{
  "schema_version": "1.1",
  "id": "archive:https://www.youtube.com/watch?v=AN65uc645mE",
  "slug": "200-million-patient-interactions-later-vivek-muppalla-hippocratic-ai-1axcolg",
  "url": "https://feed7.dev/p/200-million-patient-interactions-later-vivek-muppalla-hippocratic-ai-1axcolg",
  "title": "200 Million Patient Interactions Later — Vivek Muppalla, Hippocratic AI",
  "why_included": "Hippocratic AI’s voice stack uses specialist models, parallel checks, contextual speech recognition, and offline verification to avoid a single clinical-agent failure point.",
  "summary": "Hippocratic reports **200 million clinical interactions** across **60+ health systems**. Its fifth-generation Polaris system reached **99.89% no-harm accuracy** on its rubric, supported by continuous evaluation from more than **7,000 trained clinicians**.",
  "practical_implication": "For high-stakes voice agents, separate the main conversation model from specialist checks and asynchronous verifiers. Feed speech recognition the conversation and task context, short-circuit irrelevant specialists, and verify tool calls both live and offline where correction is possible.",
  "agent_context": "Hippocratic reports **200 million clinical interactions** across **60+ health systems**. Its fifth-generation Polaris system reached **99.89% no-harm accuracy** on its rubric, supported by continuous evaluation from more than **7,000 trained clinicians**.\n\nFor high-stakes voice agents, separate the main conversation model from specialist checks and asynchronous verifiers. Feed speech recognition the conversation and task context, short-circuit irrelevant specialists, and verify tool calls both live and offline where correction is possible.\n\nThe scale, safety, and satisfaction figures are company-reported in the presentation. The architecture is vertically optimized for clinical calls, so its latency and accuracy claims do not establish how the same approach performs in other domains.",
  "source": {
    "name": "AI Engineer",
    "url": "https://www.youtube.com/watch?v=AN65uc645mE",
    "published_at": "2026-08-19T16:00:06.000Z"
  },
  "source_class": "video",
  "content_type": "Video",
  "layer": "agent",
  "domains": [
    "audio"
  ],
  "topics": [
    "harness-engineering",
    "agent-evals",
    "agent-reliability"
  ],
  "verification": {
    "status": "source_linked",
    "label": "Source Linked",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [
    "The scale, safety, and satisfaction figures are company-reported in the presentation. The architecture is vertically optimized for clinical calls, so its latency and accuracy claims do not establish how the same approach performs in other domains."
  ],
  "connected_context": {
    "meaning": "This turns the prior case for narrow, expert-governed vertical agents into a concrete clinical voice architecture: conversation generation, specialist checks, and asynchronous verification are separate failure boundaries. It reinforces architecture-matched evals and continuous verification, while narrowing the evidence to company-reported results from one clinically optimized system rather than a transferable recipe for other domains.",
    "corpus_size": 525,
    "generated_at": "2026-08-21T10:09:17.076Z",
    "connections": [
      {
        "title": "Trading Desks to Clinical Trials: Parallels in Applied Vertical AI — Ayush Bhardwaj, Allos AI",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=Yphdry8ttAQ",
        "feed7_url": "https://feed7.dev/p/trading-desks-to-clinical-trials-parallels-in-applied-vertical-ai-ayush-1hwvsg3",
        "reason": "It supplies a clinical implementation of the candidate’s requirement for narrow scope, proprietary expertise, observability, and expert judgment."
      },
      {
        "title": "Your Agent Evolved. Your Evals Didn't. — Ameya Bhatawdekar, Braintrust",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=nxokqOq1imY",
        "feed7_url": "https://feed7.dev/p/your-agent-evolved-your-evals-didn-t-ameya-bhatawdekar-braintrust-1loaqv2",
        "reason": "The specialist and offline-verifier architecture demonstrates why evals must cover orchestration and tool use, not only the main model’s answers."
      },
      {
        "title": "Guide, Verify, Solve — Anirban Chatterjee, Sonar",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=03l29gJXpCE",
        "feed7_url": "https://feed7.dev/p/guide-verify-solve-anirban-chatterjee-sonar-1igfmbm",
        "reason": "Both place verification inside the operating loop; here that principle is extended to live and correctable offline checks for high-stakes voice calls."
      },
      {
        "title": "AI is the World’s largest Relationship Therapist — Clay Cockrell & Tony Fabrikant, CoupleWork AI",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=yoONZwV2smc",
        "feed7_url": "https://feed7.dev/p/ai-is-the-world-s-largest-relationship-therapist-clay-cockrell-tony-fabr-04obn7y",
        "reason": "Both require clinician-defined safety boundaries, but this system emphasizes layered specialist verification while the relationship agent emphasizes sycophancy, escalation, and returning users to human support."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-19T16:00:06.000Z",
  "modified_at": "2026-08-19T16:00:06.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/200-million-patient-interactions-later-vivek-muppalla-hippocratic-ai-1axcolg",
    "json": "https://feed7.dev/p/200-million-patient-interactions-later-vivek-muppalla-hippocratic-ai-1axcolg.json",
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}