This presentation was given as part of the Simulation in Healthcare Dinner sponsored by SIMUL8 at the 2014 HSPI Conference. The presentation was given by Anna Henkel of BJC Healthcare. • History of simulation at BJC HealthCare • Overview of simulation applications • Case Studies – Mobile Pharmacy – Preventable Harm Interventions – OR Bed Flow

1. Simulation Modeling at
BJC HealthCare
1
Anna Henkel
Transformation Support, Center for Clinical Excellence
afh2831@bjc.org

2. • History of simulation at BJC HealthCare
• Overview of simulation applications
• Case Studies
– Mobile Pharmacy
– Preventable Harm Interventions
– OR Bed Flow
Outline
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3. 3

4. History of Simulation Modeling at BJC
4
Mid-2009:
Identified
simulation as a
key opportunity
for the system
2009 2010 2011 2012 2013
Early 2010:
System-wide
in-house
training
2014
Early 2014:
In-house
SIMUL8
training
Mid 2013: Re-emphasis on
simulation modeling as a
valuable performance
improvement tool
Early 2010:
Begin using
simulation
system-wide
Late-2009:
Selection of
SIMUL8 as
BJC’s modeling
software
2014:
Integration
into Black Belt
curriculum
2013:
Attempt #2 to build
internal capacity
2011:
Attempt #1 to build
internal capacity
2014:
Attempt #3 to build
internal capacity

5. Simulation Applications at BJC
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• Administration
• Care Coordination
• Emergency Department
• Food Services
• Nursing Units
• Operating Room
• Outpatient Medical Practices
• Pharmacy
• Planning, Design &
Construction
• Radiology
• Revenue Cycle

6. Simulation type: Staff utilization
Questions:
1. What is the effect of variation in patient utilization on prescription
turnaround time?
2. What is the effect of staff resources on prescription turnaround time?
3. What is the impact of batching deliveries on prescription turnaround time?
Case Study 1: Mobile Pharmacy
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Inputs (variables) Outputs Controls
• Patient utilization
• Delivery batch size
• Staffing models
• Prescription turn
around time
• Resource utilization
• House-wide patient
census
• Delivery time

7. Case Study 1: Mobile Pharmacy
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Future State (45% Patient Utilization)

8. Results/Decision/Recommendations:
• With increased patient utilization, resource need less than originally
estimated
– i.e. originally anticipated adding 4 scanning stations to overall Mobile Pharmacy
workflow; simulation model revealed that only 2 additional scanning stations
necessary
Project Benefits:
• Prospective understanding of impact of increased patient utilization
• Validation of resource requests/new hires prior to initiating process
Case Study 1: Mobile Pharmacy
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9. Simulation type: Staff utilization
Questions:
1. What is the impact of varying patient acuity and patient census on staff
capacity required for executing falls and pressure ulcer interventions?
Case Study 2: Preventable Harm Interventions
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Inputs (variables) Outputs Controls
• Frequency of
interventions
• Patient length of stay
• Patient census
• Type of staff to
respond
• Staff utilization
• Intervention time by
patient fall & pressure
ulcer acuity level
• Bed capacity
• % isolation patients
• Distribution of falls
acuity
• Distribution of
pressure ulcer acuity

10. Case Study 2: Preventable Harm Interventions
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Pressure Ulcer
Prevention
Fall Prevention

11. Case Study 2: Preventable Harm Interventions
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12. Case Study 2: Preventable Harm Interventions
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“Low” fall risk patients ( ~12 patients)
“Moderate” fall risk patients (~23 patients)
“High” fall risk patients (~15 patients)

13. Results/Decision/Recommendations:
• Over 12 hours of a 24-hour time period is spent on fall and pressure
ulcer interventions for the average patient census
Project Benefits:
• Limited role differentiation for fall & pressure ulcer interventions
between staff revealed processes that neglected human potential
• Importance of clarifying standard protocol: model revealed that
some low risk patients required more staff time because of unclear
intervention protocol
Case Study 2: Preventable Harm Interventions
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14. Simulation type: Bed flow
Questions:
1. What is optimal number of pre-op and post-op beds?
2. What is the impact of shared pre-op/post-op beds?
3. How does families waiting in the pre-op/post-op bay affect flow?
Case Study 3: Pre-Op and Post-Op Bed Utilization
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Inputs (variables) Outputs Controls
• Case mix
• # Available pre-op &
post-op beds
• Use of space
(shared/separate,
families occupy room)
• Utilization by bed type
(pre-op, post-op &
shared)
• Number of ORs
• ASA scores

15. Case Study 3: Pre-Op and Post-Op Bed Utilization
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16. Results/Decision/Recommendations:
• Recommended number of beds ranged from depending on bed use
scenario (shared/separate bed pool, case load, bed use)
Project Benefits:
• Families staying in pre-op room had limited impact on number of
beds required (requirement increased by 1 bed)
• Standard ratio of pre-op/post-op beds to ORs (4:1) did not hold for
every scenario
– Impacted by unique needs of the pediatric population
Case Study 3: Pre-Op and Post-Op Bed Utilization
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17. Thank you!
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Anna Henkel
Transformation Support
Center for Clinical Excellence
BJC HealthCare
afh2831@bjc.org