Healh insurers are interested in assessing the potential for AI to improve everything from prior authorization to member management. But data fragmentation poses an enormous challenge to AI readiness in healthcare for both payers and provider health plans. On a recent webinar sponsored by Verato, panelists from SCAN Health Group and the Alliance of Community Health Plans discussed how their organizations are meeting the moment.
Martin Hougaard, Vice President, Product Marketing at Verato, served as the moderator.
Thomasina Anane, Associate Vice President of Enterprise Analytics for the Alliance of Community Health Plans, highlighted the need for shared data definitions and consistent data governance.
Vinay Kulkarni, the Chief Information Officer of SCAN Health Plan, stressed the importance of high-quality, interoperable data and robust data privacy practices. They agreed that AI readiness goes beyond technology – it requires governance and strategic planning.
The webinar also highlighted practical next steps for addressing data fragmentation and the importance of trust.
Anane identified payment accuracy and risk adjustment as key areas affected by data fragmentation, which lead to additional challenges.
“I think payment accuracy and risk adjustment are definitely the top of mind for many folks today,” Anane said. “Fragmentation shows up as documentation gaps, misdiagnoses, undercoded acuity, depending on who in the health plan industry you’re talking about. It leads to whether or not your plan succeeds, if you’re being paid accurately for the patients that you’re serving. And so, 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 pain industry.”
Anane also discussed the internal operational fragmentation within health plans, emphasizing the need for shared data definitions and consistent data governance across functions.
Kulkarni explained what AI readiness means for a health plan.
“AI readiness is an operational and a structural reality…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. You’ve got to have embedded human-in-the-loop checkpoints. A mandatory manual review. These are structures that you have to put in place. And finally, I would add that you need to ensure that you have a deterministic data lineage. By that, I mean the capability to trace AI-generated output exactly back to its raw data input transformations and applied business rules. This will help you define a real structure of how you want to get AI in your organization.”
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