,"nctId":111,"briefTitle":112,"officialTitle":112,"acronym":113,"eligibilityCriteria":114,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":4,"enrollmentInfo":115,"targetDuration":4,"studyType":21,"phases":117,"briefSummary":118,"conditions":119,"keywords":127,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":135,"lastUpdatePostDateStruct":136,"startDateStruct":138,"completionDateStruct":140,"leadSponsor":142,"locationsCount":53},"100616930","optimization-of-medical-time-in-the-emergency-department-impact-of-an-ai-based-system-on-prescription-entry-100616930","NCT07312019","Optimization of Medical Time in the Emergency Department: Impact of an AI-Based System on Prescription Entry","YGénHIAL","Inclusion Criteria:\n\n* Age ≥18 years\n* Admission to emergency department at a participating center\n* Polymedicated patients with prescriptions including ≥8 medication lines (including those for long-term illnesses)\n* Signed informed consent\n\nExclusion Criteria:\n\n* Patient under legal protection\u002Fjudicial measures (guardianship\u002Fcustody)\n* Lack of signed informed consent",{"count":116,"type":20},770,[23],"Drug-related iatrogenesis is a major public health issue, accounting for a significant proportion of adverse events and hospitalizations in emergency departments. Optimizing prescription management in this context is critical to improve both patient safety and physician efficiency This study aims to evaluate the impact of the POSOS AI-driven device on the medical time required for prescription management in polymedicated patients admitted to emergency departments. The main objective is to establish whether the use of POSOS can reduce transcription time compared to standard electronic management.",[120,121,122,27,123,124,125,126],"Drug-related Iatrogenesis","Emergency Department","Artificial Intelligence","Prescription","Transcription","Medication","Reconciliation",[128,129,33,31,130,131,132,133,134],"Drug-related iatrogenesis","emergency department","randomized trial","medication","reconciliation","prescription","transcription","2026-01-06",{"date":137,"type":45},"2026-01-08",{"date":139,"type":20},"2026-01",{"date":141,"type":20},"2027-01",{"name":143,"class":78},"Centre Hospitalier Universitaire, Amiens",{"id":145,"slug":146,"hasResults":11,"nctId":147,"briefTitle":148,"officialTitle":149,"acronym":150,"eligibilityCriteria":151,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":152,"enrollmentInfo":153,"targetDuration":4,"studyType":21,"phases":155,"briefSummary":156,"conditions":157,"keywords":159,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":163,"lastUpdatePostDateStruct":164,"startDateStruct":166,"completionDateStruct":168,"leadSponsor":170,"locationsCount":53},"100619297","artificial-intelligence-clinical-decision-100619297","NCT07342790","Artificial Intelligence Clinical Decision","Utilization of Artificial Intelligence in Supporting Physical Therapy Clinical Decision in Management of Myofascial Pain Syndrome Patients","AI\u002FCDM","Inclusion Criteria:\n\n* A- Demographic: Adult individuals 18-65 both sex\n\nB- Pain Characteristics:\n\n* Localized pain.\n* Intensity: baseline pain score of 4 or higher on the VAS . C- Duration: chronic pain 3-6 months\n\nD- Prescence of Myofascial Trigger Points (MTrPs):\n\nE- Daily Functioning limitations: moderate or severe\n\nExclusion Criteria:\n\n* • Severe cognitive impairment or illness.\n\n  * Recent history of major surgery or trauma (within 3 months).\n  * Other chronic conditions that could significantly interfere with the study.\n  * Patients with fibromyalgia which may have the Key Diagnostic Criteria for Fibromyalgia Syndrome:\n\n    1. Widespread Pain Index (WPI) (appendix (2): Measures the number of painful areas across the body. A score of 7 or more indicates a higher likelihood of FMS (Wang et al. ,2025).\n    2. Symptom Severity Scale (SSS) (appendix3): Assesses the severity of symptoms such as fatigue, sleep disturbances, and cognitive difficulties. A score of 5 or more is indicative of FMS","65 Years",{"count":154,"type":20},70,[23],"The goal of this study is to investigate the effect of AI integration into clinical physical therapy clinical decision in improving cost effectiveness and clinical outcomes purposes of the study are:\n\n1. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of pain in myofascial pain syndrome.\n2. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving joint range of motion limitations in myofascial pain syndrome.\n3. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving muscle strength in myofascial pain syndrome.\n4. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of functional limitation in myofascial pain syndrome.\n5. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on cost-effectiveness in physical therapy management of myofascial pain syndrome.",[158,27],"Myofacial Pain Syndrome",[160,33,161,162],"Clinical decision","pain","myofascial pain syndrome","2026-01-05",{"date":165,"type":45},"2026-01-15",{"date":167,"type":20},"2026-02-01",{"date":169,"type":20},"2027-04-01",{"name":171,"class":78},"Cairo University",{"id":173,"slug":174,"hasResults":11,"nctId":175,"briefTitle":176,"officialTitle":177,"acronym":178,"eligibilityCriteria":179,"healthyVolunteers":11,"sex":16,"minAge":17,"maxAge":180,"enrollmentInfo":181,"targetDuration":4,"studyType":21,"phases":183,"briefSummary":184,"conditions":185,"keywords":187,"overallStatus":41,"whyStopped":4,"lastUpdateSubmitDate":189,"lastUpdatePostDateStruct":190,"startDateStruct":192,"completionDateStruct":194,"leadSponsor":196,"locationsCount":53},"100361670","clinical-decision-support-for-familial-hypercholesterolemia-100361670","NCT03989167","Clinical Decision Support for Familial Hypercholesterolemia","Clinical Decision Support for Familial Hypercholesterolemia: a Cluster Randomized Trial in the Primary Care Setting","CDS-FH","Inclusion Criteria\n\n* Primary care centers in the county of Östergötland.\n\nExclusion Criteria\n\n* Primary care centers not using the Cambio Cosmic Electronic Health Record System.","80 Years",{"count":182,"type":20},460000,[23],"A cluster randomized study in the primary care setting to evaluate a computer-based clinical decision support system to aid in the identification and management of patients with FH. The primary outcome of the study is the number of patients diagnosed with FH thirty-six months after study initiation.",[186,27],"Hypercholesterolemia, Familial",[186,188,68],"Primary care","2024-11-22",{"date":191,"type":45},"2024-11-26",{"date":193,"type":45},"2022-12-06",{"date":195,"type":20},"2025-12-06",{"name":197,"class":78},"University Hospital, Linkoeping",{"id":199,"slug":200,"hasResults":11,"nctId":201,"briefTitle":202,"officialTitle":202,"acronym":4,"eligibilityCriteria":203,"healthyVolunteers":11,"sex":16,"minAge":152,"maxAge":4,"enrollmentInfo":204,"targetDuration":206,"studyType":207,"phases":4,"briefSummary":208,"conditions":209,"keywords":210,"overallStatus":69,"whyStopped":4,"lastUpdateSubmitDate":212,"lastUpdatePostDateStruct":213,"startDateStruct":215,"completionDateStruct":217,"leadSponsor":219,"locationsCount":4},"100567967","mortality-and-rehospitalization-risk-assessment-by-skilled-caregivers-compared-to-existing-tools-in-acute-geriatric-departments-100567967","NCT06675084","Mortality and Rehospitalization Risk Assessment by Skilled Caregivers Compared to Existing Tools in Acute Geriatric Departments","Inclusion Criteria: Patients admitted to acute geriatric departments at Shmuel Harofe Hospital for acute conditions.\n\n\\-\n\nExclusion Criteria: 1. Admission for social reasons. 2. Patients under palliative end of life care.\n\n\\-",{"count":205,"type":20},600,"1 Year","OBSERVATIONAL","Mortality and Rehospitalization Risk Assessment by Skilled Caregivers Compared to Existing Tools in Acute Geriatric Departments\n\nBackground The elderly population in Israel and worldwide is steadily increasing, leading to greater demand for medical services, including palliative care. In 2019, individuals aged 65+ accounted for 64% of hospital admissions and 70% of hospital days in Israel. Approximately 19% of these were readmissions, a rate that increases with age. Effective tools for identifying patients at high risk of rehospitalization and mortality are lacking, which, if improved, could benefit patients through targeted palliative and end-of-life care. Enhanced tools could reduce unnecessary interventions, improve patient well-being, and alleviate economic burdens on healthcare.\n\nResearch Objectives\n\n1. Evaluate mortality and rehospitalization rates in acute geriatric departments.\n2. Identify risk factors for rehospitalization and mortality in acutely hospitalized elderly patients.\n3. Compare the effectiveness of skilled caregiver assessments versus validated prediction tools for mortality and rehospitalization within one year.\n\nHypotheses\n\n1. Mortality and rehospitalization rates in acute geriatric departments are comparable to those in internal medicine.\n2. Multiple factors-such as age, family support, comorbidities, functional and cognitive status-correlate with mortality risk.\n3. Skilled caregiver assessments predict mortality and rehospitalization more accurately than existing validated tools.\n\nStudy Design Type: Prospective cohort observational study. Location: Shmuel Harofe Hospital.\n\nStudy Population Participants are elderly patients admitted to acute geriatric departments at Shmuel Harofe Hospital for acute conditions. Approximately 600 participants will be recruited, with an additional 200-300 if statistical analysis reveals trends.\n\nRecruitment Period: Two years. Follow-up Period: Up to one year post-admission.\n\nMethods and Materials\n\nData will be collected on demographic, functional, cognitive, and emotional factors, as well as clinical history, hospital admissions, comorbidities, and lab results. Predictive assessments will include:\n\n1. Mortality Prediction using the WALTER Index for the elderly.\n2. Rehospitalization Risk using the LACE Index, validated for 30-day readmission risk.\n3. Subjective Caregiver Assessments from geriatric specialists and nursing supervisors, estimating life expectancy and 30-day, 3-month, and 1-year rehospitalization risk.\n\nData Analysis Data will be coded and statistically analyzed without interventions outside of standard care. The WALTER and LACE indices will utilize existing clinical data.\n\nEthical Considerations As this is an observational study without intervention, a waiver for informed consent was granted.\n\nImportance of Research Early identification of high-risk patients will enable preventive interventions, support transitions to palliative care where appropriate, and promote advance directives, ultimately improving patient care and reducing healthcare costs by preventing costly, unnecessary readmissions and interventions.",[27],[211],"Geriatric, acute care, older adults","2024-11-04",{"date":214,"type":45},"2024-11-05",{"date":216,"type":20},"2024-11-10",{"date":218,"type":20},"2027-05-01",{"name":220,"class":221},"Shmuel Harofeh Hospital, Geriatric Medical Center","OTHER_GOV"]