This research will test and validate a machine learning predictive analytic intervention to optimize the timely and appropriate use of interpreters for hospitalized patients with language barriers and complex care needs.
This research will assess the validity of patient-level computable social factor phenotypes used to predict a patient’s risk of increased healthcare utilization and costs.
This research will compare the use of predictive modeling versus traditional questionnaires to identify those with unmet social needs, use the superior method to inform the development of a clinical decision support tool, and evaluate the tool’s impact on referrals to social providers.
This research will study the implementation of Telehealth Education for Asthma Connecting Hospital and Home (TEACHH), a novel intervention designed to provide an effective asthma educational platform appropriate for all health literacy levels. The intervention includes initial instruction in the hospital and reinforcement at home using virtual visits to reduce barriers to self-management support for children who are hospitalized due to asthma.
This research aims to examine a health system’s four telehealth programs in response to the COVID-19 pandemic.
This research aims to enhance and implement a clinical decision support tool that will support providers and underinsured patients in making personalized decisions regarding diet goal setting.
