The Illinois program gives people recovering from covid-19 a take-home kit that includes a pulse oximeter, a disposable Bluetooth-enabled sensor patch, and a paired smartphone. The software takes data from the wearable patch and uses machine learning to develop a profile of each person’s vital signs. The monitoring system alerts clinicians remotely when a patient’s vitals— such as heart rate—shift away from their usual levels.
Typically, patients recovering from covid might get sent home with a pulse oximeter. PhysIQ’s developers say their system is much more sensitive because it uses AI to understand each patient’s body, and its creators claim it is much more likely to anticipate important changes.
“It’s an enormous benefit,” says Terry Vanden Hoek, the chief medical officer and head of emergency medicine at University of Illinois Health, which is hosting the pilot. Working with covid cases is hard, he says: “When you work in the emergency department it’s sad to see patients who waited too long to come in for help. They would require intensive care on a ventilator. You couldn’t help but ask, ‘If we could have warned them four days before, could we have prevented all this?’”
Like Angela Mitchell, most of the study participants are African-American. Another large group are Latino. Many are also living with risk factors such as diabetes, obesity, hypertension, or lung conditions that can complicate covid-19 recovery. Mitchell, for example, has diabetes, hypertension, and asthma.
For example, there are 11 people in Mitchell’s house, including her husband, three daughters, and six grandchildren. “I do everything with my family. We even share covid-19 together!” she says with a laugh. Two of her daughters tested positive in March 2020, followed by her husband, before Mitchell herself.
Although African-Americans are only 30% of Chicago’s population, they made up about 70% of the city’s earliest covid-19 cases. That percentage has declined, but African-Americans recovering from covid-19 still die at rates two to three times those for whites, and vaccination drives have been less successful at reaching this community. The PhysIQ system could help improve survival rates, the study’s researchers say, by sending patients to the ER before it’s too late, just as they did with Mitchell.
Lessons from jet engines
PhysIQ founder Gary Conkright has previous experience with remote monitoring, but not in people. In the mid-1990s, he developed an early artificial-intelligence startup called Smart Signal with the University of Chicago. The company used machine learning to remotely monitor the performance of equipment in jet engines and nuclear power plants.
“Our technology is very good at detecting subtle changes that are the earliest predictors of a problem,” says Conkright. “We detected problems in jet engines before GE, Pratt & Whitney, and Rolls-Royce because we developed a personalized model for each engine.”
Smart Signal was acquired by General Electric, but Conkright retained the right to apply the algorithm to the human body. At that time, his mother was experiencing COPD and was rushed to intensive care several times, he said. The entrepreneur wondered if he could remotely monitor her recovery by adapting his existing AI system. The result: PhysIQ and the algorithms now used to monitor people with heart disease, COPD, and covid-19.
Its power, Conkright says, lies in its ability to create a unique “baseline” for each patient—a snapshot of that person’s norm—and then detect exceedingly small changes that might cause concern.
The algorithms need only about 36 hours to create a profile for each person.
The system gets to know “how you are looking in your everyday life,” says Vanden Hoek. “You may be breathing faster, your activity level is falling, or your heart rate is different than the baseline. The advanced practice provider can look at those alerts and decide to call that person to check in. If there are concerns”—such as potential heart or respiratory failure, he says—“they can be referred to a physician or even urgent care or the emergency department.”
In the pilot, clinicians monitor the data streams around the clock. The system alerts medical staff when the participants’ condition changes even slightly—for example, if their heart rate is different from what it normally is at that time of day.