[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"decision-support-systems-clinical\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:decision-support-systems-clinical":30},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,7,0,[8,51,100,130,158,180,215],{"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":32,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"100605853","developing-an-innovative-decision-support-tool-for-pediatric-neuromuscular-scoliosis-100605853",false,"NCT07167927","Developing an Innovative Decision Support Tool for Pediatric Neuromuscular Scoliosis","Developing an Innovative Decision Support Tool for Pediatric Neuromuscular Scoliosis - Aims 2 and 3","Inclusion criteria:\n\n* Parent-child dyads of children with neuromuscular scoliosis who speak English and Spanish.\n* Child is between ages 8-21 years of age and they are coming into the pediatric orthopaedic surgery clinic for consultation about potential surgery for NMS.\n* NMS is defined as having neurologic impairment (NI) and scoliosis using relevant ICD-9 or ICD-10 codes from Feudtner, et al. 2014 or Berry, et al. 2012. or a qualifying diagnosis per the Pediatric Spine Study Group definition of NMS.\n* All pediatric orthopaedic surgeons and neurosurgeons who treat neuromuscular scoliosis at our study sites will be eligible participants.\n\nExclusion criteria:\n\n* Families whose child with NMS is less than 8 years of age at time of orthopaedic consultation because surgery at a younger age usually indicates an atypical case.\n* Children with the diagnosis of Becker's muscular dystrophy due to potential disease modifying therapies that may alter curve progression.","ALL","8 Years",{"count":19,"type":20},110,"ESTIMATED","INTERVENTIONAL",[23],"NA","The goal of this pilot hybrid type I efficacy\u002Fimplementation trial is to assess a newly developed decision support tool patients, parents, and providers to use during surgical treatment decision making for neuromuscular scoliosis (NMS). Results from this pilot will inform the design of a future larger effectiveness trial of the decision support tool.\n\nParticipants will either receive usual care or receive the decision support tool. Researchers will assess the decision made, decision quality, individual affective, cognitive, and behavioral effects, and feasibility and acceptability of tool use. They will also collect potential barriers and facilitators to implementation and feedback about the tool and study design to maximize likelihood of successful deployment of the tool into clinical practice and inform the design of a future trial. The outcomes measures will be used to inform potential effect size estimates to inform a future trial.",[26,27,28,29,30,31],"Children With Medical Complexity (CMC)","Multiple Chronic Conditions","Neuromuscular Scoliosis","Shared Decision Making","Decision Support Systems, Clinical","Decision Aids",[33,34,35,36,37],"children with medical complexity","shared decision making","values clarification","uncertainty communication","decision support tool","RECRUITING","2026-08-06",{"date":41,"type":42},"2026-08-10","ACTUAL",{"date":44,"type":42},"2025-10-21",{"date":46,"type":20},"2027-03-31",{"name":48,"class":49},"University of Utah","OTHER",2,{"id":52,"slug":53,"hasResults":11,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":57,"eligibilityCriteria":58,"healthyVolunteers":11,"sex":16,"minAge":59,"maxAge":4,"enrollmentInfo":60,"targetDuration":4,"studyType":62,"phases":4,"briefSummary":63,"conditions":64,"keywords":76,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":93,"completionDateStruct":95,"leadSponsor":97,"locationsCount":99},"100649713","benchmarking-large-language-models-against-tumour-boards-for-oncology-treatment-recommendations-100649713","NCT07739121","Benchmarking Large Language Models Against Tumour Boards for Oncology Treatment Recommendations","Benchmarking AI for Clinical Oncology decisioNmaking (BEACON): A Prospective, Multicentre, Blinded Evaluation of Frontier Large Language Models Against Multidisciplinary Tumour Board Recommendations in Oncology Treatment Planning","BEACON","Inclusion Criteria:\n\n* Synthetic oncology case within one of the five predefined localisations (breast, lung, urological, digestive, gynaecological).\n* Complete structured schema: UICC 8th-edition stage, biomarkers, ECOG performance status, comorbidities and a standardised clinical question.\n* A clinically answerable treatment-planning question that is mappable to the locked guideline matrix.\n\nExclusion Criteria:\n\n* Case outside the five predefined localisations.\n* Incomplete, internally inconsistent or ambiguous schema.\n* Duplicate or near-duplicate of an existing case in the set.\n* Question not resolvable by current guidelines.","18 Years",{"count":61,"type":20},100,"OBSERVATIONAL","BEACON (Benchmarking AI for Clinical Oncology decisioNmaking) is a prospective, multicentre, comparative, blinded, non-interventional benchmark evaluating the treatment recommendations of five frontier large language models (LLMs) against the recommendations of multidisciplinary tumour boards (RCP) in oncology treatment planning. One hundred standardised synthetic cases (20 per localisation, across breast, lung, urological, digestive and gynaecological cancers) are submitted as identical structured input to two independent tumour boards per localisation and to five frontier LLMs. Each recommendation - human or model - is decomposed into five predefined decision domains (intent, surgery, radiotherapy, systemic therapy, work-up and biomarkers) and scored 0\u002F1\u002F2 for concordance against a two-tier reference: the consensus of the two tumour boards, complemented by an a priori locked guideline matrix (ESMO, NCCN). The primary endpoint is domain-level concordance between LLM and RCP consensus, expressed as a linearly weighted Cohen's kappa. A co-primary safety endpoint captures the proportion of recommendations carrying serious harm potential, because concordance alone can conceal dangerous errors. Because expert boards may disagree with one another on identical cases, model performance is always interpreted against the human consensus. BEACON is designed as reusable, openly licensed, pre-registered infrastructure: all synthetic cases, evaluation rubrics, the locked guideline matrix, scoring algorithms and verbatim prompts are released for full reproducibility.",[65,66,67,68,69,70,71,72,73,74,75,30],"Breast Neoplasms","Lung Neoplasms","Urologic Neoplasms","Prostatic Neoplasms","Urinary Bladder Neoplasms","Kidney Neoplasms","Digestive System Neoplasms","Genital Neoplasms","Artifical Intelligence","Large Language Models","Decision Making",[74,77,78,30,79,80,81,82,83,84,85,86,87,88,89],"Artificial Intelligence","Clinical Decision support","Multidisciplinary tumour board (RCP)","Patient Care Team","Medical oncology","Neoplasms","Benchmark","Benchmarking","Concordance","Weighted kappa","Synthetic data","Patient safety","Reproductibility","2026-07-28",{"date":92,"type":42},"2026-07-31",{"date":94,"type":42},"2026-05-01",{"date":96,"type":20},"2026-10-01",{"name":98,"class":49},"Assistance Publique - Hôpitaux de Paris",1,{"id":101,"slug":102,"hasResults":11,"nctId":103,"briefTitle":104,"officialTitle":105,"acronym":106,"eligibilityCriteria":107,"healthyVolunteers":108,"sex":16,"minAge":59,"maxAge":4,"enrollmentInfo":109,"targetDuration":4,"studyType":21,"phases":111,"briefSummary":112,"conditions":113,"keywords":115,"overallStatus":120,"whyStopped":4,"lastUpdateSubmitDate":121,"lastUpdatePostDateStruct":122,"startDateStruct":124,"completionDateStruct":126,"leadSponsor":128,"locationsCount":99},"100647450","testing-an-ai-tool-to-help-primary-care-clinicians-with-specialty-consultation-questions-100647450","NCT07706920","Testing an AI Tool to Help Primary Care Clinicians With Specialty Consultation Questions","Physician Usability and Output Quality Evaluation of a Retrieval-Augmented Language Model System for Specialty Medical Consultation: A Human-Computer Interaction Study","SAGE","Inclusion Criteria:\n\n* Physician practicing in primary care internal medicine and\u002For family medicine\n* Attending physician, or resident physician at postgraduate year 2 (PGY-2) or higher\n* Stanford-affiliated (faculty, staff, or trainee)\n* Able to complete a single, approximately 60-minute remote (Zoom) session conducted in English\n\nExclusion Criteria:\n\n* Not currently practicing in primary care internal medicine or family medicine\n* Member of the study team or otherwise involved in the development of SAGE\n* Unable to complete the remote study session",true,{"count":110,"type":20},15,[23],"The goal of this study is to test an artificial intelligence (AI) tool called SAGE. SAGE helps primary care doctors with questions that often need a specialist. Primary care doctors are the doctors people usually see first. SAGE reviews a case and suggests what a specialist might advise.\n\nThe main questions it aims to answer are:\n\n* Do doctors make sound, timely care decisions when they use SAGE?\n* Do those decisions match what a specialist would advise?\n\nResearchers will compare the decisions doctors make with and without SAGE.\n\nDoctors in the study will:\n\n* Review made-up patient cases (these are not real patients)\n* Make a decision for each case, their usual way and with SAGE",[30,114],"Referral and Consultation",[116,117,118,119],"clinical decision support","electronic consultation","primary care","artificial intelligence","NOT_YET_RECRUITING","2026-07-10",{"date":123,"type":42},"2026-07-16",{"date":125,"type":20},"2026-07",{"date":127,"type":20},"2026-09",{"name":129,"class":49},"Stanford University",{"id":131,"slug":132,"hasResults":11,"nctId":133,"briefTitle":134,"officialTitle":135,"acronym":136,"eligibilityCriteria":137,"healthyVolunteers":11,"sex":16,"minAge":138,"maxAge":4,"enrollmentInfo":139,"targetDuration":4,"studyType":21,"phases":141,"briefSummary":143,"conditions":144,"keywords":147,"overallStatus":120,"whyStopped":4,"lastUpdateSubmitDate":149,"lastUpdatePostDateStruct":150,"startDateStruct":152,"completionDateStruct":154,"leadSponsor":156,"locationsCount":4},"100643542","early-phase-1-clinical-evaluation-of-an-ai-risk-prediction-system-ai-trips-100643542","NCT07634185","Clinical Evaluation of an AI Risk Prediction System (AI-TRiPS)","Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients: a Stepped-wedge Cluster Randomised Trial","AI-TRiPS","Inclusion Criteria:\n\nClinician Participants\n\n* Senior clinical decision-maker involved in the initial trauma resuscitation (e.g. consultant or senior trainee in emergency medicine, anaesthesia, intensive care medicine, or surgery).\n* Based at one of the four participating Major Trauma Centres.\n* Able and willing to provide informed consent.\n* Completed the required study-specific training.\n\nTrauma Patients\n\n* Aged 16 years and above.\n* Treated and transported to a participating Major Trauma Centre by London's Air Ambulance.\n* Managed by one or more participating trauma clinicians during the resuscitation.\n\nExclusion Criteria:\n\nClinician Participants\n\n● Decline or withdraw informed consent at any stage.\n\nTrauma Patients\n\n* Aged under 16\n* Not treated by London's Air Ambulance.\n* Transported to a non-participating hospital.\n* Not managed by any participating clinicians.\n* Presenting with injuries resulting from burns, hangings, drownings, or isolated psychiatric emergencies.\n* Have registered a national NHS data opt-out or otherwise requested that their routine clinical data not be used for research.","16 Years",{"count":140,"type":20},1200,[142],"EARLY_PHASE1","The goal of this clinical study is to evaluate a software device and its impact on clinician behaviour during the initial management of trauma patients in a real-world clinical setting. Known as the AI-TRiPS Device this software uses real-time prehospital data and machine learning-based risk predictions which are displayed digitally for hospital trauma teams prior patient arrival.\n\nThe investigators will use a Stepped Wedge Cluster Randomised Controlled study design with an integrated process evaluation.\n\nThe Device will be deployed across the London Major Trauma System where the Major Trauma Centres will be the clusters. Each cluster will transition from control (standard care) to intervention at a pre-specified time (time of transition is randomised).\n\nPrimary Outcome: Clinician behaviour, assessed via the accuracy of risk prediction and clinician confidence.\n\nSecondary Outcome: Clinician acceptability, care process metrics, patient outcomes, and safety endpoints.\n\nPrimary study population: Hospital trauma clinicians, following initial resuscitation of each eligible trauma patient, who will complete electronic questionnaires.\n\nSecondary study population: Adult trauma patients, data will be collected for the duration of their index admission to hospital, to assess outcomes and enable comparison with clinician risk predictions.",[145,146,30],"Trauma","Injury",[148,116],"Device trial, prediction tool, trauma","2026-06-03",{"date":151,"type":42},"2026-06-08",{"date":153,"type":20},"2026-06-01",{"date":155,"type":20},"2027-12-01",{"name":157,"class":49},"Queen Mary University of London",{"id":159,"slug":160,"hasResults":11,"nctId":161,"briefTitle":162,"officialTitle":163,"acronym":4,"eligibilityCriteria":164,"healthyVolunteers":11,"sex":16,"minAge":59,"maxAge":4,"enrollmentInfo":165,"targetDuration":4,"studyType":21,"phases":167,"briefSummary":168,"conditions":169,"keywords":4,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":171,"lastUpdatePostDateStruct":172,"startDateStruct":174,"completionDateStruct":176,"leadSponsor":178,"locationsCount":99},"100513576","cirrhosisrx-cds-system-100513576","NCT05967273","CirrhosisRx CDS System","Pragmatic Randomized Controlled Trial to Evaluate the Effect of CirrhosisRx, a Novel Clinical Decision Support System, on Guideline-adherence and Clinical Outcomes for Patients With Cirrhosis","Inclusion Criteria:\n\n* All adult (age ≥ 18 years) patients who have cirrhosis identified based on 1+ chronic liver disease and 1+ cirrhosis (or its complications) International Classification of Diseases, Revision 10 diagnosis codes OR mention of cirrhosis or portal hypertension (or their complications) in clinical documentation admitted at our institution.\n\nExclusion Criteria:\n\n* Children (age \\\u003C 18 years)\n* patients who do not meet the cirrhosis definition criteria as noted above\n* ambulatory patients",{"count":166,"type":20},2106,[23],"The aim of the study is to compare the effect of CirrhosisRx, a novel clinical decision support (CDS) system for inpatient cirrhosis care, versus \"usual care\" on adherence to national quality measures and clinical outcomes for hospitalized patients with cirrhosis.",[170,30],"Cirrhosis","2026-03-26",{"date":173,"type":42},"2026-04-01",{"date":175,"type":42},"2025-01-14",{"date":177,"type":20},"2027-01",{"name":179,"class":49},"University of California, San Francisco",{"id":181,"slug":182,"hasResults":11,"nctId":183,"briefTitle":184,"officialTitle":185,"acronym":186,"eligibilityCriteria":187,"healthyVolunteers":108,"sex":16,"minAge":59,"maxAge":4,"enrollmentInfo":188,"targetDuration":4,"studyType":21,"phases":189,"briefSummary":190,"conditions":191,"keywords":195,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":206,"lastUpdatePostDateStruct":207,"startDateStruct":209,"completionDateStruct":211,"leadSponsor":213,"locationsCount":99},"100620042","reasoning-enrichment-with-feedback-from-ia-in-nephrology-trial-100620042","NCT07352475","Reasoning Enrichment With Feedback From IA in NEphrology Trial","Reasoning Enhancement With Feedback From a Generative AI in Nephrology (REFINe): A Randomized Evaluation of Generative AI Support in Nephrology Diagnosis","REFINe","Inclusion Criteria:\n\nAdults aged 18 years or older.\n\nAble to read and answer clinical vignettes in English or French.\n\nAccess to a computer or smartphone with an internet connection.\n\nProvides informed consent online.\n\nParticipants are expected to have at least basic medical training (e.g., medical students, residents, fellows, or practicing clinicians), although no formal verification is required.\n\nExclusion Criteria:\n\nIndividuals under 18 years of age.\n\nInability to complete online study procedures.\n\nPrior involvement in the design, development, or evaluation of the AI system used in this study.",{"count":61,"type":20},[23],"The goal of this clinical trial is to learn how artificial intelligence (AI) may help doctors make diagnoses in kidney medicine. The researchers want to know whether an AI tool called a large language model (LLM) can help doctors choose the correct diagnosis more often and feel more confident in their answers.\n\nBefore starting the study, the research team tested several AI models and chose one of the best performers, a GPT-5-class model set to use high reasoning effort.\n\nThe main questions this study aims to answer are:\n\n1. Do doctors make more correct diagnoses when they can see AI suggestions?\n2. Does seeing AI suggestions change how confident doctors feel about their diagnosis?\n\nResearchers will compare doctors who receive AI suggestions with doctors who do not receive AI suggestions to see how the AI affects accuracy, confidence, and decision-making.\n\nParticipants will complete up to 10 online clinical cases. For each case, they will:\n\n1. Read a short medical scenario\n2. Suggest up to three possible diagnoses\n\n(If in the AI group) Review the AI's suggestions and decide whether to change their answer\n\nThe study will also look at how long participants take to answer each case and how the AI's performance compares to the human answers.",[192,193,194,30],"Diagnosis","Clinical Decision-making","Artificial Intelligence (AI) in Diagnosis",[196,197,198,199,200,201,202,203,204,205],"Large Language Model (LLM)","Generative AI","Diagnostic Accuracy","Clinical Vignettes","Online Study","Randomized Controlled Trial","Nephrology Diagnosis","AI Clinical Decision Support","Human-AI Collaboration","Medical Reasoning","2026-01-12",{"date":208,"type":42},"2026-01-20",{"date":210,"type":42},"2025-11-20",{"date":212,"type":20},"2026-12-31",{"name":214,"class":49},"University Hospital, Lille",{"id":216,"slug":217,"hasResults":11,"nctId":218,"briefTitle":219,"officialTitle":220,"acronym":221,"eligibilityCriteria":222,"healthyVolunteers":11,"sex":16,"minAge":59,"maxAge":4,"enrollmentInfo":223,"targetDuration":4,"studyType":21,"phases":225,"briefSummary":226,"conditions":227,"keywords":228,"overallStatus":120,"whyStopped":4,"lastUpdateSubmitDate":231,"lastUpdatePostDateStruct":232,"startDateStruct":234,"completionDateStruct":236,"leadSponsor":238,"locationsCount":4},"100454381","clinical-decision-support-system-for-remote-monitoring-of-cardiovascular-disease-patients-100454381","NCT05196802","Clinical Decision Support System for Remote Monitoring of Cardiovascular Disease Patients","Clinical Decision Support System for Remote Monitoring of Cardiovascular Disease Patients: Promoting Self-Management and Adherence to Treatment","mHEART4U","Inclusion Criteria:\n\n* Patients attending the cardiology outpatient clinics after the onset of acute cardiac event OR\n* Patients attending the cardiology outpatient clinics who are engaged in a structured Cardiac Rehabilitation program\n* Be able to communicate with the researcher\n\nExclusion Criteria:\n\n* Participants will be excluded if they have New York Heart Association class III\u002FIV heart failure, terminal disease, or significant non-cardio vascular disease exercise limitations.",{"count":224,"type":20},212,[23],"Cardiovascular diseases (CVD) are the leading cause of death worldwide, taking an estimated 17.9 million lives each year. The reduction of CVD-related mortality and morbidity is a key global health priority. Cardiac rehabilitation (CR) is a multi-factorial and comprehensive intervention in secondary prevention, being recommended in international guidelines. Core components in CR include patient assessment, physical activity counseling, nutritional counseling, risk factor control, patient education, and psychosocial management. CR has been shown to reduce mortality, hospital readmissions, costs, as well as to improve physical fitness, quality of life, and psychological well-being. However, despite the recommendations and proven benefits, acceptance and adherence remain low. Access to health technologies in all primary and secondary healthcare facilities can be essential to ensure that those in need receive treatment and counseling.\n\nUsing mobile health (mHealth) solutions may contribute to more personalized and tailored patient recommendations according to their specific needs. Also, these technologies contribute to increasing the flexibility, quality, and efficiency of the services provided by health institutions.\n\nTime constraints, patient overpopulation, and complex guidelines require alternative solutions for real-time patient monitoring. Rapidly evolving e-health technology combined with clinical decision support systems (CDSS) provides an effective solution to these problems. There are several computerized CDSS for managing chronic diseases; however, to the best of our knowledge, there are none for the e-management of patients with CVD.\n\nThe purpose of this transdisciplinary research project is to develop and evaluate a user-friendly, comprehensive CDSS for remote monitoring of CVD patients. The CDSS will suggest a monitoring plan for the patient, advise the mHealth tools (apps and wearables) adapted to patient needs, and collect data. The primary outcome will be the reduction of recurrent cardiovascular events (a composite of cardiovascular rehospitalization or urgent consultation, unplanned revascularization, cardiovascular mortality, or worsening heart failure).",[30],[30,229,230],"Telemedicine","cardiac rehabilitation","2022-01-06",{"date":233,"type":42},"2022-01-19",{"date":235,"type":20},"2023-01",{"date":237,"type":20},"2026-12",{"name":239,"class":49},"Escola Superior de Enfermagem de Coimbra"]