Clinical Decision Support for Antimicrobial Resistance Prediction and Antibiotic Prescribing: Integrating Antimicrobial Stewardship Review into the Medication-Use Pathway

Abstract

Antimicrobial resistance complicates the selection of timely, effective, and appropriately narrow-spectrum antibiotic therapy. Clinical decision support systems can combine the suspected infection syndrome, illness severity, prior microbiology, recent antimicrobial exposure, local epidemiology, rapid organism identification, resistance markers, phenotypic susceptibility, organ function, allergies, and drug-interaction data. This capability can support empirical prescribing, accelerate the response to blood-culture results, and identify opportunities for dose optimisation, de-escalation, intravenous-to-oral conversion, and discontinuation. Yet a recommendation generated by software should not function as an autonomous prescription, particularly when it concerns reserve antibiotics, complex combination therapy, or incomplete microbiological evidence. This narrative review synthesises evidence on clinical decision support for antimicrobial resistance prediction and antibiotic prescribing and proposes a stewardship-governed medication-use pathway. Under the proposed model, requests for institutionally restricted antimicrobials require antimicrobial stewardship team authorisation before routine pharmacy release and administration. A documented emergency override protects patients with sepsis, septic shock, meningitis, febrile neutropenia, or another time-critical infection from harmful delays in treatment. A bloodstream-infection scenario involving New Delhi metallo-beta-lactamase-producing Enterobacterales illustrates the framework. Because metallo-beta-lactamases spare aztreonam while co-produced serine beta-lactamases may hydrolyse it, aztreonam-avibactam or ceftazidime-avibactam administered with aztreonam may be appropriate in selected cases. The system must not infer treatment from the resistance gene alone. It should integrate organism identification, infection source, source control, susceptibility evidence, renal and hepatic function, allergy history, pharmacokinetic feasibility, drug availability, and local guidance before routing an explainable recommendation for expert review. Effective implementation requires interoperable data, local validation, prioritised alerts, clear accountability, cybersecurity controls, and continuous measurement. Clinical decision support can make antimicrobial decisions faster and more consistent, but safe use depends on microbiology expertise, clinician judgement, and stewardship governance.