yhorg - why use simulation

Why Use Simulation?

Claire Cordeaux: Executive Director, Healthcare, SIMUL8

SIMUL8 Corporation | SIMUL8.com | [email protected]

Agenda

Simulation and…

• A bit about me

• Understanding the system

• Testing change safely

• Communication and Visualisation

• Coping with variability

• Dissemination and adoption


My Experience

• Social Care and NHS


My Experience

• Understanding the system

• A way of thinking about a solving a problem

• Evidence for the case for change

Click to edit Master title styleClick to edit Master title style


UNDERSTANDING THE SYSTEM: HOW DO WE KNOW WHETHER PLANS

WILL WORK?


Healthcare system


Scenario Generator

• Simulation tool for strategic planning in health and social services

• Framework for understanding whole systems

• Default data for immediate scenario testing

• Easy to use for those new to simulation

• Developed with NHS Institute UK and rolled out to all PCTs

• 100 organisations trained


Demand from Demographics

Patient journeys by age/condition/need

Whole system view including costs,

resources, queues

Scenario Generator

Whole system impact of change


Scenario Generator

Expected Demand

DemographicWeighting

System Points,Flows & Waits

System Pathway Models

Constrained Resources

SimulationResults

Whole Systems

System Scenarios(“What If's”)

Internal DriversRisks & Opportunities

External DriversRisks & Opportunities

Population

Prevalence

Social Care System

Health Care System


North West Surrey - Testing the strategy

Planning for locality hubs to support older people:

What is:

• the likely demand for service?

• capacity required?

• impact on acute trust?


Population and Demand


• 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)

Starting from the current state


Urgent Care Pathway


Planned Care Pathway


Validation

Scenario Generator

NHS data 14-15 12m

%

Elective admissions 1,813 1,810 1.00Day case 7,196 7,233 0.99Regular attenders 155 158 0.98First outpatients 53,657 53,692 1.00First telephone outpatients 3,322 3,321 1.00Follow up outpatients 199,394 200,786 0.99Follow up telephone outpatients 6,639 6,637

1.00A&E attendance 16,181 16,456 0.98Walk in attendance 5,644 5,569 1.01Emergency admissions 10,251 + 216 AMU 10,353 1.01


• 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%656 - Clinical psychology 60%

Analysis of activity against avoidance outpatients shows a possible drop of 19.87% in first outpatients

Outpatients avoidance


• 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

Urgent Care Scenario Locality Hubs


0

2,000

4,000

6,000

8,000

10,000

12,000

Electiveadmissions

Day case Regularattenders

Emergencyadmissions

Baseline

New

Predicted locally

Comparison with and without hub 1 year


Results for Locality Hub5 staff FTE over 24 hours – 85% utilisation

= 15 staff67,291 visits


In 5 years time baseline vs new…


5 years in detail..

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


Key Takeaways:

Use Simulation for:

• Understanding the current system

• Identifying areas for improvement


7 years on

• Sold in Italy, France, Switzerland, Canada, US, Australia, Qatar

• Now leading healthcare simulation at SIMUL8 –a global simulation software company

• Healthcare is 40% of our work



TESTING NEW MODELS OF CARE SAFELY


Simulating alternative interventions

• What is the impact on capacity?

• Where does this need to be increased or could it be decreased?

• Who are the revenue winners and losers and what is the cost?

• What are the patient outcomes?

• How long it take to show an impact?


What are the alternatives?

• Just do it?– Costly to experiment in real life

• A pilot?– but these are real patients

– it will take a long time

• Historical data? – but it’s a new service, there is no data

NO PATIENTS WERE HARMED IN THE MAKING OF A SIMULATION


Simulation to Evaluate Great Care

• Better Care Fund

• Promoting joined up care pathways across health and social care

• Aim to reduce hospital admissions by improving discharge and providing integrated care in the community

Before

Before

From Audit1.2 patients per night2 months207 patients over 6 months95% admitted

After

90% to Night Nursing


Validating the Models

• Fit with audit

• Agreeing the baseline with stakeholders

• Picking up points of insight, need for action

• Refining the model

• Asking different questions


Considering Results

• Admissions avoided

• Night Nursing Service Capacity?


Key Takeaways:

Use Simulation for:

• Testing new models of care

• No risk to patients

• Find out about problems before they happen

• Evidence for business case for change



COMMUNICATING CHANGE


It’s a tool for change

• Test everyone’s ideas

• Engage stakeholders

• Visualise the future

“Don’t put your analyst in a

darkened room with a simulation tool

and expect them to come out with the

answer”


Testing a new Emergency Department


The initial question

• Will our newly designed ED plan fall over with additional demand? led to…

• Actually, do we understand the new patient flow in the context of the new ED?

• Do we know how long patients really spend in resus, not counting when they are waiting for an inpatient bed?

• What is the likely impact of the new 24/7 short stay unit?

• We want to have the consultant at the front door – what is that going to mean?


The next iteration

• The ED won’t fall over even with additional demand – the business case can go forward

• Only one consultant on at weekends at the front door doesn’t work because they are being pulled back to other responsibilities

• Now we need to look at our beds….


Key Takeaways:

Use Simulation for:

• Engaging stakeholders

• Learning through iterations

• Gaining consensus



COPING WITH VARIATION


0

5

10

15

20

25

30

35

Num

ber o

f Bed

s

Actual Beds What actually happens

Inefficient

Increased mortality rates

Managing Bed Occupancy


Example Decisions

• How should I schedule electives?

• UK Hospital• How should I design my new build?

• US Hospital• What type of short stay unit should we have?

• UK Hospital• How many surgical beds do I need?

• US Hospital


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


• 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


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


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


Key Takeaways:

Use Simulation for:

• Understanding the impact of variability on the system

• Real world resources

• Planning to cope with a crisis



DISSEMINATION AND ADOPTION


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 and patient outcomes as well as capacity and cost

– Starting from patients and their condition means the template can be shared easily

– Speak to all stakeholders


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%


How it works

• 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.



Using the simulation: DataCapacity: 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: SCENARIOSThe PAYNE Scenario - preloaded



The PAYNE Scenario - preloaded



The CORNWALL Scenario (Living Well, Age UK)

• One cohort only

• 2% of population



Key Takeaways:

Use Simulation for:

• Sharing best practice

• Making it easy for others to learn


Finally..

• Best practice needs clinical evidence AND operational evidence

• Simulation can help:– Understand the system and impact of change

– Run a “virtual pilot”

– Provide evidence for decisions

– Engage stakeholders

– Disseminate and share operational practice

www.SIMUL8healthcare.com/YHORG

for slides and links to simulations