Clinical Research Directory

Improving Hypertension Management Through Preference List Defaults

This study aims to evaluate whether modifying the EPIC preference list to display combination blood pressure (BP) medications at the top and/or adding "(PREFERRED)" to the beginning of the medication listing increases prescribing of these medications. Combination BP medications are aligned with value-based care guidelines and may improve patient adherence and reduce pill burden. Currently, these medications may be under-prescribed in part due to their low visibility in the EPIC prescribing interface.

Closing the GAPS: Guideline Adherence, Prevention and Surveillance in Hereditary Cancer

The goal of this clinical trial is to see if a software platform can improve cancer screening in young adults with genetic risk for cancer. The trial will also help improve the software platform (Nest). The main questions it aims to answer are: * Do Nest users know more about their cancer risks and recommended care than non-users? * Do Nest users have less psychological distress than non-users? * Do Nest users share cancer risks with family and other doctors more than non-users? * Are Nest users more likely than non-users to have up-to-date care plans? Researchers will compare Nest users to non-users to see if the Nest users are more likely to do recommended cancer screening. Participants will: * Have a genetic counseling or follow up visit * Take a post-visit survey * Intervention arm only: use the Nest Patient Navigator * Complete screening and follow-up care recommended by doctors

Artificial Intelligence Clinical Decision

The goal of this study is to investigate the effect of AI integration into clinical physical therapy clinical decision in improving cost effectiveness and clinical outcomes purposes of the study are: 1. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of pain in myofascial pain syndrome. 2. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving joint range of motion limitations in myofascial pain syndrome. 3. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving muscle strength in myofascial pain syndrome. 4. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of functional limitation in myofascial pain syndrome. 5. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on cost-effectiveness in physical therapy management of myofascial pain syndrome.

Optimization of Medical Time in the Emergency Department: Impact of an AI-Based System on Prescription Entry

Drug-related iatrogenesis is a major public health issue, accounting for a significant proportion of adverse events and hospitalizations in emergency departments. Optimizing prescription management in this context is critical to improve both patient safety and physician efficiency This study aims to evaluate the impact of the POSOS AI-driven device on the medical time required for prescription management in polymedicated patients admitted to emergency departments. The main objective is to establish whether the use of POSOS can reduce transcription time compared to standard electronic management.

Clinical Decision Support to Identify Pediatric Patients With Undiagnosed Genetic Disease

This study will evaluate the effectiveness of SIGHT as a clinical support system to prompt provider/patient discussion and shared decision making regarding the need for genetic testing in the form of a chromosomal microarray. Identifying patients at high predicted probability of needing a test in clinical settings will be examined to determine if it decreases the duration of time to testing and increases diagnostic yield. SIGHT requires only data already collected in routine clinical encounters and is calculated prior to a clinical visit at VUMC.

AI-Orchestrated Workflow Versus Consultant Ophthalmologist for Refractive Surgery and Keratoconus Diagnosis (AEYE Trial)

Background and Rationale: Laser vision correction procedures, such as LASIK (Laser-Assisted In Situ Keratomileusis), PRK (Photorefractive Keratectomy), and SMILE (Small Incision Lenticule Extraction), are highly effective but require careful preoperative screening to ensure safety. One of the most critical aspects of screening is identifying keratoconus and other corneal ectatic disorders-conditions that cause progressive thinning and bulging of the cornea, often contraindicating surgery. Early detection is essential to avoid vision-threatening complications. Despite advanced corneal imaging tools such as Scheimpflug tomography and anterior segment optical coherence tomography (AS-OCT), accurate diagnosis-particularly in borderline or early-stage cases-remains challenging and subject to variability in human interpretation. Artificial intelligence (AI) offers the potential to improve diagnostic precision, reduce oversight, and standardize surgical planning. Purpose of the Study: This study evaluates the performance of AEYE (Automated Evaluation for Your Eye), a multi-agent AI system designed to support ophthalmologists in diagnosing keratoconus and determining refractive surgery eligibility. AEYE simulates the clinical workflow of an anterior segment specialist by orchestrating three specialized agents: History \& Risk Agent: Reviews patient history and extracts risk factors. Imaging Agent: Analyzes corneal tomography, AS-OCT, and epithelial mapping scans. Surgical Decision Agent: Integrates all findings, assigns a diagnosis, and recommends appropriate treatment options, including surgical eligibility or corneal cross-linking (CXL). Study Design: The study includes 50 real-world patient cases, both retrospective (from 2020 onward) and prospective, who were evaluated for refractive surgery or keratoconus. Each case is analyzed independently by AEYE and a consultant ophthalmologist (blinded to AI output), using the same multimodal clinical and imaging data. Diagnostic accuracy, agreement in surgical recommendations, and workflow efficiency are assessed. Anticipated Impact: By comparing AI-derived decisions with expert clinical judgment, this study aims to validate whether structured AI workflows like AEYE can serve as reliable, safe, and explainable decision support tools. If successful, AEYE may offer a scalable solution to reduce diagnostic variability and enhance the safety and consistency of refractive surgery screening.

Clinical Decision Support for Familial Hypercholesterolemia

A cluster randomized study in the primary care setting to evaluate a computer-based clinical decision support system to aid in the identification and management of patients with FH. The primary outcome of the study is the number of patients diagnosed with FH thirty-six months after study initiation.

Mortality and Rehospitalization Risk Assessment by Skilled Caregivers Compared to Existing Tools in Acute Geriatric Departments

Mortality and Rehospitalization Risk Assessment by Skilled Caregivers Compared to Existing Tools in Acute Geriatric Departments Background The elderly population in Israel and worldwide is steadily increasing, leading to greater demand for medical services, including palliative care. In 2019, individuals aged 65+ accounted for 64% of hospital admissions and 70% of hospital days in Israel. Approximately 19% of these were readmissions, a rate that increases with age. Effective tools for identifying patients at high risk of rehospitalization and mortality are lacking, which, if improved, could benefit patients through targeted palliative and end-of-life care. Enhanced tools could reduce unnecessary interventions, improve patient well-being, and alleviate economic burdens on healthcare. Research Objectives 1. Evaluate mortality and rehospitalization rates in acute geriatric departments. 2. Identify risk factors for rehospitalization and mortality in acutely hospitalized elderly patients. 3. Compare the effectiveness of skilled caregiver assessments versus validated prediction tools for mortality and rehospitalization within one year. Hypotheses 1. Mortality and rehospitalization rates in acute geriatric departments are comparable to those in internal medicine. 2. Multiple factors-such as age, family support, comorbidities, functional and cognitive status-correlate with mortality risk. 3. Skilled caregiver assessments predict mortality and rehospitalization more accurately than existing validated tools. Study Design Type: Prospective cohort observational study. Location: Shmuel Harofe Hospital. Study Population Participants are elderly patients admitted to acute geriatric departments at Shmuel Harofe Hospital for acute conditions. Approximately 600 participants will be recruited, with an additional 200-300 if statistical analysis reveals trends. Recruitment Period: Two years. Follow-up Period: Up to one year post-admission. Methods and Materials Data will be collected on demographic, functional, cognitive, and emotional factors, as well as clinical history, hospital admissions, comorbidities, and lab results. Predictive assessments will include: 1. Mortality Prediction using the WALTER Index for the elderly. 2. Rehospitalization Risk using the LACE Index, validated for 30-day readmission risk. 3. Subjective Caregiver Assessments from geriatric specialists and nursing supervisors, estimating life expectancy and 30-day, 3-month, and 1-year rehospitalization risk. Data Analysis Data will be coded and statistically analyzed without interventions outside of standard care. The WALTER and LACE indices will utilize existing clinical data. Ethical Considerations As this is an observational study without intervention, a waiver for informed consent was granted. Importance of Research Early identification of high-risk patients will enable preventive interventions, support transitions to palliative care where appropriate, and promote advance directives, ultimately improving patient care and reducing healthcare costs by preventing costly, unnecessary readmissions and interventions.