Nurse Practitioner


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)

Survey-based work system assessment to facilitate large-scale dissemination of healthcare quality improvement programs.

Principal Investigator

Information needs and requirements for decision support in primary care: an analysis of chronic pain care.

Principal Investigator

Decision-centered design of patient information visualizations to support chronic pain care.

Principal Investigator

An analysis of primary care clinician communication about risk, benefits, and goals related to chronic opioid therapy.

Principal Investigator

Patient and clinician perspectives on a patient-facing dashboard that visualizes patient reported outcomes in rheumatoid arthritis.

Principal Investigator

Health Information Technology and Provider Communication

Description
This is a questionnaire designed to be completed by physicians, nurse practitioners, and physician assistants in a perioperative/operative and hospital setting. The tool includes questions to assess the current state of health information technology including clinical documentation, computerized provider order entry systems, clinical decision support systems, hardware, mobile devices, secure messaging, text messaging, and EHRs/EMR.
Year of Survey