Data silos have been the Achilles Heel of the healthcare digital revolution pretty much from the beginning, and the arrival of AI on the healthcare landscape has been no different.  After all, it’s all well and good to have the ability to crunch more data than ever at previously unimagined rates, but so long as data pools remain separate and discrete, it’s difficult to take optimal advantage of new analytics technology.

Writing recently at Forbes, tech evangelist Kumar Srivinas argues that health plans may have a key, previously untapped role to play in efforts to optimize data analytics.

In his view, “as the financial center to patient healthcare, health plans are the natural hub to facilitate collaboration and communication among healthcare entities servicing patients. Each patient potentially touches multiple pharmacies, practices, hospitals, and medical device vendors and all these entities already have communication channels set up with the health plans.”

To date, he explains, while many healthcare organizations are exploring how AI can help them execute their mission, each organization, as we noted above, is operating with its own silo of data. 

In addition to obviously limiting the amount of data that can be fed to AI algorithms, Srivinas points out that “the low volume of data in a single organization's data set makes it difficult to draw actionable, explainable insights. The more information the AI model can analyze, the more refined the analysis is.”

Sharing data across organizations also minimizes the potential for AI bias, Srivinas points out, a problem that has garnered significant attention from policy makers seeking to promote equitable healthcare outcomes.

According to Srivinas, “AI bias can show up in data sets in different ways. Hospitals or health plans that work primarily with homogenous populations, say Medicare patients or within a narrow geographic region, will have a population bias. AI bias also occurs based on dissimilar treatment standards and protocols used by practices and hospitals.”

So how can health plans help?  First, he says, by reaching out proactively to members and patients. “The need for early diagnosis and prevention is key to improving patient outcomes and managing healthcare costs, and it’s where health plans can start using AI with a huge impact.”

Next, he says, “look into whether your organization is using explainable AI with your care management and clinicians as they engage with members with chronic diseases.”

Finally, “find out what steps your organization is taking to eliminate AI bias, such as combining data from multiple health plans.”

The bottom line, he says, is that “because health plans are so uniquely placed, they have a unique opportunity to lead in building the AI and pushing it out directly to members and clinicians.”

Moreover, because “the need for early diagnosis and prevention is key to improving patient outcomes and managing healthcare costs,  . . . it’s where health plans can start using AI with a huge impact.”