Working Group Webinar Library

Learn strategies for translating even the most technical information into compelling, human stories—so you can change the way people think, feel, and act. We’ll use sustainability communications as our lens for looking at messaging strategies that work (and those that don’t work). We’ll also talk about the buzzwords and cliches you should avoid in your writing. Finally, we’ll practice what we’ve learned with a simple writing exercise that brings the concepts to life. This session requires audience participation, so bring your ideas, your questions, and something to write with. Watch the Recording Presenter

This talk will introduce the technologies powering LLMs, overview the recent prevails, and examine the mirages in the hype of LLM magic. Based on the experience in developing two clinical LLMs in the clinical domain, including GatorTron and GatorTronGPT, this talk will provide insight into the potential application of LLMs for clinical NLP and healthcare.

Acute hepatic porphyria (AHP) is a rare but treatable condition with an average diagnostic delay of 15 years. Utilizing electronic health records (EHR) data and machine learning (ML) can potentially improve the timely recognition of AHP. This study used structured and notes-based EHR data from UCSF and UCLA to develop models predicting who will be referred for AHP testing and who will test positive. The referral model achieved an F-score of 86%-91%, and the diagnosis model achieved an F-score of 92%.

Learning health system (LHS) aims to leverage technology, data analytics, and evidence-based practices to create a feedback loop that continuously informs healthcare delivery, policy, and practice. It requires a multidisciplinary approach to interpret patterns observed in real-world data with the associated context.

This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is a predictive model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness.