Discrete Event Simulation f

Process Improvement: Emerg

Department Flow

T. Eugene Day, D.Sc.

Health Systems Specialist

St. Louis VA Medical Center

17 January, 2012
Outline

• Introduction

• Systems Engineering in Medicine

– Medical systems systems as dynamic dynamic systems systems– What is complexity?

• Modeling Dynamic Systems

– Discrete Event Simulation

• Simulation of the St. Louis VAMC Emergency Department

– M d li o e ng current practice and generating predi i ct ons

– Evidence based implementation of interventions

– Results– Limitations

• Acknowledgements
Conflict of Interest

• None.

Funding Source

• QI, not funded research• Software licenses, computers prov

by 2009 VHA Innovations Innovations Award,

Greenfield Incubation #123
Introduction

• Challenges in Medicine

– How do we treat all of our patients?– How do we decide where to dedicate resou– What performance performance measures measures are appropria

appropria

• Systematic approach to answers

– “Wh t a if?” and “How would?”– A different paradigm for hypothesis testing

• How can we reduce the number of long 

the St. Louis VAMC Emerg y enc Departm
Driving Questions

• Can we reduce average length of stay, 

p p ro ortion of patients with very long allowing provider assessment and disfrom triage?

– Literature suggests so[1]!– Addition of mid‐level providers to triage

– Discharge from triage

– Enhanced use of provider based fast‐trac

1) Wiler JL, Gentle C, Halfpenny JM, Heins A, Mehrotra A, Mikhail MG, Fite D(2010)

Ann Emerg Med 55(2):142‐160.e1
Driving Questions

• Can we assess the likelihood of succes

to experimentation?

– Discrete Event Simulation (DES) has been

employed in EDs before

– Useful for prediction of patient throughp

times[2]

– Comparison of triage practices[2]– Economic Evaluation[3]

2) Connelly LG, Bair AE, (2004) Acad Emerg Med 11(11):1177‐85. 3) Caro JJ, Möller J, Getsios D, (2010), Value in Health 13(8):1056‐1060.
Systems Engineering in Med

Med

• Many people think of Biomedical

Eng g ineering...
Simulation in Medicine
Systems Engineering in Med

Med

• Definition, System:

– “A collection of objects and relationships

• Definition, Systems Engineering:

– “An i di i li nterdisciplinary approach and mean

enable the realization of successful syste

• Study and design of large scale system

4) Systems Engineering Handbook; a “what to” guide for all SE practitioners

INCOSE‐TP‐2003‐016‐02, Version 2a, 1 June 2004. 
Systems Engineering in Med

Med

• Optimization of systems with human a

– How do decision makers make decisions

• Analysis of competing objectives

– Cost, quality– Speed, safety

• Examination of constraints constraints, bottlenec

bottlenec

barriers to care

– Capacity

– Policies and Regulations
Medical Systems Systems as Dynamic Dynamic Sy

Sy

Basic Emergency Department Mode
Medical Systems Systems as Dynamic Dynamic Sy

Sy

Basic Emergency Department Model?
Medical Systems Systems as Dynamic Dynamic Sy

Sy

•Multiple Dependencies•Complex Interactions•Dynamic Behavior

•“Butterfly Effect”

•Feedback Loops•Adaptive•Memory Dependant•Nonlinear State Variables

•Indistinct Boundaries
What is Complexity?

• Multiple independent subsyste• Ad ti it Adaptivity• Emergent behavior• Propagating consequences• Semi‐periodicity
Modeling Dynamic System

• Multi‐stage process• Direct observation observation and archival archival data• Multiple methods

– Systems Dynamics– Agent Based Modeling– Discrete Event Simulation

• Stochastic
Discrete Event Simulatio

• Stochastic: some state variables are ra• Dynamic: time evolution is important• Discrete Discrete‐Event: significant significant changes changes occ

discrete time instances
Discrete Event Simulatio

• Provides real time visualization of dyn

system

• Allows medical personnel to critique a

improve system fid lit e y

• Visual ind cat o i i n of p ob e r l m a eas r ao 

ll 

engineers to identify bottlenecks and solutions
System Capture

• Clinic decomposition

– Locations

• Exam rooms, waiting rooms, offic

– Resources

• Doctors, nurses, technicians, equ

– Entities

• Patients, paper records, phone ca
System Analysis

• Development of Flow

– How do Entities consume Resources at Loca– How do Entities proceed to the next Locatio

– How do Resources search for tasks?

• Location and Staff Capacities

– Single Entity, Multiple Entity, Infinite Entity– Batchable Processes

• Queue Development

– How do Entities wait while desired Resourc

unavailable?
Data Acquisition

• Measured data

–Observing resources, personnel personnel–Observing locations, activities

• Archival data

–Patient encounter sheets–Monthly status reports
Model Verification

• Model verified by clinic staff, and by input/o

identification

• External Validation

– Throughput metrics– Patient demography/statistics

• Internal Validation

– Code Review– System Stress Tests
Simulation of the St. Louis VA

Emergency Department

• Medium Sized ED

– Level 3 Trauma Facility, 120 inpatient bed– 20,000 patient visits per year– 14 emergency emergency patient patient beds

– 2 beds dedicated for mental health eme

– 2‐4 physicians, 4‐11 RNs varying by time 
The Problem:

• National Performance Measure Adher

– No more than 10% of patients should ha

longer than 6 hours (%LOS>6)

– St. Louis VAMC ED was averaging averaging 19 9%.

• Daily mean length of stay was 247 min• Long stays are associated with increas

morbidity, mortality, and elop g in in th

literature[5,6].

5) Ackroyd‐Stolarz S, Read‐Guernsey J, Mackinnon NJ, Kovacs G, (2011) BMJ Qual Saf; 20(7):564‐569

6) Fernandes CM, Daya MR, Barry S, Palmer N (1994), Ann Emerg Med 24(6):1092–96 
The Proposal

• Intervention consisted of change to tr

practices

– Consolidation of Fast Track with Triage– Placement Placement of Physician Physician and Mid‐Level pr

pr

triage

– Tt t rea ment and di iti sposition of ESI 4 5, di 

re

triage

– Treatment and disposition of roughly 30

from triage
Addressing the Problem wi

Simulation

• Simulate current practice

– According to described process

• Validate simulation against current pr

– Data used to generate mod l e cannot be 

validate model: disjoint data sets require

• Simulate proposed change to system

– Most effective effective for single changes changes– Multiple simultaneous changes not reco
Simulation of Current Current Work

Work

• Model informed by data, observation,

interview

• Validated against 6 weeks of real‐wor

– 18 S t ep – 30 O tc 2009

• Used to predict a 6 week simulated tr

period

• Implementation Implementation adopted adopted based on sim

sim

results
Pre Intervention Flow

Figure 1: Pre Intervention Flow
Post Intervention Flow

Figure 2: Post Intervention Flow
Flow Common to both Mo

Figure 3: ED Bed Processes
Simulation

Figure 4: Simulation Screen 
Validation

• Comparison of throughput metrics

– Simulation Mean Daily LOS: 249 ( ) 39.7 m– Real World pre‐intervention: 247 (39.8) 

• p=0 694 p=0.694

– Simulation %LOS>6: 19.9%– Real World pre‐intervention: 19.0%

• p=0.909

– We want non‐significance!
Accuracy of Prediction

• Implementation adopted for a 30 day 

from Oct 1 to Oct 30, 2010

– Predicted daily mean LOS: 200 (19.0) mi– Actual daily mean LOS: 210 (16 6) . min

• p=0.499

– P di d re cted %LOS 6> : 13 1%.– Actual %LOS>6: 14.3%

• P=0.880

– We want non‐significance!
Quality of Intervention

• Decrease in daily mean LOS of 37 min

– p<0.0001

• Relative decrease in %LOS>6 of 28.2%

– p=0 045 .

• No indications of negative effects on p

health, satisfaction
Limitations

• It is impossible to capture every possi

variation in flow

– Simulations are designed to capture nor

practices

• Limited sample sizes

– Patient cohorts of n=2194 and n=1699

respectively in test and trial periods
Limitations

• Some time distributions estimated fro

staff interviews

– Possible introduction of recall bias

• No access to Protected Protected Health Inform

Inform

– We don’t know the how important facto

patient age, or history might be to LOS

– Given the accuracy of prediction, it appe

factors other than ESI are likely to be les

significant than PHI‐related factors
Conclusion

• Discrete Event Simulation is a usef l u t

examining Complex Systems

– Must be thoroughly validated– Relies on assumptions– Focused on narrow predictions

• System improvements improvements can be achieve

achieve

minimizing risk

– Financial risk– Risk to patients
Acknowledgements

• VHA Innovations Program

– Provided com

p , uters

, software licenses.

• Abdul Rahim Al

‐Roubaie, MD

– Chi f e

f

o

f

Emergency

M di i e

cine,

St.

L i ou

s V

Center

• Bradley Black, RN

• Tommie Strong

, BS