van Essen Melissa H J, Twickler Robin, Weesie Yvette M, Arslan Ilgin G, Groenhof Feikje, Peters Lilian L, Bos Isabelle, Verheij Robert A
Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Reitse Poort 1, RP126, Professor Cobbenhagenlaan 125, Tilburg, The Netherlands, 31 631978419.
Nivel, Netherlands Institute for Health Services Research, Utrecht, The Netherlands.
J Med Internet Res. 2025 Jun 11;27:e64628. doi: 10.2196/64628.
The use of routinely recorded electronic health record (EHR) data is increasingly common, especially in epidemiological research. However, data must be processed and prepared for secondary use, and decisions made during this process could significantly impact research outcomes. A demonstration of the extent of these consequences is necessary.
The aim of this study was to investigate the influence of data processing steps on research outcomes derived from the secondary use of EHR data.
EHR data from 8 Dutch general practices from 2019 were used. These practices contributed data to 2 research databases: the Academic General Practitioner Development Network registry and the Nivel Primary Care Database. Data were extracted and processed through distinct extraction, transformation, and loading (ETL) pipelines, allowing the evaluation of the impact of different ETL methods by comparing the 2 datasets in three steps: (1) patient demographics, (2) epidemiology of concordant patients, and (3) health service use of patients with 3 diagnoses. A number of similarity indicators, including the number of contacts, regular consultations and visits, prescriptions, and episodes, were compared between the 2 databases. The outcomes were compared by performing paired samples t tests using 99% CIs. Prevalence, number of prescriptions, and number of regular consultations and visits per 1000 patient years were calculated and compared for 3 diagnoses (diabetes mellitus, urinary tract infection, and cough). These outcomes were compared using the SD.
Differences were observed between the datasets in the number of enrolled patients (Academic General Practitioner Development Network registry: n=47,517; Nivel Primary Care Database: n=44,247). Despite this, patient demographics were similar. All indicator outcomes of the concordant patients showed significant differences between the databases, that is, the number of contacts, prescriptions, and episodes per patient, and the number of regular consultations and visits. Differences in the indicator outcomes for the 3 diagnosis groups varied greatly in SD, however, none of the differences were deemed significant.
The findings highlight the importance of routine health data users' awareness of different ETL steps involved. Transparency and shared knowledge about these processes are critical, and making them available for research is necessary. Data processors should share their knowledge regarding their choices, and researchers and policy makers should invest in their knowledge of this type of metadata. Transparency and shared knowledge are particularly important in light of the European Health Data Space and the ever-increasing secondary use of routinely recorded health data. Future research should focus on the role of transparency, joint decision-making, and the minimization of effects of ETL steps, and on the insight into the individual influence of ETL steps on research outcomes. This could stimulate standardized approaches among data processors and researchers, resulting in increased data interoperability.
常规记录的电子健康记录（EHR）数据的使用越来越普遍，尤其是在流行病学研究中。然而，数据必须经过处理和准备才能用于二次利用，在此过程中做出的决策可能会对研究结果产生重大影响。有必要证明这些后果的严重程度。
本研究的目的是调查数据处理步骤对从EHR数据二次利用中得出的研究结果的影响。
使用了来自荷兰8家全科诊所2019年的EHR数据。这些诊所将数据贡献给了2个研究数据库：学术全科医生发展网络登记处和Nivel初级保健数据库。数据通过不同的提取、转换和加载（ETL）管道进行提取和处理，通过比较2个数据集在三个步骤中的情况来评估不同ETL方法的影响：（1）患者人口统计学特征，（2）相符患者的流行病学情况，（3）患有3种诊断疾病患者的医疗服务使用情况。比较了2个数据库之间的一些相似性指标，包括接触次数、定期会诊和就诊次数、处方数量和发作次数。使用99%置信区间进行配对样本t检验来比较结果。计算并比较了3种诊断疾病（糖尿病、尿路感染和咳嗽）每1000患者年的患病率、处方数量以及定期会诊和就诊次数。使用标准差比较这些结果。
在登记患者数量方面，数据集之间存在差异（学术全科医生发展网络登记处：n = 47,517；Nivel初级保健数据库：n = 44,247）。尽管如此，患者人口统计学特征相似。相符患者的所有指标结果在数据库之间均显示出显著差异，即每位患者的接触次数、处方数量和发作次数，以及定期会诊和就诊次数。3个诊断组的指标结果差异在标准差方面差异很大，然而，没有一个差异被认为是显著的。
研究结果强调了常规健康数据用户了解所涉及的不同ETL步骤的重要性。这些过程的透明度和共享知识至关重要，使其可用于研究是必要的。数据处理者应分享他们关于其选择的知识，研究人员和政策制定者应增加对这类元数据的了解。鉴于欧洲健康数据空间以及常规记录的健康数据二次利用的不断增加，透明度和共享知识尤为重要。未来的研究应关注透明度、联合决策以及ETL步骤影响最小化的作用，以及深入了解ETL步骤对研究结果的个体影响。这可能会促进数据处理者和研究人员之间的标准化方法，从而提高数据的互操作性。
