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.
This research will further disseminate a currently used clinical decision support tool to identify patients at risk for a life threatening, uncommon cardiac arrhythmia.
This project will examine the quality and safety impact of patient-initiated telemedicine visits with primary care providers.
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.
This project proposes a novel proactive system to reduce alert burden and thereby increase attention to situations in which patient safety is at risk.
This study will evaluate the effectiveness of patient photographs displayed in electronic health record systems for preventing wrong-patient errors.
This study showed the feasibility and value of creating a methodology and process for a health information technology black box to inform electronic health record design and usability.
This research will explore whether providing clinicians with contextual information at the point of care through the use of clinical decision support can reduce contextual errors, improve patient healthcare outcomes, and reduce misuse and overuse of medical services.
The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.
This research study used cognitive systems engineering approaches to understand the decision making process in the emergency department and proposed models and solutions to some of the biggest challenges in practicing medicine in this complex environment.