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用于生命科学和医疗保健领域知识转移与生成的应用程序编程接口。

Application programming interfaces for knowledge transfer and generation in the life sciences and healthcare.

作者信息

Woody Stephen K, Burdick David, Lapp Hilmar, Huang Erich S

机构信息

1Duke University School of Medicine, 701W. Main St, Durham NC, 27701 USA.

Stratus Medicine, 920S. Holgate St, Suite 104, Seattle, WA 98134 USA.

出版信息

NPJ Digit Med. 2020 Feb 28;3:24. doi: 10.1038/s41746-020-0235-5. eCollection 2020.

Abstract

Storing very large amounts of data and delivering them to researchers in an efficient, verifiable, and compliant manner, is one of the major challenges faced by health care providers and researchers in the life sciences. The electronic health record (EHR) at a hospital or clinic currently functions as a silo, and although EHRs contain rich and abundant information that could be used to understand, improve, and learn from care as part learning health system access to these data is difficult, and the technical, legal, ethical, and social barriers are significant. If we create a microservice ecosystem where data can be accessed through APIs, these challenges become easier to overcome: a service-driven design decouples data from clients. This decoupling provides flexibility: different users can write in their preferred language and use different clients depending on their needs. APIs can be written for iOS apps, web apps, or an R library, and this flexibility highlights the potential ecosystem-building power of APIs. In this article, we use two case studies to illustrate what it means to participate in and contribute to interconnected ecosystems that powers APIs in a healthcare systems.

摘要

以高效、可验证且合规的方式存储大量数据并将其提供给研究人员,是医疗保健提供者和生命科学研究人员面临的主要挑战之一。医院或诊所的电子健康记录(EHR)目前犹如一座孤岛,尽管EHR包含丰富且大量的信息,这些信息可用于理解、改善医疗并从中学习,但作为学习型健康系统获取这些数据却很困难,而且技术、法律、伦理和社会障碍巨大。如果我们创建一个可通过应用程序编程接口(API)访问数据的微服务生态系统,这些挑战就更容易克服:服务驱动的设计将数据与客户端解耦。这种解耦提供了灵活性:不同用户可以根据自己的需求用他们喜欢的语言编写并使用不同的客户端。可以为iOS应用程序、网络应用程序或R库编写API,这种灵活性凸显了API构建生态系统的潜在力量。在本文中,我们使用两个案例研究来说明参与并为医疗系统中为API提供支持的互联生态系统做出贡献意味着什么。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47c4/7048845/dfa4c45c2f75/41746_2020_235_Fig1_HTML.jpg

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