Evaluating AI-Based Prescription Error Detection Systems in Hospital Pharmacies

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Erik Johansson

Independent Researcher

Stockholm, Sweden, SE, 111 29

Abstract— Medication safety remains a critical quality and patient-safety priority in hospital pharmacies. Traditional approaches—prospective order review, pharmacist verification, and computerized provider order entry (CPOE) with static rules—reduce error rates but struggle with context, scale, and alert fatigue. Artificial intelligence (AI) promises faster detection of clinically significant prescription errors by combining machine learning (ML), natural language processing (NLP), and knowledge-graph reasoning with real-time clinical data streams. This manuscript presents a complete evaluation blueprint for AI-based prescription error detection in hospital pharmacies and demonstrates what robust results look like using a realistic, de-identified pilot dataset. We clarify target error types (dose/route/frequency errors, contraindications, drug–drug and drug–condition interactions, allergy conflicts, therapeutic duplications, renal/hepatic dose-inappropriateness, and look-alike/sound-alike risks) and define multidimensional outcomes: diagnostic performance (sensitivity, specificity, AUROC, AUPRC, PPV/NPV), clinical utility (accepted interventions, time-to-detection, prevented harm), implementation outcomes (adoption, fidelity, alert burden), and equity metrics (subgroup parity). Methodologically, we propose a pragmatic, prospective, stepped-wedge, before-and-after evaluation with pharmacist-adjudicated gold standards, coupled with statistical tests appropriate for paired binary outcomes and rate comparisons (McNemar’s test, Poisson/negative binomial regression, and mixed-effects modeling).

The study protocol addresses governance, data pipelines, model monitoring, human-in-the-loop validation, calibration, and alert-threshold tuning to control alert fatigue. Illustrative results from a 40,000-order pilot show a relative 49% reduction in clinically significant prescribing errors (from 4.3 to 2.2 per 1,000 orders), median detection time reduced from 42 to 4 minutes, and pharmacist acceptance of AI alerts of 68%, while alert burden halved after threshold optimization. We conclude with implications for scalability, safety governance, and future research on causal impact, calibration drift, and generalizability across services and EHRs.

Key words

hospital pharmacy; prescription errors; medication safety; artificial intelligence; machine learning; natural language processing; clinical decision support; alert fatigue; implementation science.

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