Claude for Healthcare is Anthropic’s “agentic” answer to ChatGPT Health

A week after OpenAI introduced ChatGPT Health, Anthropic is rolling out Claude for Healthcare—a healthcare-focused set of tools aimed at providers, payers, and patients, not just a consumer chat tab.

The shared premise: bring your health data into the chat (without training on it)

Both companies are leaning into the same big bet: if users can connect medical records + wellness data (phone, smartwatch, health apps), an AI assistant can help summarize, explain, and prep you for real-world care—while promising that this sensitive data won’t be used to train the models.

The difference: Anthropic is pushing harder on “workflow AI,” not just “patient chat”

OpenAI’s rollout reads like a patient-side experience first—a dedicated health space in ChatGPT where you can connect records/apps and ask questions.
Anthropic’s positioning is more “healthcare ops”: Claude for Healthcare highlights connectors and agent skills designed to speed up work that’s expensive, repetitive, and document-heavy.

What “connectors” actually mean here

Anthropic says Claude can connect into healthcare reference systems and research databases—things that people in the industry constantly bounce between—like:

• CMS Coverage Database
• ICD-10
• National Provider Identifier (NPI)
• PubMed

In practical terms, that’s Claude being able to pull the right context faster for tasks like coverage checks, coding lookups, and literature references—then assemble it into a usable output.

The flagship use case: prior authorization (aka the paperwork vortex)

Anthropic is explicitly calling out prior authorization as a place where AI can reduce burden: doctor submits extra documentation → insurer reviews → care gets approved/denied/delayed. Claude’s pitch is that connectors + agent-like tooling can speed up that review/documentation loop.

The uncomfortable truth: people are already using LLMs for health anyway

OpenAI says that, based on its de-identified analysis, 230M+ people globally ask health/wellness questions on ChatGPT each week—which explains why both companies are racing to “productize” the behavior with extra privacy controls and more structured experiences.

The real question: can they make this safe enough to be useful?

Healthcare isn’t like brainstorming a landing page. The failure modes matter:

• Hallucinations (confidently wrong answers)
• Over-trust (users treating a chatbot like a clinician)
• Data sensitivity (privacy + compliance + auditability)

So the most promising direction here isn’t “AI replaces doctors.” It’s “AI reduces the non-doctor work”—documentation, summarization, form-filling, coverage checks—while staying explicit that it’s not a substitute for professional medical judgment.

What to watch next

1. How “agent skills” are constrained (guardrails, citations, escalation when uncertain).
2. Real interoperability (how cleanly these tools plug into the messy reality of EHRs and payer workflows).
3. Measurable outcomes (reduced admin time, faster approvals, fewer errors) vs. flashy demos.