LARGE-SCALE DATA ANALYSIS TO CHARACTERIZE VARIATIONS IN CANCER TREATMENTS ACROSS THE UNITED STATES

临床护理过程中在电子健康记录和行政索赔数据库中收集的数据可用于评估护理过程和结果。观察性卫生数据科学和信息学（OHDSI）是一项大规模的开放科学倡议，旨在由科学家社区从国际数据集网络中生成证据。OHDSI拥有300多名正式成员研究人员和3000名论坛参与者，涵盖了证据生成的所有方面，包括被称为观察性医疗结果合作伙伴关系（OMOP）公共数据模型的公共数据模型，综合临床词汇，帮助数据转换和质量保证的软件，因果推理中的创新统计方法以及高效实施，帮助研究人员转换数据和运行研究的用户界面，可视化分析结果的工具，以及询问和回答重要临床问题的临床专业知识。

Data collected in the course of clinical care in electronic health records (EHRs) and in administrative claims databases may be used to assess the process and outcomes of care. Observational Health Data Sciences and Informatics (OHDSI) is a large-scale open-science initiative to generate evidence from an international network of data sets by a community of scientists. With over 300 formal member researchers and 3000 participants on its forums, OHDSI covers all aspects of evidence generation including a common data model known as the Observational Medical Outcomes Partnership (OMOP) Common Data Model, a comprehensive clinical vocabulary, software to aid data conversion and quality assurance, innovative statistical methods in causal inference along with highly efficient implementations, user interfaces to help researchers convert data and run studies, tools for visualizing analytic results, and clinical expertise to ask and answer important clinical questions. OHDSI has previously conducted a study on treatment pathways in cancer patients with three common chronic diseases: hypertension, depression, and type-2 diabetes. The study found that in cancer patients, similar to non-cancer patients, there is greatest agreement in first-line treatment for diabetes and, to a much lesser extent, treatment for hypertension. There is greater variability in second-line treatment for diabetes and for all treatments for depression.