Startups test consumer‑facing AI to show upfront prices and care plans
Loading...
Summary
General Medicine and other witnesses described using large language models and open pricing files to convert insurer benefit documents into structured, consumer‑facing prices and to create post‑visit, AI‑generated proactive care plans for patients.
Several witnesses described consumer‑facing AI efforts aimed at reducing friction in the patient experience — specifically by clarifying prices and generating actionable, personalized care plans after visits.
TJ Parker, founder of General Medicine and a former founder of PillPack, described using large language models to parse a patient's insurance coverage documents — often “80 to a 100 page” benefit booklets — into structured data. He said the company combines that output with open pricing files to show upfront prices for procedures, labs, imaging and pharmacy purchases. “We analyze the customer's insurance information, including the 80 to a 100 page coverage of benefits. We use large language models to turn these PDFs into structured data,” Parker told the subcommittee.
Parker also described “proactive care plans” that use patient permission to pull together a patient’s history, labs and prescriptions and then produce a personalized, prioritized checklist for follow‑up and preventive care. He described that approach as “the equivalent of adding to cart and checking out” — making it easier for patients to act on recommendations.
Witnesses said those consumer benefits depend on underlying infrastructure — provider directories, open price files and interoperability — and asked Congress to maintain and strengthen those data and transparency policies so AI developers can rely on accurate inputs.
