Computational Modeling of Emergency Medical Services Aaron Bair, MD Emergency Medicine UC Davis Medical Center.

Computational Modeling of Emergency Medical

Services

Aaron Bair, MDEmergency Medicine

UC Davis Medical Center

Overview

• Background

• Current status

• The future

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)