Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stage summarization system that runs entirely on embedded devices, enabling offline clinical summarization while preserving patient privacy. In our approach, a dual-device architecture first retrieves relevant patient record sections using the Jetson Nano-R (Retrieve), then generates a structured summary on another Jetson Nano-S (Summarize), communicating via a lightweight socket link. The summarization output is two-fold: (1) a fixed-format list of critical findings, and (2) a context-specific narrative focused on the clinician's query. The retrieval stage uses locally stored EHRs, splits long notes into semantically coherent sections, and searches for the most relevant sections per query. The generation stage uses a locally hosted small language model (SLM) to produce the summary from the retrieved text, operating within the constraints of two NVIDIA Jetson devices. We first benchmarked six open-source SLMs under 7B parameters to identify viable models. We incorporated an LLM-as-Judge evaluation mechanism to assess summary quality in terms of factual accuracy, completeness, and clarity. Preliminary results on MIMIC-IV and de-identified real EHRs demonstrate that our fully offline system can effectively produce useful summaries in under 30 seconds.

ECG's radio show and podcast, Healthcare Upside Down, offers unfiltered perspectives on what's working in US healthcare and what's not. Hosted by ECG principal Dr. Nick van Terheyden, each episode features guest panelists who explore the upsides and downsides of healthcare in the US--and how to make the system work for everyone. Early computing was based on programming languages that incorporated simple logic statements: if this happens, then do this; otherwise, do that. But the techniques and capabilities have moved far beyond that, and we now have high-level tools that can ingest large amounts of content and pull it together into some proxy of knowledge. Our guest on episode 68 of Healthcare Upside Down is John Lee, MD, emergency physician and digitician.

Artificial intelligence is a transformative tool in the workplace -- except when it isn't. For top managers, state-of-the art AI tools are a no-brainer: in theory, they increase revenues, decrease costs, and improve the quality of products and services. But in the wild, it's often just the opposite for frontline employees who actually need to integrate these tools into their daily work. Not only can AI tools yield few benefits, but they can also introduce additional work and decrease autonomy. Our research on the introduction of 15 AI clinical decision support tools over the past five years at Duke Health has shown that the key to successfully integrating them is recognizing that increasing the value for frontline employees is as important as making sure the tools work in the first place.

By day, he is the chief executive officer of a Tokyo startup. At night and on weekends, he works shifts as a doctor. He wants to keep it that way because he needs both to stay in balance. Sho Okiyama is one of the growing number of what are known in Japan as "entre-doctors," or doctors who are also entrepreneurs, making full use of their medical knowledge and clinical experience in doing business. What makes Okiyama distinctive is the diversity of his experience.