View clinical trials related to Electronic Medical Records.
Filter by:The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.
This is a multi-site, cluster-randomized controlled trial to test the effectiveness of patient photographs displayed in electronic health record (EHR) systems to prevent wrong-patient order errors. The study will be conducted at three academic medical centers that utilize two different EHR systems. Because EHR systems have different functionality for displaying patient photographs, two different study designs will be employed. In Allscripts EHR, a 2-arm randomized trial will be conducted in which providers are randomized to view order verification alerts with versus without patient photographs when placing electronic orders. In Epic EHR, a 2x2 factorial trial will be conducted in which providers are randomized to one of four conditions: 1) no photograph; 2) photograph displayed in the banner only; 3) photograph displayed in a verification alert only; or 4) photograph displayed in the banner and verification alert. The main hypothesis of this study is that displaying patient photographs in the EHR will significantly reduce the frequency of wrong-patient order errors, providing health systems with the evidence needed to adopt this safety practice. We will use the Wrong-Patient Retract-and-Reorder (RAR) measure, a valid, reliable, and automated method for identifying wrong-patient orders, as the primary outcome measure. The RAR measure identifies orders placed for a patient that are retracted within 10 minutes, and then reordered by the same provider for a different patient within the next 10 minutes. These are near-miss errors, self-caught by the provider before they reach the patient and cause harm. In one study, the RAR measure identified more than 5,000 wrong-patient orders in 1 year, with a rate of 58 wrong-patient errors per 100,000 orders. Real-time telephone interviews with clinicians determined that the RAR measure correctly identified near-miss errors in 76.2% of cases. Thus, the RAR measure provides sufficient valid and reliable outcome data for this study.