Clinical Research Directory

CirrhosisRx CDS System

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.

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.

Developing an Innovative Decision Support Tool for Pediatric Neuromuscular Scoliosis

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.

Decision Support for Heart Failure Prescribing

Clinical decision support (CDS) tools can 'nudge' clinicians to make the best decisions easy. Although required by "meaningful use" regulations, more than 40% of CDS lead to no change and the remaining lead to improvements that are modest at best. This is because CDS tools often ignore contextual factors and present irrelevant information. Although many tools have undergone patient-specific optimization, 'traditional CDS' are rarely clinician-specific. For example, a traditional CDS tool for beta blockers and heart failure with reduced ejection fraction (HFrEF) addresses common prescribing misconceptions by stating asthma is not a contraindication and providing a safe threshold for blood pressure. For clinicians without these misconceptions, these statements are irrelevant and distract from key information. A 'personalized CDS' would evaluate clinician past prescribing patterns to determine whether prescribing misconceptions might exist and then conditionally present information to address those misconceptions. The objective of this research is to create personalized clinician-specific CDS that overcome shortcomings of traditional CDS. The central hypothesis is a personalized CDS that minimizes irrelevant information will lead to a higher rate of prescribing guideline-directed management and therapy (GDMT) for HFrEF compared to a traditional CDS.

Evaluating the Reach of Clinical Decision Support for Patients With Heart Failure

To work best, clinical decision support tools (CDS) must be timed to provide support when healthcare decisions are made, which includes virtual visits (phone or video). Unfortunately, most CDS tools are either missing from virtual visits or not designed for the unique context of virtual visits (e.g., availability of physical assessments and labs, different workflows), which could generate new inequities for patients more likely to use virtual visits. The objective of this study is to test the reach, feasibility and acceptability of a new CDS tool for heart failure with reduced ejection fraction (HFrEF) during virtual visits. This new CDS tool was developed through an iterative design process, and will be compared to an existing HFrEF CDS tool in a randomized pilot study at outpatient cardiology clinics throughout the UCHealth system.

Clinical Decision Support System for Remote Monitoring of Cardiovascular Disease Patients

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).