<p><em>持續的人工智能監測獲得監管驗證，標誌着 Bayesian 實時臨牀智能平台的一個里程碑，以及其所提供的主動護理的新標準。</em></p>
<p>, /PRNewswire/ -- Bayesian Health 今天宣佈，其膿毒症標記設備獲得 FDA 510(k) 批准——這是首個獲得此批准的持續人工智能膿毒症監測器。早期膿毒症檢測是 Bayesian 實時臨牀智能平台的核心模塊，該平台持續監測每位患者，識別需要關注的患者，併為臨牀醫生提供工具，以採取正確的行動。</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>這一批准建立在 Bayesian 之前的 FDA 突破性設備認證基礎上，標誌着醫療系統對臨牀人工智能的期望發生了轉變：一個經過驗證的、臨牀醫生信任並在牀邊使用的實時臨牀智能層。電子健康記錄仍然是臨牀文檔的基礎，但並未設計為實時持續解讀患者數據。Bayesian 在其基礎上進行了擴展——增加了始終在線的臨牀推理，能夠在臨牀醫生已經使用的工作流程中提供洞察，並幫助他們自信地採取行動。</p>
<p><strong>膿毒症檢測的新方法</strong></p>
<p>實時臨牀智能需要一種根本不同的架構：一種能夠持續推理不斷變化的患者狀態，涵蓋所有數據類型，而不是依賴於偶發規則。這種架構是 Bayesian 平台的基礎，為醫療系統提供了一個單一解決方案，以便在膿毒症、呼吸衰竭和心臟事件等多種病症中實現早期惡化的監測。</p>
<p>&#34;膿毒症是我工作了十多年的重點——部分原因是因為我失去了我所愛的人。這個批准背後的工作歷時十多年：深入的研究、同行評審的驗證，以及證明其在牀邊有效的部署。FDA 的批准是一個關鍵的里程碑，也是多年驗證這一工具適應臨牀醫生工作流程並幫助他們提前應對惡化而非被動反應的結果。這是臨牀人工智能應達到的標準，&#34;Bayesian Health 的首席執行官兼創始人 Suchi Saria 博士説道。</p>
<p><strong>為什麼膿毒症常常被發現得太晚</strong></p>
<p>膿毒症是一個更廣泛挑戰的最明顯例子：臨牀惡化可能會微妙地出現，往往被識別得太晚。膿毒症是美國醫院的主要死亡原因之一，治療費用昂貴，每年導致超過 500 億美元的醫院費用。每延遲一小時治療，生存率可能下降多達 7.6%。儘管經過數十年的投資，膿毒症護理仍然受到早期識別不一致的困擾。</p>
<p>與 Bayesian 的解決方案不同，其他獲得 FDA 批准的膿毒症解決方案要求臨牀醫生首先懷疑膿毒症，無論是基於生物標誌物的診斷血液測試，還是使用血液測試結果計算的風險評分。持續的膿毒症監測工具歷史上一直處於 FDA 監管之外，臨牀醫生對此視而不見：它們不斷髮出警報，通常是在已經被識別的患者身上，或者結果顯示患者狀況良好。Bayesian 的平台是首個在臨牀懷疑之前標記膿毒症的批准解決方案，能夠在每位住院患者身上持續運行，並在那些尚未看起來生病的患者中識別惡化，且精確度獲得臨牀醫生的信任。</p>
<p>&#34;在臨牀醫生懷疑之前捕捉膿毒症是一個大海撈針的問題。急性護理中膿毒症的發生率僅為 2% 到 4%，而傳統診斷測試在已經懷疑有膿毒症的患者中運行的發生率為 20% 到 40%。錯過一個病例是災難性的，這要求達到大多數人工智能無法滿足的精確度，即使是那些在初步研究中看起來有前景的工具，&#34;Bayesian Health 的臨牀企業負責人 Neri Cohen 博士説道。&#34;很少有公司進行了嚴格的數據科學和獨立驗證，以達到這一標準。更少的公司進行了同樣嚴格的後續工作：將其融入臨牀工作流程，獲得臨牀醫生在牀邊的採用，並長期測量結果以確保其仍然有效。這就是臨牀人工智能在實踐中能夠交付的原因。&#34;</p>
<p><strong>經過驗證的規模</strong></p>
<p>當臨牀醫生及時對 Bayesian 的警報採取行動時，膿毒症患者在醫院死亡的可能性降低 18%。這一發現來自於 2022 年在《自然醫學》上發表的同行評審研究，涵蓋了 764,707 次患者接觸（其中 17,538 例為膿毒症）在五家醫院的學術和社區基礎設置中，使用該軟件的提供者超過 2000 人。該研究顯示，早期檢測準確（1/3 病例經過醫生確認），高靈敏度（82%）和顯著的提前時間（提前 5.7 小時），高提供者採用率（89%）——這一比例在臨牀人工智能中很少見，大多數工具被忽視或關閉。</p>
<p>&#34;醫療系統花費了數十年時間通過電子健康記錄等平台建立護理的數字基礎。缺失的是一個實時智能層，能夠持續解讀這些數據並幫助臨牀醫生更早採取行動。我們建立了這一基礎設施——不僅針對單一病症，而是針對導致最多可預防傷害的病症。主動的、持續的監測可以拯救生命。這應該是每家醫院的標準，&#34;Saria 説道。</p>
<p>&#34;在像克利夫蘭診所這樣的醫療系統中部署膿毒症檢測的人工智能需要嚴格的證據和無縫的臨牀整合，&#34;克利夫蘭診所企業膿毒症指導委員會主席 James Morrison 博士説道。&#34;我們的重點是確保這些工具在牀邊提供有意義的、可操作的洞察，這是最重要的。這一 FDA 批准是一個重要的里程碑，驗證了我們對臨牀醫生和我們服務的患者所要求的安全性和性能標準。&#34;</p>
<p>“在評估了多種膿毒症工具後，我們的臨牀醫生髮現，Bayesian 提供了高準確率的早期檢測——這是一個他們可以信賴並用於診斷和治療患者的工具，” 羅切斯特大學醫學中心首席數字與信息官 Gregg Nicandri 醫生表示。“通過使臨牀醫生能夠提前應對膿毒症，而不是追趕它，Bayesian 正在改變我們對這一領域臨牀人工智能的期望。”</p>
<p><strong>報銷途徑</strong></p>
<p>FDA 510(k) 的批准也使 Bayesian Health 在 2026 年 10 月開始的醫療保險和醫療補助服務中心的新技術附加支付（NTAP）計劃中處於有利地位。NTAP 批准的最終決定將在 8 月初發布。如果如預期獲得批准，這將使部署已獲批准的膿毒症標記設備的醫院在使用該解決方案的病例中獲得潛在的增量支付，從而支持主動膿毒症監測的臨牀和財務案例。</p>
<p><strong>Bayesian 的更廣泛平台</strong></p>
<p>該批准適用於 Bayesian 的膿毒症標記設備，這是一個更廣泛的臨牀智能平台的一部分，旨在解決導致病情惡化、死亡率、住院時間和 ICU 使用率的主要因素——使用一個持續學習的單一系統。</p>
<p><strong>關於 Bayesian Health</strong></p>
<p>Bayesian Health 是一個實時臨牀智能平台，幫助醫療系統提供主動、高可靠性的護理。通過持續讀取完整的患者記錄以建立每位患者的基線並檢測隨時間的顯著變化，Bayesian 應用複雜的臨牀推理來識別真正需要關注的患者，並提出明確的後續步驟。該平台旨在與領先的電子健康記錄系統協同工作，通過增加一個持續智能層來增強現有的臨牀工作流程，幫助團隊更早、更有效地進行干預。Bayesian 與領先的醫療系統合作，實現從反應性護理到主動護理的長期承諾，針對死亡率、再入院和住院時間的關鍵驅動因素，將質量作為一種持久的戰略，而不是一系列不相干的舉措。</p>
<p>瞭解更多信息，請訪問 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>查看原始內容以下載多媒體：https://www.prnewswire.com/news-releases/bayesian-health-receives-first-ever-fda-clearance-for-continuous-ai-sepsis-monitoring-302769190.html</p>
<p>來源：Bayesian Health</p>

<p>Bayesian Health 已獲得 FDA 510(k) 認證，批准其連續 AI 膿毒症監測設備，這標誌着臨牀 AI 的一個重要里程碑。該設備通過持續監測患者並在臨牀懷疑出現之前提醒臨牀醫生，從而實現對膿毒症的早期檢測，膿毒症是醫院內主要的死亡原因。該平台與現有工作流程無縫集成，增強了早期干預的能力，並有可能降低醫院的死亡率。這一成就經過多年的研究和驗證，使 Bayesian 的解決方案成為主動患者護理中的可信工具</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>持續的人工智能監測獲得監管驗證，標誌着 Bayesian 實時臨牀智能平台的一個里程碑，以及其所提供的主動護理的新標準。</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 今天宣佈，其膿毒症標記設備獲得 FDA 510(k) 批准——這是首個獲得此批准的持續人工智能膿毒症監測器。早期膿毒症檢測是 Bayesian 實時臨牀智能平台的核心模塊，該平台持續監測每位患者，識別需要關注的患者，併為臨牀醫生提供工具，以採取正確的行動。</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>這一批准建立在 Bayesian 之前的 FDA 突破性設備認證基礎上，標誌着醫療系統對臨牀人工智能的期望發生了轉變：一個經過驗證的、臨牀醫生信任並在牀邊使用的實時臨牀智能層。電子健康記錄仍然是臨牀文檔的基礎，但並未設計為實時持續解讀患者數據。Bayesian 在其基礎上進行了擴展——增加了始終在線的臨牀推理，能夠在臨牀醫生已經使用的工作流程中提供洞察，並幫助他們自信地採取行動。</p>
</div><p><strong>A New Approach to Sepsis Detection</strong></p>
<div class="lb-trans"><p><strong>膿毒症檢測的新方法</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>實時臨牀智能需要一種根本不同的架構：一種能夠持續推理不斷變化的患者狀態，涵蓋所有數據類型，而不是依賴於偶發規則。這種架構是 Bayesian 平台的基礎，為醫療系統提供了一個單一解決方案，以便在膿毒症、呼吸衰竭和心臟事件等多種病症中實現早期惡化的監測。</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;膿毒症是我工作了十多年的重點——部分原因是因為我失去了我所愛的人。這個批准背後的工作歷時十多年：深入的研究、同行評審的驗證，以及證明其在牀邊有效的部署。FDA 的批准是一個關鍵的里程碑，也是多年驗證這一工具適應臨牀醫生工作流程並幫助他們提前應對惡化而非被動反應的結果。這是臨牀人工智能應達到的標準，&#34;Bayesian Health 的首席執行官兼創始人 Suchi Saria 博士説道。</p>
</div><p><strong>Why Sepsis is Often Caught Too Late</strong></p>
<div class="lb-trans"><p><strong>為什麼膿毒症常常被發現得太晚</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>膿毒症是一個更廣泛挑戰的最明顯例子：臨牀惡化可能會微妙地出現，往往被識別得太晚。膿毒症是美國醫院的主要死亡原因之一，治療費用昂貴，每年導致超過 500 億美元的醫院費用。每延遲一小時治療，生存率可能下降多達 7.6%。儘管經過數十年的投資，膿毒症護理仍然受到早期識別不一致的困擾。</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>與 Bayesian 的解決方案不同，其他獲得 FDA 批准的膿毒症解決方案要求臨牀醫生首先懷疑膿毒症，無論是基於生物標誌物的診斷血液測試，還是使用血液測試結果計算的風險評分。持續的膿毒症監測工具歷史上一直處於 FDA 監管之外，臨牀醫生對此視而不見：它們不斷髮出警報，通常是在已經被識別的患者身上，或者結果顯示患者狀況良好。Bayesian 的平台是首個在臨牀懷疑之前標記膿毒症的批准解決方案，能夠在每位住院患者身上持續運行，並在那些尚未看起來生病的患者中識別惡化，且精確度獲得臨牀醫生的信任。</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;在臨牀醫生懷疑之前捕捉膿毒症是一個大海撈針的問題。急性護理中膿毒症的發生率僅為 2% 到 4%，而傳統診斷測試在已經懷疑有膿毒症的患者中運行的發生率為 20% 到 40%。錯過一個病例是災難性的，這要求達到大多數人工智能無法滿足的精確度，即使是那些在初步研究中看起來有前景的工具，&#34;Bayesian Health 的臨牀企業負責人 Neri Cohen 博士説道。&#34;很少有公司進行了嚴格的數據科學和獨立驗證，以達到這一標準。更少的公司進行了同樣嚴格的後續工作：將其融入臨牀工作流程，獲得臨牀醫生在牀邊的採用，並長期測量結果以確保其仍然有效。這就是臨牀人工智能在實踐中能夠交付的原因。&#34;</p>
</div><p><strong>Validated at Scale</strong></p>
<div class="lb-trans"><p><strong>經過驗證的規模</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>當臨牀醫生及時對 Bayesian 的警報採取行動時，膿毒症患者在醫院死亡的可能性降低 18%。這一發現來自於 2022 年在《自然醫學》上發表的同行評審研究，涵蓋了 764,707 次患者接觸（其中 17,538 例為膿毒症）在五家醫院的學術和社區基礎設置中，使用該軟件的提供者超過 2000 人。該研究顯示，早期檢測準確（1/3 病例經過醫生確認），高靈敏度（82%）和顯著的提前時間（提前 5.7 小時），高提供者採用率（89%）——這一比例在臨牀人工智能中很少見，大多數工具被忽視或關閉。</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;醫療系統花費了數十年時間通過電子健康記錄等平台建立護理的數字基礎。缺失的是一個實時智能層，能夠持續解讀這些數據並幫助臨牀醫生更早採取行動。我們建立了這一基礎設施——不僅針對單一病症，而是針對導致最多可預防傷害的病症。主動的、持續的監測可以拯救生命。這應該是每家醫院的標準，&#34;Saria 説道。</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;在像克利夫蘭診所這樣的醫療系統中部署膿毒症檢測的人工智能需要嚴格的證據和無縫的臨牀整合，&#34;克利夫蘭診所企業膿毒症指導委員會主席 James Morrison 博士説道。&#34;我們的重點是確保這些工具在牀邊提供有意義的、可操作的洞察，這是最重要的。這一 FDA 批准是一個重要的里程碑，驗證了我們對臨牀醫生和我們服務的患者所要求的安全性和性能標準。&#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>“在評估了多種膿毒症工具後，我們的臨牀醫生髮現，Bayesian 提供了高準確率的早期檢測——這是一個他們可以信賴並用於診斷和治療患者的工具，” 羅切斯特大學醫學中心首席數字與信息官 Gregg Nicandri 醫生表示。“通過使臨牀醫生能夠提前應對膿毒症，而不是追趕它，Bayesian 正在改變我們對這一領域臨牀人工智能的期望。”</p>
</div><p><strong>A Pathway to Reimbursement</strong></p>
<div class="lb-trans"><p><strong>報銷途徑</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 月開始的醫療保險和醫療補助服務中心的新技術附加支付（NTAP）計劃中處於有利地位。NTAP 批准的最終決定將在 8 月初發布。如果如預期獲得批准，這將使部署已獲批准的膿毒症標記設備的醫院在使用該解決方案的病例中獲得潛在的增量支付，從而支持主動膿毒症監測的臨牀和財務案例。</p>
</div><p><strong>Bayesian's Broader Platform</strong></p>
<div class="lb-trans"><p><strong>Bayesian 的更廣泛平台</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>該批准適用於 Bayesian 的膿毒症標記設備，這是一個更廣泛的臨牀智能平台的一部分，旨在解決導致病情惡化、死亡率、住院時間和 ICU 使用率的主要因素——使用一個持續學習的單一系統。</p>
</div><p><strong>About Bayesian Health</strong></p>
<div class="lb-trans"><p><strong>關於 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 是一個實時臨牀智能平台，幫助醫療系統提供主動、高可靠性的護理。通過持續讀取完整的患者記錄以建立每位患者的基線並檢測隨時間的顯著變化，Bayesian 應用複雜的臨牀推理來識別真正需要關注的患者，並提出明確的後續步驟。該平台旨在與領先的電子健康記錄系統協同工作，通過增加一個持續智能層來增強現有的臨牀工作流程，幫助團隊更早、更有效地進行干預。Bayesian 與領先的醫療系統合作，實現從反應性護理到主動護理的長期承諾，針對死亡率、再入院和住院時間的關鍵驅動因素，將質量作為一種持久的戰略，而不是一系列不相干的舉措。</p>
</div><p>Learn more at https://bayesianhealth.com.</p>
<div class="lb-trans"><p>瞭解更多信息，請訪問 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>查看原始內容以下載多媒體：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>來源：Bayesian Health</p>
</div>

貝葉斯健康首次獲得 FDA 批准用於連續 AI 敗血症監測