Clinical Staff/Clinician


Bedside Notes: A Multicenter Trial to Improve Family Clinical Note Access and Outcomes for Hospitalized Children

Description

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.

Grant Number
R01 HS030098
Principal Investigator(s)

Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

Description

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.

Grant Number
R18 HS029474
Principal Investigator(s)

Complexity, Incidence, and Costs Related to Delayed Diagnosis of Venous Thromboembolism in Urban and Rural Primary and Urgent Care Settings

Description

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.

Grant Number
R01 HS030221
Principal Investigator(s)

Disseminating and Implementing MedSMA℞T Families in Emergency Departments: A Randomized Control Trial to Assess Effectiveness of an Evidence-Based Gaming Intervention to Reduce Opioid Misuse

Description

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.

Grant Number
R18 HS030202
Principal Investigator(s)

Machine-Learning Prediction Model for Personalized Urinary Tract Infection Care in Children

Description

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.

Grant Number
K08 HS029526
Principal Investigator(s)

Implementation of an EHR-integrated web-based depression assessment in primary care: PORTAL-Depression.

Principal Investigator