[{"_1":2,"_112":-5,"_113":-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",[32,76,88,100],{"_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":74,"_75":74},"mediaId",6534571,"mediaKeyId","3555881-073067fd6863fc285b23effc4c7a45a1","parentId",3555881,"title","Hospitals and vendors point to early efficiency and outcome gains from AI pilots—clinical caution remains","audioSeconds",777,"startMs",2517000,"endMs",3294000,"viewUrl","/Search/View?dp=1&key=3555881-073067fd6863fc285b23effc4c7a45a1&start=2517&end=3294","thumbnailUrl","https://assets.pipeline.soar.com/3555881-073067fd6863fc285b23effc4c7a45a1/thumbnail_2517000.jpg","articleUrl","/articles/6534571/California/Hospitals-and-vendors-point-to-early-efficiency-and-outcome-gains-from-AI-pilotsclinical-caution-remains","mediaUrl","https://secure.pipeline.soar.com/6534571-9ca2688bef90c20d90d9dce1106c311c/orig.mp4?Expires=1770978769&Signature=YoXiefcGR~597tgF3~wyi3Nw3cBYJNVaQ7V6gO7WhccAulwSANY57WTd00Cadvdy7ln4zk845ig8FmLXh0THs02facUHwrd541Bc99WK4XqRPCRigdkXrm1U4iahmk3badG4-vzp0nz33ffpPbOn2Tu5y80vcB~TY9j6OC7vrgmwxHHbCzv-Tu8ahzTeaSev8W4S7Lru2BLz5w54Pef6Lmo23Gk0GqTeIguGXe0TzCP~mVhiCG6ZSyJOZKiSVXSQz9VlYEnEKYdb3Cz8Q2o6isor-Gxhzzn1q8tN7LMfZlynZrVvIVxfk~tI5H6~z0ch~PgrzIPjFMYe2oBmRb-18Q__&Key-Pair-Id=KUT5TJCACFTXG","author","House","date","2025-05-28T00:00:00Z","article","Several health‑system leaders and vendors told the Joint Informational Hearing about concrete use cases and near‑term outcomes from AI pilots in hospitals and clinics.

Cedars‑Sinai’s Craig Kwiatkowski highlighted use cases spanning administrative documentation, nursing documentation via mobile devices, imaging triage and maternal‑fetal decision support. He said an imaging workflow that ranks pulmonary embolism scans and notifies a response team produced roughly a 40 percent reduction in time to mechanical thrombectomy and related reductions in ICU and overall length of stay.

Dr. Daniel Yang of Kaiser Permanente described enterprise deployment of an ambient‑scribe product across clinicians and noted patient feedback that clinicians were more present in visits once freed from keyboard work. Yang also described a deployed predictive inpatient deterioration model that Kaiser evaluated and estimated prevented roughly 500 deaths per year in Northern California, based on local impact analysis.

Vendors and entrepreneurs urged starting with administrative and structured problems—prior authorization, coding and document summarization—where outcomes are measurable and ROI accrues quickly. Fawad Bhatt of Penguin AI said prior‑authorization automation can cut decision times from days to hours in sample deployments.

Panelists stressed that clinical uses pose higher risk and require drug‑development‑style validation, local evaluation and human‑in‑the‑loop safeguards. Multiple presenters noted that imperfect performance still can compare favorably against current practice (for example, notes written by exhausted clinicians), but that measurement and monitoring are essential.

The hearing showcased both promising early results and the limits of existing evidence: some pilots have operational metrics and preliminary outcome estimates, but independent peer‑reviewed studies were not presented for each claim. Lawmakers asked for follow‑up materials and data to evaluate the generalizability of the reported benefits.","mediaType","Video","parentTitle","Assembly Joint Hearing Health Committee and Privacy and Consumer Protection Committee","oneSentenceSummary","Health‑system executives and vendors described early clinical and operational results from pilot AI projects — faster documentation, faster imaging‑to‑treatment times, and predictive models — while acknowledging limitations and the need for careful validation and human oversight.","taxTitle","California State Assembly, House, Legislative, California","taxId",17099,"location","California","headline","","shortSummary",{"_33":77,"_35":36,"_37":38,"_39":78,"_41":79,"_43":80,"_45":81,"_47":82,"_49":83,"_51":84,"_53":85,"_55":56,"_57":58,"_59":86,"_61":62,"_63":64,"_65":87,"_67":68,"_69":70,"_71":72,"_73":74,"_75":74},6534573,"Researchers, ethicists and unions urge state governance, monitoring and clearer liability for health‑care AI",2156,6868000,9024000,"/Search/View?dp=1&key=3555881-073067fd6863fc285b23effc4c7a45a1&start=6868&end=9024","https://assets.pipeline.soar.com/3555881-073067fd6863fc285b23effc4c7a45a1/thumbnail_6868000.jpg","/articles/6534573/California/Researchers-ethicists-and-unions-urge-state-governance-monitoring-and-clearer-liability-for-healthcare-AI","https://secure.pipeline.soar.com/6534573-d06df89391b8691ccaa6dcd796ec918c/orig.mp4?Expires=1770978769&Signature=Eaf9AVGfBXCgWBCcBLf5bxRRO9NmMrCry9Pzi71aCqgBHWzC-hD~wzl9tlr50Zho3mjayolHukAQkFRwqF1ScX5l0jdDcv6C56O76jSEeIKCn1Q7bDm48uFR1AajARjkSUK7hBpCI8kiG~Lgd5x-XUQgnpShmJA8xAf2yqUDn0JhzzLUQtFwMbFwKwbs~EnkNg0fpsu-nF4KeZHSrV98aapxv90GOIRthLg3ByFJ8CM7TV7dbksvMnPsYc2sp9Q2G3NQwIIakSJaEBYN8rAThZC-g7mZ9b0ufrpShZmKVbyTqqUrfbuLKFY07nJCf9FmQdzaek9tmKZ-ggFr5zrCpQ__&Key-Pair-Id=KUT5TJCACFTXG","Experts at the Assembly hearing pressed the committee to address a governance gap: many developers disclaim liability in contracts, and most health‑care purchasers have not stood up rigorous pre‑deployment validation or ongoing monitoring for AI tools.

Stanford researcher and lawyer Michelle Mello said market incentives encourage experiments but not the personnel and processes required for robust safety oversight. “The incentives are there for health care organizations to want to try things with AI. But when it comes to the difficult task of actually governing them to ensure safe and responsible use, there are not incentives sufficient for a lot of health care organizations,” she told the committee.

Mello recommended a state framework that requires health facilities to maintain governing bodies and review procedures for AI — modeled loosely on institutional review boards used for human‑subjects research — as part of licensure or certification. She said the goal is to push hospitals to measure safety‑oriented outcomes, not just whether software is running.

Patient‑rights and civil‑society panelists asked for stronger, enforceable patient protections: notice when AI affects a care decision, patient participation in governance, clear pathways for legal recourse when errors cause harm, and privacy safeguards against re‑identification. Christine Von Riesfeld of the Light Collective proposed a seven‑point Patient AI Rights framework to codify these protections.

Labor representatives also urged stronger worker protections. Chris Nielsen of the California Nurses Association warned that employers could rely on AI to justify understaffing and called for workers to have authority to override tools and veto deployments that undermine safety.

Industry and coalition representatives offered practical complements: standardized model cards or “fact labels” (Championed by the Coalition for Health AI), a public registry of disclosures, and networks of vetted third‑party evaluators to help hospitals with pre‑ and post‑deployment assessment. Panelists agreed the state could accelerate accountability by pairing disclosure mandates with funding and technical assistance to help under‑resourced providers comply.","Legal scholars, patient-rights advocates, clinicians and union representatives told lawmakers that market incentives alone will not produce safe AI deployments and urged the Assembly to require governance structures, standardized transparency and post‑deployment monitoring as licensure or procurement conditions.",{"_33":89,"_35":36,"_37":38,"_39":90,"_41":91,"_43":92,"_45":93,"_47":94,"_49":95,"_51":96,"_53":97,"_55":56,"_57":58,"_59":98,"_61":62,"_63":64,"_65":99,"_67":68,"_69":70,"_71":72,"_73":74,"_75":74},6534574,"Safety-net providers and researchers warn AI could deepen disparities without data, funding and governance",4614,6028000,10642000,"/Search/View?dp=1&key=3555881-073067fd6863fc285b23effc4c7a45a1&start=6028&end=10642","https://assets.pipeline.soar.com/3555881-073067fd6863fc285b23effc4c7a45a1/thumbnail_6028000.jpg","/articles/6534574/California/Safety-net-providers-and-researchers-warn-AI-could-deepen-disparities-without-data-funding-and-governance","https://secure.pipeline.soar.com/6534574-3c020d2b70bd266f4959fec496d3e89a/orig.mp4?Expires=1770978769&Signature=kyvWX4Uz~JAdJM7l45dzpl8pQJ09mj8pwwmuIr6jLohW8HcnlTvBPrshvz09RYqK96TYNWHx0ooKUNHX7LgtlBAldQWrJrRkRDZkb26H03tGrI12LGBvPO7P7yMCa75tw~E0UxNu07zC7MuwgFyU0W6cWwmoRjqedKjgOHcstylu1ReAyJ2K~qu-1Figgi4w82MoMUuWDJetaOC~yaSc16~LWUY-OpQNqBrj4scTc~i5w~VtbGIfdz~WshFnFYXVHaWq5aF6r6ww8WP-oCYoFinmNwrRB~SKCbzCgjdEtUUi0l9uFwpmsgA0sGY75YqQNXSEHYwPpePUIBoGp3LjlQ__&Key-Pair-Id=KUT5TJCACFTXG","Speakers at the Joint Informational Hearing stressed that the state’s safety‑net providers face unique obstacles to safe AI adoption: limited data infrastructure, scarce funds for purchasing and sustaining tools, and insufficient capacity to run local validation and monitoring.

Cara Carter, senior vice president for programs and strategy at the California Health Care Foundation, described interviews with safety‑net providers and said many are eager to adopt AI but lack capital, staff time and technical readiness. Carter said CHCF is preparing listening activities to capture patients’ views and urged state support for data infrastructure, workforce training and governance.

Dr. Ziad Obermeyer, a physician and researcher at UC Berkeley, described prior research that found a widely used predictive algorithm prioritized patients who would generate high costs, not those who were medically high-risk — a disparity that left many Black patients without needed interventions. “Those algorithms predicted who was gonna generate high health care costs,” he said, and cautioned that automating flawed signals scales harm.

Representatives from community health‑IT networks and clinics told the committee that basic interoperability and up‑to‑date electronic health record environments are prerequisites for equitable AI. OCHIN’s public comment asked the state to invest in infrastructure, workforce training and streamlined regulations so community clinics and public‑health agencies can adopt AI safely and avoid an “AI chasm.”

Panelists proposed practical steps for policymakers: create privacy‑preserving mechanisms for aggregate data sharing, fund local validation and post‑deployment monitoring for safety‑net sites, and prioritize primary‑care use cases that directly serve underserved populations.

Panelists cautioned that policy choices could either narrow or widen gaps. Fawad Bhatt, founder of Penguin AI, advised starting with administrative automation (claims, prior authorization) that is structured and measurable before moving aggressively into higher‑risk clinical decision tools.

The committee is considering the testimony as it evaluates policy options for Medi‑Cal procurement, data sharing standards and targeted grants to ensure tools benefit rather than harm underserved Californians.","Representatives of safety-net organizations, researchers and foundations told the Assembly committees that AI can help many Californians but will widen gaps unless Medi‑Cal and other public programs support data access, validation and sustained governance for under-resourced providers.",{"_33":101,"_35":36,"_37":38,"_39":102,"_41":103,"_43":104,"_45":105,"_47":106,"_49":107,"_51":108,"_53":109,"_55":56,"_57":58,"_59":110,"_61":62,"_63":64,"_65":111,"_67":68,"_69":70,"_71":72,"_73":74,"_75":74},6534575,"California lawmakers convene joint hearing on generative AI in health care; experts urge cautious, equity-focused rollout",8506,2139000,10645000,"/Search/View?dp=1&key=3555881-073067fd6863fc285b23effc4c7a45a1&start=2139&end=10645","https://assets.pipeline.soar.com/3555881-073067fd6863fc285b23effc4c7a45a1/thumbnail_2139000.jpg","/articles/6534575/California/California-lawmakers-convene-joint-hearing-on-generative-AI-in-health-care-experts-urge-cautious-equity-focused-rollout","https://secure.pipeline.soar.com/6534575-73c687d028de569cff4dbbcef4667db2/orig.mp4?Expires=1770978769&Signature=Nf6pSj8CagtBankvd91VxBaxrMlzu2x5-LcQmZCty5RFv83VkUAa3VMrOifqZLrXBQqgLC8QO~CrMO0b3Rt2~HYtziW3~Jd3sjRKAGHlZ2HcBduIccat0Zy8wCVpie9-gmDSzRKx~Ya0VWaLR09AGc3ThPLuzfhawJ2k4wNZUTaaa8J0G3BrQbP~QP~X53yqNIrmdL7nQSjVny9xZLqFy7k6BJw1WyMEjRrrcrmY8jTHvKOL7qs3jotGX4~9nzOV4zuYdlOb1n5rgWcqm7BxJvaFOmr6-hTlrYJnQSGCwoLpPlnIPCo2LZh~JncdCHIysw1oAsoE31H9xUcKIsSPUQ__&Key-Pair-Id=KUT5TJCACFTXG","Lawmakers and health leaders met in a joint informational hearing of the California State Assembly Health Committee and Privacy Committee to review how generative artificial intelligence is being used in health care and what state policy should require to protect patients and support equitable adoption.

The hearing gathered health-system executives, developers and patient advocates to describe early deployments — ambient scribes that draft clinical notes, AI that triages imaging and speeds treatment, and predictive models used in obstetrics — and to highlight persistent risks: bias in training data, unclear developer/deployer liability, privacy and data-access constraints, and the danger that under-resourced safety-net providers will be left behind.

“We are gonna talk about generative artificial intelligence in health care opportunities, challenges, and policy implications,” Assemblymember Rob Bonta said in opening remarks. Speakers emphasized potential benefits such as reduced clinician administrative time and faster identification of high-risk patients, while repeatedly urging robust oversight and monitoring.

Cedars-Sinai’s Craig Kwiatkowski described multiple internal projects, including ambient-scribe pilots and imaging workflows that rank scans for radiologists and notify response teams when the AI flags high-risk studies. Kwiatkowski said those workflows helped reduce time to mechanical thrombectomy by about 40 percent in a pulmonary embolism pathway and that ambient scribe pilots showed roughly a 20 percent reduction in time to notes and a 17 percent reduction in clinicians’ after-hours “pajama time.”

Dr. Daniel Yang of Kaiser Permanente described his organization’s large-scale rollout of an ambient-scribe product to clinicians and said the system has been used in millions of visits. “There’s only one way for me to guarantee that we can eliminate risks associated with AI…and that is for me to never deploy these AI technologies at all,” Yang said, arguing that foregoing AI would also mean keeping in place the current workforce and access problems he said the tools can help address.

Researchers and advocates pressed officials on data, equity and governance. Dr. Ziad Obermeyer (UC Berkeley) summarized prior research identifying algorithmic bias in health systems that used cost as a proxy for need: “Those tools were supposed to identify patients who were high risk…But instead of predicting who’s sick, those algorithms predicted who was gonna generate high health care costs,” he said, illustrating how biased training data can steer resources away from underserved patients.

Panelists and advocates recommended several state actions: clarify liability between developers and deployers, require standardized disclosures (model “fact labels” or model cards), invest in data infrastructure and workforce training for safety-net providers, and build monitoring and reporting requirements so deployed tools are validated locally and reevaluated over time.

The hearing did not produce votes or formal committee action. Chairs said the discussion would inform future policy work and legislative follow-up.

Despite differing views on speed of adoption, the assembled experts and patient advocates agreed on core themes: AI can improve efficiency and some clinical outcomes, but California will need clear rules and funding to ensure safety, preserve privacy, reduce bias and extend benefits to Medi-Cal and other underserved populations.","A joint informational hearing of the California State Assembly Health and Privacy committees drew health-system leaders, vendors, researchers and advocates to discuss current uses of generative AI in clinics and hospitals, early outcome data, and gaps in governance, data access and equity that state policy could address.","actionData","errors"]

Article not found

Hospitals and vendors point to early efficiency and outcome gains from AI pilots—clinical caution remains

Researchers, ethicists and unions urge state governance, monitoring and clearer liability for health‑care AI

Safety-net providers and researchers warn AI could deepen disparities without data, funding and governance

California lawmakers convene joint hearing on generative AI in health care; experts urge cautious, equity-focused rollout