Clinical Research Directory

Teaching Doctors in Training to Reason With Artificial Intelligence

The purpose of the TEACH-AI study is to assess whether a brief, structured workshop on artificial intelligence can improve the performance of medicine doctors in training (i.e. residents) in their diagnostic and management reasoning. In this multi-site randomized controlled trial, internal medicine and family medicine residents are assigned either to receive an in-person workshop on safe, effective LLM use before a standardized AI-assisted assessment, or to complete the same assessment before receiving the workshop. Residents will review clinical cases that are fully synthetic, no protected health information is used, using a password protected LLM interface.

Effectiveness of Serious Games on Nursing Students' Clinical Reasoning

This randomized controlled trial aims to evaluate the effectiveness of serious game-based learning compared with conventional learning in improving clinical reasoning among nursing students in Vietnam. Additionally, the study will assess students' self-confidence in pediatric nursing care, intrinsic motivation, and satisfaction with the learning experience. A minimum of 104 second-year nursing students will be randomly assigned to either the intervention group (serious game-based learning) or the control group (conventional learning). Both groups will receive six teaching sessions based on the content of the Pediatric Care Nursing curriculum, conducted outside the formal curriculum, with each session lasting approximately three hours. Outcomes will be measured at multiple time points, while learning satisfaction will be assessed immediately after completion of the intervention.

Effectiveness of an Evidence-Based Respiratory and Renal Care Training With Case-Based Discussion on Nurses' Clinical Reasoning, Knowledge & Practice, Nurse-Physician Collaboration and Mechanically Ventilated Patient Outcomes in a Tertiary-Level ICU in Bangladesh

This study will evaluate the effectiveness of an evidence-based respiratory and renal care training program combined with case-based discussion on nurses' clinical reasoning, knowledge \& practice, nurse-physician collaboration, as well as selected clinical outcomes among mechanically ventilated patients in a tertiary-level ICU in Bangladesh. The intervention will provide structured education and practical training to ICU nurses on evidence-based respiratory and renal care for mechanically ventilated patients. Case-based discussions will be conducted to promote clinical reasoning, evidence-based decision-making, and nurse-physician collaboration during daily ICU rounds. Nurses' outcomes will be assessed before and after the intervention, focusing on clinical reasoning, knowledge, practice, and nurse-physician collaboration. Patient outcomes will be assessed using historical pre-intervention data and post-intervention data among eligible mechanically ventilated ICU patients, focusing on ventilator-associated events (VAE), ventilator-associated pneumonia (VAP), Acute Kidney Injury (AKI), duration of mechanical ventilation, ICU length of stay, and ICU mortality. The study is intended to determine whether evidence-based respiratory and renal care training with case-based discussion can improve ICU nurses' clinical reasoning, knowledge, practice, nurse-physician collaboration and contribute to improved outcomes among mechanically ventilated patients.

Animation-Based Orotracheal Suction Training for Expert Nurses

This randomized controlled trial aimed to evaluate the effect of animation-based orotracheal suction training on the clinical reasoning competency and knowledge levels of expert pediatric nurses. Orotracheal suctioning is a complex and invasive nursing procedure requiring advanced clinical reasoning, rapid decision-making, and patient safety awareness. Although digital learning approaches are increasingly used in nursing education, evidence regarding their effectiveness among expert nurses remains limited. A total of 66 pediatric nurses with at least five years of clinical experience and classified as expert nurses according to Patricia Benner's Novice-to-Expert Model were randomly assigned to either an intervention group receiving animation-based mobile training or a control group receiving PowerPoint-supported traditional training. Clinical reasoning competency and knowledge regarding orotracheal suctioning were assessed before and after the educational intervention.

Clinical Performance and Multitasking in Anesthesia : a Simulation-based Study

Cognitive failure under multitasking conditions has been demonstrated in various settings, including healthcare. However, in anesthesiology, the impact of multitasking on clinical performance has not yet been fully characterized. The objective of our study is to evaluate the impact of multitasking on anesthesiologists' clinical performance in a simulated setting.

Simulation-Based Learning on Clinical Reasoning, Knowledge Retention, and Transfer in Undergraduate Medical Education

This randomized controlled trial evaluates whether a digital, video-based clinical-reasoning simulation improves clinical-reasoning performance compared with conventional lecture-based teaching across multiple clinical disciplines, including orthopedics and ophthalmology. Within each discipline, participants are randomly allocated 1:1 to an intervention group or a control group. The intervention group learns the assigned topic through an interactive, web-based simulation built on real patient video cases with structured clinical-reasoning steps. The control group receives the same learning objectives and content through a standard didactic lecture, over a comparable duration. After the learning session, all participants complete the same set of standardized clinical scenarios, scored with a predefined rubric. The primary outcome is the post-intervention clinical-reasoning score. Secondary outcomes include diagnostic accuracy, confidence, and satisfaction. The trial uses a shared protocol across discipline-specific cohorts to allow consistent, pre-specified comparison of simulation-based versus lecture-based instruction.

Impact of Concept Mapping in Case-Based Learning Among Medical Students

This prospective randomized study aims to compare the impact of three Case-Based Learning (CBL) approaches on knowledge acquisition, knowledge retention, and clinical reasoning development among third-year medical students during their obstetrics and gynecology rotation. Participants are randomly assigned to one of three groups: CBL without concept mapping, CBL with a teacher-constructed concept map, or CBL with a concept map co-constructed by students during the learning session. Knowledge acquisition and retention are assessed using pre-tests, immediate post-tests, and delayed post-tests. Clinical reasoning and knowledge organization are evaluated using a synthesis exercise. The study seeks to determine whether concept mapping and the modality of its integration into CBL enhance learning outcomes and clinical reasoning skills in undergraduate medical education.

cRCT of AI Agent in Clinital Resoning Training

Traditional medical education has long emphasized one-way transmission of theoretical knowledge, which presents limitations in the systematic cultivation of clinical reasoning skills among medical students. Miller's pyramid of clinical competence emphasizes the gradual transformation from theoretical knowledge to clinical practice ability. Case-based learning (CBL), as a teaching method centered on real or simulated clinical cases, is a key strategy to address the above limitations. Artificial intelligence (AI)-assisted clinical reasoning training tools can overcome time and space constraints, and offer students repeatable, adaptive, and real-time feedback case training, thereby reinforcing the sustained role of CBL in clinical reasoning development. Currently, it still lacks high-quality evidence from randomized controlled trials on the impact of AI agents on medical students' clinical reasoning skills. This study plans to evaluate the impact of an AI clinical reasoning training agent on students' clinical reasoning training outcomes and CBL learning experience. Primary Objective: To evaluate the impact of the AI agent on student learning outcomes (course examination scores and clinical reasoning test scores). Secondary Objective: To investigate students' AI acceptance (perceived usefulness, perceived ease of use, satisfaction, and intention to use). This study adopts a two-arm parallel cluster randomized controlled trial design. The trial is designed and reported in accordance with the CONSORT statement. The study population will recruit Class of 2021 medical students (8-year program) from Peking Union Medical College and Class of 2020 medical students (8-year program) from Tsinghua University School of Medicine. Both cohorts are officially enrolled in the "Comprehensive Clinical Course" for the 2025-2026 academic year, have consistent foundational knowledge in basic medicine and diagnostics, and are in the phase of clinical medicine theory learning, not yet having entered clinical practice. Using PASS 2025 software, the sample size per arm for the cRCT is 39, with number of clusters per arm K=N/M =13, Considering a 10% attrition or exclusion rate, the target recruitment is 88 participants. Considering potential heterogeneity in baseline between students from the two schools, and possible contamination due to discussions among dormitory mates during the intervention, this study will adopt stratified cluster randomization, first stratifying by school, then using dormitory as the smallest randomization unit. Dormitories will be sorted by the random number, with the first half allocated to the intervention group and the second half to the control group. Participants' group assignment will be revealed via unique student ID only after baseline data collection and informed consent are completed. This study will select five topics from the "Comprehensive Clinical Course": "Infectious Diarrhea," "Viral Hepatitis," "Bloodstream Infection," "Infective Endocarditis," and "Central Nervous System Infection". Standardized cases will be provided by the teaching faculty, with two cases per topic, totaling 10 cases. These cases will be used to train AI agent. After class, the AI agent training tasks will be sent to the intervention group, and study materials will be distributed to the control group. Course examination scores and clinical reasoning test scores are the primary outcomes. AI technology acceptance including perceived usefulness, perceived ease of use, satisfaction, and intention to use are the secondary outcomes. This study has been approved by the Research Ethics Committee of Peking Union Medical College Hospital (Approval No.: I-26PJ0851).

Comparison of Peer Role-Playing-Based Simulation With Case-Based Learning in Physical Therapy Education

This study primarily aims to evaluate the impact of a 12-week peer role-play simulation model on the professional development of undergraduate physiotherapy students. Unlike traditional teacher-led case discussions, this study explores the effect of active participation in clinical scenarios, in which students alternate between the roles of 'clinician' and 'patient', on their academic achievement, self-efficacy and perceived clinical readiness. Specifically, the study aims to determine whether this interactive, low-cost pedagogical approach is a superior alternative to conventional lecture-based methods for preparing students for real-world clinical environments, particularly in high-enrolment academic settings.

AMIE's Clinical Conversational Abilities in an Urgent Care Setting

The purpose of this study is to determine the feasibility of a conversational artificial intelligence (AI) system to have a meaningful clinical conversation with a patient prior to an urgent care visit with their primary care physician. In this study, patients who are seeking an urgent care visit (that is, any type of medical visit with their primary care provider for a new complaint) will first have a conversation with an AI system. This interaction with the AI system will happen less than a week before their visit with their physician, and will be supervised by an independent physician who will interrupt in case there are any concerns about patient safety. After the interaction, a summary of the conversation will be sent to the patient's PCP, who will review prior to the in-person visit. The researchers will investigate: * Patient views on the AI system * PCP views on the AI system * Overall safety, as measured by the physician safety supervisor * Quality of clinical conversations, measured by standardized rubrics * Quality of diagnostic and management plans generated by the AI; these will not be shared with the patient or physician, but will be generated after the fact and compared with the actual diagnosis and management plan.

The Effect of Artificial Intelligence-Supported Intramuscular and Subcutaneous Injection Training on Nursing Students

The complexity of healthcare services and technological advances today have necessitated the adoption of innovative approaches in nursing education. Among these innovative approaches, artificial intelligence (AI) has established itself as a technology that is increasingly present in nursing education processes, offering a supportive, personalized, and interactive learning experience. AI's contributions to nursing students' acquisition of fundamental competencies such as clinical decision-making, skill development, and critical thinking are rapidly increasing. Especially in high-risk, invasive, and clinically skill-intensive applications, AI-supported educational models both enhance learning quality and support patient safety. Intramuscular and subcutaneous injections are among the basic invasive skills that nursing students must learn. These applications require a high level of cognitive and psychomotor competence from students. Incorrect injection practices can lead to complications such as drug absorption problems, nerve damage, hematoma, or infection, making it critically important to teach these skills correctly and safely. In this context, AI-supported education systems stand out as an effective tool for teaching injection skills. Artificial intelligence-based chatbots provide students with both theoretical knowledge and practical guidance. For example, before injecting a muscle group, a student can learn about the anatomy of the muscle, determine the correct angle, and remember precautions against potential complications through the chatbot. Artificial intelligence also reinforces the learning process by instantly answering students' questions, preventing the acquisition of incorrect information. Recent studies emphasize that AI-supported learning tools positively influence students' attitudes toward learning, increasing their motivation and academic satisfaction levels. Accordingly, the integration of AI-based technologies in the process of training future nurses is no longer an option but a necessity. Particularly in complex and delicate skills such as intramuscular and subcutaneous injections, AI-supported chatbots can facilitate student learning, increase skill accuracy, and support clinical safety. Therefore, it is crucial for nursing education programs to combine artificial intelligence technologies with pedagogical foundations to provide student-centered, safe, and effective learning environments.

How Pediatric Nurses' Reasoning Skills Affect Quality Nursing Care

This descriptive study aims to examine the impact of pediatric nurses' hypothetical and creative reasoning skills on the quality of nursing care. The research seeks to answer the following questions: Do the sociodemographic characteristics of pediatric nurses influence the quality of nursing care they provide? Do pediatric nurses' hypothetical and creative reasoning skills affect the quality of the nursing care they deliver? Participants will be asked to select the response that best reflects their own perspective for each item. This process will take approximately 10 minutes.

RTSS Adoption Into Physiotherapists Documentation

The Rehabilitation Treatment Specification System (RTSS) is a framework that aims to support clinicians in reasoning and documenting how to deliver rehabilitation. This was explored in a pilot study in 2023 that tested the feasibility of a 6-week teaching programme aiming to support the adoption of the RTSS into practise of physiotherapists and subsequently measure its effect on clinicians reasoning of treatment. Whilst clinical reasoning scores improved, adoption of the framework remained low. Interviews within this study revealed the difficulty clinicians experienced in transitioning to documenting the RTSS. Further local work within Guy's \& St Thomas NHS foundation trust (GSTT) resulted in modified integrations of the RTSS into electronic health record systems as well as training, yet adoption still remained limited. With documentation being cited as an influence of RTSS adoption and thus contributing to its clinical benefit, this qualitative study will attempt to make clear what factors facilitate and/or limit RTSS documentation adoption, and how they exert their influence. The findings from this study will inform the design of interventions that will aid future study of benefit of the RTSS on physiotherapy practice. To achieve these aims, interviews will be conducted virtually via MS Teams, by the lead researcher, with approximately 12-16 volunteering physiotherapists who have attempted to document the RTSS, within the site of GSTT. The interviews will last approximately 1 hour, and will be partially structured in nature to invite in-depth discussion of topic. Analyses of the data will be performed by a team of 3 clinical academics working between GSTT and King's College London with study findings to be disseminated in a peer-reviewed journal and at professional conferences