Learning Health System

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.

The study will create and implement digital tools using the SMART on FHIR framework to support Age-Friendly care in clinical practice and institutional decision-making.

This research developed and tested a machine learning classifier using natural language processing to identify hospitalized patients at risk for HIV acquisition or transmission due to substance misuse or high-risk sexual behavior. While the model performed moderately well, gaps in how patient information is documented made accurate risk prediction difficult, highlighting the need for better data and tailored interventions in hospital settings.

The annual Conference on Health Information Technology and Analytics brought together interdisciplinary stakeholders to share research; foster collaboration across academia, industry, and government; and support the next generation of researchers, producing actionable insights to improve healthcare quality, access, and efficiency.

The Clinical Decision Support Innovation Collaborative (CDSiC) created a learning community of patients, providers, policymakers, researchers, and developers to produce resources that advance patient-centered CDS, promote its adoption, practice, and measurement, and use standards to enable its implementation at scale.