Top 3 · Healthcare
- 01GPT-6 Astra$20.00 · blended price58.7 points
- 02GPT-5.6 Sol$8.00 · blended price54.4 points
- 03Claude Opus 5$10.00 · blended price54.3 points
AI in healthcare: clinical summaries, documentation and patient data
Evaluate models for clinical documentation with accuracy first
The data, today
Most affordable · Object storage
- 01Backblaze B2 · Pay as you goBackblaze$0.00695 USD / GB-month
- 02R2 · StandardCloudflare$0.015 USD / GB-month
- 03Spaces · StandardDigitalOcean$0.018626 USD / GB-month
The context
In healthcare, a model error is not a matter of style. A clinical summary that leaves out an allergy, or a note that garbles a dose, can have real consequences for a patient. So the priorities differ from other industries: accuracy and traceability come first, cost second. The most common uses are summarizing patient records, drafting documentation from clinician notes and suggesting diagnostic codes that a qualified person then reviews and confirms.
To be plain about it: Codifly does not certify anything, clinically or from a regulatory standpoint. We show LLM Stats indexes, official prices and technical criteria. Whether a provider meets HIPAA, LGPD or your local health data law is something you have to validate with that provider, its contract and your own advisors. And any model output that reaches a patient or a medical record needs review by a clinician.
What to decide
- 1
How do I evaluate a model's accuracy on clinical summaries?
Assemble de-identified cases reviewed by clinicians, each with a list of what a good summary must include. Measure omissions of critical data such as allergies, medications and active diagnoses, and count statements that do not appear in the source. General indexes help you shortlist candidates; they do not replace this test.
- 2
What should I validate with the provider on data protection?
Ask whether they sign a BAA for HIPAA or a processing agreement that meets LGPD or your local law, where data is processed and stored, what is retained and whether it is used for training. Get the answers in writing. Codifly does not issue that validation; it belongs to your legal and compliance team.
- 3
Should I de-identify data before sending it to the model?
Whenever the task allows it. Strip or replace names, identifiers and exact dates before the call and re-link the result inside your own system. Measure how much summary quality changes with pseudonymized input; if it barely moves, you cut exposure without losing usefulness.
- 4
How do clinicians fit into the workflow?
Design the output as a draft: the model proposes, the clinician edits and signs. Log what changed in every review; that history shows where the model fails and lets you compare versions. Measure review time per document, not just generation time, because that is where the real benefit shows up.
- 5
What infrastructure do I need for auditability?
Store every request with model version, prompt, input documents and output, in encrypted storage with restricted access and logs of who viewed what. Set retention according to your regulations. Compare databases and storage on encryption, available regions and access logging, not just on price.
Common mistakes
- Assuming a provider meets a health data regulation because its website mentions it.
- Filing generated summaries into the medical record without a clinician reviewing and signing them.
- Picking the cheapest model for clinical documentation before measuring what it leaves out.
Tools and comparators
Guides to go deeper
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No provider pays for its position. Indexes come from LLM Stats; prices from each provider's standard API. How we measure
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