Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

信息管理方面的技术进步迅速改变了实验室检测和兽医实践。及时和战略性的采样、当天的化验和 24 小时访问实验室结果允许快速实施干预和治疗方案。尽管病原体检测和监测系统已经取得了进步，并且存在跨兽医诊断实验室更广泛的疾病跟踪，例如通过国家动物健康实验室网络 (NAHLN)，但病原体检测和疾病表现之间的区别对于改进疾病管理至关重要。实施一致、直观和有用的疾病诊断编码系统，专门针对兽医学，适用于兽医诊断实验室内部和之间的多种动物物种，是疾病数据聚合的第一阶段。持续改进的反馈循环可以聚合现有的临床和实验室数据库，以提高诊断过程和临床干预的价值和应用，具有临床医生和诊断医生之间的交互能力，并将疾病因果关系与单纯的药物检测区分开来，但仍然不完整。第二阶段是创建一个界面，允许汇总来自临床医生的现有数据，包括最终诊断、干预或应用的治疗以及结果测量。利益相关者合作、协作和对这一愿景的 Beta 测试的原型正在开发中并成为现实。