Evaluating Veterinary Stakeholder Readiness for Standardized Clinical Terminologies to Improve Surveillance and One Health Collaboration

Introduction

Veterinary medicine currently lacks widespread adoption of standardized clinical terminology systems, limiting efficient data sharing across the industry, veterinary clinics, diagnostic laboratories, public health agencies, and academic institutions. In contrast, human healthcare systems use standardized terminologies to support interoperability, surveillance, and large-scale health data integration. Furthermore, these systems are commonly supported by regulations and governmental agencies. Despite the development of the Veterinary Extension of SNOMED CT in the United States, veterinary clinical documentation and data exchange remain fragmented across practice types and software platforms. This study uses the state of Minnesota, USA, as a case study to evaluate veterinary stakeholders’ awareness, readiness, and perceived barriers related to adopting standardized clinical terminologies to strengthen veterinary surveillance and One Health coordination.

Methods

A cross-sectional mixed-methods study was conducted. The study population included veterinary stakeholders across multiple sectors, including private clinical practice, academia, diagnostic laboratories, public health, pharmacy services, and veterinary electronic medical record (EMR) software companies. Surveys targeted veterinarians and other veterinary personnel involved in clinical documentation. Semi-structured interviews were conducted with additional veterinary industry stakeholders to further explore implementation challenges, interoperability concerns, perceived benefits, and opportunities related to standardized terminology adoption.

Results

Preliminary findings showed variable awareness and readiness regarding standardized veterinary terminologies across stakeholder groups. Commonly perceived barriers included limited training opportunities, EMR system constraints, workflow disruption concerns, financial and personnel resource limitations, and inconsistent documentation practices.

Conclusions

Standardized veterinary clinical terminologies have the potential to strengthen veterinary informatics and surveillance capacity. Implementation may facilitate more efficient data aggregation, data analysis, and support earlier detection of zoonotic threats and antimicrobial resistance patterns. Standardized datasets may also improve support for artificial intelligence and predictive analytics in veterinary surveillance and One Health decision-making.