Abridge 内幕：AI 如何旁听 1 亿次诊室对话 — Abridge 的 Janie Lee 与 Chai Asawa

The first and most important thing is context is everything as Tai alluded to. 最重要的一点是，正如 Chai 所说，上下文就是一切。 And I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. 我也在思考，我们怎样才能从被动的警报系统，转变为在最关键时刻真正主动的智能。 One thing we like to say is we want our product to feel like air conditioning. 我们常说，希望我们的产品像空调一样。 It should be in the background just making things better. 它在后台默默运转，让一切变得更好。 And maybe if and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. 如果遇到临床风险很高的情况，而我们清楚现在干预比以后更重要，那就应该立刻行动。 Before we get into today's episode, I just have a small message for listeners. 在今天的节目开始之前，我想对听众说几句话。 Thank you. 谢谢。 We would not be able to bring you the AI engineering, science, and entertainment content that you so clearly want if you didn't choose to also click in and tune into our content. 如果不是你们选择持续收听我们的内容，我们就无法继续带来这些 AI 工程、科学和娱乐内容。 We've been approached by sponsors on an almost daily basis. 几乎每天都有赞助商找上门来。 But fortunately, enough of you actually subscribe to us to keep all this sustainable without ads and we want to keep it that way. 好在足够多的你们订阅了我们，让我们不靠广告就能维持运营，我们希望一直保持这样。 But I just have one favor to ask all of you. 但我只有一个请求。 The single most powerful, completely free thing you can do is to click that subscribe button. 你能做的最有力、完全免费的一件事，就是点击订阅按钮。 It's the only thing I'll ever ask of you. 这是我唯一会求你们做的事。 And it means absolutely everything to me and my team that works so hard to bring the Inspace to you each and every week. 这对我和团队意义重大，他们每周都在努力把 Latent Space 带给大家。 If you do it, I promise you we'll never stop working to make the show even better. 如果你订阅了，我保证我们会一直努力把节目做得更好。 Now, let's get into it. 好了，让我们开始吧。 Okay, this is a special crossover late in space unsupervised learning pod. 好，这是一期特别的 Latent Space 与 Unsupervised Learning 跨栏节目。 Very, very excited to do this, you know, once a year at this point. 非常非常期待能这样合作，大概一年一次。 We get together and this is a fun occasion to get to do it on. 我们聚在一起，这次是个很好的机会。 Uh we I really wanted to talk to a bridge. 呃，我一直很想聊聊 Abridge。 Uh but I felt very underqualified because it's healthcare is not something we cover very intensely and it just so happens that points are big big investors and supporters of uh bridge. 但我觉得自己资历不够，因为医疗不是我们深度报道的领域，而 Redpoint 恰好是 Abridge 的重要投资人和支持者。 So 所以…… anytime you want to have a portfolio company on on your podcast so please by all means. 只要你想把旗下被投公司带上你的播客，尽管说。 So we introduce our guests um Chai and Janie welcome to the pod. 那么，介绍一下我们的嘉宾，Chai 和 Janie，欢迎来到节目。 Thanks for having us. 谢谢你们邀请我们。 We're excited to be here. 很高兴来到这里。 Thank you. 谢谢。 Yeah. 好的。 So for for listeners 那么对于听众来说…… uh what do you guys do just to situate you guys in the in the company? 你们能简单介绍一下自己在公司里做什么吗？ Uh Bridge is a clinical intelligence layer for health systems. Abridge 是面向医疗系统的临床智能层。 We really started with documentation and building for clinicians. 我们最初专注于文档记录，为临床医生而构建。 And we think that, you know, as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. 我们认为，医生每周要花 10 到 20 小时在文档记录上，减轻这个负担非常重要。 There's a massive doctor shortage in the country. 全国的医生缺口非常严重。 We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. 我们也认为，患者与医生之间的对话可能是医疗中最重要的工作流程。 It's obviously where care is given and received, but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given the treatment. 显然，医疗服务在这里发生，但如果想想我们 GDP 的 20% 流向医疗，几乎所有事情都衍生自这段对话，无论是理赔、付款，还是诊断和治疗。 And we've started with a conversation to reduce the burden for doctors on documentation, but we're really excited about the path ahead as we become this broader clinical intelligence layer. 我们从对话入手，减轻医生的文档负担，但更让我们兴奋的是前方的路，成为更广泛的临床智能层。 I'm Chai. 我是 Chai。 I work on clinical decision support at ABridge. 我在 Abridge 负责临床决策支持。 And so I think as Jenny said that we have this we're uniquely situated where we started off with the clinical note. 正如 Janie 说的，我们有一个独特的起点，就是从临床病历开始。 What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation during the conversation and after the conversation if you did have access to all the context about patients pair guidelines medical literature and put that together and to serve you know what how healthcare could look fundamentally different. 我真正兴奋的，也是我们正在拓展的方向，就是如果你拥有患者数据、医保政策、医学文献的全部上下文，在对话前、对话中、对话后，你能做什么，医疗会有怎样根本性的不同。 Yeah. 对。 And that's like the context engine that you guys have. 那就是你们的上下文引擎。 Is that what it's called? 是这样叫的吗？ Okay. 好的。 Uh so historically as I understand it company started in 2018. 据我了解，公司成立于 2018 年。 Uh a lot of people would be familiar with like the AI voice notes form factor that that doctors would be like well do you consent to be being recorded? 很多人可能熟悉 AI 语音记录这种形态，医生会问你是否同意录音。 It replaces handwriting and what have you. 它取代了手写记录。 Uh but I it it sounds like more recently there's been a big transition in the company or just tell me about like the the broader transition. 但听起来最近公司经历了一次重大转型，能说说这个大转型吗？ Yeah. 当然。 So from a transition perspective, we really think about our journey 从转型的角度看，我们把自己的历程想成…… as how do we, you know, first chapter was first act was how do we help save time and that's where a lot of that original product was 第一阶段，就是怎么帮人节省时间，早期产品就在那里。 which like by the way one of the interesting stats on your landing page was like people spend doctors spend like time after hours. 顺便说一句，你们落地页上有个有趣的数据，医生要花很多下班时间…… They call it pajama time. 叫睡衣时间。 Okay. 好的。 Why is that pajama time? 为什么叫睡衣时间？ Uh doctors after work in their pajamas at home or just writing and catching up on their notes every day. 医生下班后在家穿着睡衣，每天补写病历。 And you know, I think some of our favorite customer love stories. 我们最喜欢的一些客户故事…… We have a Slack channel called Love Stories. 我们有个 Slack 频道叫 Love Stories。 We have clinicians telling us a bridge has helped us, you know, from retiring early. 有医生告诉我们，Abridge 帮他们推迟了提前退休的念头。 We're now finally able to go home and eat dinner with our kids for the first time and 我们现在终于能下班回家，第一次和孩子一起吃晚饭，还有…… save their marriage and some. 挽救了婚姻之类的。 Yeah. 对。 One of your quotes was like we're not divorcing anymore. 你们有个引用，大意是我们不再离婚了。 Like why? 怎么说？ Like 就是…… cuz they're working too much, I guess. 因为他们加班太多了吧。 Yeah. 是的。 But um in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. 但我们正在扩展的第二和第三阶段，是帮助医疗系统省钱和赚更多钱。 Health systems are operating with, you know, record low operating margins. 医疗系统的运营利润率处于历史低位。 It's getting harder and harder to serve patients. 服务患者越来越难。 And they have regulatory, some tailwinds, but also a lot of headwinds coming their way. 他们面临一些监管利好，但也有很多阻力。 And we think AI is ripe for helping on the saving and make more money piece. 我们认为 AI 正好能帮上省钱和增收这一块。 And then ultimately, how do we help save lives? 而最终，我们如何帮助拯救生命？ The fact that our software and our product is open millions of times a week before, during, and after a patient walks in the room um gives us massive opportunity with products like clinical decision support, what Chai is building, but so many others to actually improve patient outcomes and probably one of the most important workflows and and problems to be going after right now. 我们的软件每周被调用数百万次，在患者进入诊室前、中、后，这给了我们巨大机会，通过临床决策支持等产品，真正改善患者预后，这是目前最重要的工作流程和问题之一。 I mean I think one thing that's that's interesting chai is obviously you came over to a bridge from glee and I think about clinical decision support uh which is you know for our listeners is basically you know in the context of a visit helping a doctor figure out the right type of care it's really a search problem in many ways right of of going through lots of different data sources very analogous to your previous role as as as one of the uh earliest engineers over at Glean um I'm sure a lot of our listeners are curious what's uh similar about the problem set you're going after now and what feels different uh now that you're you're in healthcare 我觉得有趣的是，Chai 是从 Glean 来到 Abridge 的，临床决策支持本质上是在就诊过程中帮助医生找到正确的诊疗方向，在很多方面其实是个搜索问题，要遍历大量数据源，和你之前在 Glean 的工作非常类似。听众肯定很好奇，你现在面对的问题和之前有哪些相似，哪些不同？ Yeah. 对。 Um, very similar. 嗯，非常相似。 And I I think taking a step back, I think with every wave, there's a lot of like very similar patterns that happen across different products. 退一步看，我认为每一波浪潮中，不同产品都会出现很多相似的模式。 A lot of social networking products look the same. 很多社交产品看起来都差不多。 A lot of like crowd-based products look the same. 很多基于众包的产品也是如此。 And I think we're seeing that's very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. 在 Agent 时代，我们也看到了类似现象，Redpoint 的很多被投公司都是如此。 Um, and the key insight between both companies is that like you have amazing models, but like context is king and context is what actually puts them to work. 两家公司的核心洞察是相同的：你有很棒的模型，但上下文才是王，上下文才是让模型真正发挥作用的关键。 Um so I see in a lot of ways a lot of similarities and like this is a healthcare coded version of clean but I think the differences are really interesting. 我看到很多相似之处，这基本上是一个医疗版本的 Glean，但差异也很有意思。 A couple things that come to mind. 有几点浮现在脑海里。 First and foremost uh like the rigor at which in which in in the setting we are in um the downside risk is extremely high here in healthcare. 首先，我们所处的场景中，严谨性要求极高，下行风险在医疗领域非常大。 It can actually be fatal in some cases. 某些情况下甚至可能致命。 You prescribe something that the patient is allergic to for example. 比如，你开了患者过敏的药物。 Whereas at Glean it's like oh you got the question wrong. 而在 Glean，最多是答错了一个问题。 it wasn't the end of the world in most most cases. 大多数情况下，那不是世界末日。 And so what does that mean? 那意味着什么？ That shapes our evaluation strategy, both offline evaluation, progressive roll out, and there's a lot more we could kind of go into there. 这决定了我们的评估策略，包括离线评估、渐进式发布，还有很多可以展开说的。 Second thing that comes to mind is like vertical versus horizontal. 第二点是垂直和水平的区别。 Um, in both cases, there's there's a large variance, but when Glean is it's a much more horizontal company, there's a variance of personas, companies that you're working with. 两家公司都有很大的差异性，但 Glean 更偏水平，服务的企业类型和使用人群变化很大。 Um we also have a variance of uh personas, different types of specialties, different hospital systems, but the variance is a little more narrow. 我们也有不同的使用人群，不同专科、不同医疗系统，但差异范围要窄一些。 So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. 从产品角度，你能更专注地发力，尤其是当技术趋于成熟，你在构建从未存在过的新产品时。 It lets you go specific uh go after them much more easily and especially in healthcare where so many problems have were solved with labor and process that's actually extremely ripe for AI to keep helping augment and enable. 这让你可以更专注地切入特定场景，在医疗领域尤其如此，很多问题过去靠人力和流程解决，AI 现在正好大有可为。 Um, and then the final thing that I think that's really interesting, Bridge specifically compared to many other companies in the AI area is the modality we started with. 最后一点我觉得很有意思，Abridge 与 AI 领域很多其他公司相比，我们起步时选择的交互形态。 We're we're ambient and we're always listening in the background. 我们是环境式的，始终在后台倾听。 And I think many more AI products will go that way, but it's actually how we started. 我认为越来越多的 AI 产品会走这条路，但这其实是我们最初的出发点。