Addressing Emergency Department Overcrowding Through Simulation and Optimization

Vamshidher Gankidi

Vamshidher Gankidi , MBBS, MPH

Healthcare Data Analyst

Published July 14, 2015

“Necessity is the mother of invention.”
- An English proverb

This journey from problem to solution often involves testing several ideas.
  • What if the risk of experimentation is high?
  • How can one test ideas in a low-risk environment without changing the existing process?
Emergency Department (ED) overcrowding, through its multiple influences, is associated with delays in diagnosis and treatment, decreased quality of care, compromised patient outcomes, and higher cost for patients and providers. The complex interconnections between processes can result in a series of cascading events such as the ones mentioned below. Often, this downstream impact is non-linear.

Discrete event simulation (DES) is a technology which enables risk free exploration of ideas in complex environments. It is a versatile method which is characterized by an abstract representation of complex behaviors and interactions between individuals and their environments (models) in a form a machine can understand. The number crunching prowess of computers is then used to run the DES (simulate) and explore the solution space. The term discrete in DES refers to steps in time when DES moves from one event to another. Each event requires resources (such as availability of a doctor or nurse) and takes a certain time (for example triage may take from 5 to 10 minutes).

DES has been used in a wide variety of industries such as chip design, flight safety, nuclear engineering, etc. In recent years it is finding increasing applications in health care such as health policy, in-silico experiments of biological processes, drug discovery, as well as in emergency departments.

ED Overcrowding: A Dynamic Problem

The changing landscape of health care policy adds to the complexity and uncertainty that a given solution will work. ED visits have continued to rise since the Affordable Care Act (ACA) was implemented. The estimated number of uninsured is going down from about 47 million uninsured Americans at the time it was implemented to about 35 million as of 2015. With the introduction of Value-Based Purchasing program (VBP) in traditional Medicare, 1% of Medicare payments to hospitals were withheld and then earned back in the form of incentive payments based on performance against 20 quality measures in 2012. For the year 2015, there are now 26 quality measures.

In 2012, Medicare redistributed roughly $1 Billion in bonuses and penalties among nearly 3,000 hospitals to promote the quality of care provided to patients. The percent withheld is to gradually increase by 0.25% per year until the reduction reaches a maximum of 2% in 2017. Additional quality based incentive can put up to 7% of Medicare revenue at risk by 2017 subject to the achievement of quality goals - influenced by ED performance (Chart 1).

Many of the quality metrics being tracked by VBP are determined by both patient experience and patient outcomes. Since overcrowded EDs and resulting delays in care administration have profound effects upon the patient experience and patient outcomes, addressing the overcrowded ED represents a real opportunity to capture VBP incentive payments.

Given these moving targets, how frequent should one then put on the thinking hat and come up with a strategy? What is the optimal level of reactivity? Virtual simulation models provide a potentially scalable solution for this problem. Once the model is built, it can be adapted and reused to monitor real-time performance in the context of an institution’s devised operational and strategic growth plans. By investing in simulation, a hospital can both sustain a given strategy and recognize additional opportunities for refinement and growth.

The Workflow of the Simulation and Optimization Process

The simulation and optimization process is very similar to how we learn. We constantly process stimuli from our surroundings. We build models in our minds to interpret, predict and optimally react to these environmental stimuli influenced by our goals and incentives. As opposed to something which can be felt and experienced, a virtual DES simulation happens within a computer. To bridge the communication gap between man and machine, an experienced modeler is often required to build a customized model which captures the goals of its users, provides outputs which users are comfortable interpreting and accommodates the versatility required to experiment possible future scenarios.

Information on the baseline behavior of the ED is obtained from several sources such as staff interviews, Electronic Health Records as well as the ever increasing data sources such as wearables, machine logs, etc. Observational studies such as time-motion studies may be required. Behavior is often captured in the form of stochastic variation in
  • The sequence of activities and
  • Time spent in each of these activities.
This collective intelligence is then used to build a model. Validation involves fine-tuning which can take several iterations. The objective is to test if a stimulus (such as increasing caseloads) elicits responses (such as increase in the length of stay) which match the baseline i.e. the caseloads and median length of stay observed in the ED currently. With the baseline validated, the model is then ready to play several parallel scenarios. Optimization techniques can then be applied to select the best scenarios which achieve the organizational goals.

An Informed Future within a Complex System

Decisions made within an interdependent organization simultaneously influence buildings, equipment, people, and processes. There are many interconnected parts. This reality makes it extremely difficult for individuals to recognize how one change can and will permeate across a given system.

Health care is a dynamic industry under intense pressure to reform its practices. Health care is being incentivized to both decrease cost and increase quality. Simulation and optimization technologies empower leadership and stakeholders to experiment with ideas and decisions to meet organizational goals within a virtual system - prior to making real commitments and having to endure real consequences. Simulation supports stakeholders by scoring all proposed decisions according to each decision’s comprehensive reality. By utilizing simulation, a project team can optimize the use of existing resources and reap significant efficiency gains in the short term.

Whether it is the harnessing of nuclear reactions in a safe way or in keeping up with Moore’s law in processor speeds, simulation and optimization technology has accelerated the industries embracing it. Emergency departments with their high pressure environment can greatly benefit by adopting this technology to inform their decisions.

Additional reading: Aditazz White Paper: “A key to Addressing Emergency Department Overcrowding”

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