3 Clinical Trials for Various Conditions
This study is designed to achieve the following aims: 1. Assess the relationship between the number of records open at the time of placing an order, and the risk of placing an order on the wrong patient. 2. Compare the incidence of wrong-patient orders in a "restricted environment" that limits its providers to only one record open at a time to an "unrestricted environment" where users can open a maximum of four records at once. 3. The results of this study will help inform decisions on how to safely implement EHR systems. 4. The results of this study will inform a larger scale health IT implementation research project evaluating the balance between the wrong-patient error risks and potential efficiency gains of having multiple records open at once, with rigorous research methodologies.
This clinical trial aims to evaluate the pilot implementation of a machine-learning (ML)-driven clinical decision support (CDS) tool designed to predict opioid overdose risk within the electronic health record (EHR) system at UF Health Internal Medicine and Family Medicine clinics in Gainesville, Florida. The study will use a pre- versus post-implementation design to compare outcomes within clinics, focusing on measures such as naloxone prescribing rates and opioid overdose occurrences. Researchers will also assess the usability, acceptability, and feasibility of the CDS tool through qualitative interviews with primary care clinicians (PCPs) in the participating clinics.
This study will evaluate various performance metrics of emergency department operations after the implementation of computerized physician order entry in an academic emergency department.