NeuralTools^® and StatTools^® Predict Patient Load in Richmond Hospitals

Healthcare industry consultant Barbara Tawney had a tough task ahead of her. She needed to forecast patient loads for the entire metropolitan hospital system of Richmond, Virginia. Every hospital has a finite number of beds and therefore, a maximum capacity. But unpredictable patient demand throughout the system had resulted in two occasions when all nine hospitals in the system had reached capacity and patients had to be diverted to healthcare facilities outside the area. To figure out how to anticipate and prepare for surges in patient load, Tawney turned to Palisade’s StatTools and NeuralTools data analysis products.

StatTools Provides Enough Power for an Expert
Tawney maintains an active consulting practice and is a Ph.D. candidate in the Systems and Information Engineering Department of the University of Virginia’s School of Engineering and Applied Science. She specializes in data analysis––particularly large data sets––and could have easily written her own software for the patient load project. Instead, she began her work with Palisade’s StatTools to tackle the mountain of data she was facing.

With the cooperation of Virginia Health Information (VHI), a non-profit organization that collects and warehouses all the healthcare data statewide, she was granted limited access to metropolitan Richmond patient data for the four years from 2000 to 2003. Time series data were derived from hospital billing information for about 600,000 patients being treated at area hospitals during 2000-2003. The patient level data (PLD) were detailed in 78 different fields, including dates of admission and discharge, diagnosis, and length of stay.

According to Tawney, “I was looking for a user-friendly way to do autocorrelation, and a colleague recommended StatTools to me.” She created time series for the data by “binning” the PLD according to the dates and times of activity for each case. The time series data were analyzed for daily, weekly and event trends. As Tawney observes, “StatTools does the time series autocorrelation in a user-friendly way that is quick and easy. You know you got it right the first time. I have box plots and other statistics that I did with it, and they were easy to refine for publication. StatTools did what I needed without the time and expense of a heavy-duty stats package.”

NeuralTools Uncovers Key Trends
After she analyzed the historical data with StatTools, it was time for Tawney to predict future patient loads using NeuralTools. She began by “training” NeuralTools on the existing data. Additional daily, weekly, and event trends, along with unusual days, stood out during the NeuralTools analyses. For instance, Tawney determined that patient load peaked at mid-week during most weeks of the year. Holiday periods also have a different, distinctive pattern. The number of patients entering the hospitals just before and during the Thanksgiving holiday was lower than normal but was followed on Monday by an influx of patients that stretched the facility’s resources. Similarly, patient load dropped markedly throughout the double holiday of Christmas and New Year’s. But each year there was a significant surge in demand on the Monday-Tuesday of the first full week of the New Year.

NeuralTools Yields Long-Lasting Benefits
For hospital planners and administrators, Tawney’s findings provide the basis for predicting patient load throughout the Richmond metropolitan hospital system. These predictions range from a few days to several months. Being able to predict the patient demand allows for more efficient allocation of system resources, including scheduling of services. According to Tawney, the project also led to another important discovery: NeuralTools is so accessible that it can stay on the job long after she has left. “Most folks in the medical community are not engineers,” she says, “but they can use NeuralTools to facilitate their own forecasts of future admissions, current patient demands, and the need for timely discharges using existing patient billing data. To bring this kind of forecasting to non-engineering managers is just awesome!”

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