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The team recently attended MHPA26 and contributed to a panel discussion on AI and workforce strategy. The 2026 Medicaid Health Plans of America Annual Conference (MHPA26) took place September 28–30 in San Antonio, Texas. This year’s event, themed Rooted in Resilience, Flowing Forward, centered on the future of Medicaid managed care, with sessions covering access to care, complex care, policy, and other pressing industry topics.

Instead of sending every eligible member into another outreach queue, health plans can use healthcare analytics to get more selective about who gets contacted, when outreach happens, what type of intervention makes sense, and how much effort to put behind it. That leads to fewer low-value touches and a better sense of which interventions are actually earning the resources behind them.

A payment integrity vendor can show up with an impressive savings projection, a long list of edits, and a polished demonstration. Those materials only go so far in showing how the partnership will perform against your actual claims, policies, contracts, systems, and internal processes. Payment accuracy problems can come from many places. CMS reported an estimated Medicare Advantage improper payment rate of 6.09% for fiscal year 2025, representing $23.67 billion. CMS also notes that improper payments are not automatically fraud and can include overpayments, underpayments, insufficient documentation, or payments that otherwise fail to meet program requirements.

Mature risk adjustment programs rarely struggle because they lack data. More often, the problem is that different systems, vendors, and functions tell different stories about what happened to that data. For health plan and managed care organization leaders, the more useful question is whether the organization can explain the conversion from identified opportunity to accepted data to realized financial impact, including where and why that conversion breaks down.

A member with several months of high utilization is easy to flag. The harder and more valuable task is identifying a member whose risk is changing before the pattern becomes obvious through paid claims. That requires models capable of drawing from a broader mix of information, detecting movement across time, and separating actionable risk from expensive events that a care management program is unlikely to influence. AI can strengthen that process, but health plans still have to decide what risk they want to predict, how scores will affect work queues, which interventions correspond to each signal, and how member responses will feed back into future decisions. Without those connections, a more sophisticated score can become another data point competing for attention.

Healthcare analytics is much more than building another dashboard for basic reporting. For payer executives, it’s identifying the operational signals that consistently predict financial performance, member experience, quality outcomes, and regulatory performance before quarterly results arrive. To get the most value from healthcare analytics, health plans have to focus leadership attention on the handful of metrics that reveal where performance is improving, where risk is building, and where action can make the biggest difference.