Testing the impact of policy decisions using simulation

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How simulation has been used to test the long
term and operational impact of policy idea
Tom Stephenson, Head of Customer
Success

• Head of Customer Success at SIMUL8
Corporation
• Worked as a consultant for 6 years
• Healthcare, Manufacturing, Back
Office, Policy
Tom Stephenson

Agenda
• Brief Introduction to simulation and
DES
• On overview of why simulation can be
a useful technique to aid decision
making when setting policy
• Case studies from different sectors and
different countries

What is Simulation?

An imitation of a system as it passes
through time

An imitation of a system as it passes
through time
• Investigate the impact of change
• Reduced Risk
• Helps evidence based decision
making

Example

Why is it a useful technique?

Increased Data Availability
Visualization

Lots of data available
Sometimes
• Difficult to interpret
• Difficult to understand
• Hard to use to communicate ideas
• Often sits unused
Increased Data Availability in Policy
Visualization

Bringing the Data to Life and Making it
Understandable
Understanding
Complexities-
fit pieces of the
puzzle together
EngagementCommunication
Better solutions
Visualization

Apply real life variability
 Accurately Represent Reality
 Detail to answer Long Term
and operational questions

Real Life Variability Simulation Detail
Operational Long Term
Different hours of the week Seasonality
Staff shifts Population growth/change
Opening Hours Changing External
Environment

Case Studies?

• Healthcare
• Social care
• Justice
• Environment-
Recycling
• Defence
• New technology
• Smart Cities
• Employment
Simulation in Policy

Healthcare

Common Challenges
• Budget cuts means healthcare needs to adapt
– Keep patients away from costly emergency care
• Population changes
– People are living longer
– More long term conditions
– Increased obesity
• Patient Safety a key concern
– Advice must be given to best treat patients

North West Surrey – what if we develop locality hubs
to support older people?
What is:
• the likely demand for service?
• capacity required?
• impact on acute trust?

Population and Demand

Urgent Care Pathway

Planned Care Pathway

• Emergency care avoidance – assume 3 visits,
over 3 days, 10 minutes to 1 hour each visit
• If elective care – first and 2 follow ups 10
minutes to 1 hour each visit, every 3 months
• Assume key staff are experienced
nurses/therapists
The Primary Care Hub

300 - General medicine 10%
840 - Audiology 25%
110 - Trauma & orthopaedics 25%
101 - Urology 5%
320 - Cardiology 40%
330 - Dermatology 70%
303 - Clinical haematology 70%
340 - Respiratory medicine 30%
430 - Geriatric medicine 70%
361 - Nephrology 10%
650 - Physiotherapy 90%
410 - Rheumatology 60%
301 - Gastroenterology 50%
191 - Pain management 75%
307 - Diabetic medicine 80%
812 - Diagnostic imaging 80%
302 - Endocrinology 25%
160 - Plastic surgery 25%
652 - Speech and language
therapy 25%
180 - Accident & emergency 20%
324 - Anticoagulant service 75%
400 - Neurology 80%
651 - Occupational therapy 70%
654 - Dietetics 25%
108 - Spinal surgery service 50%
350 - Infectious diseases 25%
653 - Podiatry 50%
305 - Clinical pharmacology 35%
Analysis of activity
against avoidance
outpatients shows a
possible drop of
19.87% in first
outpatients
Outpatients avoidance

0
2,000
4,000
6,000
8,000
10,000
12,000
Elective
admissions
Day case Regular
attenders
Emergency
admissions
Baseline
New
Predicted locally
Comparison with and without hub
1 year

Results for Locality Hub
5 staff FTE over 24 hours – 85% utilisation
= 15 staff
67,291 visits

NHS England

Maternity Treatment for Uninsured
• Based in Toronto, serves a diverse population of
1.17 million
• Many are uninsured
• Used simulation to build a case for decision
makers to offer prenatal services to uninsured

Maternity Treatment for Uninsured
• Common for uninsured women to enter the
hospital through the ER towards the end of their
term if prenatal services aren’t received
• Health concerns are greater when this happens
and Lengths of stay are longer
• Early interventions were tested to mitigate against
this happening
• Enough evidence gained to support initiative

• Target Setting
– 4 hour wait at A and E
– 18 week target for surgical treatment
• Informing NICE guidelines
– https://www.nice.org.uk/sharedlearning/use-of-scenario-
simulation-in-the-introduction-of-serum-natriuretic-
peptide-testing
Other Healthcare Applications

Social Care

Common Challenges
• Changing Environment- increased digitization
• Increased geographic mobility
• Aging population

Homelessness
http://www.novasim.com/case-studies-journal/homeless-services-
bed-capacity-simulation.html

Justice

• Complicated system, from a 911 call, a court case
and through to prison or parole there are many
different paths that can be taken
• Difficult to understand the long term impact of
decisions made now, for example increasing
parole
Common Challenges

• Simulating Prison Populations
• Based in Texas
• Look Long Term impact of policy and
budgetary decisions- drilling down into short
term
Applied Research Services Inc
Long Term Short Term
Release policy Jail booking
Rehabilitation policy Inmate pick up and delivery
Population change Prison Classifications
Changing Sentencing Policy

• Looked at Bed bases for population with mental
health concerns
– Likelihood to commit crimes if not placed properly
– Cost of these crimes to the state compared to the cost of
funding extra care
– Didn’t just need to know how to handle demand but also
how to handle the backlog
New Jersey Government

Smart Cities

• Poor building placement can lead to traffic Jams as
clusters of people will be trying to move the same
way
• Emergency vehicles need to be quick
• Connected vehicles- self driving cars
• Reduction in Carbon footprint
Common Challenges

• S.Huang- Designing an intelligent intersection
• Vehicle management tested with a microscopic
traffic simulator
• Tests Isolated intersection and real world
intersection
• Environmental benefits also testec
• Reduced average vehicle delay by 85%, fuel
consumption by 50% and emissions by 39-50%
Simulation Study 1
10.18535/ijecs/v5i11.45 Anaswara R, IJECS Volume 05 Issue 11 Nov., 2016 Page No.18983-18986 Page 18985 mobility and
environmental benefits

• Novel Smart parking tested with a mixed integer
linear programming simulation
• Assigns and reserves optimal parking based on
proximity to area and cost
• Compared to uncontrolled parking significant
cost and time reduction was achieved
Simulation Study 2
https://www.researchgate.net/publication/271397478_A_new_Smart_Parking_System_Infrastructure_a
nd_Implementation

Thank you for listening.
Do you have any questions?

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