Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore.
Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore.
Appl Clin Inform. 2021 Aug;12(4):757-767. doi: 10.1055/s-0041-1732301. Epub 2021 Aug 11.
Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats.
The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept.
We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard.
The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks.
Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.
糖尿病（DM）是新加坡一个重要的公共卫生关注点，给医疗保健支出带来了巨大负担。解决糖尿病等慢性病需要创新策略，整合来自不同来源的患者数据，并利用科学发现为临床实践提供信息，以帮助更好地管理疾病。观察性医疗结局伙伴关系（OMOP）通用数据模型（CDM）被选为整合具有不同格式的数据的框架。
本研究旨在评估将包含电子健康记录（EHR）、认知和抑郁评估问卷数据的新加坡本地数据源转换为 OMOP CDM 标准的可行性。此外，我们还通过使用 Atlas（一种用于 OMOP CDM 数据分析的图形用户界面工具）执行简单的治疗途径研究来验证我们的 OMOP CDM 实例是否适合研究目的，以此作为概念验证。
我们使用来自新加坡一家三级保健医院的去标识 EHR、认知和抑郁评估问卷数据将其转换为 OMOP CDM 标准的 5.3.1 版本。我们通过以下方法评估 OMOP CDM 转换：（1）评估映射覆盖率（即源术语映射到 OMOP CDM 标准的百分比）；（2）局部原始数据集与 CDM 数据集分析；（3）使用名为 Data Quality Dashboard 的开源 R 包实施 Harmonized Intrinsic Data Quality Framework。
OMOP CDM 词汇表的内容覆盖率对于临床数据超过 90%，但对于问卷数据仅约为 11%。源数据和目标数据之间的特征比较返回了一致的结果，我们转换的数据未通过 2622 次质量检查中的 38 次（1.4%）。
在我们的站点采用 OMOP CDM 表明 EHR 数据可进行标准化，且信息丢失最小，而标准化认知和抑郁评估问卷数据仍然存在挑战，需要进一步工作。
