<p><em>持续的人工智能监测获得监管验证，标志着 Bayesian 实时临床智能平台的一个里程碑，以及其所提供的主动护理的新标准。</em></p>
<p>, /PRNewswire/ -- Bayesian Health 今天宣布，其脓毒症标记设备获得 FDA 510(k) 批准——这是首个获得此批准的持续人工智能脓毒症监测器。早期脓毒症检测是 Bayesian 实时临床智能平台的核心模块，该平台持续监测每位患者，识别需要关注的患者，并为临床医生提供工具，以采取正确的行动。</p>
<p><img src="https://imageproxy.pbkrs.com/https://mma.prnewswire.com/media/2977724/BayesianHealth_Logo.jpg?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://mma.prnewswire.com/media/2977724/BayesianHealth_Logo.jpg"/></p>
<p>这一批准建立在 Bayesian 之前的 FDA 突破性设备认证基础上，标志着医疗系统对临床人工智能的期望发生了转变：一个经过验证的、临床医生信任并在床边使用的实时临床智能层。电子健康记录仍然是临床文档的基础，但并未设计为实时持续解读患者数据。Bayesian 在其基础上进行了扩展——增加了始终在线的临床推理，能够在临床医生已经使用的工作流程中提供洞察，并帮助他们自信地采取行动。</p>
<p><strong>脓毒症检测的新方法</strong></p>
<p>实时临床智能需要一种根本不同的架构：一种能够持续推理不断变化的患者状态，涵盖所有数据类型，而不是依赖于偶发规则。这种架构是 Bayesian 平台的基础，为医疗系统提供了一个单一解决方案，以便在脓毒症、呼吸衰竭和心脏事件等多种病症中实现早期恶化的监测。</p>
<p>&#34;脓毒症是我工作了十多年的重点——部分原因是因为我失去了我所爱的人。这个批准背后的工作历时十多年：深入的研究、同行评审的验证，以及证明其在床边有效的部署。FDA 的批准是一个关键的里程碑，也是多年验证这一工具适应临床医生工作流程并帮助他们提前应对恶化而非被动反应的结果。这是临床人工智能应达到的标准，&#34;Bayesian Health 的首席执行官兼创始人 Suchi Saria 博士说道。</p>
<p><strong>为什么脓毒症常常被发现得太晚</strong></p>
<p>脓毒症是一个更广泛挑战的最明显例子：临床恶化可能会微妙地出现，往往被识别得太晚。脓毒症是美国医院的主要死亡原因之一，治疗费用昂贵，每年导致超过 500 亿美元的医院费用。每延迟一小时治疗，生存率可能下降多达 7.6%。尽管经过数十年的投资，脓毒症护理仍然受到早期识别不一致的困扰。</p>
<p>与 Bayesian 的解决方案不同，其他获得 FDA 批准的脓毒症解决方案要求临床医生首先怀疑脓毒症，无论是基于生物标志物的诊断血液测试，还是使用血液测试结果计算的风险评分。持续的脓毒症监测工具历史上一直处于 FDA 监管之外，临床医生对此视而不见：它们不断发出警报，通常是在已经被识别的患者身上，或者结果显示患者状况良好。Bayesian 的平台是首个在临床怀疑之前标记脓毒症的批准解决方案，能够在每位住院患者身上持续运行，并在那些尚未看起来生病的患者中识别恶化，且精确度获得临床医生的信任。</p>
<p>&#34;在临床医生怀疑之前捕捉脓毒症是一个大海捞针的问题。急性护理中脓毒症的发生率仅为 2% 到 4%，而传统诊断测试在已经怀疑有脓毒症的患者中运行的发生率为 20% 到 40%。错过一个病例是灾难性的，这要求达到大多数人工智能无法满足的精确度，即使是那些在初步研究中看起来有前景的工具，&#34;Bayesian Health 的临床企业负责人 Neri Cohen 博士说道。&#34;很少有公司进行了严格的数据科学和独立验证，以达到这一标准。更少的公司进行了同样严格的后续工作：将其融入临床工作流程，获得临床医生在床边的采用，并长期测量结果以确保其仍然有效。这就是临床人工智能在实践中能够交付的原因。&#34;</p>
<p><strong>经过验证的规模</strong></p>
<p>当临床医生及时对 Bayesian 的警报采取行动时，脓毒症患者在医院死亡的可能性降低 18%。这一发现来自于 2022 年在《自然医学》上发表的同行评审研究，涵盖了 764,707 次患者接触（其中 17,538 例为脓毒症）在五家医院的学术和社区基础设置中，使用该软件的提供者超过 2000 人。该研究显示，早期检测准确（1/3 病例经过医生确认），高灵敏度（82%）和显著的提前时间（提前 5.7 小时），高提供者采用率（89%）——这一比例在临床人工智能中很少见，大多数工具被忽视或关闭。</p>
<p>&#34;医疗系统花费了数十年时间通过电子健康记录等平台建立护理的数字基础。缺失的是一个实时智能层，能够持续解读这些数据并帮助临床医生更早采取行动。我们建立了这一基础设施——不仅针对单一病症，而是针对导致最多可预防伤害的病症。主动的、持续的监测可以拯救生命。这应该是每家医院的标准，&#34;Saria 说道。</p>
<p>&#34;在像克利夫兰诊所这样的医疗系统中部署脓毒症检测的人工智能需要严格的证据和无缝的临床整合，&#34;克利夫兰诊所企业脓毒症指导委员会主席 James Morrison 博士说道。&#34;我们的重点是确保这些工具在床边提供有意义的、可操作的洞察，这是最重要的。这一 FDA 批准是一个重要的里程碑，验证了我们对临床医生和我们服务的患者所要求的安全性和性能标准。&#34;</p>
<p>“在评估了多种脓毒症工具后，我们的临床医生发现，Bayesian 提供了高准确率的早期检测——这是一个他们可以信赖并用于诊断和治疗患者的工具，” 罗切斯特大学医学中心首席数字与信息官 Gregg Nicandri 医生表示。“通过使临床医生能够提前应对脓毒症，而不是追赶它，Bayesian 正在改变我们对这一领域临床人工智能的期望。”</p>
<p><strong>报销途径</strong></p>
<p>FDA 510(k) 的批准也使 Bayesian Health 在 2026 年 10 月开始的医疗保险和医疗补助服务中心的新技术附加支付（NTAP）计划中处于有利地位。NTAP 批准的最终决定将在 8 月初发布。如果如预期获得批准，这将使部署已获批准的脓毒症标记设备的医院在使用该解决方案的病例中获得潜在的增量支付，从而支持主动脓毒症监测的临床和财务案例。</p>
<p><strong>Bayesian 的更广泛平台</strong></p>
<p>该批准适用于 Bayesian 的脓毒症标记设备，这是一个更广泛的临床智能平台的一部分，旨在解决导致病情恶化、死亡率、住院时间和 ICU 使用率的主要因素——使用一个持续学习的单一系统。</p>
<p><strong>关于 Bayesian Health</strong></p>
<p>Bayesian Health 是一个实时临床智能平台，帮助医疗系统提供主动、高可靠性的护理。通过持续读取完整的患者记录以建立每位患者的基线并检测随时间的显著变化，Bayesian 应用复杂的临床推理来识别真正需要关注的患者，并提出明确的后续步骤。该平台旨在与领先的电子健康记录系统协同工作，通过增加一个持续智能层来增强现有的临床工作流程，帮助团队更早、更有效地进行干预。Bayesian 与领先的医疗系统合作，实现从反应性护理到主动护理的长期承诺，针对死亡率、再入院和住院时间的关键驱动因素，将质量作为一种持久的战略，而不是一系列不相干的举措。</p>
<p>了解更多信息，请访问 https://bayesianhealth.com。</p>
<p><img src="https://imageproxy.pbkrs.com/https://edge.prnewswire.com/c/img/favicon.png/query-c2Q9MjAyNi0wNS0xMiZzbj1OWTU3MTg3?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/https://edge.prnewswire.com/c/img/favicon.png/query-c2Q9MjAyNi0wNS0xMiZzbj1OWTU3MTg3"/></p>
<p>查看原始内容以下载多媒体：https://www.prnewswire.com/news-releases/bayesian-health-receives-first-ever-fda-clearance-for-continuous-ai-sepsis-monitoring-302769190.html</p>
<p>来源：Bayesian Health</p>

<p>Bayesian Health 已获得 FDA 510(k) 认证，批准其连续 AI 脓毒症监测设备，这标志着临床 AI 的一个重要里程碑。该设备通过持续监测患者并在临床怀疑出现之前提醒临床医生，从而实现对脓毒症的早期检测，脓毒症是医院内主要的死亡原因。该平台与现有工作流程无缝集成，增强了早期干预的能力，并有可能降低医院的死亡率。这一成就经过多年的研究和验证，使 Bayesian 的解决方案成为主动患者护理中的可信工具</p>

<p><em>Continuous AI monitoring earns regulatory validation, marking a milestone for Bayesian's real-time clinical intelligence platform and the new standard of proactive care it delivers.</em></p>
<div class="lb-trans"><p><em>持续的人工智能监测获得监管验证，标志着 Bayesian 实时临床智能平台的一个里程碑，以及其所提供的主动护理的新标准。</em></p>
</div><p>, /PRNewswire/ -- Bayesian Health today announced that its sepsis flagging device has received FDA 510(k) clearance—the first continuous AI sepsis monitor ever to do so. Early sepsis detection is a core module within Bayesian's real-time clinical intelligence platform, which continuously monitors every patient, surfaces those who need attention, and gives clinicians the tools to set the right actions in motion.</p>
<div class="lb-trans"><p>, /PRNewswire/ -- Bayesian Health 今天宣布，其脓毒症标记设备获得 FDA 510(k) 批准——这是首个获得此批准的持续人工智能脓毒症监测器。早期脓毒症检测是 Bayesian 实时临床智能平台的核心模块，该平台持续监测每位患者，识别需要关注的患者，并为临床医生提供工具，以采取正确的行动。</p>
</div><p><img src="https://imageproxy.pbkrs.com/https://mma.prnewswire.com/media/2977724/BayesianHealth_Logo.jpg" alt="" original-src="https://mma.prnewswire.com/media/2977724/BayesianHealth_Logo.jpg"/></p>
<p>The clearance, which builds on Bayesian's prior FDA Breakthrough Device Designation, signals a shift in what health systems can expect from clinical AI: a validated, real-time layer of clinical intelligence clinicians trust and use at the bedside. EHRs remain the backbone of clinical documentation, but weren't designed to continuously interpret patient data in real time. Bayesian builds on their foundation—adding always-on clinical reasoning that surfaces insights inside the workflows clinicians already use and helps them act with confidence.</p>
<div class="lb-trans"><p>这一批准建立在 Bayesian 之前的 FDA 突破性设备认证基础上，标志着医疗系统对临床人工智能的期望发生了转变：一个经过验证的、临床医生信任并在床边使用的实时临床智能层。电子健康记录仍然是临床文档的基础，但并未设计为实时持续解读患者数据。Bayesian 在其基础上进行了扩展——增加了始终在线的临床推理，能够在临床医生已经使用的工作流程中提供洞察，并帮助他们自信地采取行动。</p>
</div><p><strong>A New Approach to Sepsis Detection</strong></p>
<div class="lb-trans"><p><strong>脓毒症检测的新方法</strong></p>
</div><p>Real-time clinical intelligence requires a fundamentally different architecture: one that continuously reasons over evolving patient state across all data types, rather than triggering on episodic rules. It's this architecture that underpins Bayesian's platform, providing health systems with a single solution for early deterioration across multiple conditions like sepsis, respiratory failure, and cardiac events.</p>
<div class="lb-trans"><p>实时临床智能需要一种根本不同的架构：一种能够持续推理不断变化的患者状态，涵盖所有数据类型，而不是依赖于偶发规则。这种架构是 Bayesian 平台的基础，为医疗系统提供了一个单一解决方案，以便在脓毒症、呼吸衰竭和心脏事件等多种病症中实现早期恶化的监测。</p>
</div><p>&#34;Sepsis has been the focus of my work for more than a decade — a direction set, in part, by losing someone I loved to it. The work behind this clearance spans more than a decade: the deep research, the peer-reviewed validation, and the deployments that proved it works at the bedside. FDA clearance is a critical milestone, and it's also the consequence of years spent validating that this fits into clinician workflows and helps them get ahead of deterioration instead of reacting to it. That's the bar clinical AI should be held to,&#34; said Suchi Saria, PhD, CEO and Founder of Bayesian Health.</p>
<div class="lb-trans"><p>&#34;脓毒症是我工作了十多年的重点——部分原因是因为我失去了我所爱的人。这个批准背后的工作历时十多年：深入的研究、同行评审的验证，以及证明其在床边有效的部署。FDA 的批准是一个关键的里程碑，也是多年验证这一工具适应临床医生工作流程并帮助他们提前应对恶化而非被动反应的结果。这是临床人工智能应达到的标准，&#34;Bayesian Health 的首席执行官兼创始人 Suchi Saria 博士说道。</p>
</div><p><strong>Why Sepsis is Often Caught Too Late</strong></p>
<div class="lb-trans"><p><strong>为什么脓毒症常常被发现得太晚</strong></p>
</div><p>Sepsis is the clearest example of a broader challenge: clinical deterioration can emerge subtly and is often recognized too late. Sepsis is a leading cause of death in U.S. hospitals1, and an expensive condition to treat, driving over $50 billion in hospital costs annually2. Each hour of delayed treatment drops survival by as much as 7.6 percent3. Despite decades of investment, sepsis care remains hampered by inconsistent early recognition.</p>
<div class="lb-trans"><p>脓毒症是一个更广泛挑战的最明显例子：临床恶化可能会微妙地出现，往往被识别得太晚。脓毒症是美国医院的主要死亡原因之一，治疗费用昂贵，每年导致超过 500 亿美元的医院费用。每延迟一小时治疗，生存率可能下降多达 7.6%。尽管经过数十年的投资，脓毒症护理仍然受到早期识别不一致的困扰。</p>
</div><p>Unlike Bayesian's solution, other FDA-cleared sepsis solutions require a clinician to suspect sepsis first, whether it's a biomarker-based diagnostic blood test or a software risk score calculated using the results of the blood test. Continuous sepsis monitoring tools have historically sat outside FDA oversight, and clinicians tune them out: they fire constantly, often on patients already identified or who turn out to be fine. Bayesian's platform is the first cleared solution to flag sepsis before clinical suspicion, running continuously across every hospitalized patient and surfacing deterioration in those who don't yet look sick, at a precision clinicians trust.</p>
<div class="lb-trans"><p>与 Bayesian 的解决方案不同，其他获得 FDA 批准的脓毒症解决方案要求临床医生首先怀疑脓毒症，无论是基于生物标志物的诊断血液测试，还是使用血液测试结果计算的风险评分。持续的脓毒症监测工具历史上一直处于 FDA 监管之外，临床医生对此视而不见：它们不断发出警报，通常是在已经被识别的患者身上，或者结果显示患者状况良好。Bayesian 的平台是首个在临床怀疑之前标记脓毒症的批准解决方案，能够在每位住院患者身上持续运行，并在那些尚未看起来生病的患者中识别恶化，且精确度获得临床医生的信任。</p>
</div><p>&#34;Catching sepsis before a clinician suspects it is a needle-in-a-haystack problem. Sepsis prevalence in acute care is just 2 to 4 percent, while traditional diagnostic tests run at 20 to 40 percent on patients already suspected of having it. Missing a single case is catastrophic, and that demands a level of precision most AI can't meet, even tools that look promising in preliminary studies,&#34; said Neri Cohen, MD, PhD, Head of Clinical Enterprise at Bayesian Health. &#34;Few companies have done the rigorous data science and independent validation required to meet that bar. Fewer still have done the equally rigorous work that has to follow: building it into clinical workflows, earning clinician adoption at the bedside, and measuring outcomes long enough to know it's still working. That's what makes clinical AI deliver in practice&#34;</p>
<div class="lb-trans"><p>&#34;在临床医生怀疑之前捕捉脓毒症是一个大海捞针的问题。急性护理中脓毒症的发生率仅为 2% 到 4%，而传统诊断测试在已经怀疑有脓毒症的患者中运行的发生率为 20% 到 40%。错过一个病例是灾难性的，这要求达到大多数人工智能无法满足的精确度，即使是那些在初步研究中看起来有前景的工具，&#34;Bayesian Health 的临床企业负责人 Neri Cohen 博士说道。&#34;很少有公司进行了严格的数据科学和独立验证，以达到这一标准。更少的公司进行了同样严格的后续工作：将其融入临床工作流程，获得临床医生在床边的采用，并长期测量结果以确保其仍然有效。这就是临床人工智能在实践中能够交付的原因。&#34;</p>
</div><p><strong>Validated at Scale</strong></p>
<div class="lb-trans"><p><strong>经过验证的规模</strong></p>
</div><p>When clinicians act on Bayesian's alerts in time, sepsis patients are 18% less likely to die in the hospital. That finding comes from a 2022 peer-reviewed <em>Nature Medicine</em> study4 spanning 764,707 patient encounters (17,538 with sepsis) across five hospitals in both academic and community-based settings with 2,000+ providers using the software. This research showed accurate early detection (1 in 3 cases were physician confirmed) at high sensitivity (82%) and significant lead time (5.7 hours earlier), high provider adoption (89%) — a rate rarely seen in clinical AI, where most tools are ignored or turned off.</p>
<div class="lb-trans"><p>当临床医生及时对 Bayesian 的警报采取行动时，脓毒症患者在医院死亡的可能性降低 18%。这一发现来自于 2022 年在《自然医学》上发表的同行评审研究，涵盖了 764,707 次患者接触（其中 17,538 例为脓毒症）在五家医院的学术和社区基础设置中，使用该软件的提供者超过 2000 人。该研究显示，早期检测准确（1/3 病例经过医生确认），高灵敏度（82%）和显著的提前时间（提前 5.7 小时），高提供者采用率（89%）——这一比例在临床人工智能中很少见，大多数工具被忽视或关闭。</p>
</div><p>&#34;Health systems have spent decades building the digital foundation of care through platforms like their EHR. What's been missing is a real-time intelligence layer that can continuously interpret that data and help clinicians act earlier. We've built that infrastructure — not for a single condition, but across the conditions that drive the most preventable harm. Proactive, continuous monitoring saves lives. It should be the standard in every hospital,&#34; Saria said.</p>
<div class="lb-trans"><p>&#34;医疗系统花费了数十年时间通过电子健康记录等平台建立护理的数字基础。缺失的是一个实时智能层，能够持续解读这些数据并帮助临床医生更早采取行动。我们建立了这一基础设施——不仅针对单一病症，而是针对导致最多可预防伤害的病症。主动的、持续的监测可以拯救生命。这应该是每家医院的标准，&#34;Saria 说道。</p>
</div><p>&#34;Deploying AI for sepsis detection across a health system like Cleveland Clinic requires rigorous evidence and seamless clinical integration,&#34; said James Morrison, MD, Chair of the Cleveland Clinic Enterprise Sepsis Steering Committee. &#34;Our focus is ensuring these tools deliver meaningful, actionable insights at the bedside where they matter most. This FDA clearance is an important milestone, validating the standard of safety and performance we demand for our clinicians and the patients we serve.&#34;</p>
<div class="lb-trans"><p>&#34;在像克利夫兰诊所这样的医疗系统中部署脓毒症检测的人工智能需要严格的证据和无缝的临床整合，&#34;克利夫兰诊所企业脓毒症指导委员会主席 James Morrison 博士说道。&#34;我们的重点是确保这些工具在床边提供有意义的、可操作的洞察，这是最重要的。这一 FDA 批准是一个重要的里程碑，验证了我们对临床医生和我们服务的患者所要求的安全性和性能标准。&#34;</p>
</div><p>&#34;After evaluating multiple sepsis tools, our clinicians are finding that Bayesian provides early detection with a high rate of accuracy — a tool they can trust and use to diagnose and treat their patients,&#34; said Gregg Nicandri, MD, Chief Digital and Information Officer at University of Rochester Medicine. &#34;By enabling clinicians to get ahead of sepsis instead of chasing it, Bayesian is changing what we expect from clinical AI in this category.&#34;</p>
<div class="lb-trans"><p>“在评估了多种脓毒症工具后，我们的临床医生发现，Bayesian 提供了高准确率的早期检测——这是一个他们可以信赖并用于诊断和治疗患者的工具，” 罗切斯特大学医学中心首席数字与信息官 Gregg Nicandri 医生表示。“通过使临床医生能够提前应对脓毒症，而不是追赶它，Bayesian 正在改变我们对这一领域临床人工智能的期望。”</p>
</div><p><strong>A Pathway to Reimbursement</strong></p>
<div class="lb-trans"><p><strong>报销途径</strong></p>
</div><p>FDA 510(k) clearance also positions Bayesian Health favorably to receive approval under the Centers for Medicare and Medicaid Services' New Technology Add-on Payment (NTAP) program starting in October 2026. The final decision on NTAP approval will be issued early August. If approved as expected, this would give hospitals deploying the cleared sepsis flagging device access to potentially incremental payment for cases in which this solution is used, supporting both the clinical and financial case for proactive sepsis monitoring.</p>
<div class="lb-trans"><p>FDA 510(k) 的批准也使 Bayesian Health 在 2026 年 10 月开始的医疗保险和医疗补助服务中心的新技术附加支付（NTAP）计划中处于有利地位。NTAP 批准的最终决定将在 8 月初发布。如果如预期获得批准，这将使部署已获批准的脓毒症标记设备的医院在使用该解决方案的病例中获得潜在的增量支付，从而支持主动脓毒症监测的临床和财务案例。</p>
</div><p><strong>Bayesian's Broader Platform</strong></p>
<div class="lb-trans"><p><strong>Bayesian 的更广泛平台</strong></p>
</div><p>This clearance applies to Bayesian's sepsis flagging device, part of the broader clinical intelligence platform designed to address the leading drivers of deterioration, mortality, length of stay, and ICU utilization—using a single, continuously learning system.</p>
<div class="lb-trans"><p>该批准适用于 Bayesian 的脓毒症标记设备，这是一个更广泛的临床智能平台的一部分，旨在解决导致病情恶化、死亡率、住院时间和 ICU 使用率的主要因素——使用一个持续学习的单一系统。</p>
</div><p><strong>About Bayesian Health</strong></p>
<div class="lb-trans"><p><strong>关于 Bayesian Health</strong></p>
</div><p>Bayesian Health is the real-time clinical intelligence platform that helps health systems deliver proactive, high-reliability care. By continuously reading the full patient record to establish each patient's baseline and detect meaningful change over time, Bayesian applies complex clinical reasoning to identify the patients who truly need attention and surface clear next steps. Built to work within leading EHR systems, the platform enhances existing clinical workflows by adding a continuous intelligence layer that helps teams intervene earlier and more effectively. Bayesian has partnered with leading health systems to make the long-promised shift from reactive to proactive care across key drivers of mortality, readmissions, and length of stay, hardwiring quality as a durable strategy rather than a series of disconnected initiatives.</p>
<div class="lb-trans"><p>Bayesian Health 是一个实时临床智能平台，帮助医疗系统提供主动、高可靠性的护理。通过持续读取完整的患者记录以建立每位患者的基线并检测随时间的显著变化，Bayesian 应用复杂的临床推理来识别真正需要关注的患者，并提出明确的后续步骤。该平台旨在与领先的电子健康记录系统协同工作，通过增加一个持续智能层来增强现有的临床工作流程，帮助团队更早、更有效地进行干预。Bayesian 与领先的医疗系统合作，实现从反应性护理到主动护理的长期承诺，针对死亡率、再入院和住院时间的关键驱动因素，将质量作为一种持久的战略，而不是一系列不相干的举措。</p>
</div><p>Learn more at https://bayesianhealth.com.</p>
<div class="lb-trans"><p>了解更多信息，请访问 https://bayesianhealth.com。</p>
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<p>View original content to download multimedia:https://www.prnewswire.com/news-releases/bayesian-health-receives-first-ever-fda-clearance-for-continuous-ai-sepsis-monitoring-302769190.html</p>
<div class="lb-trans"><p>查看原始内容以下载多媒体：https://www.prnewswire.com/news-releases/bayesian-health-receives-first-ever-fda-clearance-for-continuous-ai-sepsis-monitoring-302769190.html</p>
</div><p>SOURCE Bayesian Health</p>
<div class="lb-trans"><p>来源：Bayesian Health</p>
</div>

贝叶斯健康首次获得 FDA 批准用于连续 AI 败血症监测