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AI-Assisted Hospital Routing for Emergency Medical Services

What it is for

Safety & Security
  • The algorithm is used to determine the closest, appropriate hospital to the incident location based on the medical needs of a patient requiring emergency transport, improving the safety and speed of ambulance dispatch decisions.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Mobility
  • The system uses ambulance vehicle location data and transport time estimates to optimize the routing of patients to hospitals, directly supporting the logistics of emergency patient transport across New York City.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Run by
Fire Department (FDNY)
Where
No fixed location
Kept
No data retained
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

Safety & SecurityMobility

This algorithm helps ambulance crews identify the closest appropriate hospital for a patient based on their location and medical condition. It produces a ranked list of hospitals — not a binding command — so the crew retains full authority over the final transport decision. No identifying information about patients is collected or disclosed by the system.

What it collects and what happens to it

Data taken in

About a place
Anonymized data
  • The location of the patient at the incident site is used as the primary geographic input to calculate transport times from the incident to each candidate hospital.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Operational data
Anonymized data
  • The medical call type of the patient is supplied as a categorical input, determining which hospital capability list (e.g., general emergency, burns, or other specialist categories) is applied to rank the candidate hospitals.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Processing

Optimization
  • A network analysis model calibrated with historical ambulance GPS data computes incident-to-hospital transport times. The model was validated against five years of historical EMS transport data (2015–2019) to generate and correct ordered hospital lists for each medical condition category.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

What it does

Deciding (Analytical AI)
Human decides
  • The algorithm ranks hospitals from closest to furthest in transport time for each medical condition category, producing a pre-determined ordered list. The system is informative — ambulance crew members review the ranked list and make the final transport decision themselves.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Outputs

A recommendation or prediction
Anonymized data
  • The algorithm outputs a ranked list of the closest, most appropriate hospitals for the patient's medical condition, presented to the ambulance crew as an advisory suggestion. The crew is not compelled to follow the list — it is informative only.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Run by

Fire Department (FDNY)
  • The NYC Fire Department's Management, Analysis, and Planning unit deploys and operates the EMS Hospital Suggestion Algorithm to support ambulance crews in selecting the most appropriate hospital for each patient.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Built by

Not stated by the NYC Office of Technology and Innovation.

Kept for

No data retained
  • The register states that no identifying information about individuals is collected or disclosed by this system. Patient-level personal data is not retained as an output of the algorithm.
  • Duration: Not applicable — no identifying information is collected or retained

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Shared with

Not available to me
  • No identifying information about individual patients is collected or disclosed by this system. Members of the public cannot access individual-level data produced by the algorithm, as none is retained at that level.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Available to the accountable organization
  • The ranked hospital lists produced by the algorithm are available to NYC Fire Department EMS crews during active incidents, and operational outputs are accessible to the Management, Analysis, and Planning unit for monitoring and validation purposes.

NYC Automated Decision Systems — EMS Hospital Suggestion Algorithm

Stored

Not stated by the NYC Office of Technology and Innovation.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the NYC Office of Technology and Innovation, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe NYC Fire Department has publicly disclosed this algorithm's logic, inputs, and outputs through the NYC Automated Decision Systems registry. Members of the public may review the published register entry and the supporting research publication at https://doi.org/10.1016/j.scs.2022.104104.

Risks and safeguards

  • Physical harmAn incorrect or outdated hospital ranking could direct an ambulance crew to a facility that is farther away or unable to handle the patient's condition, potentially worsening patient outcomes.Safeguard: The system is advisory only — ambulance crew members retain final authority over the transport decision. The algorithm was validated against five years of historical transport data (2015–2019) before deployment, and hospital lists are updated to reflect current medical condition categories.