Search found 35 items
This research will examine the evidence around the value of order sets, while uncovering clinician perceptions that hinder their efficient use.
This research will develop, test, and evaluate a vendor-agnostic platform for clinical decision support rules that can be made available to any commercial electronic health record.
This project will evaluate the effects of a technology-based patient-reported outcomes system on patient management of type 2 diabetes in primary care practices.
This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
This project will adapt and evaluate a mobile health application to improve patient-reported asthma outcomes in New York.
This study will evaluate the effectiveness of patient photographs displayed in electronic health record systems for preventing wrong-patient errors.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
This project will develop and validate new measures needed for automatically identifying violations of the “Five Rights of Medication Safety”: right patient, right dose, right medication, right route, and right frequency.
This project will develop a patient-based dashboard to improve cognitive hygiene in the emergency department, aiding in the diagnosis of acute coronary syndrome.
This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.