vetanalyse

Vetanalyse is a term used in veterinary informatics to denote the systematic analysis of veterinary data to support animal health care and research. The concept combines data from electronic medical records, laboratory results, imaging, and field observations with statistical methods, data visualization, and predictive modeling. In practice, vetanalyse aims to improve diagnostic accuracy, treatment outcomes, and operational efficiency in clinics, hospitals, and research settings.

The word appears as a portmanteau of veterinary and analyse (or analysis) and has been used in Dutch- and English-language literature on veterinary data analytics. It is not universally standardized, and organizations may use related terms such as veterinary informatics, veterinary data analytics, or clinical analytics. The emphasis is typically on turning diverse data into actionable insights for clinicians, researchers, and managers.

Methods commonly employed in vetanalyse include descriptive statistics, inferential analyses, survival analysis, machine learning, and forecasting. Data governance, privacy, and standardization are important considerations, as is the interoperability of data with coding standards and ontologies used in veterinary medicine.

Applications of vetanalyse encompass quality assurance, outcome measurement, disease surveillance, comparative effectiveness research, and pharmacovigilance for veterinary medicines. It also supports education and training by providing case-based analytics and benchmarking across clinics or regions.

Limitations include data quality and heterogeneity across practices, missing or incomplete records, inconsistent coding, and challenges in translating complex analytics into clinical decision-making. Related topics include veterinary informatics, data analytics, epidemiology, and animal health surveillance.