# Care Signals - **Event:** [The Future of Agentic AI in Healthcare - Abridge x Anthropic x Lightspeed](https://cerebralvalley.ai/e/abridge-hackathon) - **When:** Sat, Jul 18 at 9:00 AM – 10:00 PM (PDT) - **Where:** San Francisco, CA - **Team:** [Shawn Dimantha](https://cerebralvalley.ai/u/shawnd) - **GitHub:** https://github.com/shawndimantha/Care-Signals - **Demo video:** https://youtu.be/EZkhFU1iS5k - **Gallery:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery - **Page:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/7 Care Signals: a care navigator for every patient Self-funded employers and health plans already pay care navigation teams to do this work: call around, compare prices, steer patients to lower-cost sites of service. It works. It's also human-staffed and rationed - a few high-touch cases at a time, available to a small fraction of patients, and never in the moment the order is signed. Everyone else walks out of the visit with no idea whether their scan costs $600 or $3,400. Half of commercially insured Americans are on high-deductible plans, so before the deductible is met, they pay the full negotiated rate. Faced with an unknown number, they defer necessary care - or they get it and receive a four-figure bill they never agreed to. The prices exist. CMS requires every hospital to publish machine-readable files of negotiated and cash rates. But those files run to hundreds of megabytes, every system uses a different schema, and no patient can open one. Care Signals gives every patient a navigator, automatically, at the moment of the order. From the ambient note and signed order, an agent parses the order to a CPT code, normalizes facilities' published price files, matches the patient's specific plan, computes real out-of-pocket against their deductible, and cites every dollar to its source line. Where a facility publishes no rate for a payer, it says so rather than guessing. For example in the demo, across four San Francisco facilities for a single lumbar MRI, patient out-of-pocket ranged from $586 to $3,387 - the same scan, both prices legally required to be public, neither visible when the order was signed. The agent then messages the patient the ranked options, answers their questions from the facility data using Claude, books the lower-cost site, and sends a calendar invite with a reminder. What a navigation team does over days, for a handful of patients, running in seconds for everyone. Imaging is the beachhead, not the ceiling. The same pipeline - order in, priced options out - extends to every shoppable procedure in the published files: elective surgery, endoscopy, labs, infusions. It extends to high-cost drugs too, where the same problem has a different data source: pharmacy pricing, discount cards, and manufacturer assistance programs, which patients navigate just as blindly and abandon just as often. Ambient documentation already captures the order and flags the care gap. A flagged gap isn't a closed one. This closes it. Every price is pulled from a published source and traceable to it. Patient data is synthetic. --- Markdown version of https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/7. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt