whole system modelling from prevention to integrated … from... · whole system modelling – from...

Whole system modelling –

from prevention to integrated

care Claire Cordeaux: Executive Director, Healthcare

SIMUL8 Corporation | SIMUL8.com | [email protected]

Health and Care System Flow Lack of

community /social care capacity?

Rural/ urban

population?

Lack of access? Vulnerable

groups?

Primary/ Community Not 24/7?

Staying well

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SIMULATION APPROACH:

PREVENTION


Hepatitis C Screening – preventing liver

disease

• 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

Driving business case for change



SIMULATION APPROACH: WHOLE

SYSTEM FLOW


Improving the emergency care flow North

Staffordshire

• 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

Initial Model

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


Let’s imagine…

• We have prevented disease

• We are managing long term conditions

effectively in the community

• Hospital admissions are reduced

• But how are community services coping?



SIMULATION APPROACH:

OPERATIONAL IMPACT ON SERVICES


• 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


Final Thoughts

• Simulation supporting understanding of the

health and social care system

• Evidence: – To support stakeholder/interagency dialogue

– To better understand the impact of change

– To inform financial and operational decision-making

Helping to define and solve problems



QUESTIONS?

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