Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship - Yusuf, Havan; Hillman, Alison; Stegeman, Jan Arend; Cameron, Angus; Badger, Skye (Frontiers in Veterinary Science, 2024)
Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship practices in healthcare settings. However, far fewer CDS tools are available in lower- and middle-income countries (LMICs), and in animal health settings where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decision making, by reviewing their scope and functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and grey literature. A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human health care settings and six tools for animal health care settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for antimicrobial stewardship programs was a feature in 12 (29.3%) of the tools, with 10 tools in human health care settings. The capability of the CDS tools varied when reviewed against the GUIDES checklist. We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting antimicrobial stewardship practices in areas of high need.