7 Clinical Trials for Various Conditions
The proposed study is looking to examine the effects of High-Fidelity Patient Simulation (HFPS) on clinical reasoning skills and interprofessional competencies in Physical Therapy and Nursing students. The proposed study will have two objectives: 1. Assess the use of HFPS and whether it has an effect of improving physical therapy and nursing student performance related to clinical reasoning skills during simulated clinical situations. 2. Assess the use of HFPS and whether it has an effect of improving physical therapy and nursing student self-reported changes in team work and communication based on the IPEC core competencies Researchers will compare students who do not receive a simulation (Group C) to those who receive two simulations (Group E). Participants will: 1. Be assigned to one of 2 groups (Group C, Group E) 2. Based on group assignment receive no simulation or 2 simulations 3. Each group will be assessed at the end for their inter-professional attitudes and clinical reasoning skills
Brief Summary: The goal of this study is to implement and test an intelligent perioperative system (IPS) that in real-time predicts risk for postoperative complications using routine clinical data collected in electronic health records. The accuracy of computer-generated risk scores will be compared to physician's risk scores for the same patients. Physicians will be also asked to provide the opinion regarding the computer-generated risk scores using interactive interface with the program. The information regarding the risk scores performance will be collected during the two 6-month periods. The accuracy of IPS and physicians will be compared at the end at those two time periods.
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.
This study will assess the impact of immediate access to a customized version of GPT-4, a large language model, on performance in case-based diagnostic reasoning tasks. Specifically, it will compare this approach to a two-step process where participants first use traditional diagnostic decision support tools to support their diagnostic reasoning before gaining access to the customized GPT-4 model.
This study will evaluate the effect of providing access to GPT-4, a large language model, compared to traditional diagnostic decision support tools on performance on case-based diagnostic reasoning tasks.
The PRECISION study investigates the possibility of integrating a clinical decision support analytic intelligence into the clinical care flow at University of California San Francisco Medical Center (UCSF MC). optima-for-blood pressure \[optima4BP\], our innovation, transforms the episodic and reactive nature of uncontrolled HTN pharmacological treatment management into a process that is continuous, proactive, and personalized. In addition, the clinical adoption of this "technology of the future" will be investigated to establish its feasibility in being embedded into the clinical care flow. Today's hypertension (HTN) management paradigm often fails to align current patient health status with the most effective medication treatment. Without surveillance and rapid-cycles analysis of current patient health status and treatment efficacy, HTN management remains a struggle. optima4BP addresses pharmacological intervention management by identifying the need for a drug treatment change and recommending the most appropriate drug optimization. The PRECISION study represent a pilot trial intended to address 2 critical design components of optima4BP: 1. Can optima4BP interoperate with multiple IT platforms to collect and distribute data? 2. What is the treating physician confidence in using optima4BP?
This study will evaluate the effect of providing access to GPT-4, a large language model, compared to traditional management decision support tools on performance on case-based management reasoning tasks.