As artificial intelligence (AI) gains more momentum in the healthcare sector, CIOs' use of these technologies has expanded. And as a result, the industry is now moving quickly, crafting solutions to meet this growing demand.
To this point, according to the findings of a recent Accenture report based on C-suite executive responses from more than 100 health organizations, AI is poised to become the new user interface (UI) in health IT. The report noted, “The growing role of AI in healthcare is moving beyond a back-end tool to the forefront of the consumer and clinician experience, becoming a new user interface that underpins the ways individuals transact and interact with systems. Emphasizing its growing importance of AI, more than four-fifths (84 percent) of healthcare executives surveyed as part of the research believe that AI will revolutionize the way they gain information from and interact with consumers, and nearly three-quarters (72 percent) of health organizations surveyed are already using virtual assistants to create better customer interactions.”
Peter Borden, managing director of the consulting firm Sapient Healthcare, notes that people have actually been talking about AI for ages across various sectors, and now strong use cases are staring to emerge, especially in the health space where evidence is so important. Borden points to three areas in healthcare that people are paying the most attention to as it relates to leveraging AI: population health insight, or going beyond the core data sets to analyze where populations might need the most attention; augmented intelligence, in which there is not a replacing of a function but rather making one stronger; and precision engagement in which personalization is taken to the next level.
Says Borden, “You might understand population health and what segment is at highest risk for going from pre-diabetes to diabetes, but you need to know how to engage each individual in that sub-segment in the way that makes the most natural sense for them. And there are great emerging stories from that.” He adds, “As people start to understand that it will impact the business, and not just the outcomes, there is now a lot more acceptance.”
When folks think of AI, the first thing that often comes to the minds of many is Siri, which of course is in most people’s pockets via the iPhone. And as Borden notes, “Everyone knows about Watson. Google is also making strong and interesting pushes into the space. And Amazon is making moves into the health space, too. Some of the big cloud players are doing interesting things as well, and because of the nature of the cloud, it allows for analysis of data in certain ways from a processing perspective. So the cloud is a natural partner. But really, every minute it seems like a new and interesting company is popping up,” he says.
One such healthcare AI company that has “popped up” is New York City-based Prognos, which formed just this year and is interested in developing predictive models that use massive datasets (mostly coming from an extensive network of partner labs nationwide) to determine how likely it is that a patient will undergo a specific health event in the future. Prognos builds these models using patient’s anonymized records, and has an exponentially-growing amount of them (over 8 billion by Q1 of 2017), according to company officials.
The company’s co-founder and chief medical officer Jason Bhan, M.D., a family physician who previously worked at Clinovations, a company that helps hospitals implement health IT, recalls a project he was working on for a client in which the organization’s CEO and chief medical officer turned and said to Bhan and his team, “We just spent $150 million on this [EHR] system, what did we do for ourselves?” Bhan said he responded by saying, “Let’s go find out.”
Bhan then dug into the data that was on the client’s IT systems, and the realization was that most of the data coming out of EHRs just is not very useful, he recalls. He adds that the diagnostic information, however— lab data, radiology, and tests that physicians are performing—is a gold mine, so that coupled with years of practice, and also years of making decisions based on looking at lab results, guided him towards thinking more about diagnostics in healthcare. “Prognos was a concept of eradicating disease, but how do we improve health in general by tracking and predicting disease at the earliest? That’s where we got into AI,” Bhan says.
Applying AI to Determine Risk
While the lines are sometimes blurred between machine learning and artificial intelligence, Larry Lefkowitz, Ph.D., chief scientist at SapientRazorfish, a company under Sapient that launched this year to help clients drive digital transformation, explains the difference. “Machine learning is if I have sufficient data representative of the kind of problem I am trying to solve. Then I have a reasonable chance of applying machine learning to be able to ‘understand’ that data enough so it can make the same kinds of predictions and get the same results a person would, given that next set of inputs.” He adds, “When you have tons of data like in radiology or pathology cases, it seems like a really good application of machine learning. So here is my next X-ray, I have told you what X-rays like this might look like and might mean, go ahead and give me an answer.”
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