EMS Allocation
Open thesis

Prehospital emergency care · Patient safety

Crafting the
patient safety net.

Research on how emergency medical services allocate finite response capacity under uncertainty — and how patients can be protected while they wait.

Discover the evidenceDoctoral thesis
1.29MEMS missions in the source database
4Studies in one research architecture
3Peer-reviewed journal articles
2026Doctoral thesis, Karolinska Institutet
01 / The research problem

A safety net is more than a response-time target.

Speed matters. But it cannot, on its own, describe patient safety.

Ambulance systems operate with uncertain information, fluctuating demand, geographic variation and finite resources. Every allocation decision shapes who receives help first, who waits and how safety margins are distributed across the system.

Who can safely wait — and how does the system keep that patient safe while certainty is still unavailable?

From speed to system behaviour

Response time emerges from dispatch, triage, workload, geography, availability, call handling and travel.

From queue to virtual waiting room

Queued patients require active monitoring, reassessment, escalation and organisational ownership.

From data to responsible governance

Routine EMS data can reveal patterns, provided uncertainty and interpretation boundaries remain visible.

The research journey

Four studies. One safety net.

Scroll through the evidence chain—from the emergency call to system-level delay.

01
Qualitative interview study

Uncertainty enters

The emergency call introduces incomplete information and a need for proportionate allocation.

Read study
02
Interpretable machine learning

Capacity is stewarded

Dispatchers balance the patient in front of them with readiness for the next emergency.

Read study
03
Retrospective cohort study

Risk becomes clearer

At first EMS contact, clinical assessment changes what is known about time sensitivity.

Read study
04
Systems analysis

The system leaves a trace

Workload, geography and operational intervals shape variation and extreme delays.

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DOCTORAL THESIS · 2026
KI

Published doctoral thesis

Adaptive emergency medical services allocation

Crafting the patient safety net in prehospital emergency care

The thesis connects dispatch stewardship, time-sensitive triage, response-time variability and queue governance into a systems-oriented account of EMS allocation under uncertainty.

Peter HillKarolinska Institutet124 pagesDOI 10.69622/31262914
Read the thesis
02 / Supporting studies

Four studies.
One systems question.

Qualitative insight and large-scale observational analysis examine how uncertainty, vulnerability and operational delay interact in prehospital care.

01
BMJ Open · 2026

Stewarding scarce response capacity

Qualitative interview study

How dispatchers prioritise patients, govern the queue and preserve readiness when ambulance capacity is constrained.

02
BMC Medical Informatics and Decision Making · 2025

Breathing emergencies and nonlinear risk

Interpretable machine learning

How response time, age and sex interact with high-risk time-sensitive triage among 132,395 breathing-problem missions.

03
Research Square · Preprint · 2026

Infectious presentations and concordance

Retrospective cohort study

How dispatch suspicion, on-scene phenotype and high-risk time-sensitive triage align in infectious presentations.

04
BMC Medical Informatics and Decision Making · 2025

Understanding EMS response times

Systems analysis

How priority, workload, geography, weather and operational intervals shape variability and tail delays.

Interactive systems explorer

See pressure move through the safety net.

Adjust three system conditions to explore their qualitative relationship with queue pressure, readiness and tail-delay risk.

01
Queue pressureStretched
47
02
Readiness marginStretched
52
03
Tail-delay riskStretched
45

Illustrative systems model. It uses normalized indices to communicate relationships identified in the research; it does not predict individual outcomes or reproduce an operational model.

03 / Operational relevance

Evidence for governing uncertainty.

The findings support a shift from isolated response-time targets toward distribution-aware, patient-centred governance of EMS systems.

01

See the whole distribution

Track variability and tail delays, not only averages and medians.

02

Protect the waiting patient

Treat the dispatch queue as a safety-critical virtual waiting room.

03

Preserve human judgement

Use analysis to support system understanding — not unvalidated automation.

04

Design for resilience

Balance present need with readiness for the next time-critical event.

Exploration before deployment

The research is observational and explanatory. It does not provide patient-level advice or a validated real-time dispatch tool. Operational translation requires prospective evaluation, governance, human-factors design and safeguards.