"We're working in two week sprints, where the clinicians adjust their pathways, and we adjust the algorithms to their needs," Draugelis notes.
The team builds a prototype of a new pathway for a particular condition about once every six months. Currently, it is focusing on finding a better way to predict which patients have congestive heart failure and which are likely to be readmitted after discharge from the hospital. In addition, the team is working on acute conditions such as maternal deterioration after delivery and severe sepsis.
"We're creating machine learning predictive models based on thousands of variables," Draugelis says. "We look at them in real time, but we train them up over millions of patient records."
In the case of sepsis, the team started with an expert model known as SIRS (systematic inflammatory response syndrome), which uses specific thresholds of temperature, heart rate, respiratory rate, and white blood count as key indicators of sepsis risk. After loading in all of the available data on a patient, including electronic health record (EHR) data, the computer uses the algorithm to determine how closely a patient's characteristics match those of patients who developed sepsis in the past. When a patient matches that profile, the clinician caring for the patient receives an alert, acts on it or doesn't, and feeds his or her reaction back to the algorithm to improve it.
Penn Medicine's bedside monitors continuously track vital signs and document them in the EHR. This automated documentation of vital signs didn't occur five years ago, Hanson notes. It is still not widespread outside of intensive care units, says Steinhubl, but when it does become routine, he adds, it will provide a major boost to the kind of work that Draugelis' team does.
Dean Sittig, Ph.D., a professor at the University of Texas Health School of Biomedical Informatics.
Dean Sittig, Ph.D., a professor at the University of Texas Health School of Biomedical Informatics in Houston, likes the idea of continuous monitoring and feeding data into computer algorithms. In contrast to the average floor nurse, who can only watch a patient 20 percent of the time if she has five patients, "The computer can be looking at every minute, and the idea of continuous monitoring and surveillance is very powerful," he says. "If you can teach the computer what the nurse would be looking for, the computer can be much more vigilant [than the nurse]."
To make the decision support alerts useful, however, the staff has to be ready to spring into action, especially with a condition like sepsis, Sittig says. In addition, the alerts that the algorithm triggers must be fairly accurate. "As a rule of thumb, if the computer is right more than half the time – especially with something serious like sepsis – clinicians will pay attention to it. But if it's only right 10 percent of the time, it starts to be a bother."
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