JMIR Publications

ABSTRACT

Background:

Antibiotics are among the most frequently prescribed drugs globally, yet their use carries substantial risks of adverse drug reactions (ADRs), including nephrotoxicity, hepatotoxicity, and pharmacokinetic variability driven by patient body composition. Up to 63.6% of antibiotic prescriptions are issued empirically without confirmed pathogen identification, placing the responsibility for balancing efficacy and safety on real-time clinical judgment, particularly in the emergency setting. Existing clinical decision support systems (CDSSs) for antibiotic prescribing are constrained either by the rigidity of rule-based decision trees or by the lack of explainability in machine learning approaches, and none simultaneously offers transparent, safety-focused scoring with minimal clinical input.

Objective:

This study aimed to develop and validate a quantitative, transparent CDSS that pre-filters empirical antibiotic candidates by patient-specific organ safety risk, supporting safer prescription decisions.

Methods:

We developed ASSET (Antibiotic Safety Scoring and Selection Evaluation Tool), a CDSS that quantitatively computes a composite organ safety risk score for the seven most prescribed antibiotics using a Euclidean distance-based method across renal, hepatic, and body mass index (BMI) axes. Through its mathematical scoring framework, ASSET maintains high transparency while minimizing the clinical input required for recommendations.

Results:

Among 17 in-scope test cases, ASSET achieved 100% (17/17) recommendation concordance, with no clinician-selected antibiotic classified as carrying an unacceptable safety burden. Prospective validation is warranted before clinical application.

Conclusions:

ASSET provides a transparent, quantitative safety pre-filter for empirical antibiotic selection, supporting more consistent safety-oriented prescribing in the emergency setting.