Community Health Center


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)

Implementing Personalized Cross-Sector Transitional Care Management to Promote Care Continuity, Reduce Low-Value Utilization, and Reduce the Burden of Treatment for High-Need, High-Cost Patients

Description

This research will integrate cross-sector care alerts and interoperable personalized care planning into the existing Coordinating Transitions Intervention (CTI) tool and evaluate the impact of the revised tool on patient burden, care team collaboration, and utilization value for high-need, high-cost patients.

Grant Number
R01 HS028000
Principal Investigator(s)

Integrating Patient-Reported Outcomes Into Routine Primary Care: Monitoring Asthma Between Visits

Description

This study developed, implemented, and rigorously evaluated a clinically integrated remote symptom monitoring intervention for asthma patient-reported outcomes (PROs) in primary care, with findings revealing an improvement in patient quality of life and suggesting the intervention's potential to enhance the ability of clinicians and clinical staff to manage their patients.

Grant Number
R18 HS026432
Principal Investigator(s)