With the aid of sophisticated machine learning, researchers at UPMC and the University of Pittsburgh School of Medicine demonstrated that a tool they developed can rapidly predict mortality for patients facing transfer between hospitals in order to access higher-acuity care. This research, published today in PLOS One, could help physicians, patients and their families avoid unnecessary hospital transfers and low-value treatments, while better focusing on the goals of care expressed by patients.
Each year, nearly 1.6 million patients—or as much as 3.5% of all inpatient admissions—are transferred from one hospital to another to access specialized care for complex conditions. “However, securing these services often requires burdensome travel and reduced community support for patients and their families. While some may recover from the acute illness, many others realize little benefit in terms of improved outcomes,” said Daniel E. Hall, M.D., corresponding author of the study, as well as medical director of high-risk populations and outcomes at the UPMC Wolff Center and associate professor of surgery at Pitt’s School of Medicine.
To address this gap in the coordination of patient care, which currently relies on the ad hoc judgement of bedside clinicians, Hall and his team developed a real-time tool that can predict mortality outcomes for patients at the time of hospital transfer and deliver these results to physicians in less than five minutes.
Using data from nearly 21,000 patients aged 18 and older who were transferred to a UPMC hospital during a 12-month period, the researchers developed and validated a mortality risk-assessment tool dubbed “SafeNET” (Safe Nonelective Emergent Transfers). After studying other mortality risk models currently used in hospital intensive care unit and admission settings, Hall and his team constructed a list of 70 independent variables used in one or more of these models, including patient