Why AI tools are critical to enabling a learning health system
Talk about two grand visions: widespread adoption of artificial intelligence technologies and a Learning Health System. As healthcare steps closer to each it’s becoming increasingly clear that an LHS will be almost dependent on cutting-edge technologies.
“Learning Health Systems continually improve by collecting data and processing it to inform better decision making. As the amount and complexity of big data continues to increase, organizations are challenged to fully take advantage of it,” said Kenneth Kleinberg, Vice President at Chilmark Research. “AI systems are particularly suited to analyze huge data sets to discover meaningful and actionable insights, and even to carry out actions.”
A big reason that Kleinberg pointed to is the reality that the more good data people can feed AI systems, the better the insights they get back.
What’s more, a range of software and technology vendors, including EHR makers and advanced analytics specialists, not to mention certain academic medical centers, already offer tools to glean structured and unstructured data to craft predictions that inform clinical decision support, Kleinberg added.
“Half of the health systems we surveyed believe that most medical specialties will be using AI in a major way in one to three years,” Kleinberg said.
That is a taste of the research Chilmark is conducting in conjunction with the University of Michigan Medical School surveying dozens of participants in the Learning Health Community.
Chilmark and UMMC, in fact, are undertaking the survey to prepare for a joint session Kleinberg is slated to deliver with University of Michigan Program Officer of Learning Health Initiatives Joshua Rubin at the upcoming HIMSS and Healthcare IT News Machine Learning and AI for Healthcare event at HIMSS18.
“Predictive analytics and natural language processing are technologies many respondents indicated are already having a major influence. Image recognition is also gaining traction – fast,” Kleinberg added. “Most respondents indicated that many other AI use cases will be common in 1-3 years, including Clinical decision support, use of virtual assistants, and precision medicine.”
Just about any discussion concerning AI, cognitive computing, machine learning algorithms and, of course, a Learning Health System, demands a dose of reality, too.
Hospitals face challenges on the road ahead.
“While data is increasingly abundant in healthcare, it is often captured in unusable formats or is incomplete, which can undermine the usefulness of AI,” Kleinberg said.
It doesn’t help that healthcare is lagging when it comes to the advanced processes and workflows widespread AI deployment demands — as well as the current and expected data scientist talent shortage.
“In our survey, every respondent has listed skills as a ‘significant’ barrier,” Kleinberg said. “The challenge of change management varies widely. Those organizations most willing to learn will find AI can take them where they want to go.”
HIMSS and Healthcare IT News Machine Learning and AI in Healthcare takes place at HIMSS18 on Monday, March 5, 2018 at the Wynn Hotel in Las Vegas. Register here