Framework for a Veterinary Resilience Risk Model in Small-Animal Practice

Thesis Abstract

Small-animal veterinary practice faces increasing operational risks from clinical demand surges, revenue fluctuations, regulatory changes, and biosecurity threats, which collectively threaten service continuity and animal welfare. This study develops a framework for a Veterinary Resilience Risk Model (VRRM) that quantitatively and qualitatively integrates organizational, clinical, and environmental determinants of resilience in small-animal practice. The aim is to establish a parsimonious yet robust model capable of diagnosing risk exposure, simulating adaptive responses, and guiding strategic decision-making to sustain high-quality care. Specific objectives are to (i) identify core resilience-capacity indicators across governance, human resources, clinical workflows, and infection control; (ii) formulate a multi-layered resilience index combining measurable inputs (e.g., client inflow volatility, staff turnover rates, inventory continuity, downtime due to outbreaks) with contextual moderators (practice size, geographic risk, client demographics); (iii) operationalize a mathematical model integrating a Bayesian belief network for probabilistic risk assessment with a system dynamics module to capture feedback loops between demand shifts and capacity constraints; (iv) validate the VRRM using a multi-site sample and retrospective event data; and (v) propose evidence-based contingency strategies and policy implications for small-animal clinics. The methodology adopts a mixed-methods design anchored in resilience theory and risk management. The population includes twenty small-animal veterinary clinics across diverse urban and peri-urban regions, with a purposive sample of ten clinics for in-depth case analysis and ten for broader quantitative validation. Data collection encompasses (i) archival records on annual client visits, appointment lead times, inventory stockouts, and incident reports over five years; (ii) structured surveys measuring perceived organizational resilience, psychological safety, and leadership engagement (n=300 responses); (iii) semi-structured interviews with practice owners, chief veterinarians, and practice managers (n=40) to elicit nuanced risk perceptions and adaptive practices; and (iv) observational audits of clinical workflows and biosecurity procedures. Instruments include a validated Resilience Scale for Healthcare Settings adapted for veterinary practice, inventory continuity checklists, and a bespoke VRRM data dictionary. Validity and reliability are established through content validation by a panel of five veterinary administration experts and a pilot test (n=30). Data analysis proceeds in stages exploratory factor analysis to identify latent resilience constructs, multiple regression to determine determinant strength, and structural equation modeling to test the VRRM pathways. A Bayesian network will quantify probabilistic risk relationships, while system dynamics modeling will simulate policy-informed scenarios (staffing surges, supply chain disruptions, and infection-control breaches). Sub-models address clinical risk, operational risk, and financial risk, integrated within a modular framework to enable scenario analysis under varying levels of external stress. Expected findings indicate that robust leadership engagement, cross-training, diversified supplier networks, and adaptive scheduling significantly enhance resilience scores and reduce service interruption duration during disruptive events. The Bayesian network is anticipated to reveal key conditional dependencies, such as the interdependence between client inflow volatility and staffing flexibility, moderated by inventory buffers and infection-control compliance. The system dynamics simulations are expected to demonstrate nonlinear effects of cumulative delays on client satisfaction and revenue, suggesting tipping points where proactive interventions yield disproportionately favorable outcomes. The study contributes to knowledge by operationalizing a VRRM that blends quantitative risk assessment with qualitative insights, offering a transferable framework for veterinary clinics to measure, monitor, and strengthen resilience. It also extends resilience theory in professional service settings by integrating veterinary workflows, client behavior, and biosecurity considerations into a cohesive decision-support tool. The main conclusion posits that resilience in small-animal practice is a function of integrated governance, workforce adaptability, and operational vigilance, with feedback-rich processes sustaining continuity of care. Recommendations include the adoption of VRRM-informed dashboards for real-time risk monitoring, formalized cross-training programs, strategic supplier diversification, and regular tabletop exercises for outbreak and demand-shift scenarios. The study suggests further research to refine the VRRM with longitudinal data, explore regional customization, and assess the impact of regulatory changes on resilience dynamics.