[{"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":29},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,6,0,[8,45,76,100,136,170],{"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":30,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":4},"100643542","early-phase-1-clinical-evaluation-of-an-ai-risk-prediction-system-ai-trips-100643542",false,"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.","ALL","16 Years",{"count":20,"type":21},1200,"ESTIMATED","INTERVENTIONAL",[24],"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.",[27,28,29],"Trauma","Injury","Decision Support Systems, Clinical",[31,32],"Device trial, prediction tool, trauma","clinical decision support","NOT_YET_RECRUITING","2026-06-03",{"date":36,"type":37},"2026-06-08","ACTUAL",{"date":39,"type":21},"2026-06-01",{"date":41,"type":21},"2027-12-01",{"name":43,"class":44},"Queen Mary University of London","OTHER",{"id":46,"slug":47,"hasResults":11,"nctId":48,"briefTitle":49,"officialTitle":50,"acronym":4,"eligibilityCriteria":51,"healthyVolunteers":11,"sex":17,"minAge":52,"maxAge":4,"enrollmentInfo":53,"targetDuration":4,"studyType":22,"phases":55,"briefSummary":57,"conditions":58,"keywords":62,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":67,"lastUpdatePostDateStruct":68,"startDateStruct":70,"completionDateStruct":72,"leadSponsor":74,"locationsCount":4},"100634349","the-utility-and-feasibility-of-accessible-diarrhea-etiology-prediction-tool-adept-in-an-informal-healthcare-setting-100634349","NCT07538531","The Utility and Feasibility of Accessible Diarrhea Etiology Prediction Tool (ADEPT) in an Informal Healthcare Setting","A Mobile Health Tool to Improve Antibiotics Stewardship Among Village Doctors in Bangladesh","Inclusion Criteria:\n\n* Village Doctor with antibiotic prescribing authority for children presenting with diarrheal illness\n* Practice in trial location subdistrict\n* Self-report treating a minimum of 5 pediatric diarrhea cases per week\n* Willing to participate in ADEPT training, use ADEPT in clinical practice with pediatric diarrhea patients, and to collect, via an electronic tool, data on patient characteristics and clinical management\n\nExclusion Criteria:\n\n\\- Planning to leave study site prior to completion of study","18 Years",{"count":54,"type":21},30,[56],"NA","Diarrheal disease remains a leading cause of morbidity and mortality for children under 5 globally. Accepted best practice for managing diarrhea in the absence of blood or suspicion of cholera is rehydration, however in resource poor areas antibiotics are still prescribed at high rates due to pressures such as financial incentives, caregiver expectations, and diagnostic uncertainty. Informal healthcare providers often serve as first point of care for pediatric diarrhea patients in low- and middle- income countries (LMICs) and commonly prescribe antibiotics for pediatric diarrhea at high frequencies.\n\nIn this pilot before-after feasibility trial informally trained healthcare providers will use a mobile phone-based application (Accessible Diarrhea Etiology Prediction Tool, ADEPT) which will allow for the exploration of the acceptability, feasibility, and utility of the tool, as well as ADEPTs ability to decrease inappropriate antibiotic prescribing practices.",[59,60,29,61],"Diarrhea Infectious","Algorithms","Clinical Decision-making",[63,64,65,66],"Diarrhea","Treatment Algorithm","mHealth","Antimicrobial stewardship","2026-04-13",{"date":69,"type":37},"2026-04-20",{"date":71,"type":21},"2026-04-12",{"date":73,"type":21},"2026-06-30",{"name":75,"class":44},"Daniel Leung",{"id":77,"slug":78,"hasResults":11,"nctId":79,"briefTitle":80,"officialTitle":81,"acronym":4,"eligibilityCriteria":82,"healthyVolunteers":11,"sex":17,"minAge":52,"maxAge":4,"enrollmentInfo":83,"targetDuration":4,"studyType":22,"phases":85,"briefSummary":86,"conditions":87,"keywords":4,"overallStatus":89,"whyStopped":4,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":93,"completionDateStruct":95,"leadSponsor":97,"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":84,"type":21},2106,[56],"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.",[88,29],"Cirrhosis","RECRUITING","2026-03-26",{"date":92,"type":37},"2026-04-01",{"date":94,"type":37},"2025-01-14",{"date":96,"type":21},"2027-01",{"name":98,"class":44},"University of California, San Francisco",1,{"id":101,"slug":102,"hasResults":11,"nctId":103,"briefTitle":104,"officialTitle":105,"acronym":106,"eligibilityCriteria":107,"healthyVolunteers":108,"sex":17,"minAge":52,"maxAge":4,"enrollmentInfo":109,"targetDuration":4,"studyType":22,"phases":111,"briefSummary":112,"conditions":113,"keywords":116,"overallStatus":89,"whyStopped":4,"lastUpdateSubmitDate":127,"lastUpdatePostDateStruct":128,"startDateStruct":130,"completionDateStruct":132,"leadSponsor":134,"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.",true,{"count":110,"type":21},100,[56],"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.",[114,61,115,29],"Diagnosis","Artificial Intelligence (AI) in Diagnosis",[117,118,119,120,121,122,123,124,125,126],"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":129,"type":37},"2026-01-20",{"date":131,"type":37},"2025-11-20",{"date":133,"type":21},"2026-12-31",{"name":135,"class":44},"University Hospital, Lille",{"id":137,"slug":138,"hasResults":11,"nctId":139,"briefTitle":140,"officialTitle":141,"acronym":4,"eligibilityCriteria":142,"healthyVolunteers":11,"sex":17,"minAge":143,"maxAge":4,"enrollmentInfo":144,"targetDuration":4,"studyType":22,"phases":146,"briefSummary":147,"conditions":148,"keywords":154,"overallStatus":89,"whyStopped":4,"lastUpdateSubmitDate":160,"lastUpdatePostDateStruct":161,"startDateStruct":163,"completionDateStruct":165,"leadSponsor":167,"locationsCount":169},"100605853","developing-an-innovative-decision-support-tool-for-pediatric-neuromuscular-scoliosis-100605853","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 Duchenne's or Becker's muscular dystrophy due to potential disease modifying therapies that may alter curve progression.","8 Years",{"count":145,"type":21},110,[56],"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.",[149,150,151,152,29,153],"Children With Medical Complexity (CMC)","Multiple Chronic Conditions","Neuromuscular Scoliosis","Shared Decision Making","Decision Aids",[155,156,157,158,159],"children with medical complexity","shared decision making","values clarification","uncertainty communication","decision support tool","2025-12-04",{"date":162,"type":37},"2025-12-05",{"date":164,"type":37},"2025-10-21",{"date":166,"type":21},"2027-03-31",{"name":168,"class":44},"University of Utah",2,{"id":171,"slug":172,"hasResults":11,"nctId":173,"briefTitle":174,"officialTitle":175,"acronym":176,"eligibilityCriteria":177,"healthyVolunteers":11,"sex":17,"minAge":52,"maxAge":4,"enrollmentInfo":178,"targetDuration":4,"studyType":22,"phases":180,"briefSummary":181,"conditions":182,"keywords":183,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":186,"lastUpdatePostDateStruct":187,"startDateStruct":189,"completionDateStruct":191,"leadSponsor":193,"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":179,"type":21},212,[56],"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).",[29],[29,184,185],"Telemedicine","cardiac rehabilitation","2022-01-06",{"date":188,"type":37},"2022-01-19",{"date":190,"type":21},"2023-01",{"date":192,"type":21},"2026-12",{"name":194,"class":44},"Escola Superior de Enfermagem de Coimbra"]