Automated Data Collection on Antimicrobial Use in Dogs and Cats in a Tertiary Hospital and Private Practices

在兽医中明智地使用抗菌剂是很重要的，因为不适当的 抗菌药物的使用有助于细菌抗菌素耐药性的演变 病原体，这使得随后使用这些药物对人类和 和兽医。关于兽医的实际信息很少。 美国临床医生在陪护动物实践中的抗菌素使用实践。 兽医。为了提高我们对狗使用抗菌剂的了解，以及 CATS，我们建议创建一个全国性的数字监控系统来收集关键的 使用现有电子练习信息管理系统(PIMS)的AMU数据 与兽医行业合作伙伴的合作。系统将自动收割 AMU和来自数字PIMS的患者数据。拟议的系统将收集数据 在现有PIMS系统的常规兽医检查中收集，因此 将不需要从业者付出任何额外的努力来参与该计划。 自然语言处理，一种用于非结构化分类的机器学习方法 文本，将用于审查电子病历以确定患者的诊断。 我们的目标是在北卡罗来纳州的原生数字PIMS中制作该系统的原型 大学兽医学院教学医院。然后我们就会报名 更多的私人兽医业务，包括全科、专科医院、 和急诊诊所作为哨兵，并从 更具代表性的一组诊所。与哨兵诊所密切合作将提供 深入了解我们的系统在私人诊所和最后阶段的运作方式 我们的目标是将全自动化系统扩展到全国范围内的PIMS。这两种技术的结合 哨兵诊所将与全国范围内的诊所调查一起创建一个强大的广泛和 兽医诊所抗菌药物使用深度监测系统。一套广泛的 AMU参数将根据该数据进行估计，并将结果报告给FDA 年度报告。此外，我们将通过一个 基于Web的门户和GitHub存储库。该系统将提供关键数据和 分析以了解美国的兽医AMU。

Judicious antimicrobial use in veterinary medicine is important because improper antimicrobial use can contribute to the evolution of antimicrobial resistance in bacterial pathogens, which makes subsequent use of these drugs less effective in both human and veterinary medicine. There is very little on-the-ground information about veterinary clinicians’ antimicrobial use (AMU) practices in companion animal practice in the US. veterinary medicine. To improve our understanding of antimicrobial use in dogs and cats, we propose to create a nationwide digital surveillance system to collect critical AMU data using existing electronic practice information management systems (PIMS) in collaboration with veterinary industry partners. The system will automatically harvest AMU and patient data from digital PIMS. The proposed system will harvest data collected in routine veterinary examinations from existing PIMS systems and therefore will not require any additional effort from practitioners to participate in the program. Natural language processing, a machine learning method used to classify unstructured text, will be used to review electronic medical records to determine patients’ diagnosis. We aim to prototype the system in our native digital PIMS at North Carolina State University’s College of Veterinary Medicine Teaching hospital. We will then enroll additional private veterinary practices, including general practice, specialty hospitals, and emergency clinics, as sentinels and collect the same detailed PIMS data from a more representative set of clinics. Working closely with the sentinel clinics will provide a deep understanding of how our system operates in private clinics, and in the final stage we aim to expand the fully automated system to PIMS nationwide. The combination of sentinel clinics with the nationwide survey of clinics will create a powerful broad and deep surveillance system for antimicrobial use in veterinary clinics. A broad suite of AMU parameters will be estimated from this data, and the results reported to the FDA in an annual report. Additionally, we will share the data with other researchers through an web-based portal and GitHub repositories. This system will provide the critical data and analysis to understand veterinary AMU in the US.