Increasingly intelligent medical devices and machinery have improved healthcare efficiency for millions of Americans. At the same time, wait times have risen, due to a larger patient base and an insufficient number of medical practitioners on hand. In fact, according to the latest data from the U.S. Centers for Disease Control and Prevention, the median wait time to see an emergency room physician is 55 minutes, an increase from 45 minutes back in 2000.
In short, it isn't a matter of if you'll need to wait to see the doctor, but for how long, as other factors can further delay the process. However, new research out of Columbia Business School believes predictive analytics may go a long way in quickening things up.
Predictive analytics can serve as a forecasting tool
As its name implies, predictive analytics is a method for using big data technology to make predictions about the future. In the case of the healthcare industry, the forecasts relate to the flow of ER demand.
Predictive analytics is not a new phenomenon, but the approach that these researchers are recommending is. Co-written by Carri Chan and Kuang Xu of Stanford University, the study's lead investigators say that using a revised algorithm will enable medical offices to get a better idea of how long patients will have to wait so that they can be more adequately informed and time management can be improved.
Chan, the study's co-author and an associate professor of business at Columbia Business School, indicated that managing expectations is a key component to customer sentiment.
"Patients on their way to the emergency room want to know that their emergency is going to be handled as expeditiously as possible," Chan explained. "By using predictive modeling to develop more effective diversion policies, hospitals can reduce wait times for patients by up to 15 percent, improving care and customer satisfaction while at the same time saving time and money."
Presently, patients are redirected to other care units when demand is heavy. The problem with this strategy is that it only redirects the dilemma and puts the onus on other medical facilities. The researchers argue that by using a revised predictive analytics model, hospitals can forecast when increased patient volume will occur and make the appropriate adjustments to staff and availability of equipment.
"Using predictive analytics is a step towards eliminating the over-crowding and long wait times that plague may of today's emergency rooms, ensuring patients receive the care they need when they need it," Chan said.
Titled "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion," the full details of the study are available for download at Columbia University's website.
Predictive analytics is a system that many healthcare professionals have heard about but few have actually put to use. In instances where it has been implemented, studies suggest it hasn't been used effectively, perhaps due to its relative newness. In a separate poll done by data warehousing analytics firm Health Catalyst, 8 in 10 hospital executives believe the future of healthcare management is predictive analytics. However, less than 33 percent of hospitals have used this strategy for more than 12 months.
The Affordable Care Act has proven to be a double-edged sword for healthcare providers. Has ACA fueled rise in patient volume?
Some believe that the Affordable Care Act has exacerbated overcrowding in medical facilities. Recent polling suggests as much. After the ACA went into effect in Illinois, ERs in Chicago saw a nearly 6 percent increase in patient volume according to analysis conducted by the American College of Emergency Physicians. Similar trends have developed in Massachusetts, paired with a decrease in specialist staffing.
Jason Sanders, M.D., Ph.D, who heads the Department of Emergency Medicine at Massachusetts General in Boston, said that hospitals have had to make due with what they have, forcing some patients to receive treatment in hallways.
"The proportion of emergency departments reporting any patients primarily cared for in the hallway [has] climbed from 70 percent to 89 percent," Sanders noted. "That is obviously far from ideal and is indicative of an increasingly taxed emergency medical care system."
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