Health plans face a difficult path to AI adoption without first solving deep data fragmentation, panelists from SCAN Health Plan and the Alliance of Community Health Plans said during a recent webinar. Inconsistent data definitions and weak governance lead to payment inaccuracies and risk adjustment errors that directly affect plan competitiveness.
Fragmented data erodes payment accuracy
Thomasina Anane, Associate Vice President of Enterprise Analytics for the Alliance of Community Health Plans, pointed to payment accuracy and risk adjustment as top concerns. "Fragmentation shows up as documentation gaps, misdiagnoses, undercoded acuity, depending on who in the health plan industry you're talking about," Anane said. "If you're not capturing this information accurately, you're not being paid accurately. It's affecting your competitiveness, it's affecting whether or not you can actually thrive in the competitive landscape that is the health plan industry."
She also described how fragmentation occurs internally, with different functions within a health plan using separate data definitions. Without consistent governance, analytics teams struggle to produce reliable insights, she explained.
Defining AI readiness for health plans
Vinay Kulkarni, Chief Information Officer of SCAN Health Plan, offered a concrete definition of AI readiness. "AI readiness is an operational and a structural reality," Kulkarni said. "True AI readiness means your data workflows and compliance guardrails are built so that your machine learning models and your large language models can rely on that." He emphasized the need for mandatory manual review checkpoints and deterministic data lineage, so that any AI-generated output can be traced back to its raw data source and applied business rules.
Kulkarni's framework underscores that technology alone won't suffice. Health plans need to embed human oversight and traceability into their AI processes from the start. For healthcare organizations building these foundations, developing internal expertise through AI for Healthcare training can help teams align data practices with regulatory requirements.
Governance and trust as prerequisites
The webinar discussion made clear that governance and trust are not optional add-ons. Anane stressed shared data definitions across the enterprise, while Kulkarni linked compliance structures directly to model reliability. Without these elements, even advanced AI models can produce unreliable or biased outputs.
Why this matters for healthcare professionals
For health plan leaders, the practical takeaway is that AI initiatives demand upfront investment in data quality, not just technology procurement. Payment accuracy, risk adjustment, and operational efficiency all depend on clean, interoperable data. Building the governance frameworks and human review checkpoints Kulkarni described will determine whether AI delivers real value or introduces new risks.
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