Hospital

This research explores using natural language processing and generative AI to capture and structure social determinants of health from patient narratives, aiming to improve data completeness and quality, enhance clinical decision support, and reduce the manual burden on clinical staff in routine care.

The study will develop and test a vendor-compatible clinical decision support system to support intensive care unit nurses and physical therapists at the point of care in the timely identification of patients suitable for activity interventions and the creation of personalized activity plans.

This research will develop and evaluate the Perioperative Optimization of Senior Health (myPOSH) mobile application that supports care transitions and the needs of older adults undergoing surgery and their care partners.

This research will create clinical decision support artifacts for three patient-centered outcomes research guidelines around advanced diagnostic imaging using standards to allow them to be shareable, interoperable, and scalable; and implement them in different workflows and settings measuring their impact. 

This research will refine a current health information exchange platform to improve data exchange for inter-hospital transfers, evaluate its impact, and create a dissemination toolkit so that others may adopt this model.

This research aims to examine changes in nursing clinical documentation from 2019 through the COVID-19 pandemic timeline to observe the impact of electronic health record burden on patient care.

This research will test and validate a machine learning predictive analytic intervention to optimize the timely and appropriate use of interpreters for hospitalized patients with language barriers and complex care needs.

This research will develop and test novel approaches for using electronic health record metadata to characterize and evaluate hospital discharge planning practices, with the long-term goal of providing tools to monitor and strengthen specific behaviors within discharge workflows that optimize post-acute transitions of care.

This research will adapt an existing interoperable clinical decision support (CDS) tool for prevention of venous thromboembolism (VTE) to those with traumatic brain injury, scale it using an implementation strategy called SCALED (Scaling AcceptabLE cDs), and evaluate that approach to ensure that CDS tools are based on the most current evidence.

This research will assess the validity of patient-level computable social factor phenotypes used to predict a patient’s risk of increased healthcare utilization and costs.