Kerri Webster

Across provider networks and health plans, a similar pattern keeps surfacing: everyone senses where work breaks down, but no one can point to it end-to-end with confidence. It’s not just about dashboards; it’s about the friction points between Epic, claims, and operations. Specifically, where does the 'perfect' clinical record fall apart in the authorization process? Fragmented systems, redundancy across silos, admin waste, mismatched definitions, and delayed or duplicated feeds necessitate extra work across eligibility, authorization, discharge planning, and claims. Under cost pressure, burnout, the goal is not more dashboards, it is a shared operational picture that shows where capacity is being burned and where action actually impacts outcomes.

Interoperability has been “solved” on paper for over a decade, yet data sharing still breaks in the moments that matter most. The issue is not willingness or standards adoption, it’s usability. What arrives is often late, inconsistently formatted, or incomplete, forcing teams back into manual work just to keep care and revenue moving. It’s not an issue of data access and visibility, but rather a question of whether data can be used immediately, without rework. Progress comes from agreeing on what actually needs to move, how fresh it must be, and who owns failure when the pipeline breaks.

Expectations placed on the data office have shifted faster than its mandate. Leaders are no longer judged on report delivery, but on whether data work materially protects margin, improves operational efficiency, and supports enterprise change such as M&A, platform consolidation, or new care models. Yet many teams remain stuck in a service posture, reacting to inbound demand with little authority to challenge priorities or stop low-value work. Without explicit ownership of outcomes, prioritisation rights, and clear success measures, even mature teams regress into ticket queues. The point is not tooling, it is redefining what the data office is accountable for, and what it is allowed to say no to.

The so-called “unicorn” candidate, fluent in modern data stacks and deeply grounded in healthcare operations, is effectively non-existent at scale. Leaders are realizing they cannot hire their way out of this crunch; they have to manufacture capability deliberately. This panel examines how organizations are redesigning the talent supply chain itself, moving beyond salary debates to structural solutions. From internal data academies that convert domain experts into analytics talent, to global delivery models that expand capacity without losing context, the focus is on building teams that can actually sustain data and AI ambition.