This contract provided the administration and management of the Agency for Healthcare Research and Quality's “Step Up App Challenge: Advancing Care Through Patient Self-Assessments.”
This research will support development and testing of technical tools for use within electronic health records or other systems to collect patient-reported outcomes for clinical use and research.
This research will evaluate the lifecycle of clinical decision support (CDS), as currently implemented at most health systems, against a future CDS state that incorporates the use of shareable CDS resources created using the Agency for Healthcare Research and Quality’s CDS Connect tools.
This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
This project will develop and evaluate the impact of the Prevent Diabetes Mellitus Clinical Decision Support on clinical outcomes, healthcare process measures, and associated costs.
This project will redesign approaches for collecting and using allergy information with the goal of improving healthcare quality and safety, including completeness and accuracy of allergy data.
This project will use natural language processing and dynamic logic to create a high-fidelity model of risk of death to identify patients with low life expectancy.
This research will explore whether providing clinicians with contextual information at the point of care through the use of clinical decision support can reduce contextual errors, improve patient healthcare outcomes, and reduce misuse and overuse of medical services.
The goal of this project is to generate a systematic and replicable process for transforming evidence-based research findings, including findings from patient-centered outcomes research, into shareable clinical decision support (CDS) standards and a publicly available CDS prototype.
This project will develop and evaluate an electronic health record-embedded clinical decision support tool that draws upon the strength of analytical and naturalistic decision-making to optimize the use of blood cultures in critically ill children.