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Emergency Department Throughput: Using DES as an effective tool for decision making Presenters: Johns Hopkins, Novasim The first workshop in our series will look at a challenge facing many health systems across the country. With an increase in patient demand and limited resources and capacity, the need to manage Emergency Department throughput has never been greater. Join Eric Hamrock, Senior Project Administrator for Operations Integration at Johns Hopkins Health System (JHHS), and Kerrie Paige from SIMUL8 Partner Novasim as they present some of the lessons learned through more than a decade of simulation projects at three JHHS Emergency Departments.
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Eric Hamrock, MBA, PMP- Sr. Project Administrator,
Johns Hopkins Health System
Kerrie Paige, PhD- President, NovaSim, LLC
DES for Decision Making in
the Emergency Department
4/11/2013
Roadmap
• What is discrete event simulation and why
do we use it?
• Setting up a project for success
• Gathering the right information
• Model validation
• Case Studies: Using simulation to support
decisions at JHHS
WHAT IS DES AND WHY USE
IT FOR DECISION MAKING IN
THE EMERGENCY
DEPARTMENT?
Discrete Event Simulation is…
• A powerful predictive analytics tool
• Able to capture the dynamic interactions and variation inherent in any Emergency Department
• Useful for predicting the impact of a wide range of what-if scenarios
• Highly visual
• Great for getting everyone on the same page
• Robust, flexible, powerful
• A way to create an environment of objectivity
• A method to view outcomes of complex systems in a simpler way
Sample Animation
What if We…
• Add capacity (i.e. space, staff)?
• Change a practice pattern?
• Receive more or fewer patients?
• Get a different mix of patients?
• Reduce boarding time?
• Adopt a new model of care (fast track, short
stay, etc.)?
• Want to evaluate proposed lean initiatives?
• Reconfigure or add additional care areas?
SETTING UP A PROJECT FOR
SUCCESS…
Keys to a Successful DES
Project…
• Ensure a strong project champion is identified
• Define a clear scope and desired outcomes
for the simulation
• Assess what sources and quality of data are
available
• Is the process appropriate for DES or would
another tool work better?
• Include front line staff and those affected by
the process when available
• Educate the team on DES
Key Roles
Project Manager
Decision Maker
Technical Expert/
Developer
Front Line Staff
DES Pitfalls
• Scope creep
• Did not define the question clearly up
front?
• Lack of project champion buy-in
• Lack of front-line staff buy-in
• Quality/accuracy of data
• DES not the right tool
Phases of a Simulation Study
Process Analysis
• Historical data
• Flow charts
• Interviews
• Time studies
Modeling
• Configure model
• Validate
Scenarios
• Create
• Compare & evaluate
Reporting
• Communicate
• Knowledge transfer
Process Mapping
Simulation Brings the
Process Map Alive
VALIDATING THE MODEL
Start by Gathering the Right Information
and Checking it Carefully
• Carefully mine any transactional data available for arrivals, LOS, pathways, D/C
• Supplement with manual time studies as needed
• Check patient flow and critical process elements through clinic visits and interviews with critical staff
• Verify the model by following simulated patients through the system
• Validate the model - did the baseline model predict the same performance we observed in the real system?
How Do We Know When to Trust
the Model?
• When it looks and ‘feels’ like the real system – Do we have the right number of arrivals of each patient type
into each part of the system?
– Does the hourly census pattern in each area of the ED match reality?
– Are patients waiting where they should be and for the right period of time?
– Is overall time in system about right?
– Bottom line: Does the clinical staff believe it?
• It is very common that the initial model does NOT validate very well – Tracking down the problem is often enlightening in itself
– It is also proof that validation is a critical part of the process
Sample Validation Reports
USING DES FOR ED DECISION
MAKING
All ED models help us
evaluate
• Overall ED Performance – now and in the future – LWBS
– Bed utilization
– Expected patient wait times
– ED census levels
• Potential trouble spots – Are there any areas with unsustainable bed occupancy
levels? Are we on the edge of the cliff?
– Which parts of the process are particularly sensitive to volume, patient mix or dwell time changes?
• Business cases, providing evidentiary support for proposed change
Case Study:
Howard County General Hospital
From a Basic Model, Many
Issues were Analyzed
• Addition of a psych pod
• Impact of reduced wait times for diagnostic imaging and labs
• Change in capacity/schedule in fast track
• Reduced wait for an inpatient bed
• Change in nursing bed assignment patterns
• Revised allocation of ED beds
• Reduced housekeeping (bed turnover) times?
• Increased patient volumes
Case Study: Johns Hopkins Bayview
Medical Center
• 380 bed area-wide trauma center
• Several throughput improvement
projects over the years
• Most recent: detailed model to support
capacity calculations for new ED
• Ground breaking for new ED
department, Spring 2013
Facility Size
Demand Analysis
9/4/2013 Emergency Department Operations
Review 28
Volume 59,275 63,000 75,000
Current Needed
Minor Care 8 8 8 9
Emergency 21 36 38 45
Specialty 4 7 7 7
Total 33 51 53 61
Ultimate recommendation:
55 beds for the near term, 60-62 for the longer term
Facility Size
Simulation: 65,000 Patients
9/4/2013 Emergency Department Operations
Review 29
Emergent
Beds 43 40 37 36 35
Bed
Utilization 66.8% 71.0% 75.4% 78.5% 80.0%
90th %ile
wait time
Level 3
0 mins 4.6 mins 23.5 mins 32.7 mins 48.4 mins
LWBS <1.0% <1.0% <1.0% <1.0% <1.0%
Total
Beds 58 55 52 51 50
Total includes specialty beds and 8 minor care
Add chairs for Psychiatric Evaluation Patients (6)
Case Study:
The Johns Hopkins Hospital
• How can we best manage throughput?
– Adding or reallocating capacity?
– Changing practice patterns?
– Reducing dwell times?
– Reducing boarding time?
– Adjusting unit operating hours?
Patient Service
Area Utilization
0%
20%
40%
60%
80%
100%
120%
0:3
0
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0
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0:0
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PSA Utilization
Triage % Main ED % Psych % Trauma % EACU % ICU % RAP % Super Track %
Many Interventions
Considered
• Capacity reallocation/addition
• Dwell time reductions (via process
improvements)
• Practice changes
• Shifting demand elsewhere
• Interdepartmental process changes
Ultimate goal: work toward a ‘zero-wait’ ED
Time to First Beds
(Additional Beds)
0.0 min
20.0 min
40.0 min
60.0 min
80.0 min
100.0 min
120.0 min
140.0 min
Base 1 Bed 2 Beds 3 Beds 4 Beds 5 Beds 6 Beds 7 Beds 8 Beds 9 Beds 10 Beds
Additional Beds
Main ED EACU ICU RAP SuperTrack
Time to First Bed (Dwell Time
Reduction)
0.0 min
20.0 min
40.0 min
60.0 min
80.0 min
100.0 min
120.0 min
140.0 min
Base -5% -10% -15% -20% -25% -30%
Dwell Time Reduction
Main ED EACU ICU RAP SuperTrack
Dwell Time vs Total Beds
80
85
90
95
100
105
110
115
60% 65% 70% 75% 80% 85% 90% 95%
Total Beds
Dwell Time Factor
Combination of factors necessary to get 95% of patients to first bed within 30 minutes
Bed Utilization vs. Service Level (% of patients who wait less than 5 Min)
100% 100% 100% 100% 99% 98%
96%
92%
86%
76%
61%
41%
16%
0% 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
39% 44% 49% 54% 59% 63% 68% 73% 78% 83% 88% 93% 98% 100%
Bed Utilization
Reduced Boarding Time
Lessons Learned
• Start simple, build from there
• If you have multiple EDs in your system or you are going to be running models often over the years, take some time to build a robust, easy-to-use interface
• Model building process can provide value just by getting the team to think through all aspects of the patient flow process
• Extremely useful for presenting ideas/ selling concepts to senior management
• It’s ok to start with less than perfect data, but validate end results closely before using for decisions
• Sometimes results are non-intuitive, that doesn’t make them wrong
• Look beyond the immediate symptoms – issues may be originating in another department
• Models often point out issues that had been previously unrecognized
• Invaluable for testing ideas before implementation
• Great for ED sizing/bed allocation questions
Special Thanks
• James Scheulen
• Scott Levin
• Jaret Hauge
