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 study will estimate the impact of a suite of clinical decision-support tools on structural, process, and clinical outcomes related to HIV care. The "enhanced EMR" package under investigation will include EMR monitoring tools, data quality control procedures and support, patient reports, alerts, and reminders about patient care. This intervention will be delivered by the Ministry of Health and Rwanda Biomedical Centre and monitored by the study team led by University of Rwanda's School of Public Health and Brown University.
Newborns in the neonatal intensive care unit (NICU) are at high risk for wrong-patient errors. Effective 2019, The Joint Commission requires that health systems adopt distinct methods of newborn identification as part of its National Patient Safety Goals. Displaying patient photographs in the electronic health record (EHR) is a promising strategy to improve identification of children and adults, but is unlikely to be effective for identifying newborns. This study assesses the use of Pictographs as a "photo equivalent" for improving identification of newborns in the NICU. This multi-site, two-arm, parallel group, cluster randomized controlled trial will test the effectiveness of Pictographs for preventing wrong-patient order errors in the NICU. Pictographs consist of three elements: 1) pictorial symbols of easy-to-remember objects (e.g., rainbow, lion); 2) the infant's given name (when available); and 3) a color-coded border indicating the infant's sex. The study will be conducted at three academic medical centers that utilize Epic EHR. All parents or guardians will be asked to select a unique Pictograph for each infant admitted to the NICU to be displayed on the isolette and in the EHR for the duration of the infant's hospital stay. All clinicians with the authority to place electronic orders in the study NICUs will be randomly assigned to either the intervention arm (Pictographs displayed in the EHR) or the control arm (no Pictographs displayed in the EHR). The main hypothesis is that clinicians assigned to view Pictographs in the EHR will have a significantly lower rate of wrong-patient order errors in the NICU versus clinicians assigned to no Pictographs. The primary outcome is wrong-patient order sessions, defined as a series of orders placed for a single patient by a single clinician that contains at least one wrong-patient order. The Wrong-Patient Retract-and-Reorder (RAR) measure, a validated, reliable, and automated method for identifying wrong-patient orders, will be used as the primary outcome measure. The Wrong-Patient RAR measure identifies one or more orders placed for a patient that are retracted within 10 minutes, and then reordered by the same clinician for a different patient within the next 10 minutes. In the validation study conducted at a large academic medical center, real-time telephone interviews with clinicians confirmed that 76.2% of RAR events were correctly identified by the measure as wrong-patient orders.
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.
The VHA is a leader in electronic medical records (EMR) use for patient care. It is believed that EMR use by doctors will improve patient-centeredness of visits, and improve clinical care. The proposed study will determine how doctors should use the EMR during patient consultations. We will also develop a training program to improve doctors ability to communicate with patients while using EMR.