Sorbonne Universite, Inserm, Universite Sorbonne Paris Nord, LIMICS UMR_S 1142, Paris, France.
Assistance Publique-Hopitaux de Paris, Hopital Tenon, Paris, France.
AMIA Annu Symp Proc. 2021 Jan 25;2020:1110-1119. eCollection 2020.
Medication reconciliation (MR) aims at preventing medication errors at care transitions. It is a complex, time-consuming, cognitively demanding pharmacological task. We have developed a decision support system, EzMedRec, to assist retroactive MR at hospital admission. EzMedRec compares the best possible medication history (BPMH), i.e., all medications taken by the patient before hospitalization, to the list of admission medication orders (AMO). The process includes (i) the decomposition of BPMH and AMO drugs into their active ingredients (AIs), (ii) the detection of medication discontinuations and additions, and (iii) the identification of modified medication orders. The ATC classification is used to semantically enrich MR by comparing discontinued AIs and added AIs and suggesting a potential intentional drug substitution serving the same therapeutic objective. EzMedRec has been evaluated on a sample of 52 actual MRs involving 822 medication order lines, 406 in BPMHs, and 416 in AMOs with a global accuracy of 98,3%.
药物重整（MR）旨在预防医疗转衔期间的用药错误。它是一项复杂、耗时且认知要求高的药理学任务。我们开发了一个决策支持系统 EzMedRec，以协助入院时的回溯性 MR。EzMedRec 将最佳可能的用药史（BPMH），即患者在入院前服用的所有药物，与入院用药医嘱（AMO）列表进行比较。该过程包括：（i）将 BPMH 和 AMO 药物分解为其活性成分（AIs），（ii）检测用药中断和添加，以及（iii）识别修改后的用药医嘱。通过比较中断的 AIs 和添加的 AIs，并建议具有相同治疗目标的潜在有意药物替代，ATC 分类用于通过语义丰富 MR。EzMedRec 已在涉及 822 条用药医嘱行、406 条 BPMH 和 416 条 AMO 的 52 个实际 MR 样本中进行了评估，总体准确率为 98.3%。
