Source: healthitanalytics.com
November 26, 2019 – As the foundation of nearly every healthcare trend, process, and solution, data has a vitally important role to play in care delivery and success.
From risk stratification to chronic disease management, precision medicine and medical research, data is at the center of everyday healthcare tasks and broader industry improvements, making it an incredibly valuable resource for organizations.
“If you talk to any data scientist, they’ll tell you that the more quality, scientifically validated data they have, the more likely they’re going to be able to generate useful trends and insights,” said Todd Frech, CIO at Press Ganey.
“The core of everything we do is taking the vast amounts of data that we collect and creating value for hospitals that are trying to improve their operations.”
With healthcare quickly becoming a digital industry, more and more entities are gathering meaning from this big data using artificial intelligence and other advanced analytics technologies.
“The challenge that we’re trying to overcome is that we have more data than a human can process, and we’re trying to develop insights based on those volumes of data. This issue is a natural fit for AI, so the use of this technology is going to continue to accelerate,” Frech said.
“AI can augment humans’ understanding of data, not only from the perspective of generating new insights, but also in generating those insights faster than a typical human analyst processes.”
There are countless examples of AI outperforming humans in analyzing and extracting insights from clinical data. The technology’s potential to transform the industry has led to concerns about robots encroaching on healthcare jobs, creating an environment run entirely by machines and devoid of human interaction.
While AI may disrupt standard care delivery, it’s unlikely that advanced analytics tools will completely take over the role of clinicians. In a field where high-stakes situations and sensitive data are routine, technology can’t simply be left to operate on its own, Frech stressed.
“AI is going to play a bigger part in healthcare, and humans will also continue to play a big part,” he said.
“We can’t just assume that AI is making the right decisions without human validation. There’s a trend that you’re going to see more – what’s called AI augmentation, or human augmentation with AI, more than what you would call complete robotic AI, meaning that you’re letting the AI make decisions without human intervention.”
Recent research has demonstrated that when implementing AI tools, human intervention can lead to optimal results. A study conducted by a team at NYU School of Medicine and the NYU Center for Data Science showed that combining AI with analysis from human radiologists significantly improved breast cancer detection.
Using an AI augmentation approach could also help organizations analyze and measure unstructured data.
“We collect hundreds of thousands of survey data points in the forms of responses to questions, as well as unstructured data in the form of comments. We use AI to look at the comments that come in our surveys. Those comments are obviously in the form of unstructured text, and they convey information on perception of the providers and of the service,” explained Frech.
“Those aren’t yes or no questions. Those are questions that require some soft skills to interpret. We can use AI to do an initial sentiment analysis, and that provides a way for us to really measure this type of data, which is not as binary as some of the data we typically evaluate.”
However, data-driven technologies can’t improve care if they’re fed inaccurate or incomplete information – in fact, this could have the opposite effect.
“Never underestimate the importance of data quality,” said Frech. “No AI tool is going to work well without high-quality data. People talk about data lakes and unstructured data, and these things are great tools. But without quality data, you’re going to have more of a data swamp than a data lake.”
“If you’re trying to use AI to gather insights without high-quality data, obviously the results aren’t going to pan out. Or even worse, the results could potentially offer dangerous recommendations that could negatively impact people,” he added.
Having a solid data ecosystem ensures that any innovative tools will contribute positively to a health system’s operations, Frech said, as well as communicating openly with other organizations.
“When implementing artificial intelligence or any new technology, make sure that the foundation is strong. Make sure that there’s testing and validation. If that doesn’t happen, there is potential for organizations to take steps backward rather than forward,” he said.
“Find opportunities with your peers, find case studies, talk to people who are using the technology. The more that your organizations can collaborate and learn from each other, the more ideas and successes will increase.”
AI has massive potential to revolutionize the way providers deliver care and make treatment decisions. The road to industry-wide adoption won’t be without its challenges, but the technology will likely make its way into regular clinical care.
“There are a lot of different ways to use AI, and there has been a lot of experimentation. Over time, there will be more and more successes, and those successes will come in fits and starts, depending on how the data in the market mature. There’s too much investment in AI right now to not have some of those successes come into play,” Frech concluded.