Integritas uses AI where it helps and rules where it must be right. Deterministic rules check every chart against protocol, then AI reads the narrative for context. Every flag comes with a reason you can trace, on any NEMSIS-compliant ePCR.
Every EMS software vendor now has an AI story. The question that matters is what happens when the AI flags a chart: can you explain why?
The Logical Engine checks each chart against your protocols with deterministic, written rules. Every flag traces back to a specific rule and the part of the chart that triggered it.
The AI Engine reads the provider's narrative for nuance the rules cannot catch, and helps triage which exceptions need clinical eyes.
Human-in-the-loop validation means QA officers and Medical Directors make the call. The AI makes the review smarter, not less accountable.
A QA finding can drive education, remediation, or a documented protocol deviation. If the only answer is that a model scored the chart low, clinical governance rests on something you cannot reproduce or defend.
Run the same chart twice and a purely generative model can give two different answers. That is fine for brainstorming, but not for a compliance record.
When a provider asks why a chart was flagged, "the AI decided" erodes trust and fuels the policing culture good QA programs work to avoid.
Language models can hallucinate a justification that sounds clinical but is not tied to your actual protocol.
The alternative is a glass box. Read the full argument in Black Box vs. Glass Box: Explainable AI for EMS QA.
Here is an illustrative example of the Dual-Engine on a chest-pain chart.
Logical Engine: Protocol flag, chest pain
Aspirin not documented in the Medications grid.
AI Engine: Valid variance
The narrative states the patient has an aspirin allergy and treatment was withheld per protocol.
The rule catches the gap, and the AI supplies the context. The care is validated instead of the medic being penalized. See more on the features page.
Learn more about exception-based reporting, EMS CQI software, and ePCR integrations.
It is the use of AI to help review prehospital patient care reports (ePCRs) for protocol adherence and documentation quality, so an agency can review far more charts than manual sampling allows. The key design question is where the AI sits and whether its findings can be explained.
Generative AI can, which is why Integritas does not rely on it alone. The Logical Engine is deterministic and traceable. The AI Engine is used for narrative context, after the rules have run, rather than as the source of truth for compliance findings.
No. AI handles first-pass review so people can focus on the exceptions that need clinical judgment. Human-in-the-loop validation keeps accountability with your clinical leaders.
Integritas is vendor-neutral and NEMSIS-native, so it reviews charts from any NEMSIS-compliant ePCR without a migration.