Automated Data Collection on Antimicrobial Use in Dogs and Cats in a Tertiary Hospital and Private Practices

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Judicious antimicrobial use in veterinary medicine is important because improper antimicrobial use can contribute to the evolution of antimicrobial resistance in bacterial pathogens, which makes subsequent use of these drugs less effective in both human and veterinary medicine. There is very little on-the-ground information about veterinary clinicians’ antimicrobial use (AMU) practices in companion animal practice in the US. veterinary medicine. To improve our understanding of antimicrobial use in dogs and cats, we propose to create a nationwide digital surveillance system to collect critical AMU data using existing electronic practice information management systems (PIMS) in collaboration with veterinary industry partners. The system will automatically harvest AMU and patient data from digital PIMS. The proposed system will harvest data collected in routine veterinary examinations from existing PIMS systems and therefore will not require any additional effort from practitioners to participate in the program. Natural language processing, a machine learning method used to classify unstructured text, will be used to review electronic medical records to determine patients’ diagnosis. We aim to prototype the system in our native digital PIMS at North Carolina State University’s College of Veterinary Medicine Teaching hospital. We will then enroll additional private veterinary practices, including general practice, specialty hospitals, and emergency clinics, as sentinels and collect the same detailed PIMS data from a more representative set of clinics. Working closely with the sentinel clinics will provide a deep understanding of how our system operates in private clinics, and in the final stage we aim to expand the fully automated system to PIMS nationwide. The combination of sentinel clinics with the nationwide survey of clinics will create a powerful broad and deep surveillance system for antimicrobial use in veterinary clinics. A broad suite of AMU parameters will be estimated from this data, and the results reported to the FDA in an annual report. Additionally, we will share the data with other researchers through an web-based portal and GitHub repositories. This system will provide the critical data and analysis to understand veterinary AMU in the US.