Hybrid Agent-Based/Discrete Event Simulation 

Models for Analysis of Health Care Delivery Systems 

T. Eugene Day, D.Sc. 

Sr. Improvement Advisor 

The Children’s Hospital of Philadelphia

dayt@email.chop.edu 
Contents 

• Introduction 

• Complex Systems in Healthcare 

• Discrete Event Simulation 

• Agent-Based Models 

• Integrating the Methods 

• Results 

• Discussion 

• Conclusions 
Introduction 

• VA St. Louis Healthcare System 

• VA ORD HSR&D Merit Review Pilot Study 

– PPO 11-167, “Simulation Modeling for Implementation Analysis” (PI - 

Day) 

• Equipment/Software provided by 2009 VHA Innovations 

Award #123 (PI-Day) 

• No Conflict of Interest to Report 

• Data and Some Figures Previously Published[1] or in Review. 

• Study Reviewed and Approved by VA St. Louis Healthcare 

System IRB. 

[1] Day TE, Ravi N, Xian H, Brugh A (2013) “An Agent-Based Modeling Template 

for a Cohort of Veterans with Diabetic Retinopathy” PLoS ONE, 8(6): e66812. 

doi:10.1371/journal.pone.0066812
Introduction 

• How can simulation inform policy and 

practice? 

• How can simulation be used to improve health 

services delivery? 

• How can simulation improve population 

health? 

• Specifically, can we use simulation to analyze 

the consequences of increasing the duration 

of the screening interval for DR? 
Poll Question 

• Who has computer simulation modeling 

experience? 

– I am a model developer. 

– I directly use models developed for my institution. 

– I don’t use them, but my institution does.

– I have heard of it, but my institution does no 

simulation modeling. 

– I have never heard of simulation modeling. 

– Isn’t simulation something about those creepy 

mannequins? 
Complex Systems 

in Healthcare 

• Multiple Dependencies 

• Complex Interactions 

• Dynamic Behavior 

• “Butterfly Effect”

• Feedback Loops 

• Adaptive 

• Memory Dependant 

• Nonlinear State Variables 

• Indistinct Boundaries 

6 
7 

Complex Systems 

in Healthcare 
Model Classifications

• Hybrid 

– Combination of multiple methods 

– Combination of multiple time measurements 

• Agent-based 

– Individuals within a population 

• Discrete Event 

– Step-by-step model progression 

• Multi-scale 

– Model unfolds on two time-scales 
Discrete Event 

Simulation 

• Computer simulation of real-time processes 

• Decomposes systems into basic elements 

– Entities (patients, patient records, lab samples) 

– Resources (Physicians, nurses, equipment) 

– Locations (Exam rooms, queues) 

– Paths (Networks linking locations) 

• Process Flow 

– How do entities consume resources and proceed 

along paths between locations? 
Discrete Event 

Simulation 

• Rudimentary Simulated Eye Clinic 

• Performs two functions 

– Eye screens 

– Laser eye surgeries (PRP) 

• Represents the environment of interaction for 

the agents 

– Agents compete for clinical time, resources 
Agent-Based Models 

• Agents are objects with individual rule sets 

and attributes 

• Capable of autonomous decision making 

• Agents can interact with each other, and their 

environment 

• Ideal for modeling populations of individuals 

– Birds in a flock, fish in a school 

– Cells in a tumor 

– Cars in traffic 
Agent-Based Models 

• Each agent represents a single Veteran 

• Imbued with individual characteristics 

– BMI, recent A1c, BP, age, duration of DM, 

presence of DN, stage of DR, etc. 

– Updated annually 

• Rule set governing how DR advances 

• Algorithm for when to visit clinic 

– Determine visitation schedule and then queue 

 for availability 
Agent-Based Models 

• Our Agent-Based Veteran includes: 

– A statechart governing progression of diabetic 

retinopathy 

– Numerical fields capturing the Veteran’s health 

status and demography 

– A function attached to a life-table to predict expiry 

– An “event” which updates the Veteran annually

– An “entity generator” which communicates with 

the DES clinic 

– Multiple regression model of DR progression 
Agent Based Veteran
Integrating 

the Models 

• The Agent periodically enters “clinicVisitState”

– Generates an “Entity”

– Passes all relevant information from Agent to 

Entity 

– Sends Entity to the DES Clinic 

• The Entity negotiates the clinic 

– All entity activity governed by external flowchart 

– Receives indicated care according to individual 

needs 

– Passes updated information back to Agent 
Integrating 

the Models 
Integrating 

the Models
Experiment 

• Conduct 10 simulation runs of 10 years each for 

each of five groups 

• For each group, screen veterans with no DR, or 

with Background DR, with from one to five years 

between screenings 

• Examine the incidence of vision loss in the 

population at the end of 10 years 

• Simulation allows us to isolate the contribution of 

screening interval to progression. 
Results 

• Primary outcome measure: number of 

Veterans reaching state “Vision Loss” 

– No significant difference between annual and biannual 

groups 

– Significant increase (P<.01) between 1 year interval and 3 

year interval (from 60 to 65 Veterans) 

– Increases as screening interval extends to 4, 5 years 

present, but not significant (from 65 to 67 Veterans) 

– Reduction of 76.5 appointments per year between annual 

and biannual screening groups 
Discussion

• Generally desirable to adopt policies which provide 

equivalent or improved care at lower cost 

– Provide additional care at same cost 

• Simulation allows us to identify candidates for such 

policies 

– We can test many changes and implement the most 

promising 

• Can help reduce waste in health delivery by 

identifying unnecessary outlays 

– Increased access through reduction of redundant screens 
Discussion

• Simulation structure permits bidirectional 

analysis 

– Changes to clinical policy measured in populationlevel outcomes 

• Management of chronic disease 

– Changes to population demographics/predictors 

measured at level of demand-for-services, queue 

lengths. 

• Management of healthcare facilities 

– Integrates populations and institutions in a 

comprehensive model
Discussion 

• Simulation allows us to conduct risky, 

ambitious, or unethical experiments 

– Including BDR in experimental group goes beyond 

what we see in real-world trials 

– There is no risk of harm to real patients if there 

are disastrous consequences 

– Must take great care in translating risky results to 

real world trials 

– Can function as hypothesis generation for health 

services research 
Discussion 

• Results complement other real-world and 

simulated studies 

– Chalk D, Pitt M, Vaidya B, Stein K. (2012) Can the Retinal Screening Interval Be 

Safely Increased to 2 Years for Type 2 Diabetic Patients Without Retinopathy? 

Diabetes Care 35:1663–1668

– Thomas RL, Dunstan F, Luzio SD, Roy Chowdury S, Hale SL, North RV, Gibbins 

RL, Owens DR. (2012) Incidence of diabetic retinopathy in people with type 2 

diabetes mellitus attending the Diabetic Retinopathy Screening Service for 

Wales: retrospective analysis. BMJ 22;344:e874 
Limitations 

• Cross-sectional patient data 

– Follow up work will require improved 

epidemiology 

• Does not predict disease progression in any 

individual 

– Only aggregate information should be considered 

informative 

• Simulations do not replace medical judgment 

– Provide useful supplemental evidence to 

incorporate into human studies 
Conclusions 

• Simulation can inform policy and practice 

– Using sophisticated methods to model proposed 

policy interventions and test them in silico 

– Supplying evidence that practice changes are safe 

and effective for the long-term 

• Simulation can improve health services 

delivery 

– Determine optimal screening schedule, allowing 

additional appts to be made for alternative care 
Conclusions 

• Simulation can improve population health 

– Identifying broad risk-categories for appropriate 

screening intervals 

– Optimizing delivery of care to individuals most in 

need of services 

– Target interventions likeliest to have large effectsizes 

– Providing data-driven analysis to encourage 

adoption 
Conclusions 

• Simulation may be able to inform practice 

regarding the screening interval for DR 

– Results concur with the accepted evidence 

– Inclusion of BDR in experimental group means 

that care must be taken in adopting results 

– Would require careful retrospective study of 

administrative data to support/confirm results 

– Any prospective trials must be carefully planned 

and conducted by physicians, etc. 
Poll Question 

• Who is interested in deploying simulation 

modeling? 

– This has definite potential for my institution. 

– I would be interested in exploring options in the 

future. 

– While interesting, I’m not sure simulation meets 

my institution’s needs.

– I definitely am not interested in simulation 

modeling. 

– We already have a bunch of creepy mannequins. 
Further Reading 

• Auchincloss AH, Diez Roux AV. (2008), A New Tool for Epidemiology: The Usefulness of 

Dynamic-Agent Models in Understanding Place Effects on Health. Am J Epi 168:1-8 

• Brailsford S, Schmidt B. (2003) Towards incorporating human behaviour in models of health 

care systems: An approach using discrete event simulation. Euro J Oper Res 150(1):19-31

• Chen J, Alemao E, Yin D, Cook J. (2008) Development of a diabetes treatment simulation 

model: with application to assessing alternative treatment intensification strategies on 

survival and diabetes-related complications. Diabetes Obes Metab. 10(Suppl1):33-42.

• Day TE, Ravi N, Xian H, Brugh A (2013) An Agent-Based Template for Diabetic Retinopathy in a 

Population of Veterans. PLoS ONE, 8(6): e66812. doi:10.1371/journal.pone.0066812 

• Eddy DM, Hollingworth W, Caro JJ, et al, Model Transparency and Validation: A Report of the 

ISPOR-SMDM Modeling Good Research Practices Task Force–7. Med Decis Making 2012; 

32(5):733-743

• Eldabi T, Irani Z, Paul RJ, A proposed approach for modelling health-care systems for 

understanding. J Manag Med 2002; 16(2/3):170-187

• Günal MM, Pidd M, Discrete event simulation for performance modelling in health care: a 

review of the literature J Simulation 2010 4:42–51. 

• Karnon J, Stahl J, Brennan A, et al, Modeling using discrete event simulation: a report of the 

ISPOR-SMDM Modeling Good Research Practices Task Force-4. Med Decis Making 2012; 

32(5):701-11. 
Acknowledgments 

• I wish to thank: 

– My co-authors Nathan Ravi, MD, PhD; Hong Xian, 

PhD; Ann Brugh, PsyD 

– Co-workers Jeffery Scherrer, PhD; Angelique 

Zeringue, MS, PhD(c); Lauren Garfield, PhD 

– VA ORD HSR&D 

– VHA Innovations 
Questions 

& Comments 

Image credit http://www.freedigitalphotos.net/images/Ideas_and_Decision_M_g409-Trouble_Shooting_p24080.html Retrieved 5 Nov, 2013