Search

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]

Antimicrobial Resistance is a global crisis that veterinarians contribute to through their use of antimicrobials in animals. Antimicrobial stewardship has been shown to be an effective means to reduce antimicrobial resistance in hospital environments. Effective monitoring of antimicrobial usage patterns is an essential part of antimicrobial stewardship and is critical in reducing the development of antimicrobial resistance. The aim of this study is to describe how frequently antimicrobials were used in veterinary consultations and identify the most frequently used antimicrobials. Using VetCompass Australia, Natural Language Processing techniques, and the Australian Strategic Technical Advisory Group's (ASTAG) Rating system to classify the importance of antimicrobials, descriptive analysis was performed on the antimicrobials prescribed in consultations from 137 companion animal veterinary clinics in Australia between 2013 and 2017 (inclusive). Of the 4,400,519 consultations downloaded there were 595,089 consultations where antimicrobials were prescribed to dogs or cats. Antimicrobials were dispensed in 145 of every 1000 canine consultations; and 38 per 1000 consultations involved high importance rated antimicrobials. Similarly with cats, 108 per 1000 consultations had antimicrobials dispensed, and in 47 per 1000 consultations an antimicrobial of high importance rating was administered. The most common antimicrobials given to cats and dogs were cefovecin and amoxycillin clavulanate, respectively. The most common topical antimicrobial and high-rated topical antimicrobial given to dogs and cats was polymyxin B. This study provides a descriptive analysis of the antimicrobial usage patterns in Australia using methods that can be automated to inform antimicrobial use surveillance programs and promote antimicrobial stewardship. [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]