ArticleEuropean journal of hospital pharmacy : science and practice2022
Development and validation of a ready-to-use score to prioritise medication reconciliation at patient admission in an orthopaedic and trauma department.
Article in European journal of hospital pharmacy : science and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Impact of an AI-powered hospital admission prediction dashboard to guide medication reconciliation in the emergency department: a retrospective before-after study.International journal of clinical pharmacy · 2026Article
- A machine learning-based clinical predictive tool to identify patients at high risk of medication errors.Scientific reports · 2024Article
- Best Possible Medication History Collection by Clinical Pharmacist in a Preoperative Setting: An Observational Prospective Study.Pharmacy (Basel, Switzerland) · 2023Article
- Bedside check of medication appropriateness (BED-CMA) as a risk-based tool for bedside clinical pharmacy services: A proof-of-concept study at the trauma surgery ward.Computational and structural biotechnology journal · 2023Article
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Abstract
objectiveMedication reconciliation (MR) is recognised as an important tool in preventing medication errors such as unintentional discrepancies (UDs). The aim of this study was to identify independent predictive factors of UDs during MR at patient admission to an orthopaedic and trauma department. The secondary objective was to build and validate a ready-to-use score to prioritise patients.
methodA retrospective study was performed on 3.5 years of pharmacist-led MR in the orthopaedic and trauma department of a large university teaching hospital. Independent predictors of UD were identified by multivariable logistic regression. A priority score to identify patients at risk of at least one UD was constructed from the odds ratios of the risk factors, and validated in a separate cohort. Performance was assessed with sensitivity, specificity, C-statistic and Hosmer-Lemeshow goodness-of-fit.
resultsIn total, 888 patients were included and 387 UDs were identified, mainly drug omissions (65.1%). Five independent predictors of UD were identified: age >75 years (OR 2.05, 95% CI 1.41 to 3.00; p<0.001), admission during school holidays (OR 1.69, 95% CI 1.17 to 2.44; p=0.005), female gender (OR 2.20, 95% CI 1.53 to 3.16; p<0.001), emergency hospitalisation (OR 2.05, 95% CI 1.45 to 2.92; p<0.001), and ≥5 medications on the best possible medication history (BPMH) (OR 3.29, 95% CI 2.20 to 4.94; p<0.001). Based on these predictors, a priority score ranging from 0 to 10 was built and internally and externally validated (C statistic 0.72, 95% CI 0.67 to 0.76).
conclusionsThis study confirms the high prevalence of UD in patients admitted to orthopaedic and trauma surgery departments. Five independent predictive factors of UD during MR were identified (female gender, emergency hospitalisation, hospitalisation during school holidays, age ≥75 years, and ≥5 medicines on the BPMH). The developed risk score will help to prioritise MR among patients at risk of medication error and is ready-to-use in other orthopaedic and trauma departments.
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