While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help cut costly and unnecessary hospitalizations while improving outcomes for patients, according to an article in the Harvard Business Review. In an ongoing effort with Boston-area hospitals, including the Boston Medical Center and Brigham and Women's Hospital, Dr. Yannis Paschalidis and his colleagues at Boston University's Center for Information and Systems Engineering found that they could use machine-learning algorithms to predict hospitalizations due to heart disease or diabetes approximately one year in advance with an accuracy rate of up to 82%. The team is also working with the Department of Surgery at the Boston Medical Center and can predict readmissions within 30 days of general surgery. The hospitals provide Paschalidis and his colleagues with patients' anonymous electronic health records, which include information on demographics, diagnoses, admissions, procedures, vital signs at doctor visits, prescribed medications, and laboratory results. The investigators then use their algorithms to predict who might have to be hospitalized.