Four State Disability and Health Data Analysis Collaboration

摘要 自2010年以来，临床医学受益于慢性疾病临床研究的快速激增，这些研究使用 电子健康记录(EHR)的数据。EHR之所以有吸引力，是因为它们可以提供大样本数量， 及时的信息，以及丰富的临床、诊断和实验室信息。然而，尽管数百万人 患者记录包含在大型电子病历数据库中，对其完整性了解较差， 可从EHR记录中提取的关于患者群体的信息的有效性和可靠性，以及数据 来自EHR网络的数据不具有人口代表性，限制了它们对人口健康的效用 监视系统。在这项建议中，我们建议利用我们在开发电子病历方面的长期专业知识 监测指标，我们与纽约市卫生局合作所做的工作，并扩大 包括纽约州卫生部和纽约大学调查人员在内的合作伙伴关系 综合癌症中心。我们的目标是设计和测试一个监测报告模型的可行性 包括门诊护理中癌症预防和控制的绩效措施和质量。指标将 使用基于规则的测试方法进行开发，然后使用完善的图表评审进行验证 方法评价灵敏度和特异度。我们提供：(1)访问非常大的EHR网络， 使用与整个PCORnet分布式研究网络(11个站点)相同的OMOP CDM 国家，为未来的可伸缩性提供机会；(2)获取涵盖很大比例的电子健康记录数据 居住在大都市地区的居民，包括很高比例的保险不足、低收入、 种族/民族多元化的患者；以及(3)具有丰富分析经验的调查小组 OMOP清洁发展机制电子健康数据库和进行人口健康监测。进行这项研究 在纽约市多样化的城市环境中，提供了表征高危人群差异的潜力

ABSTRACT Since 2010, clinical medicine has benefited from a rapid surge of clinical research on chronic diseases using data from electronic health records (EHRs). EHRs are appealing because they can offer large sample sizes, timely information, and a wealth of clinical, diagnostic, and laboratory information. However, while millions of patient records are included in large EHR databases, there is poor understanding about the completeness, validity and reliability of information that can be extracted from EHR records on patient populations, and data from EHR networks are not population-representative, constraining their utility for population health surveillance. In this proposal, we propose to leverage our longstanding expertise in developing EHR surveillance indicators, work we have done in partnership with the NYC Health department, and expand the partnership to include the New York State Department of Health and investigators from the NYU Comprehensive Cancer Center. Our goal is to design and test the feasibility of a model surveillance report that includes performance measures and quality of cancer prevention and control in ambulatory care. Indicators will be developed using rules-based testing approaches, and then validated using well-established chart review methods to assess sensitivity and specificity.. We offer: (1) access to a very large EHR network that actively uses the same OMOP CDM employed by the entire PCORNet distributed research network (11 sites) across the country, providing opportunities for future scalability; (2) access to EHR data covering a large proportion of residents living in a large metropolitan area, including high proportions of underinsured, low income, racially/ethnically diverse patients; and (3) an investigation team with extensive prior experience analyzing OMOP CDM electronic health databases and performing population health surveillance. Conducting this study in the diverse, urban environment of NYC offers potential to characterize disparities in at-risk populations