Care Redesign
Man vs. Machine: Leveraging AI in Health Care (08:06)

“Thus far, the use of computer assistance to help clinicians and patients has been disproportionally focused on clinical decisions — treatment A versus treatment B,” says Arnold Milstein. But there’s an enormous opportunity to use computer assistance not just in clinical decision-making, but also in assuring critical clinical work flow.

“For example, our lab has been very active in taking assurance of intended care processes in small geographic places where getting care exactly right is enormously important to value of care,” Milstein says. They focus primarily in ICUs and in the homes of cognitively impaired seniors, and ask clinicians: What are the critical facets of work flow that, if you don’t get right, will cause the patient to have severe health and cost trouble very quickly?

“What we’re doing is essentially asking, can we use economical forms of computer vision to begin to detect omissions in intended processes and alert the clinician and medical teams that something important has been missed?” For instance, turning an unconscious patient in an ICU at a certain frequency makes a big difference in preventing decubitus ulcers. With humans being human, it’s not uncommon that someone working in the unit will document that they turned the patient, when they may, in fact, not have.

Benjamin Chu agrees that artificial intelligence and machine learning can play a big role. “We have to have a much more dynamic, iterative process for trying to evaluate effectiveness, and the only way to do that is to try to decipher the patterns from the noise. It requires that we all look at data sets, big data sets, in a way that has enough power to determine whether they’re effective or not.”

Milstein adds how valuable the artificial intelligence system was in showing predictors that made intuitive clinical sense, but that no one had ever thought about before. One example was if a patient within the course of a month had three visits to a primary care doctor without a diagnosis. Another was increasing velocity of visits to subspecialists. “Think about that — in retrospect, it makes sense, there’s something happening here, and the health system is responding with a quickening of pace and moving patients into services, but it’s not a variable that any human would have thought to test. But it turned out that the supervised machine learning approach that we applied taught us that that contributed to predicting what we call cost blooms,” Milstein says.

Namita Mohta raises the importance of preserving the sacred relationship between patients and providers despite advances with machine learning. “The thing we have to keep in mind is that we don’t repeat perceived mistakes of the EMR — that it doesn’t become a technology that somehow has the unintended consequence of burdening the relationship that we’re looking to preserve between patients and their caregivers.”

Tom Lee agrees, leveraging the flood of knowledge artificial intelligence and machine learning are providing. “There’s data showing that the doubling time of knowledge in medicine has been accelerating,” he says. “It’s gone from, when I was born in the 1950s, about every 5 years knowledge was estimated to double, to now, at the end of this decade, the prediction is that doubling time will be 73 days.” How do we deal with that? “If you’ve got four neurologists, and their neurology knowledge doubles, do you hire another four neurologists? That clearly isn’t the way to do it. We’re going to have to figure out, how do we work together, and how do we work with computers in order to have people use knowledge and not just be overwhelmed by it?”

Milstein adds that we can learn from other industries and asks that we consider the example of commercial airline pilots in the 1980s, overwhelmed by the increased flow of alerts, which interfered with their “relationship” to the plane. “Eventually what they did is they got together as a group and they said, there has to be a threshold of probability that what the computer wants to tell me is going to be useful before I want to be interrupted,” he says. That turned out to be a very successful equilibrium in allowing the human brain to do what it can do best, especially in an intimate encounter, and at the same time take advantage of this supplementary form of intelligence.

From the NEJM Catalyst event The Future of Care Delivery: Relentless Redesign at Providence St. Joseph Health, January 19, 2017.

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