simulating patient safety
DESCRIPTION
This month we're discussing Patient Safety and how simulation is being used to reduce patient harm. Patient safety is a serious global public health issue. Estimates show that in developed countries as many as 1 in 10 patients is harmed while receiving hospital care (World Health Organization, June 2014). In this workshop we will examine how other high risk sectors use simulation to improve safety and how simulation could be more widely applied in healthcare. We will demonstrate simulations of delays, interruptions, adoption of best practice and interventions in sepsis.TRANSCRIPT
Simulating Patient Safety
Claire Cordeaux: Executive Director, Healthcare
SIMUL8 Corporation | SIMUL8.com | [email protected]
Presenters
Claire Cordeaux
Executive Director, Healthcare SIMUL8 SIMUL8 Corporation
SIMUL8 Corporation | SIMUL8.com | [email protected]
Housekeeping
• Audio
• Q and A
• Recording available on simul8healthcare.com
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Agenda
• What is simulation?• Why use it to improve
patient safety?• Applications in healthcare
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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
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Systems Modelling
Simulating a process in a virtual environment
From Process Map to Dynamic Process Flow
From SimLean project, Warwick University
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Adverse Drug EventsImpact of staffing and supplies on delays
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• 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
Use in other High Risk Industries
Why not healthcare?
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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
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Applying Simulation in Patient Safety
• Interruptions• Delays• Adoption of best practice• Sepsis
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Medication errors due to interruptions
• The myth of multi-tasking• Impact of designing out interruptions
Institute for Safe Medication Practices
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Recommendations
• No Interruption Zone – visual markers• “Sterile cockpit”• Good preparation – have all equipment
ready
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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
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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
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Delays and patient safetyBatching vs. Single Piece Flow
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Delays and patient safety
• 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.
Study of 5 US Hospitals
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Delays due to lack of bedsNHS IMAS model
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Parameter 1 - AdmissionsNumber of admissions by hour and day of the week
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Parameter 2 – Length of StayNumber of midnight stays by hour and day of the week
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Parameter 3 – Discharge profile% Chance of discharge hour
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Parameter 4 - BedsTotal number of emergency beds
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Improvement Scenario 1What if GP referrals arrived earlier in the day?
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Improvement Scenario 2What 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
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Improvement Scenario 3
Research suggests 65% of patients, in best performing hospitals
Increase cohort of patients who stay 2 midnights or less
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Move patient discharges forward to earlier in the day
Improvement Scenario 4
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Improvement Scenario 5Reduce 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
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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)
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Results are in…Which would you implement first?
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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
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The Simulation
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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
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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 styleClick to edit Master title style
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DEVELOPING THE SEPSIS SIMULATION MODEL
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Sepsis
Infection
Sepsis
Severe
Sepsis
Septic Shock
• Sepsis is an inflammatory response to an infection• Progressive
• Affects 100,000 people a year• Extremely high mortality rate: 35%
• Preventable
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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
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Sepsis Model
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Improvement Interventions
• More frequent and effective observations to aid earlier intervention
• To measure impact on patient outcomes and length of stay
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Assumptions – from desk research
• Progression Rates– 2.5% InfectedHSCIC Database
– 15.4% SepsisHSCIC Database
– 18% Severe Sepsis– 4% Septic Shock
• Mortality Rates – 26% Sepsis– 42% Severe Sepsis– 61% Septic shock
• A patient will progress through each state in sequence
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
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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
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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
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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
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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%
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Data requirements for baseline
• Number of patients • admitted• with infection• with sepsis• with severe sepsis• with septic shock• dying
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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
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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
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Results Reporting
• Increasing the number of patients receiving effective treatment (30%) with SSh over 36 hours
Base 5% 10% 15% 20% 25% 50% 100%
Mortal-ity (%)
35.1415755176
34.9904232234
34.9904232234
34.7856555015
34.8392674149
34.4645289035
34.0456843086
32.9612107322
29.00
30.00
31.00
32.00
33.00
34.00
35.00
Mortality (%) - Treating all SSh Patients
Per
cen
tag
e (%
)
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Results Reporting• Increasing the number of patients receiving
treatment with SSh – 3 hrs
– Reduce mortality by 5%
Base 5% 10% 15% 20% 25% 50% 100%
Mortal-ity (%)
35.1415755176
35.0663846222
34.648501725
34.1585376305
34.0765673714
33.5533647253
31.7645303695
29.6494714785
29.00
30.00
31.00
32.00
33.00
34.00
35.00
Mortality (%) - Treating Patients with SSh Within 3 Hours
Per
cen
tag
e (%
)
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Results Reporting• Increasing the number of patients receiving
treatment with SSh – 1 hour
– Reduce mortality by 6%
Base 5% 10% 15% 20% 25% 50% 100%
Mortal-ity (%)
35.1415755176
35.1456115414
34.6803144279
34.2557816824
33.9252741973
33.633004481
31.9362559827
28.9152545679798
29.00
30.00
31.00
32.00
33.00
34.00
35.00
Mortality (%) - Treating Patients with SSh Within 1 Hour
Per
cen
tag
e (%
)
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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
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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
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Future Development Work
• Measure Bed Days• Incorporate significance of other factors
– Age– Organ failure– Recovery– Staff
• Develop the user interface• Interested in collaboration
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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
SIMUL8 Corporation | SIMUL8.com | [email protected]
QUESTIONS
• Please forward any topics you would like to see covered to [email protected]
• Continue the discussion on SIMUL8 in Health – LinkedIn Group