EMS
Review
EMS Reinforcement learning
A Review of Incident Prediction, Resource Allocation, and Dispatch Models
for Emergency Management
An Online Decision-Theoretic Pipeline for Responder Dispatch
Ambulance Dispatch via Deep Reinforcement Learning
Driver behavior <-> online
RL Dispatch models
Priority in reward
A markov decision process model for the optimal dispatch
of military medical evacuation assets
Classification errors
A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities
Temporal Instability and the Analysis of Highway Accident Data
Importance of priority in reward
Priority dispatching strategies for EMS systems
Reinforcement learning for ridesharing: An extended survey✩
In other words, it is more beneficial(from a system-wide perspective) for an ambulance to bebusy serving a patient that is closer to its fixed locationthan one that is farther away.
For example, one might consider dispatchingambulances to patients in such a way that the workloadis balanced because it is necessary for all paramedics andEMTs to practice their skills. While workload may notimmediately affect a patient’s outcome, it could impactthe outcomes of future patients by degrading the work-force through the lack of skill maintenance or throughturnover
Heurisitc approach to dispatching. maximising survivability
Their study reveals that the optimal policy is to send the closest unit to the most urgent call and the next idle unit to the less urgent call, regardless of the call order.
Operations Research Tools for Addressing Current Challenges in Emergency Medical Services
Relocation of ambulances with approximate dynamic programming.
Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness
Heurisitc approach for dispatch and relocation
OTHER
relocation & dispatch
Approximate dynamic programming
Solving the dynamic ambulance relocation and dispatching problem
using approximate dynamic programming
Heuristics
Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness
importance of macroeconomics and risk taking behavior
ems system
Dispatch accuracy of physician-staffed emergency medical services in trauma care in south-east Norway: a retrospective observational study
We found that the undertriage of P-EMS dispatch in south-east Norway ranged between 20 and 32%
P-EMS dispatch in trauma care in south-east Norway suffered from an overtriage between 74 and 80% and an undertriage between 20 and 32%
Existing CAD system data is inconsistent and insufficient to provide basic data for scientific research.
Reducing the substantial level of overtriage (74–80%) of P-EMS in south-east Nor-way remains an obvious area for improvement
RL is good when predictions is hard
Real-Time Ambulance Dispatching and Relocation
The accuracy of medical dispatch - a systematic review