Are We Approaching AI’s Big Moment in Healthcare? Let’s Hope So

August 3, 2021, 11:00:00 PM

Terrific examples of the leveraging of AI and machine learning are emerging everywhere in healthcare; the time is now to address urgent cost, efficiency, and care quality issues across the U.S. healthcare system

Source:

Healthcare Innovation

Are We Approaching AI’s Big Moment in Healthcare? Let’s Hope So

Author:

Mark Hagland

It was fascinating to read an article published yesterday in the Wall Street Journal by Angus Loten, who’s been covering the Tokyo Olympics. Under the headline “Olympics Timekeeper Omega, Intel Debut New AI Tools in Tokyo,” Loten reported on the application of new artificial intelligence (AI) tools being applied to actual Olympics events. And the implications are really quite huge.

As Loten reported, “Omega SA, Intel Corp. and other technology companies have taken new software tools out of the lab and into the field at the Tokyo Olympic Games, including artificial-intelligence systems designed to keep tabs on athletes competing in everything from the 100-meter dash and beach volleyball to golf, shot put and the hammer toss. Among other venues, Omega’s latest AI-powered capabilities are on display on the Olympic beach volleyball courts, said Alain Zobrist, chief executive officer of Omega Timing, the company’s sports-technology unit. The Swiss watchmaker, which has been the games’ official timekeeper since 1932, is operating a wide range of digital tools in Tokyo across multiple events. To provide real-time data on volleyball matches, Omega’s technology team trained algorithms to recognize serves, volleys, spikes and blocks by digitizing data from motion sensors on test players doing different shots—as well as the flight path of balls in play. That data was then fed back into the platform to form a data set of patterns of volleyball plays.”

Further, “An image-tracking system following live games at the Olympics picks up movement from sensors on players’ uniforms—as well as the ball—relaying data through the platform to identify shots, track the ball speed or determine the height of a jump, among other play-by-play information. Detailed coverage of each play is then posted on scoreboards in real time, acting as a kind of automated commentator on all aspects of the game. The data can also be used by live broadcasts, trainers and coaches, Mr. Zobrist said. Omega began developing these kinds of AI-enabled software tools for the 2016 games in Rio de Janeiro, though its first significant use of motion-sensor and image-tracking technology came two years later, at the Pyeongchang Winter Games in 2018. In Tokyo, it has deployed some 400 tons of equipment, including 350 sport-specific scoreboards, along with more than 500 timekeepers and other on-site professionals, according to a company spokeswoman.” And, he told the WSJ, speaking of AI, “We are only just getting started. It is essentially the future.”

Now, obviously, the Olympic sports world is extremely different from the world of patient care delivery. But—notice the similarities? At its core, what’s happening here is that AI is being used to recognize patterns that would be very difficult for human beings to do, because they are high-speed, repetitive, and involve minute fluctuations in patterns.

And while the technology being used at the Olympics might seem futuristic, consider the significant number of case studies already emerging in U.S. healthcare, involving the leveraging of AI and machine learning.

There are the leaders at the Durham-based Blue Cross Blue Shield of North Carolina, which serves more than 3.8 million members in that state. Over a year ago, they made an organization-wide commitment to leverage artificial intelligence (AI) to firmly and comprehensively attack the readmissions problem among their covered population. After the organization’s CIO created an “Innovation Garage,” members of that “garage team” developed CarePath, which BCBSNC executives describe as “an advanced deep learning factory approach for creating predictive models that identify target populations at risk for hospital readmissions.” That approach, they note, “enables a more focused, personalized patient intervention that is implemented during the transition from the hospital to the home.” And the predictive analytical model they’ve built “applies a readmission risk score to members currently undergoing inpatient procedures,” with members further prioritized by probability of readmission, low engagement with their primary care physicians (PCPs); and being on eight or more medications.

Using these various data elements, the Innovation Garage team members have been able to predict the risk of readmission for individuals even as they’re being treated during their current inpatient hospital stays. The results? The health plan’s care management team’s engagement success rate rose within a year from 12 percent to 57 percent, and the BCBSNC people have established far more connected and productive interactions with the care management teams in medical groups and hospitals. As a result of their set of innovations, we designated them as the second-place winning team in this year’s Healthcare Innovation Innovator Awards.

And then there are the folks at the Denver-based UCHealth, whom we recognized as the first-place winning team in our Innovator Awards Program this year. As Senior Contributing Editor David Raths reported in his article on the UCHealth team, “In 2015, when Denver-based UCHealth created a Virtual Health Center (VHC) to support smaller community hospitals being added to its system, there were some use cases that were obvious, such as centralized telemetry and a virtual intensive care unit. But UCHealth executives also foresaw that more use cases would develop that weren’t initially apparent. In 2018, clinicians began work on a telemedicine-based sepsis detection and response system, using artificial intelligence to implement a nurse-driven and physician-supported care bundle that has led to a 30 percent decrease in sepsis mortality.”

Importantly, Raths noted, “Timely and effective care for sepsis, including adherence to evidence-based guidelines, continues to be a challenge and priority for every health system. Upon reviewing their sepsis data, UCHealth executives observed a significant variation in their EHR workflows and outcomes. Best practice alerts would fire frequently with the intent to aid in sepsis identification, but that would take valuable time away from health professionals. In an effort to reduce that burden and enhance sepsis outcomes, UCHealth designed and implemented a nursing-driven workflow focused on sepsis treatment, using both the local primary nurse and remote critical care nurses. The VHC’s remote teams investigate warnings generated by alerts.”

And those are just two of many, many great case studies emerging right now and involving clinical and operational leaders in patient care organizations and health plans moving forward to address some of the thorniest issues facing the U.S. healthcare delivery system, including inpatient readmissions and sepsis crises, using AI and machine learning to leap ahead into true innovation. It honestly feels right now as though the healthcare system in this country is on the verge of some major breakthroughs.

As Raths wrote in an article in early June, “With more than 10 million residents, Los Angeles County has a larger population than 41 of the states. A UCLA team has developed a predictive model that pinpoints which populations in which neighborhoods of L.A. County are most at risk from COVID-19 and which should be prioritized for vaccines. Their research, published in the International Journal of Environmental Health, describes the model that maps the county neighborhood-by-neighborhood, based on four indicators known to increase an individual’s vulnerability to COVID-19 infection: preexisting medical conditions, barriers to accessing health care, built-environment characteristics and socioeconomic challenges that create vulnerabilities.”

In that article, Raths quoted Vickie Mays, Ph.D., UCLA Fielding School of Public Health professor of health policy and management and professor of psychology in the UCLA College of Letters and Sciences, who explained in a press release about the research that she and her colleagues have been involved in. “The model we have includes specific resource vulnerabilities that can guide public health officials and local leaders across the nation to harness already available local data to determine which groups in which neighborhoods are most vulnerable and how to prevent new infections,” Dr. Mays said a statement contained in the press release.

All of these are terrific examples of innovation that is beginning to spread now across the healthcare system.

In that regard, I’m very much looking forward to attending sessions included in the Machine Learning & AI Forum taking place next Monday, August 9, at the Wynn Resort, as HIMSS21 begins in Las Vegas. Several members of the Healthcare Innovation team will be attending HIMSS21, and we’re looking forward to attending educational sessions throughout the week. The Machine Learning & AI Forum is offering sessions with titles such as “Strategies for Maturing ML and Predictive Analytics,” “Integrating Machine Learning and AI into the Clinical Workflow,” “Clinical Integration of AI into a Radiology Clinical Decision Support Tool to Minimize Workflow Disruptions,” “Eliminating Bias and Inequity in Machine Learning and AI, and “How AI Can Augment the Human Experience for Better Patient Care,” among others. The proof, of course, will be in the proverbial pudding; but I’m excited by the titles of those sessions. They seem to promise a lot of insights from leaders who are pushing ahead to really leverage AI and machine learning to help facilitate desperately needed advances in so many areas of patient care delivery and operations in U.S. healthcare.

Are we at an inflection point in healthcare right now around AI and machine learning tools? I certainly hope so. So much needs to be done in the coming years, as we as a nation are set to go over a shattering cost cliff. Back in February 2019, the Medicare actuaries predicted that overall annual U.S. healthcare expenditures would soar from $3.6 trillion in 2017 to nearly $6 trillion by 2027, with gross domestic product (GDP) spent on healthcare in this country rising from 17.9 percent to 19.4 percent by 2027. Anyone who isn’t living under a rock should be absolutely blown away by those numbers. And, after a dip in expenditures during the first several months of last year because of the pause in elective procedures necessitated by the exigencies of the COVID-19 pandemic, expenditures are roaring back again now.

If ever there were a time when we desperately needed new tools to help us collectively address deeply concerning trendlines in healthcare, now would be it. Can AI and machine learning “save” us? Not as such; they’re just tools. But powerful tools indeed they are. So I, for one, am looking forward to the next few years, as more and more leaders in pioneering hospitals, medical groups, health systems, and health plans move forward to leverage them for a nearly unlimited range of great purposes. The future is here: we just need to embrace it.

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