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The University of Pennsylvania Health System is partnering with K Health to integrate a suite of AI clinical agents into its electronic health record system, aiming to improve patient care across various specialties.
The University of Pennsylvania Health System (Penn Medicine) has announced a multi-year collaboration with K Health to deploy a suite of artificial intelligence (AI) clinical agents. These agents will be integrated into Penn’s electronic health record (EHR) system and digital front systems, starting with the virtual urgent care program, Penn Medicine On Demand. The goal is to expand these AI tools to in-person primary care and specialized areas like cardiology and dermatology.
Mitchell Schnall, M.D., Ph.D., Senior Vice President for Data and Technology Solutions at Penn Medicine, emphasized the potential of AI in clinical settings: “This work will allow us to continue to test how AI can best be used across the spectrum of care,” he said. The collaboration is not just about deploying technology but also involves joint research on the impact of clinical AI in routine healthcare.

Ran Shaul, K Health co-founder and chief product officer, highlighted the strategic importance of this collaboration: “This isn’t another point solution, but the layer that prepares the visit, and connects every patient question to a safe, navigable path inside the system.” This approach aims to create a seamless and efficient healthcare experience for patients.
The integration of K Health’s AI agents into Penn Medicine's systems represents a significant step forward in leveraging technology to improve patient care. By combining advanced AI capabilities with robust clinical expertise, this partnership aims to set new standards for healthcare delivery and research.
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Penn Medicine, K Health partner to deploy AI clinical agents
↗ https://www.fiercehealthcare.com/ai-and-machine-learning/penn-medicine-k-health-partner-deploy-clinical-ai-clinical-agents
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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