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

在临床护理过程中收集的电子健康记录（EHR）和行政索赔数据库中的数据可用于评估护理过程和结果。观察健康数据科学和信息学（OHDSI）是一项大规模的开放科学计划，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，涵盖了证据生成的各个方面，包括被称为观察性医学结局伙伴关系（OMOP）通用数据模型的通用数据模型，全面的临床词汇表，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沿着高效的实施，帮助研究人员转换数据和运行研究的用户界面，用于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。 OHDSI此前曾对患有三种常见慢性疾病的癌症患者的治疗途径进行了研究：高血压，抑郁症和2型糖尿病。 该研究发现，与非癌症患者相似，癌症患者在糖尿病的一线治疗方面存在最大的一致性，而高血压治疗的一致性要小得多。 糖尿病二线治疗和所有抑郁症治疗的差异性更大。

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.