Grants Awarded and Case Studies

Learn more about the Foundation's work to support patient safety research and educational training in these case studies:

Foundation Grant Recipients

2022 Grants

  • Reinventing Fall Risk Assessment Tools and Clinical Decision Making Through Data: To develop a tool that is automated, standardized, individualized, dynamic, and can be integrated into nursing and physician workflows (Johns Hopkins University)
  • Reducing Infections in Post-Surgical Events (RISE): To support the creation of a tool that will increase the efficiency of infection preventionists who are working to detect surgical site infection events (UMass Memorial Health Care)
  • OR Crisis Checklists, Second Generation: To support the improved capacity of clinicians to rescue patients from complications and to minimize the resultant harm (Ariadne Labs, a joint center for health systems innovation at Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health)
  • Immersive Medical and Nursing Education and Simulation for Trainees: Using a HoloPatient to teach bedside procedures to support the development of a procedural training platform that will drive hands-on skill development (University of Michigan)

2021 Grants

  • Assessing Surgical Competency Through Automated AI-Powered Surgical Video Analysis: To develop and implement an artificial intelligence (AI) system that evaluates cataract surgeon performance through automated analysis (University of Michigan)
  • Enhanced Diagnostic Reasoning (eDRx): To address the need to boost emergency department trainee diagnostic reasoning skills through ambient and artificial intelligence (University of Michigan)
  • Identifying and Mitigating Patient Safety Risks Associated with Referrals from Telehealth to In-Person Care: To support a comprehensive analysis to identify telehealth referral risks and develop a rigorous assessment tool to help facilities mitigate risks during the referral process (MedStar Health Research Institute)
  • Implementation of an All-Cause Deterioration Model for Adult Inpatients: To support the implementation of PICTURE (Predicting Inpatient Acute Care Transfers and Other UnfoReseen Events), an early warning system that predicts patient deterioration, into the clinical workflow (University of Michigan)

2020 Grants

  • An intuitive, nonintrusive approach to reduce patient harm from inappropriate dosing of high-risk drugs in older adult patients across an urban safety net hospital system (Icahn School of Medicine at Mount Sinai and NYC Health + Hospitals)
  • Immersive Virtual Reality Environment for Training Acute Care Teams (iREACT) (Department of Emergency Medicine, University of Michigan Medical School)
  • Perioperative Deterioration: Early Recognition, Rapid Response, and the End of Failure-to-Rescue (Anesthesia Patient Safety Foundation)

We are no longer accepting new applicants for the 2023 grant cycle. Please check back in early 2024.