Automated detection of wrong-drug prescribing errors.
Automated detection of wrong-drug prescribing errors.
复制标题
自动检测错误的药物处方错误。
DOI：
10.1136/bmjqs-2019-009420
复制
发表时间：
2019
影响因子：
5.4
通讯作者：
Gaunt,MichaelJ
中科院分区：
文献类型：
--
作者：
Lambert,BruceL;Galanter,William;Liu,KingLup;Falck,Suzanne;Schiff,Gordon;Rash-Foanio,Christine;Schmidt,Kelly;Shrestha,Neeha;Vaida,AllenJ;Gaunt,MichaelJ
BackgroundTo assess a specificity of a algorithm designed to detect look-alike/sound-alike（LASA）medicine prescription errors in electronic health record（EHR）data.SettingUrban，academic medical centre，including a 495-bed hospital and outpatient clinic running on the Cerner EHR.我们提取了8年来的药物处方和诊断声明。我们授权了一个药物适应症数据库，对其进行了改进，并将其与药物数据合并。我们开发了一种算法，该算法基于名称相似性、患者接受药物的频率以及药物是否被诊断声明证明是合理的来触发LASA错误。我们根据相似性对触发器进行了分类。两名临床医生审查了一份图表样本，以确定是否存在真正的错误，第三名审查者解决了分歧。我们计算的特异性，阳性预测值（PPV）和yield.ResultsThe算法分析了488 481订单，并产生2404触发（0.5%率）。临床医生审查了506例病例，确认存在61例错误，总体PPV为12.1%（95% CI 10.7%-13.5%）。无法测量灵敏度或假阴性率。该算法的特异性不同的功能的名称相似性和是否预期和分配的药物共享相同的路径administration.ConclusionAutomated检测LASA用药错误是可行的，可以揭示错误目前没有检测到其他手段。目前的系统无法进行实时错误检测，主要障碍是无法实时获得准确的诊断信息。进一步的发展应该在其他卫生系统和更大的药物组合中复制这种分析，并通过增加特异性来减少临床医生审查假阳性触发因素的时间。
BackgroundTo assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data.SettingUrban, academic medical centre, comprising a 495-bed hospital and outpatient clinic running on the Cerner EHR. We extracted 8 years of medication orders and diagnostic claims. We licensed a database of medication indications, refined it and merged it with the medication data. We developed an algorithm that triggered for LASA errors based on name similarity, the frequency with which a patient received a medication and whether the medication was justified by a diagnostic claim. We stratified triggers by similarity. Two clinicians reviewed a sample of charts for the presence of a true error, with disagreements resolved by a third reviewer. We computed specificity, positive predictive value (PPV) and yield.ResultsThe algorithm analysed 488 481 orders and generated 2404 triggers (0.5% rate). Clinicians reviewed 506 cases and confirmed the presence of 61 errors, for an overall PPV of 12.1% (95% CI 10.7% to 13.5%). It was not possible to measure sensitivity or the false-negative rate. The specificity of the algorithm varied as a function of name similarity and whether the intended and dispensed drugs shared the same route of administration.ConclusionAutomated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Real-time error detection is not possible with the current system, the main barrier being the real-time availability of accurate diagnostic information. Further development should replicate this analysis in other health systems and on a larger set of medications and should decrease clinician time spent reviewing false-positive triggers by increasing specificity.