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

在兽医学中明智地使用抗菌剂是重要的，因为不适当的 抗菌药物的使用可能有助于细菌耐药性的演变， 病原体，这使得这些药物的后续使用在人类 和兽医学。关于兽医的实地信息很少 美国伴侣动物实践中的临床医生抗菌药物使用（AMU）实践。 兽医学为了提高我们对犬用抗菌药物的理解， 猫，我们建议建立一个全国性的数字监控系统，以收集关键的 AMU数据使用现有的电子实践信息管理系统（PIMS）， 与兽医行业的合作伙伴。系统会自动采集 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 将不需要从业者参与该计划的任何额外努力。 自然语言处理，一种用于分类非结构化的机器学习方法 文本，将用于审查电子医疗记录，以确定病人的诊断。 我们的目标是原型系统在我们的本地数字PIMS在北卡罗来纳州 大学兽医学院教学医院。然后我们将注册 其他私人兽医诊所，包括全科诊所、专科医院， 和急诊诊所，作为哨兵，并收集同样详细的PIMS数据， 更有代表性的诊所。与定点诊所紧密合作， 深入了解我们的系统如何在私人诊所运作，并在最后阶段 我们的目标是将全自动系统扩展到全国范围内的PIMS。的组合 哨点诊所与全国范围内的诊所调查将建立一个强大的广泛和 兽医诊所抗菌药物使用的深度监测系统。一整套完整的 AMU参数将根据这些数据进行估计，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， 分析，以了解兽医AMU在美国。

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.