COVID-19

Improving Patient Safety and Clinician Cognitive Support Through eMAR Redesign

Description

This study will create an innovative electronic medication administration record prototype and a medication administration workflow risk assessment to improve the medication administration process and the usability and safety of the electronic medication administration records in response to challenges from COVID-19.

Grant Number
R01 HS025136
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)

Evaluating and Enhancing Health Information Technology for COVID-19 Response Workflow in a Specialized COVID-19 Hospital in a Medically Underserved Community

Description

This research will study how a safety-net hospital responds to a pandemic, specifically COVID-19, to identify how information needs are met and how decisions are made and communicated to other individuals internal and external to the institution.

Grant Number
R01 HS028220
Principal Investigator(s)

The Role of Telehealth in COVID-19 Response

Description

This research, using data from the country’s largest telehealth provider and claims from a large commercial payer, will examine the impact of the COVID-19 pandemic and telehealth on utilization, outcomes, disparities, and public health surveillance.

Grant Number
R01 HS028127
Principal Investigator(s)

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19.

Principal Investigator

An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department

Description

This study created, trained, and tested machine learning (ML) algorithms to predict emergency department returns and morbidity or mortality among returns for COVID-19 patients, with results showing ML’s potential to inform clinicians on whether admission to the hospital may be needed for the prevention of complications and to prioritize resources for higher risk patients.

Grant Number
R21 HS028563
Principal Investigator(s)

A modification of time-driven activity-based costing for comparing cost of telehealth and in-person visits.

Principal Investigator