Tom Kerr (TK): Mainstream use of generative AI has prompted claims leaders to explore how the technology can be used to improve claims management. GenAI无疑为P提供了巨大的机会&C行业, tech leaders must be diligent in ensuring the intricate knowledge and experience in claims management is integrated into these programs.
在今天的播客中, 我们邀请了Enlyte的技术专家迈克主教和迈克Cwynar来讨论这些问题. 先生们，欢迎光临.
迈克·毕夏普，从你开始吧. 当您听到客户将GenAI纳入他们的索赔程序时, 他们通常在寻找什么?
迈克主教我认为他们现在所做的就是每个人都在做的, 这是在试图理解这项技术. So, 它被炒作了, 它是最近才出现的, 我认为每个人都看到了这项技术的巨大前景, 但他们正在努力了解如何使用它.
这是所有这些技术最困难的部分? 为技术而研究技术是一回事, 而是将其整合到工作流程中, 尤其是涉及到治疗受伤员工的工作流程, 在汽车事故中受伤的人, 你必须非常小心如何使用它.
So, 我认为现在客户正在做的是试图理解用例的种类, 它会应用在哪里, 了解他们如何负责任地使用这项技术.
TK: Do they come with a general idea of how they’d like to incorporate the technology in their claims management programs, 还是仍然存在学习差距?
主教我认为肯定存在学习差距. 当然, 他们可以有自己的想法, 他们读到的最新的新闻稿, 或者他们可以想出一些技术上的想法，你可以用它来做, 正确的?
Like, "Oh, I could summarize this, or I could have a chatbot that does that," but nothing specific. 我认为, 再一次。, 他们只是想更好地理解什么类型的事情是真正可能的, 然后再一次, 他们将如何整合它们. 所以，我认为绝对存在学习差距.
迈克Cwynar:是啊, I think most come in with a list of problems that they think that they can solve with this just given an understanding of their businesses the potential behind GenAI.
作为商人，我不知道他们总是知道从哪里开始. 他们只是有个问题. 和 迈克的观点, some recent announcement sounds like maybe it would check that off their list. 我能快速预测索赔是否需要更长期的东西吗? 今天，有时他们直到这个周期的后期才知道. Tomorrow, they’d like to figure it out sooner rather than later through the use of historical data.
Many just don’t necessarily know where to start and I think that’s where the learning gap comes into play. 因为这并不一定像纸上写的那么简单.
TK: 继续这个主题, 迈克Cwynar, what other challenges do claims industry professionals face in better understanding and implementing this technology?
Cwynar坦率地说，我认为有了数据，人们对此有了更多的了解. 你开始问这样的问题，“你有这个的数据吗? 你上次打扫是什么时候?”
您最终从许多(至少是较大的付款人)那里听到的是多个系统中的数据. 它不一定那么容易到达. 我认为仅仅有大量的数据并不能解决问题. 适当数量的数据对于构建和训练这些模型非常重要.
我认为 the last part of it really is who within the organization is the subject matter expert that can help validate that these models are producing answers to the questions the same way that their business typically would? 这就是它背后的力量. Every payer can have their own sort of unique way of settling claims and handling underwriting, 等. 和, so, GenAI的力量是为了让他们在做决定时拥有自己的哲学.
但是训练这些模型需要时间，而且这些模型需要数据. 并不是所有的数据都是好的数据. 所以, 我认为这是很多人真正开始关注的地方, 意识到有很多实体都有这些潜在的强大模型, 但如果没有客户数据，它们就毫无用处.
主教我想是另一个, 太, that is not as well appreciated sometimes is the fact that the technology providers that typically serve our industry and serve all industries, 也在尝试学习这些技术，但他们真的还没有准备好.
当你和技术供应商见面的时候, they’re still trying to figure out how they’re going to make some of these core technologies available to us. 所以，它真的还没有确定在哪里你可以做出技术上的选择.
大多数人无法为外出找理由, 例如, 并且自己训练一个大的语言模型. 他们将以一个为基础, 通过不同的技术使之专业化, 包括即时工程.
但因为技术供应商现在还没有准备好, you’re almost in a wait‑and‑see mode where you’re trying to figure out who’s going to come to market with the best 太ls. 所以，在那之前，我认为这是把这些东西推向市场的另一个障碍.
TK: 我认为这是我们下一个话题的一个很好的过渡. 付费用户在选择GenAI技术合作伙伴时应该问哪些问题?
主教首先, you want to make sure that the tech provider appreciates the particular challenges in our industry. One of the euphemisms that they have around these large language models is the errors that can come up. 他们称其为幻觉或其他术语, 这就意味着模型给出了错误的结果, 这是编出来的. 所以，你必须处理所有这些事情.
If that happens when you’re trying to figure out something that you’re buying online or something where the ramifications are small, 这没什么大不了的. 如果你向某人提供医疗保健信息, 这种后果可能会危及生命. 所以, 你必须确保提供模型的人, 他们提供了技术, 是否考虑到你将如何负责任地使用这项技术. 并确保护栏在那里.
偏见之类的东西会潜入这些模型. 只是要确保他们不只是在使用技术，或者以那种方式看待它. 他们正在考虑如何将其应用到我们的行业中.
TK: 玩家可以遵循哪些策略来确保他们从GenAI中获得最佳结果?
Cwynar: I think one of the things that we’ve been talking to many about is having a very clear understanding of the problem statement and how you measure that, 你得到了你想要的结果.
所以，就像迈克刚才说的，监控不合理答案的能力. 例如, there’s a model that’s running that’s helping an adjuster make decisions that have some regulatory compliance background in them, 突然之间, a new fee schedule in Florida comes out or Michigan comes out with some of these models that could potentially become irrelevant overnight.
So, 我认为最重要的事情之一就是要确保, 在你运行这个的所有情况下, 这需要一定的专业知识, 圈内人, 与…有关, 不出意外的话, periodically audit and look at the results that are coming out of the models to make sure that they continue to be relevant.
因为这些不是一劳永逸的事情. 我们不做一次，然后它们就会一直工作下去. 你必须定期对这些东西进行管理，这样才能形成良好的数据卫生，对吧?
数据也是一样. 它会随着时间而改变, and so you got to just be really on top of that kind of governance model to make sure that these models don’t start providing answers that potentially become irrelevant at some point in the future and no one really understands it.
TK: So, 就目前GenAI在理赔管理领域的工作情况进行评分而言, are payers mostly looking at how well it can make processes easier or make claims handling more efficient? 或者他们还有其他想要实现的目标?
Cwynar 是啊，就像进步胜过完美. 你没有必要一开始就处理最复杂的问题. 从小事做起，适应它，了解如何监控、控制和审计它.
Because the possibilities of what you can apply this to become somewhat unlimited within the world of claims, 当你考虑与投保人互动时, 受伤的员工, 等.从它对承保的潜在影响到欺诈检测，都在里面. 但, it’s reliant on people that really understand the business and the ability to keep an eye on what’s going on day to day.
TK: 你对GenAI在未来一到五年内将如何影响这个行业有什么预测吗?
主教我想你会在一些特定的领域看到它. 我认为, 我和迈克给出的几乎所有答案中都有这个线索, 你是否必须考虑治理以及如何负责任地使用技术.
因此，出于这个原因, I think the impact that you’re going to see is less the stuff that is hyped and demoed; where you see an individual customer or, 在我们的例子中, 一个受伤的工人直接与技术互动. 我认为这项技术将在幕后产生更大的影响，因为这样你就可以控制它.
So I don’t see it happening in the next one or two years where we would provide a medical summary to a claimant, but we could certainly provide a medical summary that came out of GenAI to someone internal to make sure that they would look out for these errors, 幻觉, 诸如此类的事情.
So, 我认为这是负责任的使用要求, 尤其是在我们这个行业, is going to mean that the technology will be used 幕后故事 for automation to sort of help knowledge workers do their job more efficiently and better.
Cwynar是的，我同意. 我认为速度和效率是这里最大的机会. People are still going to file claims and they’re going to need help from time to time and you’re going to always want a human being available to have a conversation.
So, I don’t see this world where all of a sudden there’s a virtual adjuster who handles everything for you. 这并不意味着, 迈克的观点, 幕后故事, there isn’t a virtual adjuster that’s able to quickly and efficiently gather all the information that’s needed, 提供建议, help sort further claims along much faster than they can today given the typical amount of work that sits in front of an adjuster.
我想你会在那里看到更多的助理理算员, 虚拟调节器组件是相当强大的许多我们今天的行业. 和, 我认为是能够快速分诊的能力, 并获取信息，否则不会总是可用的. 比如说，如果有人刚刚提出索赔，他们之前发生过事故, which would alter the course of the care that somebody needs to be able to identify things like that upfront.
它只是在早期快速地创造了更多个性化的护理计划, 然后让人们更好，更快地开始他们的生活. 这些可能是许多人正在谈论和关注的一些领域, 但我认为在未来几年内最有潜力.
TK: 谢谢，迈克和迈克. 我们很快会在另一期播客中回来. 到那时候，谢谢收听.