CodeParticle tools
Answer 10 quick questions. Get a score and the one layer, from PHI access to HL7 reality, most likely to stall your healthcare AI before it ships.
Free, takes about two minutes. Built by senior engineers who ship AI into regulated production systems.
1. Do you start clinical or patient-facing AI features from a written specification of expected behavior and clinical rules?
2. Do you capture edge cases and failure conditions before a model or agent implements the feature?
3. Is what the AI can retrieve constrained by the requester's permissions and minimum-necessary PHI scope at the data/access layer, not just the application interface?
4. Can you identify and audit which PHI the AI accessed?
5. Can an AI-generated output be traced back to the source records or data that supported it?
6. Can you identify the model/version, context, and process that produced an AI-generated output?
7. Are decisions that affect care gated outside the model so the AI cannot execute them autonomously?
8. Is human or clinician review proportional to the clinical consequence of getting the output wrong?
9. Has the AI feature been tested under realistic production conditions, such as real HL7 or FHIR feeds and messy records, not just demo data?
10. Do you understand and control its cost and behavior at real patient volume?
Answer all 10 to see your score.
