Healthcare AI with patient-safe defaults
Administrative automation and clinical decision support carry different evidence bars. We separate them deliberately and instrument what clinicians and patients actually experience.
Administrative acceleration
Scheduling, coding assistance, prior authorization packets, and note summarization can move faster when PHI is segmented, workflows are deterministic, and humans stay in the loop for edge cases.
Clinical and safety posture
For decision support, emphasize provenance of guidelines, drift monitoring versus local populations, and feedback capture from clinicians. Pair with retrieval policies in RAG readiness.
Frequently asked questions
How should we partition PHI for AI workloads?
Use minimum-necessary scopes, de-identification where feasible, segregated environments, and contracts that forbid secondary use without consent.
What is a pragmatic starting point for hospitals and payers?
Back-office copilots with clear SOPs, measurable time savings, and explicit fallbacks — then expand to patient-facing tools once logging and escalation paths are proven.
Do we need different evaluations for generative vs classical ML?
Yes: generative systems need citation grounding, toxicity and bias checks, and adversarial prompting tests tuned to healthcare content.
Next step: a fixed-fee diagnostic.
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