Care outside hospital final

Simulating Care
Outside Hospital
Claire Cordeaux: Executive Director, Healthcare

SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com
Presenter
Claire Cordeaux
Executive Director, Healthcare SIMUL8
SIMUL8 Corporation
claire.c@SIMUL8.com

Housekeeping
• Audio
• Q and A
• Recording available on SIMUL8healthcare.com

Agenda
• Healthcare outside hospital – the policy agenda
• How simulation can help:
• Prevention
• Chronic Disease
• Emergency Care Flow
• Managing Community Workload
• Questions and our offer to you

• Supporting people
outside hospital:
– Provides more accessible care
– Prevents exacerbation
– Saves unnecessary visits (and
expense)
– Speeds up hospital discharge
– Reduces Length of Stay
– Improves patient outcomes
Health Policy

• International studies
• But what does that mean for us?
• Hospital at home
• Intermediate care
• Early discharge
• Admission avoidance
• Transfer of care
• Telemedicine
The Evidence

Why simulation?
• A service and system redesign
• Understanding the impact of changing service
utilization on:
– Flow
– Cost
– Capacity/Resource
• No historic data
• Different impacts on organizations, costs and
patients

Health and Care System Flow
Lack of
capacity?
Rural/
urban
population?
Lack of
access? Vulnerable
groups?
Not
24/7?

Case Study 1: Chronic Diseases
Using risk stratification to identify and manage
patients with multiple conditions and test:
• What if they are proactive managed or
unmanaged?
• What if we applied an annual tariff?

Starting to simulate a new approach
Services “consumed”
Assessment of Need
Patients at Risk
Exacerbation

• No real correlation between risk score and level of need
But…
Assessment of Need
Patients at Risk

Click to edit Master title styleClick to edit Master title style
WHAT THE DATA IS
TELLING US

Kent whole population data
Over 30% of people over 75 years have multimorbidity

Multimorbidity is more common than single morbidity

The total health and social care cost is strongly related
to multimorbidity

The main contributors to total health & social care cost
are acute non-elective admissions

People with complex health & social care needs appear
to demonstrate a ‘crisis curve’

More community, mental health and social care
services are delivered to people following a ‘crisis’
than before the ‘crisis’

Some indications that an integrated care plan changes
the pattern of services delivered to people
BHR Costing Data

• Use local
data to test
assumptions
• Ability to
update and
review
Simulation

• Level of
acuity
• Increasing
numbers of
long term
conditions
Current Simulation
• Likelihood of patients accessing services by
changing state of patients (state transition)

• Patients in each “state” have
– A likelihood of accessing certain types of service
(Acute, Community, Mental Health, Social Care),
including accessing services more than once
• Costs associated with those services
How it works

Data builds an underlying discrete event
simulation model

• Number of
patients in
each “state”
by year
• Average cost
per patient
Results

• Cost by each area of service/organisation
Results

• Costs by state per year
• Average cost per patient
• Comparison with tariff
Results

• Select population
• Select
percentage of
population
• Predict incidence
• Predict incidence
by “state”
Simulating Demand

Start up Known Unknown 2012-13
Managed vs Unmanaged

Yearly Transitions

• Trial = multiple runs sampling from distributions
in the model
• More robust results
• Allow 20-30 minutes
Running a Scenario

Results Comparison
Trial results

• Known to integrated care team or not?
• Test against proposed tariff?
• Change variation in cost for services?
• Decrease transitions through states?
Scenarios

• Planning for demand
• Testing an improvement scenario
• Negotiation between healthcare providers
How is this helping?

Case Study 2: Improving the emergency
care flow with Martin Ware
• Impact of increasing out of hospital services on
cost and capacity

• What does current unscheduled care flow
look like?
• What will it look like in 5 years taking into
account population change?
• What is the impact of increasing referrals to
domiciliary care direct from hospital?
Initially to answer following questions

Area NHS
data
Scenario
Generator
%
A+E 108,472
125,302 (17,026
out-of-area)A&E out of area (5% S Staffs) 17,000
0.99864512
Total NEL Admissions 84,297 84,470
1.00205227
Elective admissions 12,674 12,710
1.00284046
Daycase 49,983 49,895
0.9982394
Discharges to Community
Hospital
4560 4507
0.98837719
Discharge to social care teams
(Stoke)
2183 2203
1.0091617
Discharges from Community
Hospital
4347 4430
1.01909363
Intermediate Care (admission
avoidance)
590 581
0.98474576
• Ran the model
through with
the received
population
data
• Set routing
percentages
so model
matches
activity data.
Baseline Results – 10 run trial

Item £ LOS
Hospital Bed £500 a day AMU/SAU/CDU
Inpatient
Community
Hospital Bed
£263 per day 21 days
Intermediate care £47 per hour 30 hours
A&E £105.5
Cost and Length of Stay Assumptions

With population increase
In 5 years
+ £11.3m (£1m domiciliary care)
(1% annual inflation)
Increase in A&E and
admissions over 9 years

Potential Domiciliary Care Scenario
• Average 6 week package for rehabilitation
• Other packages average 48 weeks
Scenario:
• Increase direct referrals from hospital – 30% of community
hospital referrals
• Average 2 additional days in hospital
• Referrals 10% to complex, 38% maintenance, 51% re-ablement

Cost per
hour
Hours
pw
(normal)
LOS
wks
Capacity
(hrs pw)
Packages
pw
Discharges to reablement from
community 2.50% £20.98 11 6 1400 127
Discharges to reablement from acute 10.10% £20.98 11 6 1400 127
Discharges to maintenance care from
community 4.50% £13.20 7 48 4100 586
Discharges to maintenance care from
acute 7.60% £13.20 7 48 4100 586
Discharge from reablement to
maintenance 15% £13.20 7 48 4100 586
Discharge to complex £13.20 22 48 4100 186
All discharges from acute (stoke) 2183
All discharges from community
(stoke) 876
Domiciliary Care Assumptions

• £2.6m savings overall
– Plus £4m social care
– Plus 1.3m additional LOS, max bed occupancy +
10, +1% utilization
– £7.6m savings community hospital, utilisation
reduced by 25%, max bed occupancy minus 90
Domiciliary care scenario results

Impacts
• Understanding the financial impacts
• Allows negotiation across providers and
between payers and providers

• 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?
Project 3- 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

Simulation Concept

Invitation to co-develop and test the
community model
• You get to influence the design
• You get to use the model
Contact: claire.c@simul8.com
Join us?

QUESTIONS
• Please forward any topics you would like
to see covered to claire.c@simul8.com
• Continue the discussion on SIMUL8 in
Health – LinkedIn Group
• August Workshop – Improving Patient
Care Pathways

Care outside hospital final

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