University of Maryland Medical Center issued the following announcement on June 5.
Machine Learning Score Predicts Hospital Returns Better than Standard Methods
A University of Maryland School of Medicine study suggests that a novel machine learning model developed at the University of Maryland Medical System (UMMS), called the Baltimore score (B score), may help hospitals better predict which discharged patients are likely to be readmitted.
The research was led by Daniel Morgan, MD, MS, Associate Professor of Epidemiology and Public Health at the University of Maryland School of Medicine (UMSOM). Dr. Morgan analyzed data on more than 14,000 patients from three UMMS hospitals using this special predictive score to determine the likelihood these patients would be readmitted.
The research, published in the journal JAMA Network Open, could help set the stage toward improving patient care and avoiding returns to the hospital.
“A significant proportion of readmissions may be preventable with better planning and follow-up for how the patient would transition back into the community,” said Dr. Morgan.
Readmissions occur for almost 20 percent of patients hospitalized in the United States and are associated with patient harm and expenses. Furthermore, rates of unplanned readmission within 30 days after discharge are used to benchmark a hospital’s performance and quality of patient care. Nevertheless, studies have shown that clinicians are poorly equipped to identify patients who will be readmitted, and many readmissions are thought to be preventable.
“If hospitals can better target time and money in planning for discharge to home, then patients may not have to come back to the hospital, with the harm sometimes associated with hospitals, including risks for infection, falls, delirium and other adverse events,” said Dr. Morgan.
Original source can be found here.
Source: University of Maryland Medical Center