Test data generated by ~60 accredited member laboratories of the American Association of Veterinary Laboratory Diagnosticians (AAVLD) is of exceptional quality. These data are captured by 1 of 13 laboratory information management systems (LIMSs) developed specifically for veterinary diagnostic laboratories (VDLs). Beginning ~2000, the National Animal Health Laboratory Network (NAHLN) developed an electronic messaging system for LIMS to automatically send standardized data streams for 14 select agents to a national repository. This messaging enables the U.S. Department of Agriculture to track and respond to high-consequence animal disease outbreaks such as highly pathogenic avian influenza. Because of the lack of standardized data collection in the LIMSs used at VDLs, there is, to date, no means of summarizing VDL large data streams for multi-state and national animal health studies or for providing near-real-time tracking for hundreds of other important animal diseases in the United States that are detected routinely by VDLs. Further, VDLs are the only state and federal resources that can provide early detection and identification of endemic and emerging zoonotic diseases. Zoonotic diseases are estimated to be responsible for 2.5 billion cases of human illness and 2.7 million deaths worldwide every year. The economic and health impact of the SARS-CoV-2 pandemic is self-evident. We review here the history and progress of data management in VDLs and discuss ways of seizing unexplored opportunities to advance data leveraging to better serve animal health, public health, and One Health.

美国兽医实验室诊断师协会 (AAVLD) 的约 60 个认可成员实验室生成的测试数据质量卓越。这些数据由专门为兽医诊断实验室 (VDL) 开发的 13 个实验室信息管理系统 (LIMS) 中的一个获取。从 2000 年开始，国家动物健康实验室网络 (NAHLN) 为 LIMS 开发了一个电子消息系统，可以自动将 14 个选定代理的标准化数据流发送到国家存储库。这种信息使美国农业部能够跟踪和应对高致病性禽流感等高后果动物疾病的爆发。由于 VDL 使用的 LIMS 中缺乏标准化的数据收集，迄今为止，无法为多州和国家动物健康研究总结 VDL 大数据流，或为美国数百种由 VDL 常规检测到的其他重要动物疾病提供近实时跟踪。此外，VDL 是唯一能够提供早期发现和识别地方性和新出现的人畜共患病的州和联邦资源。据估计，人畜共患病每年在全世界造成 25 亿例人类疾病和 270 万人死亡。SARS-CoV-2 大流行对经济和健康的影响是不言而喻的。我们在此回顾 VDL 中数据管理的历史和进展，并讨论如何抓住未开发的机会来推进数据利用，以更好地服务于动物健康、公共卫生和 One Health。