[{"_1":2,"_122":-5,"_123":-5},"loaderData",{"_3":4,"_26":27},"root",{"_5":6,"_17":10,"_18":19,"_20":21,"_22":10,"_23":-5,"_24":-5,"_25":10},"user",{"_7":8,"_9":10,"_11":10,"_12":-5,"_13":-5,"_14":15,"_16":-5},"roles",[],"isAuthenticated",false,"isPremium","planName","subscriptionStatus","featureFlags",{},"insightsBudget","isDevelopmentHost","userPreferences",{},"recaptchaSiteKey","6LcvOJcUAAAAAHsB-gPGfGktcRlKpH7OTIU_dMTD","isImpersonating","impersonation","affiliateStatus","isFreedomPortal","routes/v2/article/layout",{"_28":29,"_30":31},"notFound",true,"relatedArticles",[32,75,86,98,110],{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60,"_61":62,"_63":64,"_65":66,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},"mediaId",6477668,"mediaKeyId","5711444-0b8114beae46c6df80e17742e1879f0b","parentId",5711444,"title","Startups test consumer‑facing AI to show upfront prices and care plans","audioSeconds",92,"startMs",1980000,"endMs",2072000,"viewUrl","/Search/View?dp=1&key=5711444-0b8114beae46c6df80e17742e1879f0b&start=1980&end=2072","thumbnailUrl","https://assets.pipeline.soar.com/5711444-0b8114beae46c6df80e17742e1879f0b/thumbnail_1980000.jpg","articleUrl","/articles/6477668/Startups-test-consumerfacing-AI-to-show-upfront-prices-and-care-plans","mediaUrl","https://secure.pipeline.soar.com/6477668-28cb37249010b396034a03857c61ec71/orig.mp4?Expires=1770455040&Signature=iqsBsTaUdW-pXzV2lJ4DTIubMtMBgDevZIomx9zT1kO-o~X5h-DfQSM8eSmwuwqyRcz-wkWiAxKyNdr9WcZqmAGiu0~9iL3XU5apX~ju3fa43FtV9KGnhJtY2TWWLruBeFHm0NtZf3bB0YFm02aeK9Yuz-23D8m-rfLDcFxEL8IExU74IGgcxmCRC~MvheSAhy4dlsOEd-oCQyN-iM2ssVcsBFbMu7h6wI7poP9JcPcwSoaLNGJPkmd7CgvtX~-e~zW9vdgzfwg0fKAA9jnYkGUcueKlrkh8WCUhTJSeCEr8kdcUMsQrPK0S03BHx9u1PxlL-TdA6p7nG0CyF3TrhQ__&Key-Pair-Id=KUT5TJCACFTXG","author","U.S. House Committee on Energy and Commerce","date","2025-09-04T05:38:06Z","article","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.","mediaType","Video","parentTitle","Examining Opportunities to Advance American Health Care through the Use of AI Technologies","oneSentenceSummary","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.","taxTitle","Energy and Commerce: House Committee, Standing Committees - House & Senate, Congressional Hearings Compilation, Legislative, Federal","taxId",1824,"location","","headline","shortSummary",{"_33":76,"_35":36,"_37":38,"_39":77,"_41":78,"_43":79,"_45":79,"_47":80,"_49":81,"_51":82,"_53":83,"_55":56,"_57":58,"_59":84,"_61":62,"_63":64,"_65":85,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},6477672,"Companies testify to clinical gains from AI: stroke triage, earlier detection and fewer readmissions",0,2610000,"/Search/View?dp=1&key=5711444-0b8114beae46c6df80e17742e1879f0b&start=2610&end=2610","https://assets.pipeline.soar.com/5711444-0b8114beae46c6df80e17742e1879f0b/thumbnail_2610000.jpg","/articles/6477672/Companies-testify-to-clinical-gains-from-AI-stroke-triage-earlier-detection-and-fewer-readmissions","https://secure.pipeline.soar.com/6477672-88c09008de9cc30290529f5b8b8c6840/orig.mp4?Expires=1770455040&Signature=IS2XXcLJj16IDNLbtk-YVa410vyPV~TnDLeeDIo2VVrIeB7GPKWgcEcrQbJCr9CiGI1zRVjIN8yxWli1qH~WfRw8641j~xgp3Gi8RqEo7GxXEX2S7HpUAQpENRE~hRM9B9Vo~zhdaK-De92kQoL5J3vx1VP6JlJ95GO8KXMX2d1xuXeQN2HahC4v~nMAk21XRvM5l4bkBqPpg3uExzWLP1ADto2N7Bx~~-MbNKZn3~iRUU3h3ZsMqkMTdMCCGRKsElaVuOh2PlhvJNnxF~KSH5vnnXUOxdbtinjiXEWZT9UvhxQuphgmyhkR2IzyvQgCOJk3U~ebSYF3A1JIIbntuw__&Key-Pair-Id=KUT5TJCACFTXG","Two vendors described clinical results their companies reported from deployment of AI tools in frontline care.

Dr. Andrew Ibrahim, chief clinical officer at Viz.ai, said his company's AI platform automatically analyzes CT scans to identify conditions like large vessel occlusion strokes, alerts stroke teams in real time and assembles clinical information for urgent decisions. ‘‘Independent studies show that this reduces treatment time by more than 30 minutes, [and] insurance hospital stays by as much as 3 days,’’ he told the subcommittee, and said Viz.ai’s platform supports care at more than 1,800 hospitals nationwide.

Andrew Toye, CEO of Clover Health, described metrics from the company’s Medicare Advantage deployments, saying clinicians using Clover Assistant identify diabetes about three years earlier on average, detect kidney disease 1.5 years earlier, and achieve higher preventive screening rates (about 11 percent more colorectal cancer screening and nearly 5 percent more breast cancer screening). Toye said certain cohorts experienced 18 percent fewer hospital stays and 25 percent fewer readmissions, and that those clinical improvements translated to better cost ratios for the plan.

Both witnesses and members stressed safeguards: they described human‑in‑the‑loop workflows, institution‑level validation before deployment and continuous monitoring after deployment. Witnesses said interoperability, access to data and appropriate reimbursement models are necessary for smaller or rural providers to realize similar gains.","Viz.ai and Clover Health presented study results and operational metrics to the House subcommittee, saying AI tools have shortened treatment times for stroke, increased screening rates and reduced hospital stays and readmissions in populations they serve.",{"_33":87,"_35":36,"_37":38,"_39":88,"_41":89,"_43":90,"_45":91,"_47":92,"_49":93,"_51":94,"_53":95,"_55":56,"_57":58,"_59":96,"_61":62,"_63":64,"_65":97,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},6477677,"Experts urge safeguards for AI chatbots used for mental‑health support, especially for youth",1814,3306000,5120000,"/Search/View?dp=1&key=5711444-0b8114beae46c6df80e17742e1879f0b&start=3306&end=5120","https://assets.pipeline.soar.com/5711444-0b8114beae46c6df80e17742e1879f0b/thumbnail_3306000.jpg","/articles/6477677/Experts-urge-safeguards-for-AI-chatbots-used-for-mentalhealth-support-especially-for-youth","https://secure.pipeline.soar.com/6477677-9835741e1e6541b1c3b36a4958d42e5c/orig.mp4?Expires=1770455040&Signature=cvE-uKEonhkPja3IWdY1n49xUGqN-3qCekaxDqOP3irQJzifj1iZUnoYYficSRFWgPl-8oZ5jN878cJtaSCgFOw8A2vvLyFzBt5TMYIckBwmhN4VDRbxxFS9JxKWfQv78wlRvMdsplvUoMMH0EnBII~HsJwTzXVzHDdzPfoFed3jkiCBtUePYx9BthuMxVSdJaA7T~XR-atF8yZSBELUxzrZAuH3S8uYhgQTBQ10dWJFZK2oQHuCRtFT7YD9LQFw66gmPVSZXFzfw~CiXCT0a39NCV0DT8QU3yjW5PXkSxtImeriHMuLMC2RtMBLBBM7eQq8m1MrqAQmFjH7u4IIlg__&Key-Pair-Id=KUT5TJCACFTXG","Witnesses and members repeatedly raised alarms about direct‑to‑consumer chatbots marketed for mental‑health support and their effects on adolescents and vulnerable adults.

Dr. Vail Wright, senior director for health care innovation at the American Psychological Association, told the committee that chatbots can “amplify existing health inequities” and that systems trained on inappropriate proxies have already harmed patients. Wright cited cases where chatbots validated violent or self‑harm ideation and said the APA had requested federal investigations into some products.

Wright urged five policy actions: clear regulatory guardrails to ban misrepresenting chatbots as licensed professionals; mandatory independent testing for harms across diverse populations before market deployment; age‑appropriate safeguards for adolescents; federal investment in research and public AI literacy; and comprehensive federal privacy protections that would include mental‑health and biometric data.

Members pointed to widely reported incidents and requested that news articles and op‑eds be submitted for the record; witnesses used those examples to illustrate risks but also described positive use cases, such as chatbots used for social‑skills practice under supervision. Wright said the problem is not universal but that current commercial products often prioritize user engagement over safety and urged regulators to require labeling, meaningful age verification and reporting of serious adverse events.

Witnesses recommended independent, pre‑deployment testing (a “sandbox” or certification model), mandatory disclosures when an AI system is providing health‑related recommendations, and stronger privacy rules for sensitive mental‑health and biometric data.","The American Psychological Association and other witnesses told the House subcommittee that unregulated chatbots can produce harmful responses, calling for age‑appropriate safeguards, independent testing and a human‑in‑the‑loop requirement for clinical uses.",{"_33":99,"_35":36,"_37":38,"_39":100,"_41":101,"_43":102,"_45":103,"_47":104,"_49":105,"_51":106,"_53":107,"_55":56,"_57":58,"_59":108,"_61":62,"_63":64,"_65":109,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},6477682,"Lawmakers and experts warn against AI‑driven prior‑authorization programs, cite CMS 'WISER' pilot",5148,1672000,6820000,"/Search/View?dp=1&key=5711444-0b8114beae46c6df80e17742e1879f0b&start=1672&end=6820","https://assets.pipeline.soar.com/5711444-0b8114beae46c6df80e17742e1879f0b/thumbnail_1672000.jpg","/articles/6477682/Lawmakers-and-experts-warn-against-AIdriven-priorauthorization-programs-cite-CMS-WISER-pilot","https://secure.pipeline.soar.com/6477682-98fc9aea4e54a023592215db97a4c052/orig.mp4?Expires=1770455040&Signature=dX5ypvWLn3qAHK24Fm4mM0kt-FqnxMEWwBnuZ9KC9zAi4-pYW3RTNJGLscDIb~dPmevoSkgoM~sP6B1XbE9zYU88QId4p0oJCmrZJgwnop2gjr0doL36K411dy3H8ya4QC7Ecs4Ldpaiek6Cc-aJue~1pdj~R9bN~ActW7deaSx8FORdo7sl-yaMU07grW0QNV3eY~rd0C3ejVplphILxS2rTQHnW2q0TbnPwwhN61lNi5RJEpJNSjVUVNr78ZqKJzA-Whzr9AI4iArZc~3~w1svPaNwynjnutTa6mU00G6nXdXU6nFnd9Mrm3NoKv5x-K4WXMHFTVn2Bu-ELsHVag__&Key-Pair-Id=KUT5TJCACFTXG","Members of the House Energy and Commerce hearing focused extensive questioning on the use of AI in prior authorization and on a CMS pilot program discussed in testimony.

Michelle Mello, professor of law and health policy at Stanford, told the committee that prior authorization is already “fraught” and that adding AI could make outcomes better or worse, but that the effect is unknown without independent study. “We don't know because there are no publicly available information that would enable somebody like me to be able to tell you, does using AI make prior authorization better for patients or worse?” she said.

Multiple members and witnesses described the administration’s pilot program — referred to in testimony as a WISER pilot — that would extend use of automated reviews to parts of Medicare. Lawmakers and witnesses flagged two core concerns: (1) financial incentives tied to denying claims and (2) the lack of public performance data and standards for the human review that follows an algorithmic recommendation.

Andrew Toye, CEO of Clover Health, said his company does not use AI to make coverage denials and that “AI should not be used to review, to deny in any case.” Toye and others told members that if AI is used to speed administrative work, human clinicians must remain the final decisionmakers and peer‑to‑peer clinician reviews should be available when denials occur.

Witnesses recommended specific policy responses: require disclosure of when AI is used in coverage decisions; mandate independent, post‑deployment evaluation of algorithms; define what “human review” must include (for example, a fresh, unprimed clinician review rather than a cursory sign‑off on a packaged denial); and extend reporting and oversight to commercial employer‑sponsored plans that are currently less transparent.

Members pressed CMS and the committee for immediate oversight and asked for clearer statutory authority and modernized review processes at FDA and CMS so agencies can evaluate software products more effectively before they influence coverage and care.","Witnesses and members raised concerns that using AI to automate prior‑authorization reviews could delay or deny medically necessary care, questioned incentive structures that pay vendors from savings tied to denials, and urged human‑in‑the‑loop standards and independent validation.",{"_33":111,"_35":36,"_37":38,"_39":112,"_41":113,"_43":114,"_45":115,"_47":116,"_49":117,"_51":118,"_53":119,"_55":56,"_57":58,"_59":120,"_61":62,"_63":64,"_65":121,"_67":68,"_69":70,"_71":72,"_73":72,"_74":72},6477687,"House Energy and Commerce hearing showcases benefits and risks of AI in health care",11304,706000,12010000,"/Search/View?dp=1&key=5711444-0b8114beae46c6df80e17742e1879f0b&start=706&end=12010","https://assets.pipeline.soar.com/5711444-0b8114beae46c6df80e17742e1879f0b/thumbnail_706000.jpg","/articles/6477687/House-Energy-and-Commerce-hearing-showcases-benefits-and-risks-of-AI-in-health-care","https://secure.pipeline.soar.com/6477687-00669d2f3b122342b85971c9b63a7cbc/orig.mp4?Expires=1770455040&Signature=YmzAK1dH1GSFDgrKn~VjTo~C6q0jf16lPWXgw~cfhgYZrTNEcE6oJGSP90s~2MnQYB67gAoqAvQMUrUuVmcI4mXD0dkVjGR2NzACGfelxe3VQMd9UR0hJFHwwn2x5uAPYUUwIWHXYPE9ZzTfUyNE8tvpagG0rnfjhZv712o1N79f3E1MlJf0zINLVAGXvbeoTX891vd5ky8pzJoR82KIO9wdfnGOB0j8V-5YSUb1NAcUccl0Urd9noU1LOfrHnzsVUI1-liJduC3~6sLWeFK4TTO5yyI3YAiKzgZ5JYHggov2X6Gsd9ryAxOUjJkLF0F55AyNaVntSWZJ1q3LdRgKA__&Key-Pair-Id=KUT5TJCACFTXG","At a House Energy and Commerce subcommittee hearing, industry founders, practicing clinicians and academic experts outlined how artificial intelligence is already being used across the U.S. health system — and where lawmakers say federal guardrails are needed.

The witnesses described concrete clinical gains from platforms that triage strokes and other time‑sensitive conditions, insurer and plan tools that surface missed preventive care, and consumer products that attempt to show upfront prices. At the same time, experts and members warned about a “foundational trust deficit” in health care AI, unregulated direct‑to‑consumer chatbots that can produce harmful advice, and pilot programs that would let AI play a central role in coverage decisions.

“By combining this infrastructure with AI, we can offer a vastly better health care experience for every American regardless of location, insurance, or background,” said TJ Parker, founder of General Medicine, describing the company’s use of large language models to parse insurer benefit documents and produce upfront prices. Andrew Toye, chief executive of Clover Health, and Dr. Andrew Ibrahim of Viz.ai described clinical results their companies attribute to AI tools: earlier detection of disease, higher screening rates and shorter time to treatment for strokes.

Stanford Law and health‑policy professor Michelle Mello told the panel that adoption of clinical AI has been limited by a lack of trust and uneven institutional governance. “There is a foundational trust deficit,” she said, and recommended institutional vetting, developer disclosures (model cards) and more independent performance research. The American Psychological Association’s Dr. Vail Wright urged particular caution for mental‑health applications and for children, saying AI should “augment, not replace the clinical judgment and therapeutic relationship.”

Members of both parties used the hearing to press for policy steps: some urged modernization of FDA review and clearer reimbursement pathways to encourage adoption and monitoring; others pressed CMS and Congress to prevent AI from becoming a tool that causes inappropriate denials of medically necessary care.

Committee members also placed the hearing in a broader political context: multiple lawmakers criticized recent actions at HHS and the CDC, arguing that agency instability increases the need for congressional oversight of both AI and public‑health institutions.

Looking forward, witnesses recommended a mix of actions: (1) clearer institutional governance and disclosure by developers, (2) independent post‑market performance studies, (3) reimbursement changes to help smaller hospitals adopt and monitor tools, and (4) targeted safeguards for high‑risk use cases such as mental‑health chatbots and automated prior authorization.","A House Energy and Commerce subcommittee hearing brought industry leaders, clinicians and scholars to Washington to discuss real-world uses of artificial intelligence in health care, including clinical tools, price‑transparency products and risks from unregulated mental‑health chatbots and automated prior‑authorization systems.","actionData","errors"]

Article not found

Startups test consumer‑facing AI to show upfront prices and care plans

Companies testify to clinical gains from AI: stroke triage, earlier detection and fewer readmissions

Experts urge safeguards for AI chatbots used for mental‑health support, especially for youth

Lawmakers and experts warn against AI‑driven prior‑authorization programs, cite CMS 'WISER' pilot

House Energy and Commerce hearing showcases benefits and risks of AI in health care