This is where the researchers’ new predictive tool can help. Electronic medical records are being used in hospitals now more than ever, which means that researchers have access to a patient’s data in real time. The researchers used this data to create an algorithm based on six years’ worth of patient data from the Beth Israel Deaconess Medical Center in Boston, comparing information from more than 11,000 non-septic patients, and about 1,800 septic patients. They used 27 types of data often aggregated in the electronic medical records, such as respiratory rate, blood pressure, white blood cell count, and body temperature. The algorithm estimates a patient’s risk of going into septic shock by giving the patient a number called a TREWScore.