From the Emergency Services edition of October 3, 2026
EMS1/Seattle Times: Seattle Fire used Corti AI on 911 medical calls without public disclosure or outcome metrics
Seattle Fire Department / The Seattle Times (via EMS1) · Municipal fire/EMS 911 medical dispatch · United States (Seattle, Washington)
- Publisher
- EMS1 (Seattle Times)
- Original publication
- 2026-06-15
- Source retrieved
- 2026-10-04
What happened
EMS1 republishes Seattle Times reporting (Daniel Beekman, June 15, 2026) that for more than two years Denmark-based Corti’s AI has analyzed Seattle residents’ 911 medical calls without caller disclosure or public review, confirmed by the Fire Department in late May after Times inquiries. Corti prompts dispatchers and can nudge routing of some patients to a nurse-staffed Texas call center rather than immediate ambulance dispatch. Officials reportedly cannot explain success metrics or current contract details publicly; no public website disclaimers; no shared outcome metrics; use not assessed under Seattle’s surveillance ordinance. Why now for 2026-10-03: same-week Waukesha-scale PSAP automation (53% of ~92k Ava-handled calls) makes undisclosed 911 AI assist a live governance comparator for SLED agencies expanding conversational or triage AI.
Why it matters
Cautionary U.S. fire/EMS 911 AI governance case directly in emergency-services; newly relevant as PSAP automation metrics become public elsewhere.
Evidence and measured results
Investigative journalism with department confirmation of non-disclosure and missing public metrics; not a technical performance evaluation of Corti.
Limitations and uncertainty
June 2026 reporting; Seattle-specific law; clinical accuracy of Corti not measured here; why-now rests on contemporaneous PSAP automation scale-up.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-03; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Agencies adopting 911 AI assist face trust/legitimacy risk if callers and oversight bodies are not informed.
- Stakeholders
- Fire/EMS chiefs
- City council/oversight
- Dispatchers
- Public/privacy advocates
- Discovery questions
- Was AI disclosed to callers?
- What KPIs exist?
- Did surveillance review occur?
- Who can audit the vendor?
- Value hypothesis
- Transparent AI assist with published metrics is more durable than silent optimization of diversion.
- Potential engagement
- Two-week governance gap assessment: disclosure, ordinance, metrics, contract audit rights.
- Unsupported claims
- Corti clinical harm/benefit proven here
- All PSAPs have identical legal duties
Pre-sales engineering
Role takeaway
- Fit
- Real-time 911 medical-call AI prompting/diversion assist.
- Architecture and integration
- Audio analysis alongside dispatcher workflow with pop-up prompts and nurse-line routing options.
- Prerequisites
- Legal/privacy review
- Disclosure script
- KPI definitions
- Vendor audit rights
- Constraints
- Local surveillance law
- Public records exposure
- Cross-state nurse-line data flows
- Security
- Third-party listening to 911 audio raises privacy, retention, and secondary-use risks.
- Proof of value
- Published diversion outcomes
- Complaint/appeal rates
- Undertriage audits
Delivery
Role takeaway
- Work
- Stand up disclosure, metrics, ordinance review, and contract transparency before scale.
- Dependencies
- City attorney
- Council/oversight
- Vendor
- Nurse-line partner
- Ownership
- Fire/EMS executive owns public accountability; privacy officer owns data controls.
- Skills and adoption
- Train dispatchers on when to accept/reject AI nudges and how to document overrides.
- Governance checkpoints
- Surveillance review
- Public notice
- Quarterly metrics release
- Acceptance criteria
- Caller disclosure live
- KPI dashboard public
- Audit clause executed
- Risks
- Secret deployment backlash
- Unmeasured diversion harm
- Contract opacity
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?
Real-time call audio AI prompting integrated into dispatcher workflow with nurse-line diversion path.
Governance
Who approves, reviews and stays accountable for outcomes?
Highlights gaps in disclosure, surveillance-ordinance review, contract transparency, and public KPI reporting for consequential 911 AI.
Security and privacy
What data, permissions and controls need testing?
Third-party AI listening to medical 911 calls without notice raises privacy, secondary-use, and interstate nurse-line data questions.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Nurse-line diversion changes who callers reach; without disclosure, accessibility and trust for limited-English or vulnerable callers may be affected.
Procurement
What should contracts, pricing and exit terms secure?
Demands contract clauses for public metrics, audit rights, and disclosure obligations—not only accuracy SLAs.
Operating model
Which teams own the service once it runs?
AI assist that alters ambulance vs nurse-line routing is an operating-model change requiring explicit public policy, not a silent IT add-on.
Publication history
- 2026-10-03Emergency Services · Issue 275 resources
Stable resource ID: ems1-seattle-corti-911-ai-disclosure-20260615