Aalsma Matthew C, Schwartz Katherine, Haight Konrad A, Jarjoura G Roger, Dir Allyson L
Indiana University School of Medicine, US.
American Institutes for Research, US.
EGEMS (Wash DC). 2019 Jul 11;7(1):26. doi: 10.5334/egems.258.
Integrating electronic health records (EHR) with other sources of administrative data is key to identifying factors affecting the long-term health of traditionally underserved populations, such as individuals involved in the justice system. Linking existing administrative data from multiple sources overcomes many of the limitations of traditional prospective studies of population health, but the linking process assumes high levels of data quality and consistency within administrative data. Studies of EHR, unlike other types of administrative data, have provided guidance to evaluate the utility of big data for population health research.
Here, an established EHR data quality framework was applied to identify and describe the potential shortcomings of administrative juvenile justice system data collected by one of four case management systems (CMSs) across 12 counties in a Midwest state. The CMS data were reviewed for logical inconsistencies and compared along the data quality dimensions of plausibility and completeness.
After applying the data quality framework, several patterns of logical inconsistencies within the data were identified. To resolve these inconsistencies, recommendations regarding data entry, review, and extraction are offered.
The recommendations related to achieving quality justice system data can be applied to future efforts to link administrative databases from multiple sources. Increasing trust in administrative data quality related to vulnerable populations ultimately improves knowledge of pressing public health concerns.
将电子健康记录(EHR)与其他行政数据来源相结合,是识别影响传统上服务不足人群(如司法系统中的个人)长期健康因素的关键。将来自多个来源的现有行政数据相链接,克服了传统人群健康前瞻性研究的许多局限性,但链接过程假定行政数据具有高水平的数据质量和一致性。与其他类型的行政数据不同,电子健康记录研究为评估大数据在人群健康研究中的效用提供了指导。
在此,应用一个既定的电子健康记录数据质量框架,来识别和描述由中西部一个州12个县的四个案件管理系统(CMS)之一收集的行政少年司法系统数据的潜在缺陷。对CMS数据进行逻辑不一致性审查,并在合理性和完整性的数据质量维度上进行比较。
应用数据质量框架后,识别出了数据中的几种逻辑不一致模式。为解决这些不一致性,提供了有关数据录入、审查和提取的建议。
与实现高质量司法系统数据相关的建议,可应用于未来链接多个来源行政数据库的工作中。增强对与弱势群体相关行政数据质量的信任,最终会增进对紧迫公共卫生问题的了解。