Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA.
International Training and Education Center for Health (I-TECH), Seattle, WA, USA.
BMC Health Serv Res. 2023 Oct 23;23(1):1139. doi: 10.1186/s12913-023-10133-2.
In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya.
We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data.
A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators.
We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics.
在本次评估中，我们旨在通过数字化数据质量评估（DQA）流程来加强常规卫生信息系统（RHIS）。我们利用基于 District Health Information System version 2（DHIS2）的肯尼亚卫生信息系统（KHIS）中的电子数据来大规模执行 DQA。我们提供了一个系统的指南，用于开发综合数据质量评分，并使用这些评分来评估肯尼亚的数据质量。
我们评估了肯尼亚 187 家拥有电子病历的艾滋病毒护理机构。使用 2011 年 1 月至 2018 年 6 月的季度纵向 KHIS 数据（总 N=30 个季度），我们提取了涵盖一般艾滋病毒服务的指标，包括预防母婴传播（PMTCT）服务。我们使用三个数据驱动的综合评分来评估这些数据的准确性（数据的正确性和无误差程度）：1）完整性评分；2）一致性评分；3）差异评分。完整性是指数据的适当数量。一致性是指多个指标的数据的一致性。差异（在 Z 标度上测量）是指数据与定义数据可能有效值的规则之间的一致性（或缺乏一致性）程度。
从 KHIS 中总共提取了 5610 个独特的设施季度数据。完整性评分的平均值为 61.1%（标准差（SD）=27%）。一致性评分的平均值为 80%（SD=16.4%）。差异评分的平均值为 0.07（SD=0.22）。一致性评分和差异评分之间存在很强的正相关性（相关系数=0.77），而与完整性评分的相关性较低，相关系数为-0.12（与一致性评分相关）和-0.36（与差异评分相关）。一般艾滋病毒指标比 PMTCT 指标更完整，但一致性和可信度较低。
我们观察到完整性评分与其他两个评分之间缺乏相关性。因此，对于全面的 DQA，完整性评估应与一致性或差异的衡量相结合，以反映数据质量的不同维度。鉴于差异评分的复杂性，我们建议使用更简单的一致性评分，因为它们高度相关。在卫生信息数字化不断扩展的情况下，对 KHIS 数据使用综合评分进行常规使用可以提高大规模 DQA 的效率，并且可以应用于艾滋病毒诊所以外的其他卫生部门。
