Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation.
Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation.
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DOI：
10.1111/acem.12743
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Froehle CM
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文献类型：
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作者：
Ward MJ;Self WH;Froehle CM
评估电子健康记录（EHR）中的数据错误如何影响常见急诊科（艾德）运营绩效指标的准确性。使用一个3个月，7,348访问数据集的电子时间戳从郊区的学术艾德作为基线，蒙特卡罗模拟被用来引入四种类型的数据错误（替代，缺失，随机和系统偏差）在三个频率水平（2%，4%和7%）。计算了三个常用的艾德操作指标（到达临床医生评价、住院患者退出的处置决定和住院患者的艾德住院时间），并确定了实现每个性能目标的艾德访视比例。即使是很小的数据错误也会对临床组织准确确定其是否满足其运营性能目标的能力产生可测量的影响。系统性替代错误、错误频率增加和使用较短持续时间指标导致报告为符合相关性能目标的艾德访视比例较低。然而，其他错误类型的存在在一定程度上减轻了系统替代错误的影响。较长的持续时间的指标被认为是不太敏感的数据错误比较短的持续时间的指标。EHR时间戳中的罕见和小幅度数据错误可能会损害临床组织准确确定其是否满足性能目标的能力。通过了解组织EHR中数据错误的类型和频率，组织领导者可以使用数据管理最佳实践来更好地衡量真实绩效并增强运营决策。
To estimate how data errors in electronic health records (EHR) can affect the accuracy of common emergency department (ED) operational performance metrics. Using a 3-month, 7,348-visit dataset of electronic timestamps from a suburban academic ED as a baseline, Monte Carlo simulation was used to introduce four types of data errors (substitution, missing, random, and systematic bias) at three frequency levels (2%, 4%, and 7%). Three commonly used ED operational metrics (arrival to clinician evaluation, disposition decision to exit for admitted patients, and ED length of stay for admitted patients) were calculated and the proportion of ED visits that achieved each performance goal was determined. Even small data errors have measurable effects on a clinical organization's ability to accurately determine whether it is meeting its operational performance goals. Systematic substitution errors, increased frequency of errors, and the use of shorter-duration metrics resulted in a lower proportion of ED visits reported as meeting the associated performance objectives. However, the presence of other error types mitigated somewhat the effect of the systematic substitution error. Longer time-duration metrics were found to be less sensitive to data errors than shorter time-duration metrics. Infrequent and small-magnitude data errors in EHR timestamps can compromise a clinical organization's ability to determine accurately if it is meeting performance goals. By understanding the types and frequencies of data errors in an organization's EHR, organizational leaders can use data-management best practices to better measure true performance and enhance operational decision-making.