Methods for determining indications for antibiotic prescriptions to facilitate antimicrobial stewardship: a scoping review.

BACKGROUND

Effective antimicrobial stewardship (AMS) requires accurate information on the reason for prescribing antimicrobials. To design targeted interventions aimed at improving guideline adherence, accurate information on the indication for each prescription is necessary. This scoping review maps the existing methods used for systematically determining indications for antibiotic prescriptions.

SOURCES: A search in Medline and Embase was performed until 1 October 2025. Studies determining indications of antibiotic prescriptions for the purpose of AMS were included.

CONTENT: A total of 157 studies were identified. Most were conducted in high-income countries, involved multiple centres, and used retrospective designs. Manual indication extraction was reported in 43% of studies, whereas 57% used automated methods, most commonly ontology-based systems using standardized medical coding schemes, followed by prescriber-registered indications. Rule-based and artificial Intelligence (AI)-driven methods were only used in 3% of studies. The predominant data source types were structured datasets, followed by electronic health record data, clinical notes, and billing or insurer records. Most studies reported multiple infection categories (e.g. respiratory tract infection), and over half provided infection-level granularity (e.g. pharyngitis or pneumonia).

IMPLICATIONS: Indication registration approaches were heterogeneous and with notable limitations. Manual approaches are detailed but resource intensive. Regarding automated methods, structured codes lack granularity, and prescriber-registered systems capture the intended indication for therapy but face workflow and usability barriers that limit accuracy and adoption. Rule-based and AI-driven methods are only rarely used to identify indications for AMS purposes. Standardized definitions, improved data capture, and scalable AI models that leverage both structured data and unstructured clinical text are required to enable precise and sustainable monitoring of antibiotic use.