UCHealth’s healthcare AI methodology currently enables 1 nurse to monitor 14 fall-risk patients, with plans to scale to 140, then 1,400 through computer vision and predictive analytics. Instead of exhausting pilots, they deploy in phases: test, prove, optimize, then scale. This has created a system that prioritizes force multiplication of current staff rather than replacing them, enabling healthcare professionals to work at the top of their scope. Richard Zane, Chief Innovation Officer also tells Ravin how their computational linguistics system automatically categorizes thousands of chest X-ray incidental findings into risk levels and manages closed-loop follow-up communication, ensuring critical findings don't fall through administrative cracks. Richard's three-part evaluation framework for technology partners — subject matter expertise, technical deep dive, and financial viability — helps them avoid the startup graveyard.    Topics discussed: UCHealth's phase deployment methodology: test, prove, optimize, scale Force multiplication strategy enabling 1 nurse to monitor 14+ patients Computational linguistics for automating incidental findings Three-part startup evaluation: subject matter, technical, and financial assessment FDA regulatory challenges with learning algorithms in healthcare AI Problem-first approach versus solution-seeking in healthcare AI adoption Cultural alignment and operational cadence in multi-year technology partnerships Listen to more episodes:  Apple  Spotify  YouTube Website