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Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studies using VetCompass Australia and Natural Language Processing (NLP) have demonstrated antimicrobial usage patterns in companion animal practices across Australia. Doing so has highlighted the many obstacles and barriers to the task of converting raw clinical notes into a format that can be readily queried and analysed. We developed NLP systems using rules‐based algorithms and machine learning to automate the extraction of data describing the key elements to assess appropriate antimicrobial use. These included the clinical indication, antimicrobial agent selection, dose and duration of therapy. Our methods were applied to over 4.4 million companion animal clinical records across Australia on all consultations with antimicrobial use to help us understand what antibiotics are being given and why on a population level. Of these, approximately only 40% recorded the reason why antimicrobials were prescribed, along with the dose and duration of treatment. NLP and deep learning might be able to overcome the difficulties of harvesting free text data from clinical records, but when the essential data are not recorded in the clinical records, then, this becomes an insurmountable obstacle. [ABSTRACT FROM AUTHOR]

The Australian Veterinary Prescribing Guidelines for antimicrobial prophylaxis for surgery on dogs and cats are evidence‐based guidelines for veterinary practitioners. Validation of these guidelines is necessary to ensure quality and implementability. Two validated tools, used for medical guideline appraisal, were chosen to assess the guidelines. The terminology from the GuideLine Implementability Appraisal (GLIA) and the Appraisal of Guidelines for Research and Evaluation version 2 (AGREE II) were adapted for use by veterinarians. A two‐phase evaluation approach was conducted. In the first phase of the evaluation, the GLIA tool was used by two specialist veterinary surgeons in clinical practice. The results of this phase were then used to modify the guidelines. In the second phase, the AGREE II tool was used by 6 general practitioners and 6 specialists to appraise the guidelines. In phase 1, the specialist surgeons either agreed or strongly agreed that the guidelines were executable, decidable, valid and novel, and that the guidelines would fit within the process of care. The surgeons were neutral on flexibility and measurability. Additional clarity around one common surgical procedure was added to the guidelines, after which the surgeons agreed that the guidelines were sufficiently flexible. In phase 2, 12 veterinarians completed the assessment using the AGREE II tool. In all sections the scaled domain score was greater than 70%. The overall quality of the guidelines was given a global scaled score of 76%. This assessment has demonstrated that the guidelines for antimicrobial prophylaxis for companion animal surgery are valid and appear implementable. [ABSTRACT FROM AUTHOR]

Background: Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs. Methods: VetCompass Australia collects medical records from 181 clinics in Australia (as of May 2018). These records contain detailed information from individual consultations regarding the medications dispensed. One unique aspect of VetCompass Australia is its focus on applying natural language processing (NLP) and machine learning techniques to analyse the records, similar to efforts conducted in other medical studies. Results: The free text fields of 4,394,493 veterinary consultation records of dogs and cats between 2013 and 2018 were collated by VetCompass Australia and NLP techniques applied to enable the querying of the antimicrobial usage within these consultations. Conclusion: The NLP algorithms developed matched antimicrobial in clinical records with 96.7% accuracy and an F1 Score of 0.85, as evaluated relative to expert annotations. This dataset can be readily queried to demonstrate the antimicrobial usage patterns of companion animal practices throughout Australia. [ABSTRACT FROM AUTHOR]

This was a collaborative project, with representation from Australian and New Zealand veterinary schools, supported by the Veterinary Schools of Australia and New Zealand and funded by the Australian Government Department of Agriculture and Water Resources. The program was supported by Veterinary Schools of Australia and New Zealand and funded by the Commonwealth Department of Agriculture and Water Resources. [Extracted from the article]