From the Emergency Services edition of November 3, 2026
Uppsala study validates open-source routing plus gradient-boosting and regression models to predict ground and helicopter EMS travel times for dispatch decisions
Uppsala University; Uppsala University Hospital ambulance, HEMS and emergency dispatch services · Emergency medical services dispatch and resource allocation · Uppsala region, Sweden
- Publisher
- Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
- Original publication
- 2026-11-02
- Source retrieved
- 2026-11-04
What happened
Spangler et al. (published Nov. 2, accepted early version) estimated ground EMS travel times with the open-source OSRM routing tool, then adjusted them with linear regression and gradient boosting trained on historical travel times; helicopter times used great-circle distance adjusted by historical data. On validation data collected after the methods were finalized, the best ground models explained much of the variation in drive time to scene and to hospital (R2 0.79 and 0.70), but helicopter flight times much less (R2 0.46 and 0.38). Mean average percentage error was 18-20% across all techniques and intervals. The authors release open-source code and say it could help dispatchers judge the time benefit of HEMS versus ground units.
Why it matters
Helicopter-versus-ground decisions are a costly dispatch choice for US state EMS offices, regional dispatch centers and air medical programs. This is a prospectively validated, open-source machine-learning approach rather than a vendor claim. Transferability caveat: single Swedish region and road network; US agencies would need to retrain on their own historical CAD/AVL times. Interpretation: an ~18-20% error band suggests decision support, not automated assignment.
Evidence and measured results
Peer-reviewed, accepted article (early version, subject to edits); abstract-level results inspected, full methods tables not reviewed. Validation on data collected after method finalization. Ethics approval by the Swedish Ethics Review Authority. No patient outcome or dispatcher-use evaluation reported.
Limitations and uncertainty
Single Swedish region; early accepted version; abstract-level inspection; helicopter predictions weak (R2 0.38-0.46); no evaluation of dispatcher decisions or patient outcomes.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-11-04; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Dispatchers must choose helicopter or ground EMS without reliable estimates of which arrives and transports faster.
- Stakeholders
- EMS medical director
- Dispatch center manager
- Air medical program
- State EMS office
- CAD administrator
- Discovery questions
- How clean are your historical CAD/AVL travel-time records?
- How are HEMS activation decisions made today?
- What prediction error would dispatchers accept?
- Can predictions display inside CAD?
- Value hypothesis
- Interpretation: locally validated travel-time predictions could make HEMS-versus-ground choices more consistent; outcome benefit is unmeasured.
- Potential engagement
- A data-readiness review and shadow-mode validation of travel-time predictions on one region's history.
- Unsupported claims
- Faster patient arrival
- Reduced HEMS costs
- Improved patient outcomes
- Accuracy outside Sweden
Pre-sales engineering
Role takeaway
- Fit
- Fits regional dispatch centers coordinating ground and air medical resources.
- Architecture and integration
- OSRM routing with gradient-boosting and regression correction on historical times; helicopter great-circle estimates adjusted by history; output fed to CAD.
- Prerequisites
- Historical CAD/AVL timestamps
- Current road network data
- HEMS base and flight records
- Constraints
- Single Swedish region
- Helicopter R2 only 0.38-0.46
- Early accepted version subject to edits
- Security
- Restrict access to historical incident location data and model outputs; log model versions used in dispatch.
- Proof of value
- Measure local MAPE on held-out recent data
- Run shadow mode against actual travel times
- Compare helicopter predictions to flight logs
Delivery
Role takeaway
- Work
- Prepare data, retrain locally, validate in shadow mode, then pilot as advisory display.
- Dependencies
- CAD vendor integration
- GIS team
- Air medical provider data
- Ownership
- EMS medical director with dispatch operations manager.
- Skills and adoption
- Dispatchers learn to read predictions with uncertainty ranges and keep final activation authority.
- Governance checkpoints
- Medical director approval of use rules
- Shadow-mode results review
- Periodic recalibration review
- Acceptance criteria
- Proposed: local ground MAPE at or below an agreed threshold
- Proposed: uncertainty shown with every estimate
- Proposed: quarterly recalibration documented
- Risks
- Over-trust in point estimates
- Stale road data
- Weak helicopter accuracy
- Model drift
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?
Open-source routing engine (OSRM) plus ML correction layer trained on historical CAD/AVL travel times; helicopter estimates from great-circle distance with historical adjustment. Could run on-premises alongside CAD.
Governance
Who approves, reviews and stays accountable for outcomes?
Use as decision support requires documented error bands and a rule that dispatchers or medical directors keep the HEMS activation decision.
Security and privacy
What data, permissions and controls need testing?
Training uses historical incident locations and times, which are operationally sensitive; patient data needs are minimal but location records require access controls.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Gives dispatchers a quantified time comparison rather than relying on experience alone; requires training on interpreting error ranges.
Procurement
What should contracts, pricing and exit terms secure?
Open-source code lowers licensing cost; agencies still need data engineering and validation effort. Interpretation: buyers can ask CAD vendors whether travel-time predictions are validated on local data.
Operating model
Which teams own the service once it runs?
Supports a more explicit, evidence-based HEMS activation step in dispatch protocols.
Publication history
- 2026-11-03Emergency Services · Issue 583 resources
Stable resource ID: sjtrem-uppsala-ml-ground-helicopter-ems-travel-time-prediction-20261102