Prompted to download the UCSF EdgeMed application. Medical records from other institutions are synced automatically.
The Build
extends beyond clinic walls and continuously connects back to care.
Edge Medicine is proactive, comprehensive medicine powered by AI and wearable sensors reaching beyond clinic walls. It's where doctors can, for the first time, see and anticipate their patients' true disease states between visits. Patients can have always-on intelligent support between visits like never before because of the continuous flow of information.
Edge Medicine is a paradigm shift: the frontline of healthcare is wherever people manage their health and chronic conditions.
75% of Americans are living with at least one chronic condition, such as heart disease, cancer, diabetes, or kidney disease. These conditions are 24/7, occurring where people live, work, and play. Yet today's healthcare remains siloed, fragmented, episodic, and reactive.
We should be preventing chronic disease, yet most of our effort goes toward treating it after the fact. Chronic conditions drive 90% of the U.S.'s $5 trillion in annual healthcare costs, yet billions of dollars continue to go toward apps, companies, and technologies that address one disease at a time, building isolated solutions. But patients rarely live with just one condition.
Edge Medicine is designed to be:
- 01 Human-centered, privacy-preserving
- 02 Powered by streamlined data and compute
- 03 Clinically validated and built into care workflows
Breaking Down Silos with Open Source Infrastructure
Through JupyterHealth, we are bringing the proven Jupyter ecosystem, used by millions of scientists worldwide, into everyday clinical care at the Edge. JupyterHealth combines data interoperability with the analytics power of Project Jupyter to create a scalable, interactive foundation for healthcare transformation.
Edge Medicine uses JupyterHealth to replace fragmented, one-off healthcare solutions with open, standardized infrastructure that lets data and AI move freely from home to health system and back.
Software Developers: See How We BuildThe Coordinated, Continuous, and Contextual Flow of Care
Signals from the Edge
Devices collecting data are inputs that provide health signals through continuous data streams, allowing for a complete picture of health to be a click away for health teams.
JupyterHealth
Infrastructure layer that makes Edge Medicine possible. Facilitates data integration from multiple sources (e.g., wearable devices), Epic integration, analytic tooling and algorithms to support standardization and clinical insights. Learn more about JupyterHealth
Patient-facing Apps
Wholistic single view of personal health history (e.g., Medical records, Labs, Current medications) integrated with health systems and health care providers.
Clinician-facing Portals
Provider and patient generated cohesive view allowing for cross-specialty coordination and real-time monitoring of condition progression.
The Edge
The true frontline of care where health is lived continuously in everyday life.
An AI model trained on millions of electronic health records assigns Alex a risk score. Labs are automatically ordered by the system.
A clinical team member from UCSF reaches out to set up a continuous glucose monitor and a contactless, smartphone-based blood pressure sensor. Alex's Oura ring data is synced.
UCSF's AI model projects a concerning health trajectory.
Alex's primary care appointment is expedited. Instead of waiting 5 months for his primary care appointment and a return appointment for 3 months after his initial visit, Alex waits a week to be seen. His sensor data is available to his care team, and AI can assist with triage to subspecialty care.
The iCKM collaboration portal allows specialists and primary care physicians to model and discuss treatment options.
A case across the Edge Medicine continuum
Alex, a 37 year old man with high blood pressure (BP). He has gained 20 lbs in the last 2 years. Both his parents have high blood pressure and diabetes. He doesn’t know that he is at high risk for Cardio-kidney-metabolic (CKM) syndrome, and checks in as a patient at UCSF.
Click through Alex's story (on the left) to see how it changes under a JupyterHealth-integrated care model with Edge Medicine.
Who This Is For
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Software Developers & Engineers
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