What You Should Know:

你应该知道的：

–

–

Layer Health

健康层级

, a

，一个

health technology

健康技术

company leveraging advanced

公司利用先进的技术

AI

人工智能

to transform chart review, raises $21M in Series A funding round led by

以转型图表审查，筹集了由...领投的2100万美元A轮融资

Define Ventures

定义风险投资

with participation from other prominent investors.

其他著名投资者也参与其中。

– The funding will enable Layer Health to scale its

这笔资金将使Layer Health能够扩大其

AI-powered

人工智能驱动的

platform, expand its team, and further its mission to improve healthcare efficiency, reduce costs, and enhance patient outcomes.

平台，扩大其团队，并进一步实现提高医疗效率、降低成本和改善患者预后的使命。

AI to Tackle Healthcare’s Data Challenge

人工智能应对医疗保健的数据挑战

Founded by veteran AI and clinical leaders from institutions such as MIT, Harvard, Microsoft, and Google, Layer Health is addressing the persistent challenge of extracting actionable insights from fragmented medical records. Its AI platform employs advanced large language models (LLMs) trained on longitudinal patient data to automate the review and interpretation of both structured and unstructured clinical data at scale, achieving clinician-level accuracy..

Layer Health 由来自麻省理工学院、哈佛大学、微软和谷歌等机构的资深人工智能和临床领导者创立，致力于解决从分散的医疗记录中提取可行见解的长期难题。其人工智能平台利用在纵向患者数据上训练的先进大型语言模型 (LLMs)，大规模自动化审查和解读结构化与非结构化的临床数据，达到与临床医生相当的准确性。

How Layer Health’s AI Differs

Layer Health的人工智能有何不同

Unlike traditional software solutions that primarily rely on predefined rules, Layer Health’s AI reasons across a patient’s entire chart, similar to a clinician, enabling it to handle complex and nuanced scenarios. This capability allows health systems to reduce costs and facilitate timely interventions, ultimately leading to better and more personalized patient care, as well as new revenue opportunities..

与主要依赖预定义规则的传统软件解决方案不同，Layer Health 的人工智能能够像临床医生一样推理患者的整个病历，从而应对复杂且微妙的场景。这种能力使医疗系统能够降低成本并促进及时干预，最终带来更好、更个性化的患者护理，以及新的收入机会。

Use Cases and Value Proposition

用例与价值主张

Layer Health’s platform is designed to deliver significant value across a wide range of chart review use cases, including:

Layer Health的平台旨在为广泛的图表审查用例提供重要价值，包括：

Quality Reporting & Clinical Registries:

质量报告和临床注册：

Automating data extraction for improved accuracy and efficiency in quality measurement programs and clinical registries.

自动化数据提取，以提高质量测量计划和临床注册的准确性和效率。

Clinical Research & Real-World Data Abstraction:

临床研究与真实世界数据抽象：

Accelerating patient cohort identification for research studies and streamlining real-world evidence generation.

加速患者队列识别以用于研究，并简化真实世界证据的生成。

Hospital Operations & Revenue Cycle Management:

医院运营和收入周期管理：

Enhancing clinical documentation integrity (CDI) and coding processes to optimize reimbursement, reduce denials, and improve financial performance.

提高临床文档完整性 (CDI) 和编码流程以优化报销、减少拒绝并改善财务绩效。

Clinical Decision-Making & Patient Care Optimization:

临床决策与患者护理优化：

Providing clinicians and care teams with real-time, AI-powered insights for personalized, evidence-based treatment decisions.

为临床医生和护理团队提供实时的、由人工智能驱动的洞察，以制定个性化、基于证据的治疗决策。

Addressing Inefficiencies in Chart Review

解决图表审查中的低效率问题

The current manual process of chart review is time-consuming, resource-intensive, and prone to human error. Trained professionals dedicate thousands of hours annually to analyzing health records, a costly inefficiency that can drain millions of dollars from health systems, impede clinicians’ ability to practice at the top of their license, and potentially compromise clinical outcomes.

当前的图表审查手动流程耗时、资源密集且容易出现人为错误。训练有素的专业人员每年投入数千小时分析健康记录，这种成本高昂的低效工作可能使卫生系统损失数百万美元，阻碍临床医生充分发挥其执业能力，并可能影响临床结果。

Manual chart reviews can also lead to inaccuracies in reporting to clinical registries, increasing compliance risks and limiting providers’ ability to improve care quality..

手动图表审查还可能导致向临床登记处报告不准确，增加合规风险，并限制提供者改善护理质量的能力。

Early Success and Future Plans

早期成功与未来计划

Layer Health’s AI is already delivering significant returns for its early ecosystem partners:

Layer Health的人工智能已经为其早期生态系统合作伙伴带来了显著的回报：

Health systems:

卫生系统:

Layer Health’s technology has streamlined quality data abstraction for the Froedtert & the Medical College of Wisconsin health network, reducing the time required by more than 65%, allowing staff to focus on higher-value tasks.

Layer Health的技术简化了Froedtert & the Medical College of Wisconsin卫生网络的质量数据提取流程，所需时间减少了65%以上，使员工能够专注于更高价值的任务。

Life science and clinical research partners:

生命科学和临床研究合作伙伴：

Layer Health’s technology facilitates real-world data (RWD) abstraction to support clinical research. In collaboration with a leading cancer organization, Layer Health completed RWD extraction for dozens of new patients in a few hours, a process that previously took over a year.

Layer Health的技术促进了真实世界数据（RWD）的提取，以支持临床研究。在与一家领先的癌症机构的合作中，Layer Health在几小时内完成了数十名新患者的真实世界数据提取，而这一过程以往需要一年多的时间。

With this new funding, Layer Health plans to expand its offerings, advance its AI models, and deepen partnerships with health systems and other stakeholders across the U.S. and beyond.

通过这笔新的资金，Layer Health 计划扩展其产品 offerings，提升其 AI 模型，并深化与美国及其他地区卫生系统和其他利益相关者的合作伙伴关系。

“Medical chart review has historically been a costly and time-consuming challenge for health systems, yet scaling it is key to decreasing much of the friction in healthcare. That’s why we’re committed to revolutionizing this process and to building technology that providers trust, empowering them to enhance care quality, drive financial growth and identify new revenue opportunities,” said David Sontag, Ph.D., CEO and Co-Founder of Layer Health and a MIT professor.

“病历审查历来是卫生系统成本高昂且耗时的挑战，但扩大其规模是减少医疗摩擦的关键。这就是为什么我们致力于革新这一过程，构建医疗提供者信任的技术，使他们能够提高护理质量、推动财务增长并发现新的收入机会，”Layer Health首席执行官兼联合创始人、麻省理工学院教授David Sontag博士说道。

“We are thrilled to partner with these stellar investors who deeply understand healthcare, our long-term vision and our technology’s transformative power. By reducing administrative burdens and streamlining inefficiencies, we allow providers to focus on their ultimate priority – delivering exceptional patient care.”.

“我们非常高兴能与这些非常了解医疗保健、我们的长期愿景以及我们技术的变革力量的杰出投资者合作。通过减轻行政负担和简化低效问题，我们让供应商能够专注于他们的最终优先事项——提供卓越的患者护理。”