Caregiver


An evidence-based IT program with chatbot to support caregiving and clinical care for people with dementia: The CareHeroes development and usability pilot.

Principal Investigator

Feasibility Study of a Mobile Digital Personal Health Record for Family- Centered Care Coordination for Children and Youth with Special Healthcare Needs - Final Report

Principal Investigator

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)

Patient Intestinal Failure-ECHO Project (PIF-ECHO)

Description

This study will evaluate the feasibility and effectiveness of providing chronic intestinal failure patients and their family caregivers with direct access to live, virtual, multi-disciplinary (multi-D) support and education on best practices from a team of experts and peers. Such support can improve patient outcomes by enhancing knowledge, increasing confidence in self-care, and fostering a support network where patients can share lived experiences.

Grant Number
R03 HS030321
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)

Incorporating patient, caregiver, and provider perspectives in the co-design of an app to guide Hospital at Home admission decisions: a qualitative analysis.

Principal Investigator