Can AI Really Improve Care?

Arnold Milstein, MD, came to Stanford eight years ago with a simple assignment: Find out how to lower the national cost of producing great health care. Put another way, if we could find more affordable ways to deliver better care for conditions that consume the bulk of the country’s health care spending, more monies would be available for other ways to improve human well-being — like education and social services.

Milstein was ideally suited to the task. He spent two decades working to improve health care value in the private sector, after which he served as an advisor to Congress and the White House. In 2011 he created Stanford’s Clinical Excellence Research Center (CERC). It is the first university-based research center exclusively dedicated to discovering, testing, and disseminating cost-saving innovations in clinically excellent care.

One of CERC’s areas of emphasis is discovering how artificial intelligence (AI) can prevent inadvertent and costly failures in intended care delivery. This focus began with a call from Professor Fei-Fei Li, PhD, director of the Artificial Intelligence Lab in the Stanford School of Engineering.

“Our subsequent conversations sparked a decision to create a unique cross-school Partnership in AI-assisted Healthcare, which we call PAC. We imagined a world in which AI improves the performance of a broad range of human services that affect health,” Milstein says.

“We initially focused solely on health care in order to learn and make a difference before we expand our use of AI to improve performance across a broad range of health-affecting services,” adds Milstein, who turns to a favorite initial target: lowering the incidence of hospital-acquired conditions or HACs.

“Every time a patient in a U.S. hospital acquires an infection that they didn’t come in with, human misery and tens of thousands of dollars to the cost of a hospitalization follow,” he explains.

“No clinician wants to impose hospital-acquired infections on their patients. But clinicians are busy. They’re human. They’re imperfect. So they don’t always notice when they’ve just skipped a critical intended action step.”

That led to thinking about how artificial intelligence could be used to help detect and correct — in real time — deviations in essential clinical actions, like maintaining hand hygiene, which is a primary way to prevent hospital-acquired infections.

In 2015 CERC researchers, alongside graduate students and faculty in the AI Lab, began developing a system that detects whether someone used the alcohol hand dispenser that sits on the wall next to every hospital room entrance. Their system relies on computer vision, a rapidly progressing domain of artificial intelligence used in the automotive and other industries.

“If computer vision can detect when drivers initiate dangerous lane changes and safely control vehicular steering, can it similarly analyze motion to detect unintended deviations in important clinician behaviors or patient activities?” asked Milstein and Li’s research team in a New England Journal of Medicine article.

AI systems that take advantage of computer vision are relatively inexpensive. By using them, the team has shown it can achieve greater than 95 percent accuracy in detecting inadvertent omissions in the use of the hand sanitizer before staff enter patient rooms.

The vision of making excellent care more effective and efficient also targets behaviors that affect lifelong health trajectories. In collaboration with Stanford researchers in child development and pediatrics, the team is testing how computer vision can let mothers know if their eyes inadvertently drift to their smartphone screen instead of responsively returning their infant’s gaze.

The hope, Milstein says, is to unite technology and human care. “By mobilizing emerging science and technology from engineering, behavioral sciences, and medicine, Stanford can address a seemingly intractable national challenge to make affordable all forms of human caring that powerfully affect health.”