AI就绪始于解决医疗保健的数据碎片化问题
AI Readiness Starts with Solving Healthcare’s Data Fragmentation Problem
由...呈现
医疗保险公司有兴趣评估AI在从预先授权到会员管理等方面的改进潜力。但数据碎片化对医疗保健领域的AI就绪构成了巨大挑战，无论是对于支付方还是提供者健康计划。在最近由Verato赞助的网络研讨会上，来自SCAN Health Group和社区健康计划联盟的小组成员讨论了他们的组织如何应对这一时刻。
Martin Hougaard，Verato产品营销副总裁，担任主持人。
Thomasina Anane，社区健康计划联盟企业分析副总裁，强调了共享数据定义和一致数据治理的必要性。
Vinay Kulkarni，SCAN Health Plan首席信息官，强调了高质量、互操作数据以及健全的数据隐私实践的重要性。他们一致认为，AI就绪超越了技术本身——它需要治理和战略规划。
网络研讨会还强调了解决数据碎片化的实际后续步骤以及信任的重要性。
Anane指出支付准确性和风险调整是受数据碎片化影响的关键领域，这些领域又导致了额外的挑战。
“我认为支付准确性和风险调整无疑是当今许多人最关心的问题，”Anane说。“碎片化表现为文档缺口、误诊、编码不足的严重程度，具体取决于你在健康计划行业中与谁交谈。它决定了你的计划能否成功，是否为你所服务的患者获得准确的支付。因此，如果你没有准确捕获这些信息，你就不会得到准确的支付。这会影响你的竞争力，影响你是否能在健康计划行业的竞争格局中真正蓬勃发展。”Anane还讨论了健康计划内部的运营碎片化，强调了跨职能共享数据定义和一致数据治理的必要性。
Kulkarni解释了AI就绪对健康计划意味着什么。
“AI就绪是一种运营和结构性现实……真正的AI就绪意味着你的数据工作流和合规护栏已经建立，以便你的机器学习模型和大型语言模型可以依赖它们。你必须嵌入人在回路的检查点，以及强制的人工审查。这些是你必须建立的结构。最后，我会补充说，你需要确保拥有确定性的数据血缘。我的意思是，能够将AI生成的输出精确追溯到其原始数据输入转换和应用的业务规则。这将帮助你定义如何在组织中引入AI的真实结构。”要观看完整的网络研讨会，请填写以下表格：
图片：
Alllex，Getty Images
presented by
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 plan 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.” To watch to the full webinar, fill in the form below:
Picture:
Alllex, Getty Images