Quantifying the Effect of Data Quality on the Validity of an eMeasure
Quantifying the Effect of Data Quality on the Validity of an eMeasure
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DOI：
10.4338/aci-2017-03-ra-0042
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Westra, Bonnie L.
中科院分区：
文献类型：
--
作者：
Johnson, Steven G.;Speedie, Stuart;Westra, Bonnie L.
目的本研究的目的是证明医疗保健数据质量框架的实用性，通过使用它来衡量的影响，综合数据质量问题的有效性的eMeasure（CMS 178-导尿管取出手术后）。方法数据质量问题是人为创建的系统性退化的基本质量的EHR数据使用两种方法：独立和相关的退化。描述eMeasure中包含的事件变化的线性模型量化了每个数据质量问题的影响。结果导管持续时间对CMS 178 eMeasure的影响最大，数据质量每降低1%，缺失事件数量就会增加1.21%。对于出生日期和入院类型，每减少1%的数据质量导致了1%的增加missingevents.Conclusion数据质量问题的影响可以量化使用一个通用的过程，CMS 178电子测量，目前定义的，可能无法衡量如何以及一个组织是满足预期的最佳实践目标。只有在数据质量足够高的情况下，才有必要对EHR数据进行二次利用。本研究中描述的评估方法演示了如何量化数据质量问题对eMeasure的影响，该方法可以推广到其他数据分析任务。医疗保健组织可以优先考虑数据质量改进工作，重点关注对有效性影响最大的领域，并评估报告的值是否值得信任。
Objective The objective of this study was to demonstrate the utility of a healthcare data quality framework by using it to measure the impact of synthetic data quality issues on the validity of an eMeasure (CMS178-urinary catheter removal after surgery).Methods Data quality issues were artificially created by systematically degrading the underlying quality of EHR data using two methods: independent and correlated degradation. A linear model that describes the change in the events included in the eMeasure quantifies the impact of each data quality issue.Results Catheter duration had the most impact on the CMS178 eMeasure with every 1% reduction in data quality causing a 1.21% increase in the number of missing events. For birth date and admission type, every 1% reduction in data quality resulted in a 1% increase in missing events.Conclusion This research demonstrated that the impact of data quality issues can be quantified using a generalized process and that the CMS178 eMeasure, as currently defined, may not measure how well an organization is meeting the intended best practice goal. Secondary use of EHR data is warranted only if the data are of sufficient quality. The assessment approach described in this study demonstrates how the impact of data quality issues on an eMeasure can be quantified and the approach can be generalized for other data analysis tasks. Healthcare organizations can prioritize data quality improvement efforts to focus on the areas that will have the most impact on validity and assess whether the values that are reported should be trusted.