Comparison of Artificial Intelligence and Lexicomp for Drug--Drug Interaction Screening in Hospitalized Patients
Year: 2025
Type: preprint
Study design: Observational Study
Abstract: Background and Purpose Adverse drug reactions are a major source of healthcare burden, with drug–drug interactions (DDIs) contributing up to 20% of cases. Conventional electronic checkers often show heterogeneity in interaction detection and severity assessment. This study aimed to compare an LLM-based artificial intelligence system with UpToDate/Lexicomp for DDI detection, severity classification, and processing time in hospitalized patients. Experimental Approach We conducted a comparative, cross-sectional study at Hospital Samel, Brazil, including 60 inpatients (ICU and ward) prescribed ≥2 medications. Active drug lists were assessed with both UTD and an AI system, which integrated demographic, clinical, and laboratory data to classify DDIs on the same severity scale. Key Results Among 284 interaction pairs, AI identified 191 and UTD 184, with 91 detected by both. Severity concordance was 63.7% (κ=0.19). AI tended to assign higher severity, often upgrading category C to D. Compared with UTD, AI detected more interactions with NSAIDs (22 vs 1; q<0.001) and antibiotics (21 vs 3; q=0.003), while UTD identified more non-opioid analgesic and antipsychotic interactions. Post-hoc prompting showed AI acknowledged 71% of initially missed UTD interactions, largely filtered as “low priority.” Processing time was significantly shorter with AI (36.2s vs 102.4s per patient; p<0.001), a 68% reduction. Conclusion and Implications LLM-based AI complemented conventional checkers, detecting additional interactions and delivering faster and contextualized assessments. Despite single-center and small-sample limitations, this study advances understanding by demonstrating AI’s potential to enrich drug–drug interaction evaluation and reduce processing burden in clinical workflows.
Authors: Noemi Santos de Oliveira, Tricila Ribeiro de Matos, Eduardo de Araújo Oliveira, Luiza Fernanda Mendonça Nicolau, Emilyn Barbosa de Oliveira Guerreiro, Fernando Chiodini Machado, Zaira Fernanda Martinho Nicolau
Language: English
Subfield: Geriatrics and Gerontology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4414417653
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