Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Diagnostic Accuracy for Discrepancy Detection
Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Diagnostic Accuracy for Discrepancy Detection
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DOI：
10.1055/s-0038-1645889
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发表时间：
2018-04-01
影响因子：
2.9
通讯作者：
Dorr, David A.
中科院分区：
文献类型：
--
作者：
Lesselroth, Blake J.;Adams, Kathleen;Dorr, David A.
背景退伍军人事务部波特兰医疗保健系统开发了一种药物历史收集软件，显示处方名称和药物images.Objective本文测量的频率用药差异报告使用的药物历史收集软件，并与报告的频率使用基于纸张的过程进行比较。本文还确定了每种方法的准确性，通过比较这两种策略，以最好的药物治疗history.Study设计随机，对照，单盲试验。设置三个社区为基础的初级保健诊所与退伍军人事务部波特兰医疗保健系统：300-床教学设施和门诊护理网络服务的退伍军人在太平洋西北美国。参与者212例原发性肝癌患者，干预患者随机分为软件指导的用药史组和纸质用药史组。使用基于计算机的随机数发生器进行随机化和分配至治疗组。将样本放入密封的信封中，并在参与者同意后打开。研究协调员不知道或有机会获得的治疗分配，直到presentation.Main结果measurements的主要分析比较组之间的差异检测率相对于健康记录和最好的可能的药物history.Results的3,500药物审查，我们检测到1,435差异。这些差异中有46%是导致药物不良事件的潜在高风险。治疗组之间的检出率无差异。软件敏感性为83%，特异性为91%;纸张敏感性为81%，特异性为94%。没有参与者失去了follow-up.Conclusion用药史收集软件是一种有效的和可扩展的方法，用于收集用药史和检测高风险的差异。虽然它包括药物图像，但与最好的药物史相比，该技术并没有提高纸质列表的准确性。
Background The Veterans Affairs Portland Healthcare System developed a medication history collection software that displays prescription names and medication images.Objective This article measures the frequency of medication discrepancy reporting using the medication history collection software and compares with the frequency of reporting using a paper-based process. This article also determines the accuracy of each method by comparing both strategies to a best possible medication history.Study Design Randomized, controlled, single-blind trial.Setting Three community-based primary care clinics associated with the Veterans Affairs Portland Healthcare System: a 300-bed teaching facility and ambulatory care network serving Veteran soldiers in the Pacific Northwest United States.Participants Of 212 patients with primary care appointments, 209 patients fulfilled the study requirements.Intervention Patients randomized to a software-directed medication history or a paper-based medication history. Randomization and allocation to treatment groups were performed using a computer-based random number generator. Assignments were placed in a sealed envelope and opened after participant consent. The research coordinator did not know or have access to the treatment assignment until the time of presentation.Main Outcome Measures The primary analysis compared the discrepancy detection rates between groups with respect to the health record and a best possible medication history.Results Of 3,500 medications reviewed, we detected 1,435 discrepancies. Forty-six percent of those discrepancies were potentially high risk for causing an adverse drug event. There was no difference in detection rates between treatment arms. Software sensitivity was 83% and specificity was 91%; paper sensitivity was 81% and specificity was 94%. No participants were lost to follow-up.Conclusion The medication history collection software is an efficient and scalable method for gathering a medication history and detecting high-risk discrepancies. Although it included medication images, the technology did not improve accuracy over a paper list when compared with a best possible medication history.