Patient Safety

This research will evaluate the effectiveness of Bedside Notes, a digital health solution designed to provide caregivers with real-time access to clinical notes during their child’s hospitalization, with the goal of improving caregiver engagement in identifying and reporting safety concerns to reduce medical errors.

Using a conference model, this study convenes a multidisciplinary group of experts to explore the integration of human factors engineering approaches in the implementation of artificial intelligence in healthcare, providing an opportunity for ongoing collaboration and research to disseminate knowledge and implement best practices that enhance efficiency, prevent provider burnout, and ultimately improve healthcare quality, safety, and value.

This research develops and evaluates an artificial intelligence-enhanced pretreatment peer-review process in radiation oncology, aiming to improve patient safety by reducing variability among providers in treatment planning, minimizing clinical errors, and enhancing overall treatment outcomes.

This research aims to improve the early detection of venous thromboembolism in primary and urgent care by using mixed methods (stakeholder interviews and surveys, electronic health records, and machine learning) to better understand missed and delayed diagnoses, identify risk factors, and develop tools to enhance patient safety.

This research tests the effectiveness of MedSMA℞T Mobile, a mobile adaptation of the MedSMA℞T Families game-based tool, in delivering preventive education that enhances opioid safety knowledge and communication among adolescents, parents, and healthcare providers in emergency departments, with the goal of reducing opioid misuse and improving family medication safety practices.

The study will develop and implement a validated machine learning model to optimize voiding cystourethrogram timing and use for diagnosing vesicoureteral reflux (VUR) in children, aiming to reduce the significant health and economic impacts of VUR and recurrent febrile urinary tract infections (fUTIs) by standardizing practices, minimizing unnecessary procedures, and ensuring timely diagnosis for those at highest risk, ultimately seeking to prevent renal injury from fUTIs.

The study will develop, validate, implement, and assess the usability of a machine learning based clinical decision support tool designed to reduce laboratory testing overutilization in pediatric intensive care unit patients.

This research uses large language models and machine learning to retrospectively analyze electronic health records of patients with suspected sepsis and identify patterns in treatment outcomes, with the goal of shaping future clinical guidelines that help doctors select the most effective antibiotics for each patient, reduce unnecessary use of broad-spectrum antibiotics, lower the risks of drug resistance, and ultimately improve patient outcomes.

This study will develop a prototype guide for the safe and effective integration of ambient digital scribes into primary care, providing insights into how this artificial intelligence-driven technology transforms workflows while addressing critical safety and burnout concerns in diverse healthcare settings.

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.