[{"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":29},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,5,0,[8,42,82,110,138],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":23,"briefSummary":25,"conditions":26,"keywords":4,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":31,"lastUpdatePostDateStruct":32,"startDateStruct":35,"completionDateStruct":37,"leadSponsor":39,"locationsCount":4},"100648374","cite-clinical-inference-tethered-to-evidence---a-retrieve-and-verify-layer-for-ai-care-plans-100648374",false,"NCT07718893","CITE: Clinical Inference Tethered to Evidence - a Retrieve-and-verify Layer for AI Care Plans","A Randomized Controlled Trial of CITE (Clinical Inference Tethered to Evidence), an Evidence-Grounding Retrieve-and-Verify Layer That Flags Unsupported and Inappropriate Recommendations in AI-Generated Care Plans, Versus AI With Safety Guardrails Alone and Unassisted Care, in Medicaid Primary Care","CITE","INCLUSION CRITERIA:\n\n1. Age 18 years or older.\n2. Medicaid-enrolled and attributed to a participating Waymark primary care site.\n3. Primary care encounter that requires clinical reasoning (not administrative-only).\n4. English-language clinical documentation.\n\nEXCLUSION CRITERIA:\n\n1. Age less than 18 years.\n2. Hospice or palliative-care-exclusive care plan.\n3. Administrative-only or pharmacy-only encounter that does not surface a clinical decision to the supervising clinician.\n4. Encounter where the supervising clinician is the principal investigator.\n5. Enrollment in a competing AI-safety study within the prior 90 days.","ALL","18 Years",{"count":20,"type":21},240,"ESTIMATED","INTERVENTIONAL",[24],"NA","This trial evaluates CITE, a retrieve-and-verify layer that audits an AI-generated care plan against a full-text evidence corpus and flags patient-specific codifiable safety hazards to the clinician. The co-primary outcomes are how accurately CITE flags these hazards (sensitivity and specificity versus blinded clinician adjudication) and its clinician alert burden and acceptance, compared with AI care plans using safety guardrails alone and with unassisted clinician care, in Medicaid primary care.",[27,28,29],"Quality of Health Care","Patient Safety","Clinical Decision Support","NOT_YET_RECRUITING","2026-07-16",{"date":33,"type":34},"2026-07-22","ACTUAL",{"date":36,"type":21},"2026-09",{"date":38,"type":21},"2027-09",{"name":40,"class":41},"Waymark","INDUSTRY",{"id":43,"slug":44,"hasResults":11,"nctId":45,"briefTitle":46,"officialTitle":46,"acronym":47,"eligibilityCriteria":48,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":49,"targetDuration":4,"studyType":22,"phases":51,"briefSummary":52,"conditions":53,"keywords":61,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":71,"lastUpdatePostDateStruct":72,"startDateStruct":74,"completionDateStruct":76,"leadSponsor":78,"locationsCount":81},"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":50,"type":21},770,[24],"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.",[54,55,56,29,57,58,59,60],"Drug-related Iatrogenesis","Emergency Department","Artificial Intelligence","Prescription","Transcription","Medication","Reconciliation",[62,63,64,65,66,67,68,69,70],"Drug-related iatrogenesis","emergency department","artificial intelligence","clinical decision support","randomized trial","medication","reconciliation","prescription","transcription","2026-01-06",{"date":73,"type":34},"2026-01-08",{"date":75,"type":21},"2026-01",{"date":77,"type":21},"2027-01",{"name":79,"class":80},"Centre Hospitalier Universitaire, Amiens","OTHER",1,{"id":83,"slug":84,"hasResults":11,"nctId":85,"briefTitle":86,"officialTitle":87,"acronym":88,"eligibilityCriteria":89,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":90,"enrollmentInfo":91,"targetDuration":4,"studyType":22,"phases":93,"briefSummary":94,"conditions":95,"keywords":97,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":101,"lastUpdatePostDateStruct":102,"startDateStruct":104,"completionDateStruct":106,"leadSponsor":108,"locationsCount":81},"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":92,"type":21},70,[24],"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.",[96,29],"Myofacial Pain Syndrome",[98,64,99,100],"Clinical decision","pain","myofascial pain syndrome","2026-01-05",{"date":103,"type":34},"2026-01-15",{"date":105,"type":21},"2026-02-01",{"date":107,"type":21},"2027-04-01",{"name":109,"class":80},"Cairo University",{"id":111,"slug":112,"hasResults":11,"nctId":113,"briefTitle":114,"officialTitle":115,"acronym":116,"eligibilityCriteria":117,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":118,"enrollmentInfo":119,"targetDuration":4,"studyType":22,"phases":121,"briefSummary":122,"conditions":123,"keywords":125,"overallStatus":128,"whyStopped":4,"lastUpdateSubmitDate":129,"lastUpdatePostDateStruct":130,"startDateStruct":132,"completionDateStruct":134,"leadSponsor":136,"locationsCount":81},"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":120,"type":21},460000,[24],"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.",[124,29],"Hypercholesterolemia, Familial",[124,126,127],"Primary care","Clinical decision support","RECRUITING","2024-11-22",{"date":131,"type":34},"2024-11-26",{"date":133,"type":34},"2022-12-06",{"date":135,"type":21},"2025-12-06",{"name":137,"class":80},"University Hospital, Linkoeping",{"id":139,"slug":140,"hasResults":11,"nctId":141,"briefTitle":142,"officialTitle":142,"acronym":4,"eligibilityCriteria":143,"healthyVolunteers":11,"sex":17,"minAge":90,"maxAge":4,"enrollmentInfo":144,"targetDuration":146,"studyType":147,"phases":4,"briefSummary":148,"conditions":149,"keywords":150,"overallStatus":30,"whyStopped":4,"lastUpdateSubmitDate":152,"lastUpdatePostDateStruct":153,"startDateStruct":155,"completionDateStruct":157,"leadSponsor":159,"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":145,"type":21},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.",[29],[151],"Geriatric, acute care, older adults","2024-11-04",{"date":154,"type":34},"2024-11-05",{"date":156,"type":21},"2024-11-10",{"date":158,"type":21},"2027-05-01",{"name":160,"class":161},"Shmuel Harofeh Hospital, Geriatric Medical Center","OTHER_GOV"]