Clinical Decision-making

This study will evaluate whether, relative to conventional information retrieval approaches, direct large language models (LLM) access and LLM use training can improve the overall clinical decision-making ability of rural physicians in low-resource grassroots healthcare settings.

This randomized controlled trial aims to evaluate the effects of VRS on decision-making skills, self-confidence levels, and anxiety of psychiatric nursing students. Students participating in the study will be divided into two groups: intervention and control groups. VRS, which includes a scenario of a patient with schizophrenia, will be applied to the intervention group at the beginning of the academic term, before students start clinical practice, and in the middle of the term. The control group will be subjected to theoretical courses and clinical practice. Data will be collected using the Clinical Decision-Making, Self-Confidence, and Anxiety Scale, Personal Information Form, and Modified Simulation Effectiveness Tool. Measurements will be performed at three time points: before the simulation, in the middle of the academic term, and at the end of the academic term. In addition, debriefing sessions will be held with students participating in VRS in groups of 4-5 within the scope of the simulation process. It is expected that the findings obtained as a result of the study will make significant contributions to the literature in understanding the effects of VRS on psychiatric nursing education. At the same time, this study aims to demonstrate that this VRS, designed specifically for the Turkish language and culture, can be an effective tool to increase the self-confidence levels of psychiatric nursing students in their clinical decision-making processes and to reduce their anxiety levels within the scope of the psychiatric nursing course. The results of the study are intended to guide the development of new approaches to the use of technology in psychiatric nursing education and the integration of technology-based VRS into the psychiatric nursing curriculum.

This multicenter, scenario-based cross-sectional study aims to examine the effect of operating room nurses' knowledge of medical device-related pressure injuries on their clinical decision-making skills. The study will be conducted with approximately 165 operating room nurses working in three different hospitals in Gaziantep, Türkiye. Data will be collected using a descriptive information form, a validated medical device-related pressure injury knowledge scale, and a scenario-based clinical decision-making assessment form. The study will evaluate the relationship between nurses' knowledge levels and their clinical decision-making performance, as well as identify factors influencing these outcomes. The findings are expected to contribute to improving patient safety, enhancing nursing education, and supporting evidence-based clinical decision-making in perioperative care.

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.

The goal of this interrupted time-series analysis is to evaluate the impact of the de-implementation of urine dipsticks as a diagnostic tool for urinary tract infections (UTIs) in hospitalized patients in the North Denmark Region. The main question it aims to answer is: How does de-implementation of urine dipsticks affect the diagnosis and management of UTIs and related disorders? Specifically, does it change the following parameters: * Number and severity of UTI infections (lower and upper UTI, non-severe and severe) * Antibiotic prescription (overall, antibiotic classes, administration routes, duration, dosages) * Number of urine cultures and number of positive urine cultures * Risks of admission to intensive care units and 30-day mortality * Risk of drug toxicity * Length of hospital stay * Risk of admission to intensive care unit * 30-day risk of readmission after discharge * 6-month risks of Clostridioides difficile enterocolitis and de novo antimicrobial resistance in cultures obtained during routine clinical care. Researchers hypothesize that de-implementing urine dipsticks will lead to a reduced frequency of diagnosed cystitis, reduced antibiotic use, and fewer urine cultures without negatively affecting patient mortality or readmission risk. Researchers will compare the outcomes before and after the discontinuation of urine dipsticks across hospitals in the North Denmark Region. Furthermore, results will be compared to another Danish administrative healthcare region where dipsticks are still in use as well as urine culture data from the primary sector in the North Denmark Region. Since this is a registry-based observational study utilizing data from the electronic patient record system in the North Denmark Region, no direct contact will be made with participants.