Medication table mappings.

Background The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and ‘validation’ analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. Methods We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Results Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A ’FAIL’ occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. Conclusion The OMOP CDM’s widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.研究背景 将常规收集的健康数据用于二次研究，已日益被认可为一种可推动医学研究进展、改善患者预后并指导政策制定的研究方法。这类源自电子病历（electronic medical records, EMRs）的二次数据，可通过转换为统一数据结构得到优化，从而能够与其他同类健康指标数据集开展联合分析。这一目标可通过观察性医学结果协作组通用数据模型（Observational Medical Outcomes Partnership Common Data Model, OMOP-CDM）实现，该模型采用标准化词汇体系，助力跨多类观察性数据库开展系统化分析。OMOP-CDM的核心思路是，通过在统一存储库中统一术语、词汇体系与编码方案，将各类数据转换为通用格式。该模型通过以下方式提升研究能力：开发共享的分析与预测技术、开展药物安全主动监测的药物警戒工作，以及在澳大利亚、美国、欧洲与亚太地区的多机构间开展‘验证性’分析。本研究旨在探究开源OMOP-CDM在PATRON基层医疗数据存储库中的应用场景。 研究方法 我们采用标准结构化查询语言（structured query language, SQL）构建抽取、转换与加载脚本，将数据转换为OMOP-CDM格式。从不同EMRs中抽取的非结构化自由文本术语的映射流程面临极大挑战：多数术语无法通过直接文本比对自动匹配至标准词汇体系，因此有大量术语需通过人工方式完成映射赋值。为解决该问题，我们制定了一项策略：要求临床映射专员仅关注出现频次足够高的术语。我们为每个领域设定了特定阈值，确保在完成合理映射后，超过95%的记录可关联至SNOMED等经认可的词汇体系。为评估生成的OMOP数据集的数据质量，我们采用OHDSI数据质量仪表板（Data Quality Dashboard, DQD），依据Kahn框架对PATRON存储库中数据的合理性、合规性与全面性开展评估。 研究结果 我们将3套基层医疗EMR系统中203万名活跃患者的数据转换为OMOP通用数据模型5.4版本格式。本次DQD评估共开展3570项独立测评，每项测评均将结果与预设阈值进行比对：当不符合要求的记录行占比超出指定阈值时，即判定为‘不通过’。针对本次所述的基层医疗OMOP数据库的评估中，我们整体通过率达到97%。 研究结论 OMOP CDM在全球范围内的广泛应用、配套支持与培训体系，为协作研究中的数据标准化提供了成熟可行的路径。其良好的兼容性支持本地与国际研究团队共享分析工具包，助力实现快速且可复现的数据比对。包括OHDSI数据质量仪表板（版本1.4.1）在内的一系列开源工具可为该模型提供支撑。其操作简便且基于标准体系的设计思路，便于该模型被采纳并集成至现有数据流程中。