This document proposes using a digital twin approach to simulate an emergency service. A discrete event simulation model was developed based on historical patient data from a hospital emergency department. The model consists of modules representing different patient pathways and models staff activities. The digital twin can operate in two modes: 1) real-time monitoring of the current system and 2) running simulation scenarios to evaluate "what-if" situations. Experimental results showed the digital twin could replicate key performance metrics like length of stay. Opportunities exist to improve data collection during patient stays and generate the model automatically. The approach provides a proof of concept for using digital twins to evaluate hospital emergency departments.

1. PILOTAGE INTELLIGENT
DU SERVICE DES
URGENCES PAR UNE
APPROCHE JUMEAU
DIGITAL
VINCENT AUGUSTO

2. 1. INTRODUCTION
2. GENERAL APPROACH
3. MODEL CONCEPTION
4. DIGITAL TWIN AND RESULTS
5. DISCUSSION, CONCLUSIONS AND PERSPECTIVES
SOMMAIRE

3. INTRODUCTION
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3Context
 A digital twin can be defined, fundamentally, as:
 an evolving digital profile of the historical and current behavior of a physical object or
process,
 that helps optimize business performance.
 The DT concept is a modern twist on an old idea:
 Data-driven simulation, stochastic simulation
 What-if scenarios, design of experiments
 Test through experimentation (by opposition to decision aid, optimization)
Observations
Historical data
Real system
Model (automatically
generated or not)
Results
Simulation
Scenarios
Data from sensors
and Health
Information System Data-driven model
Simulation
Scenarios

4. INTRODUCTION
Context – Scientific challenges
 Scientific challenges related to DT in healthcare
• How to get access to data?
► HIS are getting better but lack accurate data (e.g. patient admission
time in a service)
► Implementation of sensors in the hospital is difficult and costly
► Privacy
• How to automatically generate and initialize an accurate model
from these data?
► The DT model should be generated/updated and validated
automatically from the data
• How to implement scenarios and use results to improve the real
system?
► Scenarios depend on stakeholders
► From a scenario to be tested, how to extrapolate on interesting but
unforeseen situations?
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5. OBJECTIVES
 Objective: propose a data-driven simulation tool for emergency service
with 2 main functions:
• Real-time monitoring of the system
• Prediction of the service activity from any state using simulation
 Motivation:
• Test the concept of the digital twin using available data (without further costs)
• Propose a proof of concept that may be applied to any other hospital
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6.  General approach
GENERAL APPROACH
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Data from Health
Information System
- Patient arrival time
- Triage outcome
Mode 1:
Monitoring
Real time
Model-based
Mode 2:
What-if
Scenarios
Simulations
Real system Discrete-event Model
Observations on site
Model conception and validation

7. STUDY DESIGN
 Retrospective and observational study
 Monocentric (Saint-Etienne hospital)
 Data extraction of 40,000 stays between 2014/01/01 and 2014/12/31
 All patients aged over 15 years (separate pediatric emergency department)
 Data collection and observations for duration data (care duration…)
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8. MODEL
Conception and validation
 Patient pathway is modelled within modules:
• One module = One possible patient pathway depending on triage
• Implementation of modules depending on hospital organization
• 3 pathways:
► P1 (life threatening),
► P2 (functional care),
► P3 (quick care pathway)
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Patient pathway 2/3
Patient pathway 1

9. MODEL
Conception and validation
 Human resources activities are modeled as state machines:
• One mission = One type of activity
• Add up missions depending on abilities of practitioners
• Special features such as experience are also taken into account
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10. DIGITAL TWIN RUNNING MODES
 Mode 1: Real-time monitoring
• The model is supposed to be precise enough to monitor the service
• Updates are performed regularly with data from the system
► Patient arrivals are driven from the data every x minutes
► Each generated patient comes with several attributes (currently, only triage information)
 Mode 2: Scenarios simulation
• The simulation can be paused at any moment to play a scenario
• A scenario is defined by:
► Patient arrival rate and triage information
► Resource capacities
• Performance indicators are displayed (waiting time, resource occupation…)
• Once the experiment is over, it reverts to mode 1
► Fast replay of the day until the break
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11. RESULTS
Design of experiments
 Design of experiments
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1 2 3 4 5 6 7 8 9 10 11 12 13
Scenario
Replicationcount
Replication
length
Waitingtime
hospitalization*
Waitingtime
hospitalizationfor
vitalpatients*
Waitingtimefor
surgeonnotice*
Waitingtimefor
imagingresults*
Waitingtimefor
imagingresults
forvitalpatients*
Inclusionof
psychiatric
patients
Inclusionof
additional
patientseachday
Arrivals
(1:real,0:Poisson)
Conditioning
durationforvital
patient
(min,mod,max)
%ofvitalpatients
Additional
arrivalsof
patients
A-1 Ideal 10 8640 120 25 15 30 15 NO NO 0 24 30 36 1.73 0
A-2 Real 10 8640 240 45 60 90 30 YES YES 0 24 30 36 1.73 0
A-3
Validation
10 8640 240 45 60 90 30 YES YES 1 24 30 36 1.73 0
B-1 (D-1) 10 8640 180 45 60 90 30 YES YES 0 24 30 36 1.73 0
B-2 (D-2) 10 8640 240 25 60 90 30 YES YES 0 24 30 36 1.73 0
B-3 (D-3) 10 8640 240 45 60 30 30 YES YES 0 24 30 36 1.73 0
B-4 (D-4) 10 8640 240 45 60 90 15 YES YES 0 24 30 36 1.73 0
B-5 (D-5) 10 8640 240 45 15 90 30 YES YES 0 24 30 36 1.73 0
B-6 (D-6) 10 8640 240 45 60 90 30 NO YES 0 24 30 36 1.73 0
B-7 (D-7) 10 8640 240 45 60 90 30 YES NO 0 24 30 36 1.73 0
B-8 (D-8) 10 8640 180 25 60 90 30 YES YES 0 24 30 36 1.73 0
B-9 (D-9) 10 8640 240 45 15 30 15 YES YES 0 24 30 36 1.73 0
C-1 (E-1) 200 816 180 25 15 30 15 NO NO 0 24 30 36 17.30 1
C-2 (E-2) 200 816 240 45 60 90 30 YES YES 0 24 30 36 17.30 1
C-3 (E-3) 200 816 120 25 60 90 30 YES YES 0 24 30 36 17.30 1
C-4 (E-4) 200 816 240 45 60 90 30 YES YES 0 8 10 12 17.30 1
C-5 (E-5) 200 816 120 25 60 90 30 YES YES 0 8 10 12 17.30 1
C-6 (E-6) 200 816 120 25 60 30 15 YES YES 0 8 10 12 17.30 1
Validation
scenarios
Regular
activity
scenarios
Exceptional
activity
scenarios

12. RESULTS
Length of stay
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4
4,5
5
5,5
6
6,5
7
7,5
8
A-1 A-2 A-3 B-1 B-2 B-3 B-4 B-5 B-6 B-7 B-8 B-9 C-1 C-2 C-3 C-4 C-5 C-6
Average length of stay (hours)
3
4
5
6
7
8
9
10
11
A-1 A-2 A-3 B-1 B-2 B-3 B-4 B-5 B-6 B-7 B-8 B-9 C-1 C-2 C-3 C-4 C-5 C-6
Length of stay for
each care pathway (hours)
LOS UF LOS UFR LOS UG
Psychatric patients treated in a dedicated unit
Reduced time to get complementary exams
Reduced time to get an external bed
Pathway 1
Pathway 3
Pathway 2
Validation
Regular
Exceptional

13. LIMITATIONS AND OPPORTUNITIES
 Limitations
• Lack of information between patient admission and discharge
• Modular but fixed model based on Saint-Etienne hopital
• Problems related to external resources
 Opportunities
• A real and accuration decision aid tool for practitioners
• Sexy representation of the service using 3D and VR (take into account architecture)
• Serious game for practitioners (training) and patients (why is it so long?)
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14. CONCLUSIONS AND PERSPECTIVES
 Conclusions
• An approach between traditional discrete-event simulation and digital twin
• A first attempt to apply the 4.0 idea to a real hospital service
• Proof of concept used to confirm practitioners’ intuition
• Possible retrospective evaluation of the system
 Perspectives
• Validate the tool using historical data (was the DT accurate enough considering missing
data?)
• Move from a modular fixed model to a model generated automatically using process
mining (or a mixed approach?)
• Get data during the stay of a patient (location, tests, examinations)
• Integrate the model in a global representation of the hospital
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