CLEVELAND — Seven floors, and long odds, were stacked against John S. He was undergoing a test on the first floor of a Cleveland Clinic hospital when his nursing team — on the eighth floor — got a call, telling them the 57-year-old had developed a dangerously rapid heartbeat that was spiraling toward cardiac arrest.
It is a predicament that often ends badly. Only about 25 percent of U.S. patients survive when their hearts stop in hospitals. Crucial minutes elapse before help arrives, sometimes because alarms are missed amid the din of beeping monitors.
But the call that day didn’t come from within the hospital. It came from a darkened room in an office park several miles away, where a technician in the clinic’s Central Monitoring Unit (CMU) was watching the patient’s vital signs on a computer monitor and noticed the onset of ventricular tachycardia.
The subsequent emergency response pulled John (not his real name) back from the brink, but the CMU’s success in this case was just the first step toward a more ambitious goal to use artificial intelligence to predict such events ahead of time, so that patients like John get effective treatment before a crisis arises in the first place.
“This is where we think machine learning has the great opportunity to help us,” said Dr. Daniel Cantillon, a cardiologist who serves as medical director of the CMU. “The challenge is we have to be able to call our shots in advance, and that’s something we’re deeply invested in.”
Hospital command centers have proliferated across the country in recent years, with medical centers from Oregon to Florida deploying them to tackle a range of data-monitoring tasks, such as maximizing bed capacity, calibrating staffing levels, and detecting the onset of sepsis, a life-threatening response to infection that is a common killer in hospitals.
Recent advances in artificial intelligence promise to help hospitals identify new warning signs of patient deterioration and intervene earlier in the process. Administrators of command centers at Johns Hopkins and Yale New Haven Hospital both said they are exploring the use of machine learning to deliver more timely care.
The Cleveland Clinic’s ultimate goal is to give front-line clinicians notice of serious cardiac events an hour or more before they happen. That would be a significant leap forward from the system’s current capabilities. Right now the CMU can offer some advance notice of cardiac emergencies, but it is heavily reliant on technicians to pluck out the signals from massive data streams on hundreds of patients and quickly route them to caregivers.
Nemours Children’s Hospital, which operates facilities in Delaware and Florida, is a pioneer in the command-center model. The health system created a logistics center more than a decade ago to track vital signs of non-ICU pediatric patients across its system. Paramedics who work in the center monitor data feeds and details of patient electronic health records to ensure timely follow up.
“If there is an abnormal laboratory result, the logistics center makes sure someone has looked at it,” said Dr. Stephen Lawless, the health system’s chief clinical officer. He added that Nemours is beginning to apply artificial intelligence to better predict nursing demand on floors throughout its facilities.
Lawless has been tinkering with the logistics center for more than 15 years. The theory behind it is derived from the military, where tactical command centers removed from the chaos of battle are used to direct soldiers on the front lines and coordinate the use of air power and other resources.
The need for such facilities in health care has increased in recent decades, as an array of monitoring devices produces tens of thousands of alarms on a daily basis. The vast majority of alarms do not require a clinical response, which makes it harder for nurses and doctors to pick out the rare ones that do.
“We had reports of alarms going off for an hour,” Lawless said. “Most times it’s not a harm. But when you realize that it takes that long to answer, you begin to think, ‘Oh my God, how would they know if this is real or not?’”
It is an ideal task for artificial intelligence systems whose power of pattern recognition can surface crucial information that otherwise would get lost in the noise. At the Cleveland Clinic, a customized algorithm developed by clinicians crunches an array of data, such as blood pressure, heart rate, and oxygen saturation levels, to flag the patients that are at highest risk of deterioration.
On a recent afternoon, those patients were marked in red at the top of the computer monitors inside the CMU, a dimly-lit conference room in a medical building in Beachwood, a Cleveland suburb. Teams of three — usually made up of nurses and emergency medical technicians — clustered in front of computers arranged in parallel rows.
A radio played Billy Joel in the background while the workers clicked in and out of patient reports and electronic records detailing their medical histories and lab results. Each time they noticed a problematic heart rhythm, or blood pressure change, they picked up the phone. In April alone, CMU workers logged 77,000 calls to nursing units across the health system, according to Alicia Burkle, the unit’s program manager.
Most calls are routine, providing a quick nudge to a charge nurse to check on a particular patient. But occasionally the technicians will notice a situation like John’s — an urgent problem that requires activation of the emergency response team.
Kris Rhode, a registered nurse, was on the other end of the line when the CMU called that day a few years ago to notify her of John’s decline. “We looked (at his telemetry data) and said, ‘Oh my gosh, this patient is in a lethal arrhythmia,” Rhode said, adding that John was off her floor having his heart imaged. “We figured out where he was and got down there within two minutes.”
A 2016 paper published in the Journal of the American Medical Association sought to quantify the performance of the CMU in notifying front-line caregivers of impending emergencies. It found that the unit accurately alerted clinicians of 79 percent (772 of 979) of heart rate or rhythm changes that eventually led to the activation of an emergency response team. It also provided notice of 27 cardiac arrests, with caregivers restoring circulation in 25 of those patients.
The challenge now, Cantillon said, is to get outside that hour window, to give doctors even greater visibility into future events. “If we can unlock the secrets to the subtle pattern changes happening with the patient’s digital biomarkers, then we can do a better job of getting out in front of those events,” he said.
That depends on being able to identify those changes and link them to actual cardiac events. Cantillon said the clinic is analyzing an array of different measures, such as variations in heart rate and cardiac repolarization (or the return of the heart to its resting state during a heartbeat.)
“We have to weave them into an algorithm that is already very good at identifying sick patients and we have to do that in real time and validate it,” Cantillon said. “It’s a tremendous amount of work and development.”
He said the clinic has been doing all of the heavy lifting on its own but is now working with external AI vendors to further develop the system’s capabilities.
“We need the right commercial partners to take us that last mile of this race,” Cantillon said, adding that any future improvements to the algorithms will augment — not replace — the workers in the CMU. “What we’re trying to do, “ he said, “is to make the machines work better for them, so they’re able to more efficiently handle all of the data coming their way.”
The leader of a command center at Johns Hopkins said the hospital is exploring the use of machine learning to predict changes in patients’ conditions and monitor the delivery of treatment. Currently, the center is focused on managing hospital capacity to allow for timely transfers and prevent patients from languishing in the emergency department.
But Jim Scheulen, chief administrative officer for emergency medicine and capacity management, said the hospital is contemplating a variety of clinical uses. “If a patient’s lab values are supposed to be heading in one direction, but they suddenly veer off in another direction, should we get an alert for that,” he said. “But some of this is just in discussion.”
At Yale New Haven Hospital, which also created a command center focused on managing capacity, clinical leaders instituted a program to monitor use of Foley catheters to prevent infections and other complications and are beginning to look at possible predictors for patient deterioration.
Most of that work is now done by floating ICU nurses and other clinicians. “ But the goal is to bring that into the capacity coordination center,” said Dr. Robert Fogerty, director of bed resources at the hospital. “It’s a natural fit.”