12th PenCHORD Seminar, Showcase and Workshop Event

Supporting Pathway
Modelling and Decision-
Making with Simulation
Claire Cordeaux: Executive Director, Healthcare, SIMUL8

SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
Agenda
• Simulation and healthcare
• How simulation can help
• Patient Safety
• Hospital Flows
• New Models of Care
• Disease Management and Prevention

My Experience
• Planning for health and social care
• Understanding the system
• A way of thinking about a solving a problem
• Evidence for the case for change

Simulation and Healthcare Systems

Models a flow of individual patients
Small scale
operations
Service
operations
Whole system
Passing of time
Arrivals
Duration of
treatment
Time
between
treatments
Waiting
times and
bottle necks
Experimentation
What if?....
No risk to
patients
through
pilots
Results
Costs
Resource
utilisation
Waiting
times
High level flow and operational questions, individual
patient variability, graphical visualisation

Insight
• Understanding the current system
• Learning about current impacts
• Identifying areas for improvement

Current Healthcare Challenges
• Patient Safety
• Bed Occupancy
• New Models of Care
• Disease management and prevention

Patient Safety Example

Improvement methods in safety
• Looking back and
looking forward
“safety management must look ahead,
not only to avoid that things go wrong
but also – and more importantly –
to ensure that they go right.”
Eric Hollnagel 2012
“Evidence-based design is a form of risk
management” (Becker 2007)

Using Simulation in Patient Safety
• Simulation can improve patient safety by:
– Testing interventions and impacts before
implementation
– Providing a robust evidence base for change
– Sharing best practice messages and allowing
experimentation to understand in local context
between stakeholders
Planning for healthcare to go right

Sepsis
• Over 100,000 cases last year
• increasing by between 8-13% each year
• Mortality rate: 35%
• Preventable
• Simulation used to test impact of early
intervention solution on reducing the number
of patients who die directly as a result of
sepsis

Sepsis
Increased nursing time to identify sepsis vs
patient outcomes and resource
Patient Arrives
Given invasive
surgery
Patients’ wound
becomes infected
Infection is Treated
Treatment is
delayed
Severe Infection
Patient Dies
Increased LOS
Patient recovers as
normal

Sepsis Simulation
Point of Care
Diagnostic?
Specialist
Nurse?
Access to diagnostic
machines?

Click to edit Master title styleClick to edit Master title style
HOSPITALS FLOWS

0
5
10
15
20
25
30
35
NumberofBeds
Actual Beds What actually happens
Inefficient
Increased mortality rates
Managing Bed Occupancy

We asked 20 hospitals and service
improvers….

1. Staff shift patterns are changed?
2. Patient mix changes?
3. Beds are flexed between specialties?
4. Short and long term ward closures?
5. Length of stay changes?
6. Discharge planned in advance?
7. Services outside hospital change?
8. Bring forward decision-making
What If…

This is an expensive problem
A patient in the wrong bed costs
• A patient in the wrong bed extends their stay by 1 day, costing $1,600 per day per
patient
• If just 10% of patients are in the wrong bed that’s $10,000 per day
A patient in the right bed has better outcomes
• A patient placed in the wrong bed has increased mortality of 2.57%
• If just 10% of patients are placed in the wrong bed, that’s 26 lives per year that can
be saved
Cancelled Ops cause patient pain and lose income
• 4% of scheduled surgery is cancelled for non surgical reasons
• Surgery generates revenue around $1,500 per case. That adds up to $75,000 per
month in lost revenue.
• Cancelled ops leave your whole team idle. Your anaesthetist, surgeon and nurses.
That’s also wasted time and money.

Using Historical Data
One year of data from PAS
• Admissions
• Discharges
• LOS
• By Cohort, Month, Day
and Hour

Simulation
automatically builds
the parameters for
bed modelling.
.
Auto build

• Increase/decrease arrivals
• Increase/Decrease LOS
• Change Discharge Pattern
• Change number of beds
Experiment

• Number admissions
• Number discharges
• Wait Time
• Number of Outliers
• Ave/Max Beds in Use
• Empty Beds
Results – Short Term and Long Term

Summary results highlights potential problem
days.
Results Overview3

Each day has detailed results by hour of the day
which highlight clearly where problems might
occur and at what time.
Result Detail

NEW MODELS OF CARE

Testing the strategy
Planning for locality hubs to support older
people:
What is:
• the likely demand for service?
• capacity required?
• impact on acute trust?

Starting from the current state
• Simulation reflecting current state
2014-15)
• Driven by over 75 population (data
provided by CCG) and age-banded
disease prevalence (from RCGP annual
prevalence survey)

Population and Demand

Urgent Care Pathway

Planned Care Pathway

Validation
Scenario
Generator
NHS data 14-
15 12m
%
Elective admissions 1,813 1,810 1.00
Day case 7,196 7,233 0.99
Regular attenders 155 158 0.98
First outpatients 53,657 53,692 1.00
First telephone outpatients 3,322 3,321 1.00
Follow up outpatients 199,394 200,786 0.99
Follow up telephone
outpatients
6,639 6,637
1.00
A&E attendance 16,181 16,456 0.98
Walk in attendance 5,644 5,569 1.01
Emergency admissions 10,251 + 216 AMU 10,353
1.01

The Primary Care Hub
• 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

Outpatients avoidance
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%
656 - Clinical psychology 60%
Analysis of activity
against avoidance
outpatients shows a
possible drop of
19.87% in first
outpatients

Urgent Care Scenario Locality Hubs
• Clinical analysis of HRGs showed potential
avoidance of 4855 emergency admissions
• Simulated by rerouting to Primary Care Locality
Hubs:
– 50% of ambulance calls
– 50% of care home
– 50% of out of hours
– 50% of primary care
Each avoided admission would have 3 Hub
visits over 3 days

Comparison with and without hub 1 year
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

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

In 5 years time baseline vs new…

5 years in detail..
Year 1 Year 2 Year 3 Year 4 Year 5
Elective admissions 1813 1872 1899 1944 2001
New Elective Admissions 1431 1479 1498 1543 1581
Day case 7196 7456 7644 7835 8058
New Day case 5694 5880 6023 6183 6361
Regular attenders 155 166 165 163 173
New Regular attenders 122 132 131 132 133
First outpatients 53657 54824 56184 57714 59462
New First outpatients 42373 43261 44298 45520 46937
Follow up outpatients 199394 205720 210269 215919 222104
New Follow up outpatients 157465 162277 165960 170244 175224
A&E attendance 16182 16877 16684 17604 18176
New A&E attendance 11819 12294 12117 12859 13261
Emergency admissions 10467 11000 10767 11429 11751
New Emergency admissions 7714 7852 7686 8210 8430

Results
At the end of year
2 resources start to
struggle and the
service will fall over
in year 3
Adding one more
FTE community
clinician 24/7 = 3
clinicians results
in a 77%
utilisation

Regional Implications
• Population 4.3m
• Assume same solution as NW Surrey
• Total “Hub” activity 886,200 visits 54%
urgent alternatives
• Resource testing:
– 50 FTE, 100% utilised and queues building
after 3 months

How Many Staff?
• 100 staff 56% utilised
• 80 staff 70% utilised

• What is the impact of improvement interventions
on a community team workload?
• For example: what is the impact of faster healing
wounds on workload (60%)?
– More time to care?
– More time to see other patients?
• Engaging with community team – what are the
pain points?
Impact on Community team capacity

Community
Team
Patients
Daily allocation to
staff matching
patient need to
competencies
Referrals
Visits
Discharge or
Death
Ageing Population
Clinical
Assessment
Wound
care only
Multi-
morbidity
Not
wound
care

Localising and testing improvements on
capacity and patient throughput.
• Change referrals, patient
types and priority
• Change visit times and
frequency
• Include travel times
• Test impact of
improvements by condition
on capacity and patient wait
times

Results – supporting evidence for
change

DISEASE MANAGEMENT AND
PREVENTION

Patient care pathways
• Routinely used to understand and
improve practice
– Services
– Clinical practice
– Disease

Patient care pathways: Services
• Process flow through services

Patient care pathways: Clinical practice
Best clinical practice (Map of Medicine
example)

Patient care pathways: Disease
progression
• Progression through disease states

Our Approach to Modelling Pathways
• Combining disease state transitions with
best practice, service access and
utilization.
• Why?
– Test impact of service redesign on disease
progression
– Genericized – can be used and localised by
any group interested improving pathways for
patients with the same condition

Best Clinical Practice
New interventions, best clinical practice,
service redesign can all be tested for:
• Impact on cost, resource, activity, waiting
times
AND
• Impact on progression

Hepatitis C
• Impact of future demand if a new Hep C
service is delivered locally, increasing
patient attendance:
– on future burden of disease
– projected treatment costs for the service

Simulating demand
Age-banded
population
projections
Age-banded
disease
prevalence
Demand
749,805 X Hep C 0.45% = 2771
(2.5% diagnosed, 55% not Genotype 1)

Rising demand
Simulated Demand:
Catchment population

Diagnosis (Current State)

Burden of Disease
All patients undiagnosed or not cured go to
“warehouse” where disease progresses with
an annual cost

Treatment – 30% Dual, 70% PI

Costs
Step Unit Cost
Initial screening 6.3
Further Screening 42.83
Nurse Consultation 25 20
Genotype test 52.61
FBC 3 2.66
HCVRNA and FBC 16.34
HCVRNA 13.68
Consultant at initial treatment and 6
months SVR 55.98
Warehouse disease progression p.a. 882
Compensated Cirrhosis p.a. 1400
Decompensated cirrhosis p.a. 11,218
Carcinoma p.a. 9,996

Validation
Activity Data Source Scenario
Generator result
Confirmed cases 56 Annual Hep
Report 2012 p46
56
Commencing
treatment
42 Annual Hep
Report 2012 p44
42 (moderate
and
compensated
cirrhosis)

Treated as current vs Treated in local
hospital– cost over 5 years
• Assumed 50% of currently treated patients
do not attend after initial appointment
• Costs reduce with increased diagnosis
• Costs include annual inflation

Patients cured and cost – 5 years
By:
• current state
• future state
• increasing diagnosis by 5%, 10% and 20%
Patients Cured by Scenario
Increased diagnosis = increased
patients cured
Cost per patient cured - decreases
with increasing diagnosis

Annual Savings by Scenario with new
local provider

LTC Year of Care Commissioning – why
simulation?
• “Bottle” the processes of the
Early Implementers
• Easily enable Fast Followers and
others to understand and test
impacts

LTC Year of Care Commissioning
39%
9%
40%
22%
Simulation modelling tool - Developed as part of the LTC Year of Care Commissioning Programme

LTC Year of Care – next level
56%
13%
62%
40%

• Group
patients by
level of
acuity
• Increasing
numbers of
long term
conditions
What drives the model?
• Patients with long term conditions by acuity

• Patients in each
group access
services
• Often more than
once
• Each service is a
associated with a
range of costs
• Each service has an
associated capacity
How it works
Patient
Services
Costs
Capacity

Using the simulation
Population and Eligible Patients, acuity breakdown
and annual incidence

Using the simulation: Data
Service Access: the proportion of patients in each
group accessing a service.

Service Attendance: The frequency of patients in
each group accessing a service.

State Transitions: Patient increasing/decreasing
acuity or dying year on year.

Costs: Cost of each service.

Capacity: the type of resource for each service and
the average time a patient would stay in each
service.

Tariff: annual capitated budget

Using the simulation: RESULTS
Tariff results

Using the simulation: RESULTS

Using the simulation: SCENARIOS
The PAYNE Scenario - preloaded

The PAYNE Scenario - preloaded

The CORNWALL Scenario (Living Well,
Age UK)
• One cohort only
• 2% of population

Finally..
• Best practice needs clinical evidence AND
operational evidence
• Simulation can help:
– Understand the impact of change
– Run a “virtual pilot”
– Provide evidence for decisions
– Engage stakeholders

www.SIMUL8healthcare/penchord
for slides and links to simulations

12th PenCHORD Seminar, Showcase and Workshop Event

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12th PenCHORD Seminar, Showcase and Workshop Event