“Artificial intelligence is not going to be optional for most healthcare organizations in the near future, so organizations that start getting their infrastructure prepared now will have an advantage in the short term and the long term.” 

So noted Josh Gluck, vice president of global healthcare technology strategy at Pure Storage, in a recent article at HealthITAnalytics.

The problem, as healthcare stakeholders have long known, is that healthcare data is immensely complex and immensely voluminous, and AI is only going to add significantly and quickly to that complexity.

Indeed, as Gluck sums it up, “Artificial intelligence in healthcare has absolutely exploded over the past year. There are people doing amazing things in proteomics, radiomics, genomics, and so many other areas that have the potential to drastically improve the way we deliver care. We’re going to continue to see AI move into the patient environment, and healthcare providers will need to prepare for that in a number of ways.”

With that preparation in mind, Gluck says the first step for healthcare organizations is to assess their long-term goals for artificial intelligence.  Many organizations are beginning the AI adoption process by approving limited pilots and research projects in order to generate real-world evidence that machine learning can solve their business problems.

While this approach can help providers avoid wasted larger investment that doesn’t end up bringing a return on investment, Gluck says it can also expose organizations to the challenges of “shadow IT.”

Shadow IT occurs when smaller teams within an organization start to develop infrastructure without sufficient oversight from the IT department or a C-suite IT executive.  Such isolated AI projects can lead to pockets of data being stored separately from the organization’s main data assets, and they may even expose the organization to privacy and security issues if the data is being stored, shared, or used in a manner inconsistent with established compliance protocols.

The solution, say experts, is for healthcare organizations to focus on developing a single data hub that becomes a “one-stop shop” for AI researchers.

“When you create a single, seamless hub for your data, you ensure that all of your applications are drawing on a unified source of truth,” notes Esteban Rubens, global enterprise imaging principal at Pure Storage. “There is no need to duplicate data or maintain multiple versions of the same infrastructure, which can get very costly very quickly.”

He adds that a unified approach to data management can also allow organizations to innovate where it matters.  Instead of getting creative with how to access the data itself, researchers can focus on developing high-value algorithms that offer clinical decision support and other key insights that will support the delivery of high-quality, cost-effective care.

In short, with AI bound to expand quickly over the next few years, developing a robust, future-proof, and seamless data architecture can help ensure that a healthcare organization can “be disruptive instead of disrupted.”