Spread of AI highlights need for clean and timely health data

Jeff Rowe
It hasn’t taken long for AI to dominate conversation in the world of healthcare data analytics, but that doesn’t mean all the pieces are fully in place for optimal success.

If HIMSS19 in Orlando was any indication, “AI appears to have moved from uncertain novelty to absolute necessity in the blink of an eye.”

That’s according to tech writer Jennifer Bresnick, who, based on her observations of the HIMSS exhibition floor,  says “major solutions providers have joined regulatory agencies in promoting AI as both a competitive advantage and an inevitability, urging organizations to accept machine learning as a fact of life.”

That said, Bresnick cautions that AI may not be ready for primetime quite yet.  As she puts it, “the industry has another big problem on its hands before AI can become operational: accessing the enormous volumes of clean, complete, timely data required to train, validate, and deploy AI for use in the real-world environment. 

“Creating a fluid, accessible data aggregation environment in which AI can flourish means overhauling everything from basic infrastructure design to the business case for sharing information. Organizations may be more willing to take on the challenge as AI starts to prove its value, but the industry still has a lot of work to do before provider groups can consistently access the prescriptive insights that are critical for achieving their long-term goals.”

According to Mark Morsch, VP of Technology at Optum, which works with both payers and providers to generate data-driven insights, healthcare organizations have entered a pivotal year for artificial intelligence and advanced analytics.

“We do see 2019 as a significant year for adoption and for the continuing maturity of AI,” he told Bresnick.  “It’s progressing very quickly, especially in the realms of deep learning and natural language processing (NLP). I certainly see more organizations moving from the diagnostic and descriptive level up to the more predictive and prescriptive levels – that is very exciting. People are starting to appreciate the challenges and the opportunities of putting AI into practice.”

Similarly, Seth Hain, Director of Analytics and Machine Learning at Epic Systems, noted,

“The hype is dying down and folks are really starting to move from getting their arms around AI. Many, many of our customers are starting to build the teams and processes that integrate machine learning into the standard way they do business. It’s been nice to see that shift and it’s great to be able to start getting deeper into optimizing clinical workflows and gaining operational efficiencies with machine learning as part of the toolset.”

But as Bresnick observed, data siloes are everywhere in healthcare, and breaking down the barriers to data access while still adhering to privacy and security principles “is an immense challenge.”

Still, industry stakeholders seem to be ready to embrace the challenge. Bresnick points to a 2017 survey from Accenture which found that more than 80 percent of healthcare executives had started working on creating centralized data platforms to underpin their artificial intelligence plans. 

 “Eighty-two percent believed that a good leader in the modern health IT landscape will be defined by how well they architect seamless, interoperable environments for conducting large-scale analytics.”

Nonetheless, as with most health IT initiatives, solving the problem is easier said than done.