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首页> 外文期刊>JMIR Medical Informatics >Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study
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Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study

机译：集成和评估智能泵信息的数据质量和效用在检测药物管理错误中的检测：评估研究

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Background At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized. Objective This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration. Methods We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data, respectively. A novel concordance assessment was also developed to understand the detec-tion power and capabilities of SPR and EHR data. Results Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data. Conclusions We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting.
机译：目前，电子健康记录（EHRS）是临床信息学的中心焦点，赋予其作为临床数据的主要来源的作用。尽管他们的粒度虽然粒度，但EHR数据严重依赖手动输入并容易发生人类错误。许多其他数据来源存在于临床环境中，包括诸如智能输注泵等数字医疗器械。当与来自EHRS的处方数据结合起来时，智能泵记录（SPR）能够在药物使用过程中发生的动作上脱光。然而，谐波2个来源受到多种技术挑战的阻碍，SPR的数据质量和效用尚未完全实现。目的本研究旨在评估SPR的质量和效用，其在检测药物管理误差时掺入EHR数据。我们的总体假设是SPRS将在Med-Ancation使用过程中提供独特的信息，从而能够更全面地检测药物管理中的差异和潜在误差。方法我们评估了9种高危药物的药物使用过程，为1年期间患者录取了新生儿Inten-Sive护理单元的患者。开发了一种自动算法，以使用患者ID，药物名称和时间戳将SPR与EHRS中的Medica-tion订单对齐。通过临床研究协调员和2个儿科医生手动重新观察对齐的数据，以确定药物宣传的差异。 SPR的数据质量被评估为与有效的EHR订单相关的信息的比例。为了评估其实用程序，我们分别比较了SPR和EHR数据捕获的差异的频率和严重程度。还制定了一种新颖的一致性评估，以了解SPR和EHR数据的撤销功率和能力。结果大约70％的SPR包含有效的患者ID和药物名称，使其可行的数据集成。结合了2个来源后，调查团队审查了2307名药物命令，10,575名药物管理记录（火星）和23,397 SPRS。鉴定了总共321月和682分SPR乳腺癌，血管加压剂显示出最高的差异率，其次是麻醉品和全肠胃外营养。与EHR火星相比，使用SPRS更常见的给药差异差异。 Concordance分析显示MAR和SPR STOMELIPA-CIE之间的重叠很小，最大多数由SCR数据捕获的差异。结论我们将智能输注泵信息与EHR数据进行了整合，分析了药物生命周期最容易出错的阶段。结果表明，SPRS可能是用于药物错误检测的更可靠的数据源。最终，必须将SPR信息与EHR数据集成到临床环境中完全检测和减轻药物管理误差。

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