Decision Support Systems, Clinical

The goal of this pilot hybrid type I efficacy/implementation 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. Participants 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.

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 0/1/2 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.

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. The main questions it aims to answer are: * Do doctors make sound, timely care decisions when they use SAGE? * Do those decisions match what a specialist would advise? Researchers will compare the decisions doctors make with and without SAGE. Doctors in the study will: * Review made-up patient cases (these are not real patients) * Make a decision for each case, their usual way and with SAGE

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. The investigators will use a Stepped Wedge Cluster Randomised Controlled study design with an integrated process evaluation. The 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). Primary Outcome: Clinician behaviour, assessed via the accuracy of risk prediction and clinician confidence. Secondary Outcome: Clinician acceptability, care process metrics, patient outcomes, and safety endpoints. Primary study population: Hospital trauma clinicians, following initial resuscitation of each eligible trauma patient, who will complete electronic questionnaires. Secondary 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.

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.

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. Before 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. The main questions this study aims to answer are: 1. Do doctors make more correct diagnoses when they can see AI suggestions? 2. Does seeing AI suggestions change how confident doctors feel about their diagnosis? Researchers 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. Participants will complete up to 10 online clinical cases. For each case, they will: 1. Read a short medical scenario 2. Suggest up to three possible diagnoses (If in the AI group) Review the AI's suggestions and decide whether to change their answer The study will also look at how long participants take to answer each case and how the AI's performance compares to the human answers.

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. Using 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. Time 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. The 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).