This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.
This research aims to examine a health system’s four telehealth programs in response to the COVID-19 pandemic.
This research, using data from the country’s largest telehealth provider and claims from a large commercial payer, will examine the impact of the COVID-19 pandemic and telehealth on utilization, outcomes, disparities, and public health surveillance.
This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.
The goal of the CEPI Evidence Discovery and Retrieval (CEDAR) project is to make patient-centered outcomes research findings within AHRQ repositories more FAIR - findable, accessible, interoperable, and reusable - through technologies used by clinicians, researchers, implementers, patients, and others.
The aim of this research is to implement a clinical decision support tool to provide clinicians patient-specific and evidence-based treatment recommendations regarding the recognition and management of high blood pressure and hypertension in children and adolescents.
This research aims to determine the effectiveness of a program designed to reduce medication-related issues among patients during the hospital-to-skilled nursing facility transition.
This research will implement a personalized and electronically integrated shared clinical decision support system for left ventricular assist devices in patients with advanced heart failure.
The research team will implement and evaluate an integration application that incorporates relevant health information exchange data directly into the electronic health record in the emergency department.