Research at the edge of uncertainty
Adaptive EMS Allocation is a research portfolio by Peter Hill, PhD in Medical Science and RN, focused on prehospital emergency care, EMS allocation, patient safety, dispatch prioritisation, response-time variability, and public-sector health system governance.
EMS allocation as a patient safety problem
The central idea behind Adaptive EMS Allocation is that prehospital patient safety is not only about speed. It is also about how scarce response capacity is prioritised, preserved, reassessed, and governed while information remains incomplete.
Why allocation matters
Ambulance systems operate under fluctuating demand, uncertain call information, geographic variation, resource limitations, and time-sensitive clinical risk. Allocation decisions determine who receives help first, who waits, and how safety margins are distributed across the system.
Why response time is not enough
Response time remains important, but it cannot alone explain patient safety. The safety meaning of waiting depends on dispatch information, clinical presentation, age, vulnerability, priority, queue dynamics, geography, workload, and system strain.
Why uncertainty is central
EMS decisions begin before diagnostic certainty is available. Dispatchers, clinicians, and systems must act on partial information, reassess over time, and make trade-offs between individual urgency and population-level readiness.
Adaptive emergency medical services allocation
The doctoral thesis Adaptive emergency medical services allocation: crafting the patient safety net in prehospital emergency care was published through Karolinska Institutet in 2026.
Thesis contribution
The thesis examines EMS as a safety net shaped by dispatch prioritisation, time-sensitive triage, response-time variability, queue governance, and allocation decisions that distribute delay exposure and safety margins across patients and time.
The thesis should be interpreted as system-oriented research. It does not present a deployable dispatch automation tool or validated operational decision-support system.
What the research connects
The research brings together qualitative understanding of dispatch work and large-scale observational analysis of EMS system behaviour.
Dispatch under uncertainty
How emergency medical dispatchers prioritise, reassess, and steward scarce response capacity when system demand exceeds immediately available resources.
Queue governance
How queued patients are monitored, reprioritised, escalated, and protected while waiting for response in a dynamic EMS system.
Response-time variability
How workload, geography, priority, weather, call handling, travel, and operational intervals shape response-time distributions.
Data-informed governance
How routine EMS data can support system understanding while respecting uncertainty, confidentiality, ethics, and interpretation boundaries.
A safety net is not a single target. It is the system’s ability to interpret uncertainty, preserve readiness, reassess waiting patients, and distribute scarce response capacity responsibly.
Exploration before deployment
The research uses qualitative analysis, register-based cohort studies, response-time modelling, and interpretability-focused machine learning to understand system behaviour.
The four-study architecture
The thesis combines one qualitative dispatch study with three register-based studies examining risk patterns, concordance, and response-time variability.
Stewarding scarce response capacity
Qualitative interview study of emergency medical dispatchers prioritising ambulance resources when system capacity is constrained.
Open Study IBreathing emergencies and nonlinear risk
Exploratory machine-learning study of response time, age, sex, and high-risk time-sensitive conditions in breathing-problem missions.
Open Study IIInfectious presentations and concordance
Retrospective study of concordance between dispatch suspicion, on-scene phenotype, and high-risk time-sensitive triage.
Open Study IIIResponse-time variability
Machine-learning-based analysis of how workload, priority, geography, weather, and operational intervals shape EMS response times.
Open Study IVWho this site is for
This site is intended for researchers, EMS leaders, public-sector healthcare analysts, clinicians, doctoral students, and stakeholders interested in how emergency medical services allocate finite response capacity under uncertainty.
