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EMERGENCY SERVICE: A GENERALIZED FLEXIBLE SIMULATION MODEL. Paola FACCHIN Department of Paediatrics, University of Padova, Italy Giorgio ROMANIN JACUR Department of Management and Engineering, University of Padova, Italy AIRO 2003. EMERGENCY SERVICE 1. Any emergency service:
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EMERGENCY SERVICE:A GENERALIZED FLEXIBLE SIMULATION MODEL Paola FACCHIN Department of Paediatrics, University of Padova, Italy Giorgio ROMANIN JACUR Department of Management and Engineering, University of Padova, Italy AIRO 2003
EMERGENCY SERVICE 1 Any emergency service: • is devoted to supply first aid to outpatients • may alternatively serve: • a hospital or a group of hospitals, • a department or a group of departments, according either to pathologies (e.g., obstetrics, surgery) or to types of patients (e.g., neonatology, paediatrics)
EMERGENCY SERVICE 2 Any emergency service is characterized by: • high variability of patient arrivals, depending on: • time (different intervals of the day, week, year) • randomness (e.g., accidents, weather, epidemics) • hard requirements for: • quick (sometimes immediate) response, also in case of congestion
EMERGENCY SERVICE 3 Any emergency service shall be correctly designed and managed for what concerns: • structures (major treatment rooms, minor treatment rooms, beds) • technological resources (specifical instruments) • human resources (doctors,nurses, engineers and related working rules, turns of duty, etc.) in order to supply a high quality service at minimum cost
EMERGENCY SERVICE 4 Discrete simulation may be a useful tool: • in the design phase, to find the right dimension of employed structures and resources • in the working phase, to analyze, check and possibly adapt the system behaviour Discrete simulation requires to: • build up a model with the right level of detail • implement it in a friendly language
PAPER CONTRIBUTION • We build up a generalized flexible model, able to correctly describe many existing emergency services, by adjusting fundamental parameters • We implement it by specialized software Micro Saint, which presents a good compromise between abstraction level and user friendliness, and therefore may be easily employed both by those who adapt the general model to the particular application and those who utilize the application for design or management
RESOURCES: DOCTORS NURSES INSTRUMENTS QUEUE SERVICE ARRIVALS QUEUE SERVICE TRIAGE OUT QUEUE SERVICE ARRIVALS QUEUE SERVICE OUT EXTERNAL EXTERNAL QUEUE EXTERNALS SERVICE ARRIVALS EMERGENCY SERVICE GENERAL MODEL (LOW DETAIL)
RESOURCES: DOCTORS NURSES INSTRUMENTS QUEUE SERVICE ARRIVALS 1 QUEUE SERVICE TRIAGE OUT QUEUE SERVICE ARRIVALS 2 QUEUE SERVICE OUT EXTERNAL EXTERNAL QUEUE EXTERNALS SERVICE ARRIVALS EMERGENCY SERVICE MODEL ARRIVALS
EMERGENCY SERVICE MODEL ARRIVALS • “Average arrivals”: • random independent • time dependent mean • groupable in classes (to be defined now) • “Special arrivals” (e.g., epidemics, extraordinary weather): • randomness to be defined according to the particular case • time dependency to be defined according to the particular case • “Pseudo arrivals”: • change of orders (deterministically at every duty change) • other
RESOURCES: DOCTORS NURSES INSTRUMENTS QUEUE SERVICE ARRIVALS QUEUE SERVICE TRIAGE OUT QUEUE SERVICE ARRIVALS QUEUE SERVICE OUT EXTERNAL EXTERNAL QUEUE EXTERNALS SERVICE ARRIVALS EMERGENCY SERVICE ARRIVAL SELECTION (TRIAGE)
EMERGENCY SERVICE ARRIVAL SELECTION (TRIAGE) All arrivals are selected by the same unique filter (triage) which is ruled by specialized nurses The selection is based on the presented life parameters (predefined and universal) which determine the immediate life danger The selection assignes a colour code, according to immediate life danger, and related urgency: red code, aid shall be immediate, any wait may cause death yellow code, aid shall not be delayed more than some minutes, wait may increase severity green code, aid may be delayed, a limited wait (tenth of minutes) is permitted without danger white code, no life danger, any wait cannot cause damage
PATIENT ROUTING AND SUPPLIED SERVICES • According to the assigned colour code the patient is characterized by: • different routing • different treatments • different resources utilized • different service time • different priority and possible preemption in the queues, in particular for external services, if required
RESOURCES: DOCTORS NURSES INSTRUMENTS QUEUE SERVICE ARRIVALS QUEUE SERVICE TRIAGE OUT QUEUE SERVICE ARRIVALS QUEUE SERVICE OUT EXTERNAL EXTERNAL QUEUE EXTERNALS SERVICE ARRIVALS PATIENT ROUTING AND SUPPLIED SERVICES
RESOURCES AT DISPOSITION • DOCTORS • duty chief = responsible of the service • (possible) assistant(s) on duty • available within minutes (e.g., ten, twenty) • working in external services • NURSES • triage nurses • nurses on duty • STRUCTURES AND INSTRUMENTATION • Major treatment room(s) • Minor treatment room(s) • Special instruments
MODEL IMPLEMENTATION BY SW MICRO SAINT Micro Saint is based on task network modelling
MODEL IMPLEMENTATION BY SW MICRO SAINT Network is easily built up by a CAD method
MODEL IMPLEMENTATION BY SW MICRO SAINT Main elements of networks are tasks, decisions and queues
MODEL IMPLEMENTATION BY SW MICRO SAINT Example of task description
MODEL IMPLEMENTATION BY SW MICRO SAINT Example of description of a decision
MODEL IMPLEMENTATION BY SW MICRO SAINT Example of queue description
MICRO SAINT MODEL NETWORK: DATA DEFINITION ARRIVALS are ruled by all time dependency parameters TRIAGE chooses different routing for every patient group PATIENT ROUTING defines access to resources and to external services and related priorities (and preemption) RESOURCES WHICH MAY BE SEIZED, PRIORITY AND PREEMPTION are different for the various patient groups EXTERNAL SERVICE UTILIZATION are ruled by time dependency and different priority for the patients HUMAN RESOURCE PRESENCE is ruled by turns of duty ALL ABOVE PARAMETERS ARE ADJUSTED IN ORDER TO CORRECTLY DESCRIBE THE ACTUAL STUDIED SERVICE
MICRO SAINT MODEL NETWORK: SIMULATION RESULTS Results report:flow times queues parametersresource utilization They may be supplied referred to: single groups whole performance
CONCLUSIONS • The presented model has been tested on an actual service • It is sufficiently detailed and extremely flexible, therefore it may be easily employed to simulate different emergency services • It is a useful managing instrument to improve the service performance, as it permits to detect all possible critical points and consequently to suggest suitable correctingactions