Scott MacLean has spent eight years building MedStar Health’s IT environment around a simple conviction: fewer tools used better.
As senior vice president and CIO at Columbia, Md.-based MedStar, a 10-hospital integrated delivery system affiliated with Georgetown University Medical Center in Washington, D.C., he applies the same discipline to artificial intelligence.
“The advent of generative AI gives great opportunities,” he said on a recent episode of the “Becker’s Healthcare Podcast.” “McKinsey and others have talked about whether you’re a taker, shaker or maker in the AI space. We certainly want to take advantage of the AI that’s offered from all of the platforms that we’ve invested in.”
For MedStar, that means starting with what the vendors have already built and embedded in the platforms the system relies on — its EHR, ERP and business process automation software. Then others can absorb the early implementation risk, reflecting how Mr. MacLean has long approached technology to simplify the environment first, then use it deeply.
But platform AI only goes so far. MedStar has accumulated a vast data lake house with years of clinical, operational and financial data that off-the-shelf vendor tools aren’t built to fully exploit. That’s where the second tier of Mr. MacLean’s strategy takes shape.
“We, like other healthcare systems, have an enormous amount of data in a data lake house where we will be making some AI interventions on the data that are not provided by the platforms and probably some of our own custom development using proprietary information or data that we have,” he said. “The best outcome is our ability to take advantage of what’s provided by our vendors that we’ve invested in so far.”
That discipline is also a financial one. MedStar faces the same reimbursement-expense squeeze confronting health systems nationally, and Mr. MacLean sees end-to-end automation as a lever for cost management that doesn’t require sacrificing quality. The system is examining its workflows for repetitive, data-intensive processes that can be automated, freeing staff capacity in an environment where revenue growth is consistently outpaced by rising costs.
The longer view is more ambitious. With MedStar’s Epic go-live set for September 2027, Mr. MacLean anticipates the platform consolidation will deliver better data infrastructure and more mature AI tooling from the vendor — building the foundation for AI’s real promise in healthcare.
“I think the next step is to be able to have customizable at the point of care support for clinicians making decisions about the specifics of a patient, their genetic makeup, what various therapies would best fit them,” he said. “I think that is obviously very complex and therefore is ripe for automation and artificial intelligence being able to process a lot of information and provide good suggestions while obviously keeping the human in the loop for decision making, at the point of care.”
The ideas for AI at the point of care have existed for years. What’s changing is the data infrastructure and processing power to make them real.
“For a long time as we’ve labored in healthcare technology, we have methodically — at least over my time — it’s really only been limited by time and money,” he said. “There’s great opportunities to automate the clinical workflows. Now we have most of our information online. We’re gathering a lot of data.”
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