Simulating Patient Safety

Claire Cordeaux: Executive Director, Healthcare

SIMUL8 Corporation | SIMUL8.com | info@SIMUL8.com

Presenters

Claire Cordeaux

Executive Director, Healthcare SIMUL8 SIMUL8 Corporation

claire.c@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

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 EventsImpact of staffing and supplies on delays

• 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

Use in other High Risk Industries

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

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 safetyBatching vs. Single Piece Flow

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

Delays due to lack of bedsNHS IMAS model

Parameter 1 - AdmissionsNumber of admissions by hour and day of the week

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

Parameter 3 – Discharge profile% Chance of discharge hour

Parameter 4 - BedsTotal number of emergency beds

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

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

Improvement Scenario 3

Research suggests 65% of patients, in best performing hospitals

Increase cohort of patients who stay 2 midnights or less

Move patient discharges forward to earlier in the day

Improvement Scenario 4

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

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 styleClick to edit Master title style

DEVELOPING THE SEPSIS SIMULATION MODEL

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

• 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% 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

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:

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%

Mortal-ity (%)

35.1415755176

34.9904232234

34.7856555015

34.8392674149

34.4645289035

34.0456843086

32.9612107322

Mortality (%) - Treating all SSh Patients

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

Mortality (%) - Treating Patients with SSh Within 3 Hours

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

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

Healthcare

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