How Predictive Modeling Cuts Hospital Readmissions
This article appears in the April 2012 issue of HealthLeaders magazine.
With the looming threat of reimbursement losses for preventable 30-day readmissions, healthcare organizations nationwide are analyzing care transitions in an effort to achieve better outcomes and keep patients from returning to their facilities unnecessarily. While transition programs show promise in helping hospitals reduce their readmission rates, predictive models are also being used successfully in tandem with these programs. Three early adopters of these models are achieving positive results thanks to tactics and technology that identify at-risk patients from the outset of care and influence treatment approaches and the level of transitional care needed.
Parkland Health & Hospital System, Dallas: Data algorithm and readmission rates
Since December 2009, Parkland Health & Hospital System in Dallas has been using what it calls the e-Model, one of the first electronic readmission predictive models of its kind. The organization's center for clinical innovation began development on the electronic predictive model in 2007 with an eye toward making real-time identifications of heart failure patients at high risk for hospital readmission or death. Since then, it has expanded the program for all-cause readmissions.
"We are automating the patient care transaction. It's akin to what finance institutions did in the 1960s and '70s. The challenge is: How can we turn this into data that can help clinicians make real-time decisions that affect outcomes? We're taking information from various sources, and based on it we can say with high probability that [the clinicians] may want to suggest a different course of treatment," says John Dragovits, executive vice president and CFO at the $1.1 billion-net-revenue Parkland Health & Hospital System.
"I think people mistakenly believe that getting an EMR is the end of the process, but it's just the beginning. [Predictive modeling] is showing us how to use this information as a stepping stone … to provide better information to caregivers," he says.
The Center for Clinical Innovations at Parkland received grants from several sources, including the National Cancer Institute and the National Institutes of Health to support its work on the model, which combines 29 data points extracted from its EMR. The data includes physiologic, laboratory, demographic, and utilization variables that can be pulled from a patient's EMR within 24 hours of hospital admission. The comprehensive algorithm has proven to be accurate at predicting readmission or death, says Ruben Amarasingham, MD, director of Parkland's center for clinical innovation and assistant professor of medicine at UT Southwestern. Preliminary results show 33% reductions in readmission of Medicare heart failure patients and 20% reduction in readmissions for all HF patients.
Parkland's predictive model compiles a daily report of all admitted patients and essentially profiles patients and places them into risk categories. Clinicians and case managers are then notified which patients are at highest risk for complications, so those patients can be treated accordingly from the early stages of care, explains Amarasingham.