How hospitals are using control centers to predict patient emergencies

Cleveland Clinic is one of several hospitals in the U.S. that has developed a Central Monitoring Unit designed to alert front line staff when a patient's vital signs indicate a life-threatening condition, reports STAT.

A team of technicians in an office several miles from the hospital monitor patients' vital signs. In one success story, a technician was able to alert a nurse that a patient was developing ventricular tachycardia. The 57-year-old patient never went into cardiac arrest because of the CMU.

Cleveland Clinic is now looking to use artificial intelligence to predict serious cardiac events an hour or more before they even happen. This is currently heavily reliant on technicians to identify signals from massive amounts of data on hundreds of patients.

Baltimore-based Johns Hopkins Hospital is also exploring machine learning and AI. While the hospital is currently focused on managing hospital capacity to ensure timely transfers, Johns Hopkins is exploring the use of machine learning to predict changes in patient conditions, according to STAT.

Yale New Haven (Conn.) Hospital created a command center to mange capacity. The hospital aims to launch a program that will monitor the use of Foley catheters in order to prevent infections and other complications. Clinical leaders at the hospital are also contemplating how machine learning can be leveraged to identify predictors for patient deterioration.

To read the full report, click here.

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