使用电子医疗记录相关事件报告评估STAMP EMR:案例研究:制造商和用户设施设备体验数据库

F. Mason-Blakley, R. Habibi, J. Weber, Morgan Price
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引用次数: 6

摘要

在世纪之交,医学研究所(IOM)发现,医疗失误每年导致多达98000名美国人死亡。针对这一发现,他们建议实施广泛的健康信息和通信技术(HICT),包括电子病历(EMRs)。尽管从业人员广泛遵守这些建议,但医疗保健研究和质量机构(AHRQ)不仅没有观察到错误率的改善,事实上他们观察到的是相反的情况。我们认为,这种失败源于对错误的根本误解,这种误解可以通过应用系统理论框架来纠正。为此目的,我们提出了一个先前评估的系统模型,该模型使用系统理论事故模型和过程(STAMP)框架的原理开发。在这项工作中,我们的目标是进一步评估我们的模型STAMP EMR的价值,通过调查它与事故报告中报告的危险和事故的一致程度。我们将针对STAMP EMR的事件报告语料库作为先验模型执行初始编码。我们分析原始结果,并进一步提供多重协调分析(MCA)。我们首先发现,在MAUDE中没有报告不能使用STAMP EMR模型进行编码。其次,我们发现四个主要的贡献因素集群代表的报告-验证,验证,工程管理和临床管理问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing STAMP EMR with Electronic Medical Record Related Incident Reports: Case Study: Manufacturer and User Facility Device Experience Database
At the turn of the millennium the institute of medicine (IOM) discovered that medical error was responsible for the deaths of as many as 98000 Americans each year. In response to this discovery they recommended the implementation of a wide range of Health Information and Communication Technology (HICT) including electronic medical records (EMRs). In spite of the broad based adherence of practitioners to these recommendation, the Agency for Healthcare Research and Quality (AHRQ) has not only failed to observe an improvement in the rate of errors, they have in fact observed the opposite. We propose that this failing arises from a fundamental misunderstanding of error which may be remedied by the application of a system theoretic framework. We propose a previously evaluated system model developed using the principles of the System Theoretic Accidents Models and Process (STAMP) framework for this purpose. In this work we aim to further assess the value of our model, STAMP EMR, by investigating the degree to which it aligns with the hazards and accidents reported in incident reports. We perform an initial coding of a corpus of incident reports against STAMP EMR as an a priori model. We analyze the raw results and further provide a multiple coordination analysis (MCA). We find firstly that no reports in the MAUDE could not be coded using the STAMP EMR model. We find secondly that four primary clusters of contributing factors are represented for the reports - validation, verification, engineering management and clinical management issues.
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