A Multi-Disciplinary Narrative Review: Medication Safety in the Surgical Patient from Prescription to Administration
Bibliographic record
Abstract
Background: Medication errors in surgical patients represent a critical, multifaceted threat to patient safety, occurring at any point in a complex, multi-handler pathway. The unique perioperative environment, involving high-stakes pharmacology and numerous handoffs between diverse professionals, creates distinct vulnerabilities. Aim: This narrative review aims to synthesize contemporary evidence (2010-2024) on the epidemiology of medication errors in surgical care and to trace the medication-use process across the interconnected disciplines involved, identifying systemic risks and collaborative strategies for mitigation. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Scopus, and Web of Science databases. Results: Errors are prevalent, with high-risk points at prescribing (especially antimicrobials and analgesics), transcription/communication, anaesthesia administration, and post-operative monitoring. Fragmented systems, ambiguous communication, and role overload are key contributors. Evidence supports structured interventions like computerised physician order entry with decision support, standardised handoff protocols, barcode-assisted medication administration, and enhanced interdisciplinary training (e.g., simulation, crew resource management) as effective in reducing errors. Successful implementation is fundamentally dependent on strong health services administration policies and a pervasive culture of safety. Conclusion: Medication safety in surgery is an inherently interdisciplinary challenge. A "siloed" approach is ineffective. Future strategies must be architected around integrated care pathways, leveraging health information technology and fostering a culture of shared responsibility from the executive suite to the bedside, involving every link in the chain from the medical secretary to the resident doctor and surgical team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".