Patient Safety

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 conference convenes interdisciplinary experts from the United States and abroad to define priorities and goals for researching technology use in Long-Term Post-Acute Care (LTPAC), and to identify factors that support or hinder its adoption in LTPAC settings, aiming to promote safer, higher quality, more accessible, and equitable LTPAC globally.

This research will use deep learning models to move a reactive full capacity protocol (FCP) for emergency department (ED) overcrowding interventions into a proactive FCP by predicting patient flow measures so that interventions may be activated to avoid ED overcrowding.

This research will implement, evaluate, and disseminate DDInteract, a shared decision making clinical decision support tool that aims to reduce harm from oral anticoagulant drug-drug interactions.

This research will design, implement, and evaluate an emergency department triage machine learning algorithm, with an emphasis on predicted patient acuity and complexity, and incorporate it into a clinician-facing clinical decision support tool to promote safer, higher quality, and more equitable care.

This research will develop, validate, and evaluate a decision support tool using a machine learning algorithm to standardize the approach to discontinuing disease-modifying therapies for multiple sclerosis.

This research will develop and evaluate a machine learning-augmented and telemedicine-augmented sociotechnical intervention for postoperative handoffs to reduce the risks of patient complications and improve patient-centered care.

This research evaluates an artificial intelligence risk predictive tool called CoMET that uses visual outputs of patient data to serve as an early warning system for patients at risk of cardiac decompensation to allow for earlier intervention and reduction in morbidity and mortality.

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 study explored the use of and experiences with sharing inpatient notes during pediatric oncology hospitalization between parents and clinicians, with findings suggesting its potential to positively impact parent engagement and understanding in pediatric inpatient care and providing an opportunity to identify potential patient safety issues.