New computer program helps health departments halt outbreaks

Imagine that a terrorist has just released the smallpox virus in Atlanta, and suddenly there’s a race against time to vaccinate and treat every last man, woman and child in metro Atlanta before the deadly virus can spread.

In a bioterror scenario such as this, the speed at which emergency health care facilities treat patients can mean the difference between life and death for thousands (or even millions) of people. And the logistics of such a large-scale emergency plan are dizzyingly complex.

But Eva Lee, a professor of industrial and systems engineering at the Georgia Institute of Technology, has created a computer program that is up to the task.

Based on a software model created by the Centers for Disease Control and Prevention (CDC), Lee developed the program, called RealOpt©, to help U.S. state, city and county healthcare departments organize the most efficient plan for treating infectious illness, whether it’s a natural or man-made outbreak.

While government health departments have emergency plans in place, it is difficult to test a plan’s efficiency against the urgency and sheer number of patients an outbreak would create. And when a severe outbreak of influenza starts to spread through the population, treatment facilities are faced with a number of problems as they attempt to treat or vaccinate many thousands of patients in just a few days.

How many doctors will be needed? How many nurses? How long will it take frightened or unprepared patients to fill out paperwork? How will infected patients be separated from healthy patients?

The CDC, recognizing that local public health departments needed guidance on what human resources would be required to treat the affected population, created a model that could assist in this effort. Then Lee, who is also an associate professor at the Winship Cancer Institute at Emory University, and her Georgia Tech team used the CDC model as a guide to build a new, more powerful program.

RealOpt can be used to prepare for a possible outbreak, as well as for emergency re-assignment of health care workers within the clinic and between clinics during an actual outbreak. By determining their preparedness, health departments will have a thorough estimate of what resources and funds they will need to treat their communities before an actual outbreak occurs.

The program takes the numerous variables associated with an emergency health care facility’s treatment of a very large group of people, and through simulation and optimization, pinpoints the most efficient way to move patients through the facility. Using the program, a health care department can determine the most efficient facility layout, the number of health care professionals needed in certain areas, the number of vaccinations needed and the time it will take to treat patients.

In addition to its role in planning, one of RealOpt’s significant advantages is its ability to process data in real time as the emergency treatment occurs. As patient flows fluctuate, the program can reallocate the facility’s resources in a fraction of a second, sending more doctors or nurses to one station or more attendants to the paperwork processing area.

The program will be tested by health agencies in several states and was recently installed in Georgia. Installation is also scheduled for North Carolina. While the program is still in the testing phase, it will soon be available free to any government health department that requests it from Georgia Tech.

The next phase of the project, which is already underway, will expand the scope of the program to include an even more complex problem — how to quickly and efficiently get thousands or millions of patients to treatment facilities. The program will puzzle out the best locations to set up emergency treatment facilities based on roads and population density. These facilities can include anything from a school gymnasium to a football stadium.

This phase of the program is expected to be ready for testing in three to six months, Lee said, and a future phase will include simulations of the spread of infectious disease through the population and within treatment clinics.