Clinical Research Directory

Artificial Intelligence as a Decision Making Tool in Emergency Department

This study will evaluate the performance of a large language model (LLM)-based clinical decision support system in the emergency department at Rambam Health Care Campus. The system analyzes structured patient data from the electronic health record and generates diagnostic and treatment recommendations for physicians. The study will assess the system's ability to support diagnostic reasoning, its impact on diagnostic accuracy when used by physicians, and its perceived clinical usefulness. In addition, a retrospective analysis of de-identified patient records will be conducted to compare LLM-generated recommendations with actual clinical outcomes, including diagnosis, disposition decisions, and length of stay. The study will also examine the performance of the system in a multilingual clinical environment where both Hebrew and English are used in medical documentation and communication.

The Utility and Feasibility of Accessible Diarrhea Etiology Prediction Tool (ADEPT) in an Informal Healthcare Setting

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. In 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.

Assessing Intensive Care Unit (ICU) Indications: Human vs. ChatGPT-4o Predictions

This retrospective study evaluates the accuracy of ICU admission indications by comparing clinical decisions with predictions from ChatGPT-4. Patient data, including demographics, vital signs, laboratory results, imaging findings, and clinical decisions, will be retrospectively collected and documented systematically using Case Report Forms. The model will be trained using ICU admission guidelines and tasked to predict ICU needs based on collected patient data. This study aims to systematically assess the alignment between AI-based predictions and clinical decisions for ICU admissions.

Clinicians' Trust in AI-Based Fetal Growth Estimates

This study examines how clinicians trust and use artificial intelligence (AI) when estimating fetal weight during pregnancy. Accurate assessment of fetal growth is important for identifying growth problems that may affect pregnancy management. New AI-based tools can estimate fetal weight from ultrasound images, but little is known about how clinicians trust these estimates or how uncertainty information influences their decisions. In this study, clinicians will review anonymized ultrasound cases and compare fetal weight estimates generated by an AI model with traditional estimates. Some clinicians will also be shown information about the AI model's performance and uncertainty, while others will not. Participants will be asked to choose which estimate they find most reliable, indicate their level of confidence, and decide whether they would recommend follow-up scans. The study aims to better understand how AI and uncertainty information affect clinical decision-making and trust among clinicians with different levels of experience.

Reasoning Enrichment With Feedback From IA in NEphrology Trial

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 Impact of De-implementing Urine Dipsticks for Diagnosis of UTIs in Hospitals

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.