Patient Safety


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)

Identifying Sepsis Phenotypes Associated with Antibiotic-Resistant Pathogens Using Large Language Models and Machine Learning

Description

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.

Grant Number
K08 HS030118
Principal Investigator(s)

A Roadmap for Research: The International Summit on Innovation and Technology in Care of Older People (IS-ITCOP)

Description

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.

Grant Number
R13 HS030051
Principal Investigator(s)

Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Description

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.

Grant Number
R21 HS029410
Principal Investigator(s)