启动 HN:BitBoard(YC X25) – 面向医疗后勤的人工智能代理
嗨,HN!我们是Connor和Ambar,正在开发BitBoard(<a href="https://bitboard.work">https://bitboard.work</a>)。我们构建AI代理,处理医疗诊所中的重复行政任务,比如填写接收表、准备病历或管理转诊。
我们曾是Forward的早期员工,该公司在美国提供初级护理。为了扩大规模,我们依赖成千上万的远程承包商来处理重复的行政工作,比如对患者记录进行核对,以及根据护理计划安排后续跟进。这成为了一个巨大的瓶颈——成本高、容易出错,并且总是分散临床护理的注意力。我们的软件解决方案总是太脆弱,无法处理我们所监督的临床数据的变化。
当AI应用得当时,能够执行我们手动完成的许多任务。因此,我们决定再次尝试这个问题,构建我们当时希望拥有的工具,并帮助诊所使用它。
诊所将他们的标准操作程序(SOPs)发送给我们(例如,“在就诊前根据这些记录准备患者病历”),我们将其转化为执行工作的AI代理。这些代理像远程承包商一样:它们登录电子健康记录(EHR),导航内部工具,并在后台完成工作。与传统的机器人流程自动化(RPA)不同,我们内置了验证和确定性检查,以便客户可以确认任务已正确完成。
与低代码工具不同,客户无需学习任何新知识。他们不需要接触用户界面或维护逻辑。只需将任务交给我们,我们就会完成。临床医生不想要更多的屏幕!这些屏幕会分散注意力,并在操作中造成奇怪的瓶颈,因为总得有人来操作它们。我们的产品旨在解决这个问题。
这里有一个演示视频:<a href="https://www.youtube.com/watch?v=t_tQ0fYo85g" rel="nofollow">https://www.youtube.com/watch?v=t_tQ0fYo85g</a>。我们还没有自助服务,但在客户入职后几天内就能部署。我们正在努力加快这一进程。
我们的早期客户之一是一家快速发展的肥胖医学集团。他们的医疗助理(MA)每位患者在电子健康记录中输入接收表数据需要花费15到20分钟。这个任务占用了他们30%的时间。我们在一周内接手了这个工作。现在这个过程已经完全自动化,他们清除了积压,并加快了就诊速度。
在构建医疗代理时,有几个技术问题特别相关:
- 不可靠的接口:许多电子健康记录和诊所工具不遵循现代网络标准,使得自动化变得脆弱。我们已经分叉了浏览器使用,以解决其中的一些挑战。我们正在开发类似的基础设施,以便代理能够在桌面上和广泛的API上操作。
- 验证:在医疗领域,任务需要可证明的正确性。我们在每个工作流程中嵌入确定性检查,以便代理能够确认任务按预期完成,输出是准确的。
- 工作流程生成:诊所的SOP通常用自然语言编写,差异很大,但仍然描述了适用于诊所的实际流程。
我们的收费是按任务计算的,基于复杂性。我们符合HIPAA标准,具有审计日志,并在零保留环境中运营,除非审计要求另行处理。
在医疗这样高风险的环境中,建立信任是一个重要的部分。这部分内容包括使产品可靠。但另一个教育性的部分是学习如何向诊所介绍“代理”这一新概念。我们正在努力找到合适的方式来描述它们、引导它们、衡量它们。令人亲切的是,我们的一位客户的代理名叫“Robert Ott”,他们在每周更新中提到他,就像他是团队的一员一样 :) 我们正在学习很多,还有很长的路要走。
我们希望能认识其他在医疗集团或健康系统工作并希望减轻重复工作负担的人,以及在这一领域构建产品并希望交流经验的人。我们乐意分享迄今为止所学到的一切。
这是一个广阔的领域,包含了来自临床医生、技术专家、管理人员等的许多个人故事和经验教训。你对此有什么看法?我们期待听到你的声音。
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Hi HN! We’re Connor and Ambar, and we’re working on BitBoard (<a href="https://bitboard.work">https://bitboard.work</a>). We build AI agents that handle repetitive administrative tasks in healthcare clinics like filling out intake forms, prepping charts, or managing referrals.<p>We were early employees at Forward, which provided primary care across the US. To scale this, we relied on thousands of remote contractors to do repetitive administrative work like reconciling patient records, and scheduling follow-ups based on care plans. It was a huge bottleneck—expensive, error-prone, and always pulling attention away from clinical care. Our software solutions were always too brittle, never managing to handle the variance of clinical data we oversaw.<p>AI, when applied well, is capable of performing a lot of the tasks we manually did. So we decided to take another crack at the problem by building today what we would have liked to have back then, and to help clinics use it.<p>Clinics send us their SOPs (Standard Operating Procedures—for example, “prep a patient chart from these records before a visit”), and we turn them into AI agents that do the work. These agents act like remote contractors: they log into EHRs, navigate internal tools, and do the work in the background. Unlike classical RPA, we build in verification and deterministic checks, so customers can confirm it was done right.
Unlike low-code tools, there’s nothing new to learn. Customers don’t have to touch a UI or maintain logic. They just hand us the task, and we do it. Clinicians don’t want more screens! They erode attention and cause weird bottlenecks in operations because someone has to drive them. Our product is built to address this.<p>Here’s a demo video: <a href="https://www.youtube.com/watch?v=t_tQ0fYo85g" rel="nofollow">https://www.youtube.com/watch?v=t_tQ0fYo85g</a>. We’re not self-serve yet, but we deploy with customers in days after onboarding them. We’re working on speeding that up.<p>One of our early customers is a fast-growing obesity medicine group. Their MAs were spending 15 to 20 minutes per patient just entering intake form data into the EHR. That one task was taking up 30% of their MA time. We took it over in a week. It’s now fully automated, and they’ve cleared the backlog and sped up visits.<p>A few technical problems are specifically relevant to building healthcare agents:<p>- Unreliable interfaces: many EHRs and clinic tools don’t follow modern web standards, making automation brittle. We’ve forked browser-use to solve some of these challenges. We’re working on analogous infrastructure to let agents operate on desktops and across a wide range of APIs.<p>- Verification: in healthcare, tasks need to be provably correct. We embed deterministic checks into each workflow so agents can confirm the task was completed as expected and the output is accurate.<p>- Workflow generation: clinic SOPs are written in natural language and vary widely, yet still describe the actual process that works for clinics.<p>We charge per task, based on complexity. We’re HIPAA compliant, audit-logged, and operate in a zero-retention environment unless auditing requires otherwise.<p>A meaningful part is building trust in a high-stakes environment like healthcare. Part of that is making the product reliable. But another educational part is learning how to introduce a new concept like “agents” to clinics. We’re working on the right ways to describe them, to onboard them, to measure them. Endearingly, one of our customers’ agents is named “Robert Ott”, and they refer to him by name in their weekly updates like he’s a member of the team :) We’re learning a lot and have a long way to go.<p>We’d love to meet other folks who 1. work in medical groups or health systems and want to offload repetitive work, and 2. are building in this space and want to trade notes. We’re happy to share everything we’ve learned so far.<p>And this is a big space, with a lot of learnings from personal stories, from clinicians, technologists, administrators, and more. What do you make of it? We’d love to hear from you.