Real-Time Healthcare Analytics: Monitor, Predict, Nudge, Act

By Jennifer Knapp, Vocera

It was a Monday night, the busiest time in the ED. The ED charge nurse received an alert on her Vocera smartphone app.

The alert was from Qventus, which predicts operational bottlenecks and recommends course corrections.

The alert was telling the charge nurse that the ED was going to have a surge of patients in two hours. Qventus predicted this with machine learning based on the current census in the ED, historical admit and discharge times, the practice patterns of the clinicians currently working in the ED, and the current queue and expected turnaround times for diagnostic tests.

The alert was delivered through the Vocera Platform, which knew who the on-duty charge nurse was, what device to contact her on, and whom to escalate the alert to if she was not available for some reason.

The course-correction alert advised the charge nurse to work with the transport, lab, and radiology teams to expedite the flow of patients through the ED and ensure beds would be available for the anticipated surge.