OpenAI announced ChatGPT can now plug directly into Epic's EHR system, letting clinicians summarize charts and access patient data without switching apps. The author argues this is a major distribution win that absorbs the entire business model of thousands of healthtech startups—but it's not a technological breakthrough, and most specialized health AI companies will still die because they lack the workflow integration, liability ownership, and regulatory capabilities that actually matter.

• OpenAI's Epic integration directly threatens startups built on the pitch "AI that plugs into your EHR and saves time"—that exact value proposition is now baked into ChatGPT for free or cheap
• Distribution and deep workflow integration are the real competitive moats in healthcare AI, not better models or clever prompting; specialized companies with regulated write-back capabilities and liability ownership will survive, others won't
• OpenAI's 99.1% safety claim is marketing spin, not peer-reviewed validation, and the company still lacks the medical expertise and liability framework that established players like UpToDate have built over decades

Researchers developed CAPT, a method that combines a new general-purpose language model with an older clinical model without requiring retraining. The technique uses contrastive decoding to blend clinical knowledge into the newer model while keeping its reasoning abilities intact, and it outperformed existing ensemble methods by 17-41% on clinical tasks. This approach helps hospitals with limited computing resources adopt newer models without the expensive process of retraining them on clinical data.

• CAPT combines a new general-purpose LLM with an old clinical model at inference time via contrastive decoding, no retraining needed, even across mismatched vocabularies.
• It beat UniTE by 17.6% and standard proxy tuning by 41.4% on average across six clinical tasks.
• Physician review and token-level analysis showed more clinically specific language, fewer context errors, and better actionable recommendations.
• Lets resource-constrained hospitals adopt newer models without expensive retraining cycles on clinical data.