<p><em>人口規模的人工智能系統使用 Claude 分析數百萬份醫療記錄，以識別符合指南推薦護理的患者，並支持及時的臨牀審查</em></p>
<p>, /PRNewswire/ -- Qualified Health 和 Anthropic 在德克薩斯大學系統（UT System）推出了一項具有里程碑意義的人工智能部署，以應對醫療保健中最持久的挑戰之一：確保符合循證標準的患者能夠持續被識別、適當評估，並在大規模和多樣化的人羣中考慮高質量護理。</p>
<p>在這一倡議下，Qualified Health 的人工智能系統由 Anthropic 的 Claude AI 模型提供支持，正在德克薩斯州 UT 系統龐大的健康網絡中部署，以持續分析大量臨牀數據集並發現指南推薦護理中的差距。該系統應用經過驗證的臨牀指南和適宜性標準，使 UT 系統的護理團隊能夠持續識別可能需要進一步臨牀審查和協調護理計劃的患者。</p>
<p>儘管經過數十年的臨牀研究和成熟的指南，仍有數千萬美國人符合循證護理的標準卻從未及時被識別和評估。僅在德克薩斯州，每年就有估計 400 萬到 600 萬患者被忽視，導致可預防的併發症和死亡、獲取醫療服務的不平等，以及對臨牀醫生和醫療系統的日益壓力。</p>
<p>問題不在於缺乏醫學知識，而在於現代醫療的操作現實。確定患者是否符合基於指南的適宜性標準通常需要在分散的系統、非結構化的筆記、實驗室、影像和歷史記錄中進行有意且勞動密集的圖表審查。在人口規模上，跨越數百萬患者和數 PB 的複雜臨牀數據，系統性識別循證患者護理中的差距在歷史上一直是不可行的，直到現在。</p>
<p>&#34;醫療保健是人工智能最具挑戰性的環境之一，因為它需要解析大量複雜的非結構化臨牀數據，同時在嚴格的治理框架內安全操作，&#34; Anthropic 生命科學負責人 Eric Kauderer-Abrams 表示。&#34;Claude 能夠可靠地做到這一點，當與 Qualified Health 的治理平台和德克薩斯大學系統這樣的前瞻性健康系統結合時，創造了安全大規模部署先進人工智能的條件，並最終幫助彌補數百萬患者的護理差距。&#34;</p>
<p>Qualified Health 的人工智能系統整合來自不同來源的數據，解析複雜的臨牀數據，並應用經過驗證的臨牀指南和適宜性標準，以保持對護理差距的持續更新的人口級視圖。需要進一步考慮的患者會直接被納入護理團隊的工作流程中進行審查，支持的臨牀背景會自動匯總，以支持高效、高質量的決策。</p>
<p>&#34;挑戰不在於我們不知道什麼有效，而在於將數十年的證據和適宜性指導轉化為一致的臨牀實踐，&#34; Qualified Health 首席執行官 Justin Norden 博士表示。&#34;該系統旨在增強，而不是取代臨牀判斷，使臨牀醫生能夠在以前無法實現的規模上應用他們的專業知識。合作伙伴們共同建立了一個可複製的框架，以大規模操作循證醫學。曾經需要大量手動圖表抽象和跨系統協調的工作現在可以在整個羣體中持續進行。&#34;</p>
<p>經過廣泛評估和測試，該系統現在在德克薩斯大學醫學分校（UTMB）上線，這是 UT 系統內的第一個部署地點。初步部署重點識別指南推薦的心臟病護理中的差距，包括指南指導的藥物治療和適當的藥物劑量，以及心力衰竭和瓣膜疾病的必要介入治療。重要的是，適宜性標準也被提出，以確保高質量的評估。</p>
<p>初步部署的早期結果顯示出人口規模的影響：</p>
<ul>
<li>複雜的臨牀數據被解析並匯總成統一的患者檔案，整合了筆記、實驗室結果、影像和程序信息</li>
<li>統一的患者檔案根據廣泛的心臟病程序的精確、基於指南的標準進行評估</li>
<li>大量以前未被識別的高可能性候選人被提出供臨牀醫生審查</li>
<li>臨牀醫生的審查與系統輸出顯示出高度一致，增強了臨牀信任</li>
<li>適合的患者的護理路徑得到了加速</li>
</ul>
<p>通過確保臨牀考慮在患者羣體和護理環境中更為一致地應用，該方法支持更及時、適當的護理交付，並減少德克薩斯州患者因操作或獲取限制而被忽視的可能性。</p>
<p>在 UTMB 成功的基礎上，該平台正在全系統擴展，評估工具也將延伸到心臟病以外的領域。到 2026 年底，額外的部署將支持在初級護理、血管、胃腸、風濕病和神經學專業中識別合格患者，幫助更多的健康系統在德克薩斯州各地擴展獲得經過驗證的護理。</p>
<p>&#34;UT 健康機構為德克薩斯州每個角落的患者提供服務。我們不是在現有系統上疊加解決方案，而是在 UT 系統的健康企業中建立一個新的共享基礎，使新的人工智能部署能夠以一致性、問責制和長期影響引入，&#34; UT 系統健康事務首席數字與分析官兼副校長 Zain Kazmi 表示。&#34;這一新夥伴關係有助於我們快速行動、共同學習，並在患者護理環境中有效應用智能。&#34;</p>
<p>作為德克薩斯大學醫療人工智能研究、工程與應用實驗室（UT REAL Health AI）計劃的一部分，該部署圍繞兩個共同目標展開：擴大基於證據的治療的可及性，特別是針對服務不足的人羣，並建立在臨牀環境中安全、負責任地部署人工智能的新標準。</p>
<p>“UT REAL Health AI 計劃旨在改善德克薩斯州人的生活，” 德克薩斯大學醫學分校首席人工智能與數字官員、UT REAL Health AI 計劃主席彼得·麥卡弗裏（Peter McCaffrey）表示。“通過在我們的醫療機構中部署新的人工智能技術，我們可以提升患者的護理體驗，促進人口健康，並降低整體護理成本。”</p>
<p>該部署在 Anthropic 的溝通和公共論壇中得到了展示，包括在摩根大通醫療會議上，隨着全國各地的醫療系統評估大規模人工智能以系統性地擴展基於證據的護理的可及性。</p>
<p><strong>關於 Qualified Health</strong></p>
<p>Qualified Health 是一個企業人工智能平台和戰略人工智能合作伙伴，幫助醫療系統部署安全且可擴展的人工智能，以推動可衡量的臨牀和財務成果。我們的平台覆蓋全國頂級醫療系統的 40 多萬用户，結合了工作流程自動化、代理開發、臨牀保障、實時監控和端到端治理，以及深厚的醫療和人工智能專業知識，幫助醫療領導者實現規模價值。</p>
<p>Qualified Health 由前醫療系統高管、來自領先機構的一線醫生、臨牀轉型專家和硅谷工程師構建和領導，具備部署、擴展和安全、負責任地治理企業級人工智能應用所需的領域深度和運營嚴謹性。</p>
<p>欲瞭解更多信息，請訪問 www.qualifiedhealthai.com。</p>
<p>來源：Qualified Health PBC</p>
<p><img src="https://imageproxy.pbkrs.com/https://rt.prnewswire.com/rt.gif/query-RGF0ZUlkPTIwMjYwMTIxJk5ld3NJdGVtSWQ9U0Y2ODMwNiZUcmFuc21pc3Npb25fSWQ9MjAyNjAxMjExMzU0UFJfTkVXU19VU1BSX19fX19TRjY4MzA2?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://rt.prnewswire.com/rt.gif/query-RGF0ZUlkPTIwMjYwMTIxJk5ld3NJdGVtSWQ9U0Y2ODMwNiZUcmFuc21pc3Npb25fSWQ9MjAyNjAxMjExMzU0UFJfTkVXU19VU1BSX19fX19TRjY4MzA2"/></p>

<p>合格健康公司（Qualified Health）與 Anthropic 在德克薩斯大學系統推出了一項人工智能部署，以增強對基於證據的醫療保健的獲取。該人工智能系統分析數百萬份醫療記錄，以識別符合指南推薦護理的患者，解決及時臨牀審查的挑戰。初始部署集中在心臟病學領域，計劃在 2026 年前擴展到其他專業。該倡議旨在填補數百萬德克薩斯州居民的護理空白，確保患者獲得適當的評估和治療，最終改善全州的醫療保健結果</p>

<p><em>Population-scale AI system uses Claude to analyze millions of medical records to identify patients who qualify for guideline-recommended care and support timely clinical review</em></p>
<div class="lb-trans"><p><em>人口規模的人工智能系統使用 Claude 分析數百萬份醫療記錄，以識別符合指南推薦護理的患者，並支持及時的臨牀審查</em></p>
</div><p>, /PRNewswire/ -- Qualified Health and Anthropic have launched a landmark AI deployment across the University of Texas System (UT System) to tackle one of healthcare's most persistent challenges: ensuring that patients who meet evidence-based criteria are consistently identified, appropriately evaluated, and considered for high-quality care across large and diverse populations.</p>
<div class="lb-trans"><p>, /PRNewswire/ -- Qualified Health 和 Anthropic 在德克薩斯大學系統（UT System）推出了一項具有里程碑意義的人工智能部署，以應對醫療保健中最持久的挑戰之一：確保符合循證標準的患者能夠持續被識別、適當評估，並在大規模和多樣化的人羣中考慮高質量護理。</p>
</div><p>Under this initiative, Qualified Health's AI system, powered by Anthropic's Claude AI models, is being deployed across UT System's vast health network in Texas to continuously analyze vast clinical datasets and surface gaps in guideline-recommended care. The system applies validated clinical guidelines and appropriateness criteria to enable care teams across the UT System to consistently identify patients who may benefit from further clinical review and coordinated care planning.</p>
<div class="lb-trans"><p>在這一倡議下，Qualified Health 的人工智能系統由 Anthropic 的 Claude AI 模型提供支持，正在德克薩斯州 UT 系統龐大的健康網絡中部署，以持續分析大量臨牀數據集並發現指南推薦護理中的差距。該系統應用經過驗證的臨牀指南和適宜性標準，使 UT 系統的護理團隊能夠持續識別可能需要進一步臨牀審查和協調護理計劃的患者。</p>
</div><p>Despite decades of clinical research and well-established guidelines, tens of millions of Americans who meet criteria for evidence-based care are never identified and evaluated in time. In Texas alone, an estimated 4-6 million patients fall through the cracks each year, leading to preventable complications and mortality, inequities in access, and growing strain on clinicians and the healthcare system.</p>
<div class="lb-trans"><p>儘管經過數十年的臨牀研究和成熟的指南，仍有數千萬美國人符合循證護理的標準卻從未及時被識別和評估。僅在德克薩斯州，每年就有估計 400 萬到 600 萬患者被忽視，導致可預防的併發症和死亡、獲取醫療服務的不平等，以及對臨牀醫生和醫療系統的日益壓力。</p>
</div><p>The problem is not a lack of medical knowledge, but the operational reality of modern healthcare. Determining whether patients meet guideline-based and appropriateness criteria often requires intentional and labor-intensive chart review across fragmented systems, unstructured notes, labs, imaging, and historical records. At population scale, across millions of patients and petabytes of complex clinical data, systematic identification of gaps in evidence-based patient care has historically been infeasible, until now.</p>
<div class="lb-trans"><p>問題不在於缺乏醫學知識，而在於現代醫療的操作現實。確定患者是否符合基於指南的適宜性標準通常需要在分散的系統、非結構化的筆記、實驗室、影像和歷史記錄中進行有意且勞動密集的圖表審查。在人口規模上，跨越數百萬患者和數 PB 的複雜臨牀數據，系統性識別循證患者護理中的差距在歷史上一直是不可行的，直到現在。</p>
</div><p>&#34;Healthcare is one of the most demanding environments for AI because it requires parsing vast amounts of complex, unstructured clinical data while operating safely within strict governance frameworks,&#34; said Eric Kauderer-Abrams, Head of Life Sciences at Anthropic. &#34;Claude can do that reliably, and when paired with Qualified Health's governance platform and a visionary health system like the University of Texas System, it creates the conditions to deploy advanced AI safely at scale and ultimately help close care gaps for millions of patients.&#34;</p>
<div class="lb-trans"><p>&#34;醫療保健是人工智能最具挑戰性的環境之一，因為它需要解析大量複雜的非結構化臨牀數據，同時在嚴格的治理框架內安全操作，&#34; Anthropic 生命科學負責人 Eric Kauderer-Abrams 表示。&#34;Claude 能夠可靠地做到這一點，當與 Qualified Health 的治理平台和德克薩斯大學系統這樣的前瞻性健康系統結合時，創造了安全大規模部署先進人工智能的條件，並最終幫助彌補數百萬患者的護理差距。&#34;</p>
</div><p>Qualified Health's AI system integrates data across sources, parsing complex clinical data, and applying validated clinical guidelines and appropriateness criteria to maintain a continuously updated, population-level view of care gaps. Patients who warrant further consideration are surfaced directly into care team workflows for review, with supporting clinical context automatically assembled to support efficient, high-quality decision-making.</p>
<div class="lb-trans"><p>Qualified Health 的人工智能系統整合來自不同來源的數據，解析複雜的臨牀數據，並應用經過驗證的臨牀指南和適宜性標準，以保持對護理差距的持續更新的人口級視圖。需要進一步考慮的患者會直接被納入護理團隊的工作流程中進行審查，支持的臨牀背景會自動匯總，以支持高效、高質量的決策。</p>
</div><p>&#34;The challenge isn't that we don't know what works. It's translating decades of evidence and appropriateness guidance into consistent clinical practice at scale,&#34; said Justin Norden, MD, MBA, MPhil, CEO of Qualified Health. &#34;The system is designed to augment, not replace, clinical judgment and enable clinicians to apply their expertise at a scale that was previously not possible. Together, the partners are establishing a replicable framework for operationalizing evidence-based medicine at scale. What once required extensive manual chart abstraction and cross-system coordination can now happen continuously, across entire populations.&#34;</p>
<div class="lb-trans"><p>&#34;挑戰不在於我們不知道什麼有效，而在於將數十年的證據和適宜性指導轉化為一致的臨牀實踐，&#34; Qualified Health 首席執行官 Justin Norden 博士表示。&#34;該系統旨在增強，而不是取代臨牀判斷，使臨牀醫生能夠在以前無法實現的規模上應用他們的專業知識。合作伙伴們共同建立了一個可複製的框架，以大規模操作循證醫學。曾經需要大量手動圖表抽象和跨系統協調的工作現在可以在整個羣體中持續進行。&#34;</p>
</div><p>Following extensive evaluation and testing, the system is now live at the University of Texas Medical Branch (UTMB), the first deployment site within the UT System. Initial deployment focuses on identifying gaps in guideline-recommended cardiology care, ranging from guideline directed medical therapy and appropriate medication dosage to necessary interventional treatments for heart failure and valvular disease. Importantly, appropriateness criteria are also surfaced to ensure high-quality assessment.</p>
<div class="lb-trans"><p>經過廣泛評估和測試，該系統現在在德克薩斯大學醫學分校（UTMB）上線，這是 UT 系統內的第一個部署地點。初步部署重點識別指南推薦的心臟病護理中的差距，包括指南指導的藥物治療和適當的藥物劑量，以及心力衰竭和瓣膜疾病的必要介入治療。重要的是，適宜性標準也被提出，以確保高質量的評估。</p>
</div><p>Early results from the initial deployment demonstrate population-scale impact:</p>
<div class="lb-trans"><p>初步部署的早期結果顯示出人口規模的影響：</p>
</div><ul>
<li>Complex clinical data were parsed and assembled into unified patient profiles, integrating notes, laboratory results, imaging, and procedural information</li>
<li>Unified patient profiles were evaluated against precise, guideline-based criteria across a broad set of cardiology procedures</li>
<li>Large cohorts of previously unrecognized, high-likelihood candidates were surfaced for clinician review</li>
<li>Clinician review demonstrated a high level of agreement with system outputs, reinforcing clinical trust</li>
<li>Care pathways for appropriately eligible patients were accelerated</li>
</ul>
<div class="lb-trans"><ul>
<li>複雜的臨牀數據被解析並匯總成統一的患者檔案，整合了筆記、實驗室結果、影像和程序信息</li>
<li>統一的患者檔案根據廣泛的心臟病程序的精確、基於指南的標準進行評估</li>
<li>大量以前未被識別的高可能性候選人被提出供臨牀醫生審查</li>
<li>臨牀醫生的審查與系統輸出顯示出高度一致，增強了臨牀信任</li>
<li>適合的患者的護理路徑得到了加速</li>
</ul>
</div><p>By ensuring that clinical consideration is applied more uniformly across patient populations and care settings, the approach supports more timely, appropriate care delivery and reduces the likelihood that patients across Texas are overlooked due to operational or access constraints.</p>
<div class="lb-trans"><p>通過確保臨牀考慮在患者羣體和護理環境中更為一致地應用，該方法支持更及時、適當的護理交付，並減少德克薩斯州患者因操作或獲取限制而被忽視的可能性。</p>
</div><p>Building on UTMB's success, the platform is expanding systemwide and evaluation tools are extending beyond cardiology. By the end of 2026, additional deployments will support identification of eligible patients across primary care, vascular, gastrointestinal, rheumatology, and neurology specialties, helping additional health systems extend access to proven care all across Texas.</p>
<div class="lb-trans"><p>在 UTMB 成功的基礎上，該平台正在全系統擴展，評估工具也將延伸到心臟病以外的領域。到 2026 年底，額外的部署將支持在初級護理、血管、胃腸、風濕病和神經學專業中識別合格患者，幫助更多的健康系統在德克薩斯州各地擴展獲得經過驗證的護理。</p>
</div><p>&#34;UT health institutions serve patients in every corner of Texas. Rather than laying solutions on top of existing systems, we are building a new shared foundation across the UT System's health enterprise that allows new AI deployments to be introduced with consistency, accountability, and long-term impact,&#34; said Zain Kazmi, chief digital &amp; analytics officer and associate vice chancellor of health affairs at the UT System. &#34;This new partnership contributes to our ability to move quickly, learn collectively, and apply intelligence effectively within patient care environments.&#34;</p>
<div class="lb-trans"><p>&#34;UT 健康機構為德克薩斯州每個角落的患者提供服務。我們不是在現有系統上疊加解決方案，而是在 UT 系統的健康企業中建立一個新的共享基礎，使新的人工智能部署能夠以一致性、問責制和長期影響引入，&#34; UT 系統健康事務首席數字與分析官兼副校長 Zain Kazmi 表示。&#34;這一新夥伴關係有助於我們快速行動、共同學習，並在患者護理環境中有效應用智能。&#34;</p>
</div><p>As part of the University of Texas Research, Engineering, and Application Laboratory for Healthcare Artificial Intelligence (UT REAL Health AI) initiative, the deployment is anchored around two shared goals: expanding access to evidence-based treatment, particularly for underserved populations, and establishing a new standard for the safe, responsible deployment of AI in clinical environments.</p>
<div class="lb-trans"><p>作為德克薩斯大學醫療人工智能研究、工程與應用實驗室（UT REAL Health AI）計劃的一部分，該部署圍繞兩個共同目標展開：擴大基於證據的治療的可及性，特別是針對服務不足的人羣，並建立在臨牀環境中安全、負責任地部署人工智能的新標準。</p>
</div><p>&#34;The UT REAL Health AI Initiative is about improving the lives of Texans,&#34; said Peter McCaffrey, chief AI &amp; digital officer at the University of Texas Medical Branch and chair of the UT REAL Health AI initiative. &#34;Through new AI deployments across our health institutions, we can enhance patients' experience of care, advance population health, and reduce the overall cost of care.&#34;</p>
<div class="lb-trans"><p>“UT REAL Health AI 計劃旨在改善德克薩斯州人的生活，” 德克薩斯大學醫學分校首席人工智能與數字官員、UT REAL Health AI 計劃主席彼得·麥卡弗裏（Peter McCaffrey）表示。“通過在我們的醫療機構中部署新的人工智能技術，我們可以提升患者的護理體驗，促進人口健康，並降低整體護理成本。”</p>
</div><p>The deployment was featured in Anthropic's communications and public forums, including at the J.P. Morgan Healthcare Conference, as health systems nationwide evaluate population-scale AI to systematically extend access to evidence-based care.</p>
<div class="lb-trans"><p>該部署在 Anthropic 的溝通和公共論壇中得到了展示，包括在摩根大通醫療會議上，隨着全國各地的醫療系統評估大規模人工智能以系統性地擴展基於證據的護理的可及性。</p>
</div><p><strong>About Qualified Health</strong></p>
<div class="lb-trans"><p><strong>關於 Qualified Health</strong></p>
</div><p>Qualified Health is the enterprise AI platform and strategic AI partner helping health systems deploy safe and scalable AI to drive measurable clinical and financial outcomes. Reaching over 400,000 users across top health systems nationwide, our platform combines workflow automation, agent development, clinical safeguards, real-time monitoring, and end-to-end governance with deep healthcare and AI expertise, helping healthcare leaders realize value at scale.</p>
<div class="lb-trans"><p>Qualified Health 是一個企業人工智能平台和戰略人工智能合作伙伴，幫助醫療系統部署安全且可擴展的人工智能，以推動可衡量的臨牀和財務成果。我們的平台覆蓋全國頂級醫療系統的 40 多萬用户，結合了工作流程自動化、代理開發、臨牀保障、實時監控和端到端治理，以及深厚的醫療和人工智能專業知識，幫助醫療領導者實現規模價值。</p>
</div><p>Qualified Health is built and led by former health system executives, frontline physicians from leading institutions, clinical transformation experts, and Silicon Valley engineers, bringing the domain depth and operational rigor required to deploy, scale, and govern AI applications safely and responsibly at enterprise scale.</p>
<div class="lb-trans"><p>Qualified Health 由前醫療系統高管、來自領先機構的一線醫生、臨牀轉型專家和硅谷工程師構建和領導，具備部署、擴展和安全、負責任地治理企業級人工智能應用所需的領域深度和運營嚴謹性。</p>
</div><p>For more information, visit www.qualifiedhealthai.com.</p>
<div class="lb-trans"><p>欲瞭解更多信息，請訪問 www.qualifiedhealthai.com。</p>
</div><p>SOURCE Qualified Health PBC</p>
<div class="lb-trans"><p>來源：Qualified Health PBC</p>
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13:54 ET
			
			
				Qualified Health and Anthropic Launch Landmark AI Deployment at University of Texas Institutions to Expand Access to Life-Saving, Evidence-Based Care for Millions of Texans

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Qualified Health 和 Anthropic 在德克薩斯大學的機構推出了具有里程碑意義的 AI 部署，以擴大數百萬德克薩斯人獲得拯救生命的循證護理的機會