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Delivering care outside of the hospital is seen as one of the ways of managing increasing demand for healthcare services, whilst also improving patient outcomes. Effective delivery means a huge rethink of service delivery as a system as well as by organizations, and whilst there are some blueprints for good practice, on the whole the evidence for system-wide management is sketchy. Simulation is a really helpful technique to use when trying to predict uncertain futures. Bringing together clinical evidence for best practice with available data for current service utilization for population groups and ideas for improvement into a simulation can help drive forward decision-making for change, underpinned with the best evidence available. This workshop will draw on a variety of projects and models to consider how simulation can help to model the impact of care outside hospital. From prevention activity (planning a new obesity and weight management service), to applying an annual capitated tariff for people with chronic disease, to managing workload in community teams, we will examine how simulation has been helping to understand the current position and to develop and negotiate a plan for change across health systems.
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Simulating CareOutside Hospital
Claire Cordeaux: Executive Director, Healthcare
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Presenter
Claire Cordeaux
Executive Director, Healthcare SIMUL8 SIMUL8 Corporation
claire.c@SIMUL8.com
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Housekeeping
• Audio
• Q and A
• Recording available on SIMUL8healthcare.com
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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
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• 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
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Health Policy
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• International studies
• But what does that mean for us?
• Hospital at home• Intermediate care• Early discharge
• Admission avoidance• Transfer of care• Telemedicine
The Evidence
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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
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Health and Care System Flow
Lack of capacity?
Rural/urban
population?
Lack of access? Vulnerable
groups?
Not 24/7?
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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?
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Starting to simulate a new approach
Services “consumed”
Assessment of Need
Patients at Risk
Exacerbation
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• 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
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WHAT THE DATA IS TELLING US
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Over 30% of people over 75 years have multimorbidity
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Multimorbidity is more common than single morbidity
Kent whole population data
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The total health and social care cost is strongly related to multimorbidity
Kent whole population data
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The main contributors to total health & social care cost are acute non-elective admissions
Kent whole population data
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People with complex health & social care needs appear to demonstrate a ‘crisis curve’
Kent whole population data
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More community, mental health and social care services are delivered to people following a ‘crisis’ than before the ‘crisis’
Kent whole population data
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Some indications that an integrated care plan changes the pattern of services delivered to people
BHR Costing Data
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• Use local data to test assumptions
• Ability to update and review
Simulation
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• Level of acuity
• Increasing numbers of long term conditions
Current Simulation
• Likelihood of patients accessing services by changing state of patients (state transition)
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• 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
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Data builds an underlying discrete event simulation model
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• Number of patients in each “state” by year
• Average cost per patient
Results
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• Cost by each area of service/organisation
Results
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• Costs by state per year• Average cost per patient
• Comparison with tariff
Results
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• Select population• Select
percentage of population
• Predict incidence• Predict incidence
by “state”
Simulating Demand
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Start up Known Unknown 2012-13
Managed vs Unmanaged
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Yearly Transitions
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• Trial = multiple runs sampling from distributions in the model
• More robust results• Allow 20-30 minutes
Running a Scenario
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Results Comparison
Trial results
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• Known to integrated care team or not?• Test against proposed tariff?• Change variation in cost for services?• Decrease transitions through states?
Scenarios
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• Planning for demand• Testing an improvement scenario• Negotiation between healthcare providers
How is this helping?
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Case Study 2: Improving the emergency care flow with Martin Ware
• Impact of increasing out of hospital services on cost and capacity
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• 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
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Initial Model
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Area NHS data
Scenario Generator
%
A+E 108,472125,302 (17,026 out-of-area)A&E out of area (5% S Staffs) 17,000
0.99864512
Total NEL Admissions 84,297 84,4701.00205227
Elective admissions 12,674 12,7101.00284046
Daycase 49,983 49,8950.9982394
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
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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
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With population increase
In 5 years
+ £11.3m (£1m domiciliary care)(1% annual inflation)
Increase in A&E and admissions over 9 years
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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
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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 127Discharges to reablement from acute 10.10% £20.98 11 6 1400 127Discharges to maintenance care from community 4.50% £13.20 7 48 4100 586Discharges to maintenance care from acute 7.60% £13.20 7 48 4100 586Discharge from reablement to maintenance 15% £13.20 7 48 4100 586
Discharge to complex £13.20 22 48 4100 186All discharges from acute (stoke) 2183All discharges from community (stoke) 876
Domiciliary Care Assumptions
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• £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
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Impacts
• Understanding the financial impacts
• Allows negotiation across providers and between payers and providers
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• 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
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CommunityTeam
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
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Simulation Concept
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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?
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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
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