How AI Is Shaking Up Healthcare, Beyond Diagnostics

Alex Lubetkin
APRIL 26, 2018

When the dust settles, the 2017-2018 flu season will go down as the worst in at least a decade, by several measures, according to the CDC. With countless hospital visits for flu-like symptoms and the number of states battling widespread bouts of influenza—the continental 48, plus Alaska and various territories such as Puerto Rico—Americans are battling a challenging foe that can be deadly.

Public health officials, clinicians, and hospital executives are charged with responding to scattered outbreaks. Part of what makes the flu a difficult puzzle to solve each year is the level of required human input. “Everybody who works in primary care over the last couple of months probably encountered the same issues,” said Michael Cantor, MD, associate professor in the Departments of Population Health and Medicine at New York University Langone Health. “Your office is getting flooded with people with upper respiratory infection, who, because of what’s going on in the news, are worried. It’s hard to triage those patients over the phone, and you end up seeing the doctor for something that’s relatively benign.”

The effects of incorrect diagnoses and time invested in patients who don’t need medical intervention can be severe. The most vulnerable patients get drowned out by a chorus of coughing fits. On the macro level, overwhelmed hospital systems find day-to-day operations challenging, if not impossible. In Alabama, for example, health systems got so clogged that Republican Gov. Kay Ivey declared a public health emergency in January, noting that hospitals were “taxed to such an extent that care of patients may now no longer be provided in the traditional, normal, and customary manner.”

But help is arriving, and some is already here. More and more, state-of-the-art diagnostics are being piloted, many using hyper-advanced analytic and artificial intelligence (AI) technologies. But would simply knowing who has the flu and who has a severe cold be enough to save an Alabama hospital system overwhelmed during influenza’s witching hours? Probably not: There is a laundry list of labor-intensive applications carried out in a hospital on a day-to-day basis, many of which rely on human input, which means they may not be running efficiently.

Fortunately, many corners of healthcare are due for an AI-infused upgrade, from pharmaceutical delivery systems to surgery scheduling and even technologies that save doctors time on transcribing patient notes. These innovations will have different looks and applications, but if successful, they will share a common outcome. “The hope is that the field will be more orderly,” Cantor said. “If you’re using these AI systems to help with workflows, there will be more balance. There won’t be [as much] chaos.

“There [are] always going to be emergencies, but the day-to-day operations will be a lot smoother.”

The Hospital of the Future

Branded the “hospital of Silicon Valley” and a 5-mile drive from Apple’s headquarters in Cupertino, California, El Camino Hospital is on the cutting edge of many new forms of medical technology. It’s developing AI systems that predict which patients are at risk of falling and robotic delivery modules that hum across hospital corridors. “We are really interested in innovation here,” said Chief Information Officer Deborah Muro, “especially because we’re in the middle of Silicon Valley.” Many of her efforts remain in their infancy, but she made the hospital’s goal clear: “We’re really looking at developing a hospital room of the future, a clinic room of the future.”

Some of the new tech that El Camino is pursuing falls outside the umbrella of AI and analytics. The hospital is partnering with Apple, for instance, to integrate iOS devices into the hospital’s information systems, using customized apps to streamline communications between doctors, nurses, and patients.

But AI systems are slowly rolling out. The hospital recently partnered with Qventus, an AI-based operational decision management platform, to pilot software that attempts to predict patients at risk of falling out of bed. The software uses an algorithm that analyzes 3 risk factors: patients’ medical histories, communication patterns with nurses, and how patients move in bed. El Camino then allocates nursing power accordingly.

So far, the data show, the tech has helped the hospital reduce its fall numbers by 30%.

El Camino and Qventus are also beginning to implement similar programs that identify patients at risk for sepsis, which kills roughly 250,000 Americans per year, and those who are prime for readmission. The systems take into account different factors but are all considered “predictive,” which differs from the “preventive” models of understanding healthcare that have been in place for decades, Muro said. “That’s going to be the future, being able to predict healthcare scenarios for patients rather than waiting for them to come to us when they have a healthcare event,” she added.

El Camino has also pointed its analytical gaze inward to increase efficiency. Muro described a smart surgical system being installed that alerts doctors and surgery teams to open time slots. This technology could help patients undergo surgeries more quickly, and the hospital could then schedule more surgeries. There is no time wasted on human error.

Become a contributor