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Computational Modeling of Emergency Medical
Services
Aaron Bair, MDEmergency Medicine
UC Davis Medical Center
Background
• Multiple contributing factors make this necessary and possible– Crisis-level overcrowding problems have led to
increased interest in studying and promoting ED efficiency
– Bioterror and disaster preparedness (surge)– Computer simulation has been used successfully
in other industries for decades (manufacturing)– Hardware and software advances
What is a “model”?
• Epidemiological, Statistical and CS definitions – Overlapping considerations
• Discrete Event Simulation– Ability to model multiple discontinuous
events with probabilistic input
Limitations
• GIGO applies!– Limited by the accuracy of input data– Limited by understanding of complex
processes– Limited by interpretation of complex output
EDSIM 2.12©
• 12,500+ hierarchical computational modules• Representative model of UCDMC ED• Stochastic inputs for laboratory turn around times• 3,000 representative patients drawn from UCDMC ED cohort • Patient path step approach• Full activity pre emption
The Team
• Aaron Bair, MD – Emergency Medicine• Lloyd Connelly, MD, PhD – Model engineer• Beth Morris, MPH – Project Manager, Data Manager• Alex Tsodikov, PhD Statistician• Lauri Dobbs, PhD – Engineer, LLNL• Michael Johnson, PhD – Engineer, Sandia• Nathaniel Hupert, MD, MPH – Modeling and
Outcomes research, Cornell• Nathan Kuppermann, MD, MPH – Research Mentor
EDSIM recent applications
• Triage strategy analysis– Standard v. Acuity Ratio Triage1
• Nursing shortage: RN allocation strategy analysis– Partial v. Complete area closure
• Quality of care– Implications of crowding: Resource saturation
impact on cardiac chest pain patients2
1. Connelly LG, Bair AE. Discrete Event Simulation of Emergency Department Activity: A Platform for System Level Operations Research. Acad Emerg Med. 2004; 11: 1177-1185.
2. Connelly LG, Bair AE. Computer Simulation and Observational Study of the Cardiac Chest Pain Patient in a Variably Overcrowded ED. Acad Emerg Med. In Press.
Advantages of modeling
• Detailed model can be used for more mundane work flow efficiency projects
• Representative model can be used as “pretrial” for extraordinary what-if scenarios– Scenarios that will probably never be
prospectively studied
Next steps
EDSIM
Cornell GeneralHospital Model
Cornell GeneralHospital Model
Validate and merge
Goal: A generalized hospital model to study both routine work flow and crisis optimization (disaster response)
The BioNet model
Combined HospitalSimulator
Modification sizeand resources
The program seeks to improve the ability of a major urban area in the United States to manage the consequences of a biological attack on its population and critical infrastructure by integrating and enhancing currently disparate military and civilian detection and characterization capabilities.
A vision of the future
• Expand collaborative relationships to create a model that can be used to analyze and optimize patient flow under variable circumstances– UCDMC: Emergency Services model (EDSIM)– Cornell University: Hospital based services model (AHRQ
project)– Oregon Health Sciences: Center for Policy Research in EM– Sandia National Laboratories: BioNet project and regional
model (http://bionet.calit2.net/project.php) (NDA in place)– Lawrence Livermore National Laboratories: model validation
(HS grant funded)– Look Ahead Decisions Inc: Optimization project (NLM grant
decision pending)– NCEMI – Project Sentinel: azyxxi (Washingon D.C.)
More thoughts on the future
• Optimization research• Dual supervised PhD grad student
-Funding source for training:DHS/Sandia?HRSA?
• UC Davis EM researcher role?– Non-clinical funding
• Grants?– Expansion from prior training grants?
• Institutional support?
Conclusions
Model uses: Preparation, Policy and Administration• Computer modeling of complex and variable systems
is increasingly possible • Modeling can lead to better understanding of flow
(bottleneck identification) and resource optimization strategies
• Particularly valuable for rare scenario analysis and preparedness (disaster response)