The project team successfully developed and implemented an automated system for measuring the rate of adverse drug events in pediatric patients.
This project piloted an embodied conversational agent to support shared decisionmaking for prostate cancer treatment and found that the tools was technically feasible however, many men preferred receiving information from their providers.
The project team developed automated methods for identifying relevant new information versus redundant information in electronic health record clinical notes.
The research team created a systematic way to understand and measure how household context—such as storage adequacy, lighting, and privacy affordance—influences personal health information management.
This project tested a reinforcement learning (RL) text messaging system, which tailors and adapts messages to the needs of patients with uncontrolled hypertension to promote medication adherence, and found it to be feasible and adaptive.
This project explored whether the use of data from pain management practices can be used to develop more robust evidence-based approaches to chronic pain management.
This research developed an interactive and engaging computer tablet-based, virtual program to prepare children with cancer for radiation therapy.
This project developed a relational agent system--a human animation program that interacts with patients--to deliver depression treatment to patients with chronic illness and comorbid depression and found the system to be feasible and user-friendly.
Analysis of 5,200 patient safety event reports showed an association between electronic health record (EHR) usability and patient safety in both adults and children and led to development of an EHR usability and safety assessment tool that healthcare facilities can use to identify usability and safety issues.
This project developed and pilot-tested a novel, outcomes-based emergency department triage tool and found that risk stratification and waiting times were improved for some patients.
