The

The

American Cancer Society

美国癌症协会

is betting on generative AI’s potential to accelerate cancer research, having struck a multi-year collaboration with

正在押注生成式人工智能加速癌症研究的潜力，并与之达成了多年的合作关系。

Layer Health

健康层

this month.

这个月。

Boston-based Layer, founded in 2023, is a healthcare AI company that uses large language models (LLMs) to improve data abstraction and draw insights from EHR data. Working together, the partners will abstract data from thousands of medical charts of patients enrolled in the American Cancer Society’s research studies..

总部位于波士顿的 Layer 成立于 2023 年，是一家医疗保健 AI 公司，利用大型语言模型 (LLM) 改进数据抽象并从电子健康记录 (EHR) 数据中获取洞察。合作伙伴将共同努力，从参与美国癌症协会研究项目的数千名患者的病历中提取数据。

The types of data elements being abstracted include information about a patient’s cancer, such as their cancer stage or biomarkers, as well as information about a patient’s treatment journey, such as their dates of imaging or surgeries.

被抽象的数据元素类型包括有关患者癌症的信息，如癌症阶段或生物标志物，以及有关患者治疗过程的信息，如影像检查或手术的日期。

presented by

呈现者

Artificial Intelligence

人工智能

Why Your Patients Are Leaving and How to Stop It

为什么你的病人在离开，以及如何阻止他们离开

Patient expectations continue to evolve, and healthcare practices that fail to adapt risk more than just revenue—they risk losing trust.

患者的期望不断变化，未能适应的医疗实践风险的不仅仅是收入——他们还可能失去信任。

By Bryan Sequeira, Director, Product Management Greenway Health

由布莱恩·塞凯拉（Bryan Sequeira），产品管理总监，Greenway Health

In the past, the American Cancer Society manually abstracted information from charts to conduct their cancer prevention research. This manual process would take more than a year, said Layer CEO David Sontag.

过去，美国癌症协会为了进行癌症预防研究，手动从图表中提取信息。Layer首席执行官大卫·松塔格表示，这个手动过程需要一年多的时间。

AI can make the process happen much faster, he noted.

他指出，人工智能可以大大加快这一进程。

Once a health system configures Layer’s platform to abstract a given data element, the AI can be run against thousands of patient notes in a matter of hours, Sontag said.

Sontag 表示，一旦一个健康系统配置了 Layer 的平台来提取特定的数据元素，人工智能就可以在数小时内对数千份患者记录进行分析。

In May of last year, the American Cancer Society conducted a pilot program that applied Layer’s AI to 200 patient charts. The pilot found that Layer’s AI outperformed human abstraction with 95-100% accuracy.

去年五月，美国癌症协会开展了一项试点项目，将 Layer 的人工智能应用于 200 份患者病例。试点发现，Layer 的人工智能准确率达到了 95%-100%，优于人类抽象化处理。

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A Data-Driven Guide to Patient Access Success

数据驱动的患者获取成功指南

A new report from Relatient, A Data-Driven Guide to Patient Access Succes, highlights how focusing on data accuracy and relevance can enhance the performance of healthcare practices.

Relatient 的一份新报告《数据驱动的患者访问成功指南》强调了如何通过关注数据的准确性和相关性来提升医疗实践的绩效。

By Relatient

由 Relatient 发布

“The significance of being able to get results that are equally accurate as humans, but in a fraction of the time, means that ACS researchers can start to analyze the data quicker to accelerate their vital research. Having a scalable solution for abstraction also enables a broader scope of data elements to be pulled from the charts — enabling researchers to ask and answer even more nuanced/deeper questions about the study population,” Sontag explained..

“能够获得与人类同样准确但耗时更少的结果，这意味着ACS研究人员可以更快地开始分析数据，以加快他们至关重要的研究。拥有一个可扩展的抽象解决方案还能够从图表中提取更广泛的数据元素，使研究人员能够提出并回答关于研究人群更加细致和深入的问题，”Sontag解释道。

By scaling its collaboration with Layer, the American Cancer Society is seeking to accelerate its chart abstraction, as well as expand the scope and number of data elements abstracted from each chart, all while preserving accuracy, he said.

他说，通过扩大与 Layer 的合作，美国癌症协会希望加快其图表摘要的速度，并扩大从每个图表中提取的数据元素的范围和数量，同时保持准确性。

To measure the success of these goals, Sontag noted that Layer will closely track the accuracy of the extraction for each data element and the speed of turnaround for the charts.

为了衡量这些目标的成功，桑塔格指出，Layer 将密切跟踪每个数据元素提取的准确性以及图表周转的速度。

The generations of natural language processing technology that came before LLMs failed to generalize well across patient records from different health systems — only performing well if the input data was very standardized, Sontag pointed out.

Sontag 指出，在 LLM 之前的一代自然语言处理技术无法很好地跨不同健康系统的患者记录进行泛化——仅在输入数据非常标准化时表现良好。

“LLMs are more flexible and contextually aware. This enables LLMs to perform strongly even when data from thousands of different clinics and hospitals looks different — therefore enabling this work to scale,” he declared.

“大型语言模型更加灵活且上下文感知能力更强。这使得即使来自数千家不同诊所和医院的数据看起来有所不同，LLMs依然能够表现出色——因此使这项工作得以扩展，”他说道。

Photo: Carol Yepes, getty Images

照片来源：Carol Yepes，getty Images