Why Provider Data Must Be the Foundation, Not an Afterthought, In Health Plan AI
In today’s healthcare landscape, many health plans are investing heavily in AI, automation, and analytics to cut administrative costs and improve performance. But as our CEO Megan Schmidt explains, most of these initiatives struggle not because the technology is immature, but because the foundational provider data feeding them is unreliable and fragmented.
Across payer systems, from enrollment and credentialing to claims and directories, provider information exists in disconnected silos. When data changes (such as specialties, locations, or affiliations), it often has to be updated manually across multiple platforms, leading to inconsistencies, inaccuracies, and ongoing manual reconciliation. That work isn’t directly tracked in budgeting, but it quietly inflates administrative costs and erodes ROI on digital transformation efforts.
As AI technologies expose these data weaknesses, rather than solve them, plans find that automation stalls in pilot mode, staff revert to manual verification, and efficiency gains remain unrealized. For CFOs under pressure to reduce SG&A while supporting growth, this represents a significant financial blind spot with real margin impact.
The solution isn’t merely data cleanup after the fact, but building a continuously updated, authoritative provider data infrastructure that acts like a shared supply chain across systems. With a reliable data foundation, plans can finally unleash the power of AI, lower operating expenses, boost auditability, and support scalable growth without proportional increases in headcount.
— From an article by Megan Schmidt, President & CEO at Madaket READ HERE
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