2021 journal article

Visualization and application of disease diagnosis codes for population health management using porcine diseases as a model

Journal of Veterinary Diagnostic Investigation, 33(3), 428–438.

By: G. Trevisan*, K. Schwartz*, E. Burrough*, B. Arruda*, R. Derscheid*, M. Rahe*, E. Souza Magalhães, M. Almeida*, R. Main*, D. Linhares*

co-author countries: United States of America 🇺🇸
MeSH headings : Animals; Clinical Coding / statistics & numerical data; Population Health Management; Sus scrofa; Swine; Swine Diseases / diagnosis; Veterinary Medicine / methods
Source: ORCID
Added: June 8, 2023

Accurate and timely results of diagnostic investigations and laboratory testing guide clinical interventions for the continuous improvement of animal health and welfare. Infectious diseases can severely limit the health, welfare, and productivity of populations of animals. Livestock veterinarians submit thousands of samples daily to veterinary diagnostic laboratories (VDLs) for disease diagnosis, pathogen monitoring, and surveillance. Individual diagnostic laboratory reports are immediately useful; however, aggregated historical laboratory data are increasingly valued by clinicians and decision-makers to identify changes in the health status of various animal populations over time and geographical space. The value of this historical information is enhanced by visualization of trends of agent detection, disease diagnosis, or both, which helps focus time and resources on the most significant pathogens and fosters more effective communication between livestock producers, veterinarians, and VDL professionals. Advances in data visualization tools allow quick, efficient, and often real-time scanning and analysis of databases to inform, guide, and modify animal health intervention algorithms. Value is derived at the farm, production system, or regional level. Visualization tools allow client-specific analyses, benchmarking, formulation of research questions, and monitoring the effects of disease management and precision farming practices. We present here the approach taken to visualize trends of disease occurrence using porcine disease diagnostic code data for the period 2010 to 2019. Our semi-automatic standardized creation of a visualization platform allowed the transformation of diagnostic report data into aggregated information to visualize and monitor disease diagnosis.