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Health Informatics in Veterinary Medicine: State of the Literature, Day-1 Competencies, Perceptions of Telemedicine and Application of Predictive Modeling

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Title: Health Informatics in Veterinary Medicine: State of the Literature, Day-1 Competencies, Perceptions of Telemedicine and Application of Predictive Modeling
Author: Ouyang, Zenhwa
Department: Department of Population Medicine
Program: Population Medicine
Advisor: Bernardo, Theresa
Abstract: Modern technologies have brought the veterinary clinic into the animal owner’s home. Like human healthcare, veterinary medicine is expected to be affected by these technologies. Competency in health informatics (HI) will allow veterinarians to leverage these technologies in practice. The overarching objective was to examine the role health informatics plays in veterinary medicine. To answer this, we 1) mapped the technological landscape of veterinary medicine; 2) created a set of HI competencies for new veterinarians; 3) examined the perceptions of practicing veterinarians around telemedicine; and 4) combined traditional explanatory techniques with predictive analytics to identify risk factors for a multifactorial disease (canine infectious respiratory disease complex (CIRDC)). Unlike human medical literature, animal health and veterinary medical literature did not see a rapid increase in big data research. This could be attributed to the changing definition of big data. New veterinarians will need to develop competencies in the internet/social media, communication technologies, electronic medical records and data. Veterinarians will need to be able to establish relationships with partners in the rapidly-evolving technology sector to stay updated. The delivery of healthcare through information and communication technologies (ICT) ii can be practiced with or without a plan to generate revenue. Distinguishing between these two types of practices may help prevent confusion among veterinarians regarding telemedicine. The use of traditional explanatory techniques (e.g. linear and logistic regression) and predictive analytics (random forest) can be used together to enhance analyses. This combined approach provided evidence that canine parainfluenza virus and canine respiratory coronavirus were associated with CIRDC diagnosis and that coinfection may be a risk factor for disease severity. This research provides an overview of technology in veterinary medicine and provides an option for educating veterinary students and veterinarians about health informatics. Such education can help address misconceptions around specific technologies. Finally, while primarily used for the purpose of prediction, some frequently-used methods with established interpretability, such as random forest modeling, can also provide useful insights alone or when combined with explanatory analyses.
URI: https://hdl.handle.net/10214/23749
Date: 2021-01
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
Related Publications: Ouyang, Z., Sargeant, J., Thomas, A., Wycherley, K., Ma, R., Esmaeilbeigi, R., et al. (2019). A scoping review of “big data”, “informatics”, and “bioinformatics” in the animal health and veterinary medical literature. In Animal Health Research Reviews (pp. 1–18). Cambridge University Press. https://doi.org/10.1017/S1466252319000136
Embargoed Until: 2022-01-08


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