Most EMS QA/QI programs still run on manual chart review— someone reading through run reports one at a time, looking for missing fields, unclear narratives, or protocol deviations.

That takes time reviewers don’t have, and it means issues often don’t get caught until weeks after the call. By then, it’s too late to fix the records, coach the crew in the moment, or catch a pattern before it shows up again on the next run.

This session looks at how AI is starting to change that by helping crews document more completely at the point of care, and helping reviewers catch problems faster instead of digging for them manually.

We’ll walk through what this looks like in practice with real incident data. Less time spent on manual review, faster feedback loops, and a clearer read on what’s actually happening across your EMS operations — without adding another system or process on top of what you’re already doing.

Key Takeaways:
✅ Identify documentation gaps as reports are written, instead of catching them weeks later in review
✅ Spot patterns across incidents by reviewing QA/QI data at scale, not just on report at a time
✅ Cut down manual review time so your team can focus on coaching, not chasing paperwork
✅ Apply AI within your existing workflows — no separate system, no manual data pulls