The Veterinary record · 2026
Reusing health records from farm animal practices at scale: A potential complementary method of surveillance.
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Hopkins Beverley, Davies Peers, Noble Peter-John, Bunford-Davies Anna, Lawson April, Pinchbeck Gina, Lloyd Ifan, Smith Robert, Radford Alan D
Аннотация
BACKGROUND: Disease in primary care frequently represents a surveillance blind spot, particularly for diseases affecting farm animals.
METHODS: Electronic health records (EHRs) were collected from four farm animal veterinary practices in Wales (February 2024‒January 2025) as part of a pilot study. Information collected included species treated, date, owner postcode, products sold and clinical free text. Text mining and topic modelling were used to describe treatments and classify syndromes.
RESULTS: In total, 32,799 records were collected. Antimicrobials were prescribed in 32.6% and 63.8% of cattle and sheep records, respectively. The most frequent antibiotic classes in both species were tetracyclines, macrolides, penicillins and penicillin‒aminoglycoside combinations. There were no recorded category A antimicrobials, and category B antimicrobials were prescribed in only 0.12% and 0.04% of cattle and sheep EHRs, respectively. Text mining and topic modelling seemed efficient methods to identify key syndromes, including mastitis, joint ill, lameness and pneumonia, and how these were treated.
LIMITATIONS: Some EHRs described more than one animal with different diagnoses, obfuscating the attribution of treatment to syndrome.
CONCLUSION: The increasing availability of EHRs at scale and in real-time represents a complementary opportunity to survey disease and treatment on farms. Text mining methods, including artificial intelligence, could efficiently identify important syndromes and provide novel insight into use of antibacterials.
METHODS: Electronic health records (EHRs) were collected from four farm animal veterinary practices in Wales (February 2024‒January 2025) as part of a pilot study. Information collected included species treated, date, owner postcode, products sold and clinical free text. Text mining and topic modelling were used to describe treatments and classify syndromes.
RESULTS: In total, 32,799 records were collected. Antimicrobials were prescribed in 32.6% and 63.8% of cattle and sheep records, respectively. The most frequent antibiotic classes in both species were tetracyclines, macrolides, penicillins and penicillin‒aminoglycoside combinations. There were no recorded category A antimicrobials, and category B antimicrobials were prescribed in only 0.12% and 0.04% of cattle and sheep EHRs, respectively. Text mining and topic modelling seemed efficient methods to identify key syndromes, including mastitis, joint ill, lameness and pneumonia, and how these were treated.
LIMITATIONS: Some EHRs described more than one animal with different diagnoses, obfuscating the attribution of treatment to syndrome.
CONCLUSION: The increasing availability of EHRs at scale and in real-time represents a complementary opportunity to survey disease and treatment on farms. Text mining methods, including artificial intelligence, could efficiently identify important syndromes and provide novel insight into use of antibacterials.