Development and Evaluation of a Telemedicine Triage System for Veterinary Clinics

Thesis Abstract

This study addresses the rising demand for timely triage in veterinary practice amid increased client expectations and limited clinical capacity, by developing and evaluating a telemedicine triage system designed to optimize decision-making, reduce unnecessary in-clinic visits, and improve animal welfare outcomes. The aim is to design, implement, and evaluate a structured telemedicine triage workflow capable of classifying presenting complaints, prioritizing cases, and guiding owners on immediate at-home care versus in-person consultation. Specific objectives include (1) to develop a standardized triage protocol informed by the Veterinary Emergency and Critical Care Society guidelines and the Theory of Planned Behavior to understand clinician and client adoption; (2) to implement a secure, user-friendly telemedicine platform enabling asynchronous data capture (photos, videos, owner-reported history) and synchronous video consultations; (3) to evaluate diagnostic concordance, referral accuracy, and triage appropriateness compared with in-clinic assessments; (4) to assess user satisfaction, perceived usability, and perceived quality of care among veterinarians and clients; (5) to determine the impact on clinic throughput, wait times, and revenue using a quasi-experimental design. The methodology adopts a mixed-methods, multi-site design over 18 months. The population comprises small-animal veterinary practices (n=12) within a regional network and their client base; a purposive sample of 240 cases will be recruited across diverse presenting problems (emergency, urgent, and non-urgent). Data collection instruments include a standardized triage checklist and decision-support algorithm, validated user experience questionnaires (System Usability Scale and Client Satisfaction Questionnaire), semi-structured interviews with clinicians (n?30) and clients (n?40), and platform usage analytics. Validity and reliability will be ensured through pilot testing of the triage protocol (n=30) and inter-rater reliability assessment (Cohen’s kappa) for triage categorization. Quantitative analyses will utilize regression models to examine relationships between triage level, time-to-intervention, and clinical outcomes; ANOVA will compare efficiency metrics pre- and post-implementation; diagnostic concordance will be assessed via Cohen’s kappa and percent agreement. Time-series analyses will evaluate trend changes in clinic throughput and appointment demand. Thematic analysis will be applied to interview transcripts to elucidate perceived barriers, enablers, and contextual factors influencing adoption, guided by the Technology Acceptance Model as a supplementary lens. Expected findings include high diagnostic concordance between teletriage decisions and subsequent in-clinic findings for urgent cases (kappa >0.6), significant reductions in non-urgent in-clinic visits by 15–20%, decreased average wait times by 20–25%, and improved owner satisfaction scores (mean SUS >70). The study anticipates that contextual factors—practice workflow integration, clinician confidence in remote assessment, and perceived data quality from owners—will significantly mediate outcomes. The contribution to knowledge lies in providing a rigorously developed, theory-informed telemedicine triage framework for veterinary clinics, including a validated decision-support algorithm, implementation guidelines, and evidence on clinical efficacy, economic impact, and user acceptance. The study will extend implementation science in veterinary telemedicine by integrating the Theory of Planned Behavior and Technology Acceptance Model to explain adoption dynamics within animal health services, and it will offer a replicable model for scalable tele-triage across species and practice sizes. The main conclusion is that a structured telemedicine triage system can safely reduce unnecessary in-person visits, streamline clinic operations, and maintain or enhance quality of care when integrated with clinician oversight and owner education. Recommendations emphasize iterative refinement of the triage algorithm, ongoing training for veterinarians and clients, data security enhancements, and policy development for reimbursement and regulatory compliance to support broader adoption.