Wednesday, November 2, 2016

Modelling Trauma Physiology for Large Crisis Management

In recent years, there has been a rise in Major Incidents with big impact on the citizens health and the society. Without the possibility of conducting live experiments when it comes to physical trauma, only an accurate in-silico reconstruction allows us to identify organizational solutions with the best possible chance of success, in correlation with the limitations on available resources (e.g. medical team, first responders, treatments, transports, and hospitals availability) and with the variability of the characteristic of event (e.g. type of incident, severity of the event and type of lesions).
Utilizing modelling and simulation techniques, a simplified mathematical model of physiological evolution for patients involved in physical trauma incident scenarios has been developed and implemented. The model formalizes the dynamics, operating standards and practices of medical response and the main emergency service in the chain of emergency management during a Major Incident.

To run simulations, the following functions have been preliminarily implemented in Matlab and then made available as webservices:
  • GeneratePatients: this function randomly generates patients, affected by different lesions. The degree of severity of each patient can be sampled according to different (choosable) distributions: gaussian, uniform or triangular;
  • EvolvePatients: this function simulates the patients evolution from (1)-(2), with and without therapeutic maneuvers;
  • TimeToDeath: this function calculates the time to death for each patient, if there is not a medical treatment with therapeutic maneuvers;
  • TriagePatients: this function simulates a patients triage, based on the time to death, and gives the color code according to literature review.
The syntax and the input-output description of each service, in terms of number and type of input-output parameters, is contained in the Web Services Description Language (WSDL), publicly available at the web address

The results could provide a benchmark for potential introduction and proliferation of applications to be employed in real operation during MIs medical response, with potential improvements on the safety and security of citizens. In particular, it will allow the development of health monitoring applications aiming at: saving data remotely; producing reports on the health status of each patient; supporting decision-making during MIs, where medical staff act in limited time, under pressure, without having a second decision-making chance, outside their own medical specialties, and with high load of casualties.

More information are available here.

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