From the Emergency Services edition of October 5, 2026
ImageTrend case study: St. Johns County Fire Rescue uses AI Assist on 85% of fire and EMS reports, 422 rejections in 20,000+ completions
St. Johns County Fire Rescue (Florida) / ImageTrend · County fire rescue and EMS / patient care documentation · United States (Florida)
- Publisher
- ImageTrend
- Original publication
- September 2026
- Source retrieved
- 2026-10-06
What happened
ImageTrend's case study (marked published September 2026) says St. Johns County Fire Rescue, with nearly 500 field personnel and about 67,000 incidents a year, uses ImageTrend Elite AI Assist (voice-to-text, image scanning of medication lists and insurance cards, auto-population) on 85% of fire and EMS reports, about 2.9 uses per incident. AI Assist populated 472,948 fields (44.7 per incident); more than 20,000 completions were applied with 422 rejected. Shorter completion time, less QI review and faster billing are stated without figures. A CQI Check feature is next.
Why it matters
One of the few fire/EMS AI documentation deployments reporting usage and acceptance counts at agency scale, relevant to any department weighing AI-assisted ePCR.
Evidence and measured results
Vendor case study with agency quotes. Counts (fields populated, completions, rejections) are system telemetry; time savings, accuracy and billing effects are asserted without baselines or numbers. Measurement period not stated.
Limitations and uncertainty
Vendor-authored; month-level date only; no stated timeframe for the totals; no independent accuracy audit; 'rejected completions' undefined.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-05; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- EMS crews split attention between patients and screens, and manual entry errors create billing and QI rework.
- Stakeholders
- EMS deputy chief
- QI/QA coordinator
- Billing office
- Field crews
- Privacy officer
- Discovery questions
- How long do reports take today?
- How much QI rework comes from data entry errors?
- Do you already have AI features in your ePCR?
- Value hypothesis
- Embedded AI capture may cut manual entry and rework if accuracy holds on audit.
- Potential engagement
- Baseline and field-level accuracy audit for AI-assisted ePCR.
- Unsupported claims
- Specific time savings
- Accuracy improvement percentages
- Billing revenue gains
Pre-sales engineering
Role takeaway
- Fit
- AI-assisted ePCR data capture for fire-based EMS.
- Architecture and integration
- Voice, OCR and text analysis inside ImageTrend Elite auto-populating report fields.
- Prerequisites
- ePCR with AI features enabled
- Device cameras and microphones
- QA workflow
- Constraints
- Vendor telemetry only
- No timeframe stated
- Rejection metric undefined
- Security
- Confirm where scanned IDs, insurance cards and audio are processed and retained.
- Proof of value
- Field-level accuracy on audited sample
- Report completion time vs baseline
- QI rework rate
Delivery
Role takeaway
- Work
- Train at go-live, baseline metrics, audit accepted fields, review PHI capture.
- Dependencies
- ImageTrend
- Billing
- QI staff
- Ownership
- Fire rescue EMS operations with QI.
- Skills and adoption
- Crews learn scanning and dictation workflows and to check auto-filled fields.
- Governance checkpoints
- Go-live training sign-off
- Monthly accuracy audit
- Annual PHI review
- Acceptance criteria
- Audited AI-field accuracy reported monthly
- Completion time measured against baseline
- Risks
- Accepted but wrong fields
- PHI over-capture
- Adoption counted as success
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
AI features embedded in the existing ePCR platform: speech-to-text narratives, OCR of IDs, insurance cards and medication lists, and automatic field population.
Governance
Who approves, reviews and stays accountable for outcomes?
A low rejection count measures crew acceptance, not correctness; agencies should audit a sample of accepted AI-filled fields against source documents.
Security and privacy
What data, permissions and controls need testing?
Image scanning captures IDs, insurance details and medication lists at the bedside; PHI handling and model processing location are not described.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Deputy chief says crews spend less time on screens and more on patients; providers adapted the tool to their own workflows.
Procurement
What should contracts, pricing and exit terms secure?
Comes as a feature of an incumbent ePCR platform, so adoption may bypass a separate AI procurement review; contract terms on AI processing should be checked.
Operating model
Which teams own the service once it runs?
Documentation shifts toward real time, including before leaving the hospital; QA moves toward pre-submission checks.
Publication history
- 2026-10-05Emergency Services · Issue 305 resources
Stable resource ID: imagetrend-st-johns-fire-rescue-ai-assist-case-study-202609