At UPMC, clinicians use predictive analytic tools to help reduce the risk of disease. They’re pursuing narrow but still vital goals like reducing hospitalizations and applying new diagnostic tools that help patients self-manage their own conditions.
UPMC and some other providers see potential in AI helping determine whether a patient’s condition is terminal. That would allow providers to prescribe palliative care rather than treatment.
In other words, smaller goals are better.
“We’ve done ourselves a disservice in propagating the hype around AI,” said Dr. Rasu Shrestha, chief innovation officer at the UPMC system. Shrestha says more palatable uses for AI might come from a use-case perspective rather than placing too grand expectations on AI. “I think we would start to get to the future that we are desiring,” he said.
Shrestha sees AI as being used to augment—rather than completely redefine—healthcare.
The use of any technology in population health would be an advance given that previous solutions have involved simply connecting patients to community resources in order to address non-clinical health determinants.
When New York University Langone Health launched its predictive analytics unit in 2016, it was looking to reduce unnecessary hospitalizations by enhancing clinical decisionmaking and increasing efficiencies.
The unit’s first project was developing an AI model that can identify patients with congestive heart failure by evaluating their medical records upon admittance.
“We did it to remind clinical staff that if you have someone with pneumonia, for example, but they also have CHF, you don’t give them fluids because that might exacerbate their CHF,” said Dr. Michael Cantor, an internist and associate professor at NYU Langone Health.
The heart-failure project led to an analytics model that predicts which patients are prone to sepsis—a condition that affects more than 1.5 million Americans annually and accounts for 1 in every 3 deaths that occur in hospitals.
“Basically, we’ve been rolling out models every few months,” Cantor said. Clinical demand dictates which models will be built. Once they are developed and evaluated, they’re included in NYU Langone’s electronic health record system for integration into the clinical workflow.
“A big part of the project planning and development is making sure that once the model is live that it gives that information that people will act on,” Cantor said. The goal is not simply to “throw information out there.”
During the clinical evaluation phase of some projects, the unit develops best practices on how to handle conditions flagged by the AI model.
Cantor said AI has been helpful in treating acutely ill patients, but he hopes to see models that identify ways to prevent people from ever needing to come to the hospital.
But it would take time to factor in social determinants of health. It also would require training the healthcare staff or hiring professionals with new skills.
“A lot of places don’t have the personnel who know enough about predictive modeling to adopt them effectively,” Cantor said. “When you’re trying to find people like that, you’re competing with Google and Amazon.”
Improving 30-day readmission rates, flagging patients at risk, shortening hospital stays and mitigating disease risk are just some of issues AI is helping hospitals currently address, said Brian Kalis, managing director of digital health and innovation for consulting firm Accenture.
But the technology could also help providers improve patient engagement.
AI can help patients self-manage their conditions at home and skip in-office doctor visits.
“We’re discharging patients not just with a bag of pills but with technology,” Shrestha said, referring to UPMC’s patient-monitoring program that uses machine learning to manage chronic conditions. UPMC invested in the technology in 2016. It was created by Texas-based Vivify Health.
Upon discharge, patients with conditions such as heart failure or diabetes are given a tablet computer or instructed to use their own mobile device to transmit their health information to UPMC. The device monitors a patient’s symptoms of the disease, blood pressure, weight and oxygen levels while at home. It also contacts their physician if needed.
The data are analyzed to predict when a patient is at risk of ending up back in the emergency department so clinicians can intervene by phone or a nurse visit.
“They’re able to stay in an environment where they can eat, work, stay and play and not have to come back to the ED,” Shrestha said.
More than 1,100 patients were enrolled in the program the first year it was launched, Shrestha said. It has a 92% compliance rate among patients and a satisfaction score of 91%. During that first year, Pittsburgh-based UPMC reported Medicare beneficiaries enrolled in the program were 76% less likely to be readmitted within 90 days of discharge than patients without the remote monitoring.
“In the past we’ve been able to achieve some level of success by taking more of a low-tech approach,” Shrestha said. “But what we’re seeing right now increasingly is when it comes to identifying or risk-stratifying the patient population, this entire loop of population health management becomes more efficient and effective if you’re able to bring capabilities that would allow for you to really get at these data elements in ways that we’ve not been able to previously.”
Piali De, CEO of Senscio Systems, maker of another home-based AI patient-monitoring application for patients with multiple chronic health conditions, said AI facilitates remote patient monitoring.
“There’s just a lot of data and a lot of correlations,” De said. “Discovering those correlations is the essence of population health management because it’s impossible to know what’s working for the most complex populations without the use of AI.”