Simulating Patient Safety

Simulating Patient Safety
Claire Cordeaux: Executive Director, Healthcare

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

Housekeeping
• Audio
• Q and A
• Recording available on simul8healthcare.com

Agenda
• What is simulation?
• Why use it to improve
patient safety?
• Applications in healthcare

Explaining simulation
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

Systems Modelling
Simulating a process in a virtual environment
From Process Map to Dynamic Process Flow
From SimLean project, Warwick
University

Adverse Drug Events
Impact of staffing and supplies on delays

Use in other High Risk Industries
• Nuclear Power
– Operator
Performance/multi-tasking
• Airline
– Fleet Maintenance/when to
replace parts
• Maritime
– Ship Collisions/routing
• Oil
– Oil transportation/making
sure the right product goes
to the right place
• Prison Population
– Forecasting the number of
cells
Why not healthcare?

Using Simulation to improve
Patient Safety
– Test interventions and impacts before
implementation
– Provide a robust evidence base for change
– Sharing best practice messages and allowing
experimentation to understand in local context
between stakeholders

Applying Simulation in Patient
Safety
• Interruptions
• Delays
• Adoption of best practice
• Sepsis

Medication errors due to
interruptions
• The myth of multi-tasking
• Impact of designing out interruptions
Institute for Safe Medication Practices

Recommendations
• No Interruption Zone – visual markers
• “Sterile cockpit”
• Good preparation – have all equipment
ready

Simulating interruptions
• 9am-9pm for 1 month
• Medication rounds 9am and 3pm (120 minutes duration)
• Interruptions every 45 minutes (10 minutes duration)
• Each interruption gives 10% chance of error

Simulating intentional rounding
• Good practice from the USA (Studer Group 2007)
• Adopted in “Harm Free Care”
• Reported improvements in pain management , decrease
in falls and pressure ulcers and reduction in call bell
usage and waits
• Unanswered questions: who does it, how often, and for
which patients? What are the implications for skill mix
and nurse staffing? What are the costs?
Intentional Rounding:
what is the evidence?
nursingtimes.net / Vol 108
No 28 / Nursing Times 10.07.12 23

Delays and patient safety
Batching vs. Single Piece Flow

Delays and patient safety
Study of 5 US Hospitals
• Patients closer to the automated
dispensing machine less likely to have a
medication error because the nurse was
less likely to batch all of the medications
for several patients at the same time.

Delays due to lack of beds
NHS IMAS model

Parameter 1 - Admissions
Number of admissions by hour and day of the week

Parameter 2 – Length of Stay
Number of midnight stays by hour and day of the week

Parameter 3 – Discharge profile
% Chance of discharge hour

Parameter 4 - Beds
Total number of emergency beds

Improvement Scenario 1
What if GP referrals arrived earlier in the day?

What if our ambulatory care patients were treated
elsewhere?
• Select a percentage of
admissions classed as
“ambulatory” (default 25%)
• Select likely impact on
number of midnight stays

Increase cohort of patients who stay 2 midnights or
less
Research suggests 65% of patients, in best
performing hospitals

Move patient discharges forward to earlier in the day

Reduce 14 night length of stay for over 75s
• Enter LOS cohort (14days)
• Select a percentage of
which will have impact
(default 50%)
• Select likely impact on
number of midnight stays

Poll: Which scenario has the
most impact?
1. GP referrals earlier?
2. Ambulatory care treated elsewhere? (25% of
admissions)
3. Increase proportion of stays that are less than 2
midnights?
4. Move 50% of discharges earlier in the day?
5. Reduce 14 night stays for over 75s? (25% of
admissions)

Results are in…
Which would you implement first?

Delays due to lack of decision-makers
and diagnostics:
David Halsall and Team NHS England
• Services and the case mix of patients at
weekend are different compared to a weekday.
• Medical inpatients more likely to die if admitted
to hospital as an emergency at a weekend

The Simulation

Conclusions
• Need more senior decision-makers AND
diagnostic services at weekends to reduce
mortality rates
• Length of stay reduces at weekend, but
increases at start of the week

Accelerating Adoption –
Simulating Pathways
• BNP Blood Test (East Riding, NHS
Improvement)
• Serum NP testing for suspected heart
failure patients
• Not implemented in 46% of PCTs
• Realised savings of @£100k per PCT

Click to edit Master title style
DEVELOPING THE SEPSIS
SIMULATION MODEL

Sepsis
• Sepsis is an inflammatory response to an
Infection Sepsis
Severe
Sepsis
Septic
Shock
infection
• Progressive
• Affects 100,000 people a year
• Extremely high mortality rate: 35%
• Preventable

Aim
• Simulate probability of infection and
progression to sepsis from hospital
admission
• Test with one hospital
• Allow easy localisation of key parameters
so model can be used in different hospital
trusts
• Test impact of improvement strategies

Sepsis Model

Improvement Interventions
• More frequent and effective observations
to aid earlier intervention
• To measure impact on patient outcomes
and length of stay

Assumptions – from desk
research
• Progression Rates
– 2.5% Infected
HSCIC Database
– 15.4% Sepsis
HSCIC Database
– 18% Severe Sepsis
– 4% Septic Shock
• Mortality Rates
– 26% Sepsis
– 42% Severe Sepsis
– 61% Septic shock
The Dynamics of Disease Progression in Sepsis: Markov Modelling Describing the
Natural History and the Likely Impact of Effective Antisepsis Agents: Clin Infect Dis
1998; 27:185-190
Systemic Inflammatory response and Progression to Severe Sepsis in
Critically Ill Infected Patients: AM J Respir Crit Care Med. 2005 Mar
1;171(5): 461-8
• A patient will progress through each state
in sequence

Transitions
• Admissions to Infection
– 2.5% of all admissions develop an infection
– Over a patient’s total stay they will have a
2.5% chance of developing an infection
– Transition is:
2.5%
Total Length of Stay

Transitions
• Progression and Mortality
– The number of days a patient has spent in
each state is recorded
– Data is stored in a spreadsheet
– As each day a patient spends increases the
progression rate is read from a new row in the
spreadsheet

Transitions
• Recovery
– Each patient is assigned a Length of Stay
based on an average distribution.
• Infection 3.7 days - HSCIC database
• Sepsis 11.5 days - HSCIC database
• Severe Sepsis 16 days http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=7069
• Septic Shock 20 days VHA, Inc. Transformation of the intensive care unit: sepsis data
collection toolkit. Irving (TX): VHA, Inc.; 2007 Jan 1. 29 p
– A patient recovers once they have spent the
required amount of time in that state

Progression Assumptions
• Expected increase in risk of progression each
day a patient is in one of the states
– Sepsis to Severe Sepsis – 7%
– Severe Sepsis to Septic Shock – 9%
• Risk of mortality each day a patient is in one of
these states
– Sepsis – 2%
– Severe Sepsis – 6%
– Septic Shock – 8%

Data requirements for baseline
• Number of patients
• admitted
• with infection
• with sepsis
• with severe sepsis
• with septic shock
• dying

Scenarios Tested
• Increasing the number of patients
receiving treatment
• Increasing the number or patients treated
within 1 hour and 3 hours
• Looked at septic shock and severe sepsis
independently and together
• Understanding impact on mortality

Assumptions from research
Kumar et al , Duration of hypotension before initiation of effective antimicrobial
therapy is the critical determinant of survival in human septic shock, Crit Care
Med 2006 Vol. 34, No. 6
30% unlikely to
receive effective
treatment
Even with
treatment only
80% success

Results Reporting
• Increasing the number of patients receiving
effective treatment (30%) with SSh over 36
hours
Base 5% 10% 15% 20% 25% 50% 100%
35.50
34.50
33.50
32.50
31.50
30.50
29.50
Mortality (%) 35.14 34.99 34.99 34.79 34.84 34.46 34.05 32.96
28.50
Percentage (%)
Mortality (%) - Treating all SSh Patients

Results Reporting
treatment with SSh – 3 hrs
Base 5% 10% 15% 20% 25% 50% 100%
35.50
34.50
33.50
32.50
31.50
30.50
29.50
Mortality (%) 35.14 35.07 34.65 34.16 34.08 33.55 31.76 29.65
– Reduce mortality by 5%
28.50
Percentage (%)
Mortality (%) - Treating Patients with SSh Within 3
Hours

Results Reporting
treatment with SSh – 1 hour
Base 5% 10% 15% 20% 25% 50% 100%
35.50
34.50
33.50
32.50
31.50
30.50
29.50
Mortality (%) 35.14 35.15 34.68 34.26 33.93 33.63 31.94 28.92
– Reduce mortality by 6%
28.50
Percentage (%)
Mortality (%) - Treating Patients with SSh Within 1
Hour

Results Reporting
• Ideal Scenarios
– Treat 50% with SS and SSh
• Progressively
• Last minute
– Treat 100% with SS and SSh
• Progressively
• Last minute
– Treat everyone with SS and SSh within 1 hour

Results Reporting
Scenario Base 100%
Progressively
100%
Last Min
50%
Progressively
50%
Last Min
SS<1 and
SSh<1
Mortality (%) 35.14 30.49 31.89 32.76 33.55 26.28

Future Development Work
• Measure Bed Days
• Incorporate significance of other factors
– Age
– Organ failure
– Recovery
– Staff
• Develop the user interface
• Interested in collaboration

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

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

Simulating Patient Safety

Recommended

Recommended

More Related Content

Similar to Simulating Patient Safety

Similar to Simulating Patient Safety (20)

More from SIMUL8 Corporation

More from SIMUL8 Corporation (20)

Recently uploaded

Recently uploaded (20)

Simulating Patient Safety