That said, breaking ground on a net-new AI investment isn’t necessarily the heavy lift many fear it would be.
Like all healthcare technology implementations, a successful go-live begins with a solid strategy. For example, AI doesn’t need to solve all problems right out of the gate. Instead, consider a “crawl, walk, run” strategy that begins with automating specific tasks.
Iterate around your most essential pain points; AI is best done in an agile, experimental environment, rather than one that is broad and formless. Look at your available data, then pick a business process that has potential for optimization. There’s little risk in leveraging data around specific pain points. With each incremental iteration, you can move onto more ambitious initiatives. Next, create a culture and overall framework for rapid innovation. Set up a feedback loop that allows you to run experiments and gain results that provide value to end-users.
Acquiring the infrastructure, technology, and brain trust needed to uncover insights from incomprehensibly large and continuously growing data sets is the industry’s next great challenge. Millions of lives and billions of dollars of revenue and cost efficiency is at stake.
The promise of AI is no longer on the distant horizon, and it’s the farthest thing from a buzzword. It exists in the here and now. The unique complexities of the financial, administrative, and consumer experiences sides of healthcare are not only ideally suited to AI’s capabilities, but can deliver immediate and transformative results.
About the author: Brian Robertson is the CEO of VisiQuate.
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