Leveraging the EHR to Collect and Analyze Social, Behavioral & Familial Factors

描述（由申请人提供）：了解社会、行为、环境和遗传因素之间的相互作用及其与健康的关系的重要性，使生物医学研究界对研究这些疾病决定因素产生了更大的兴趣。虽然对社会经济地位、教育背景、烟酒使用以及对特定疾病或病症的遗传易感性等特定决定因素的作用有一些了解，但需要改进方法来分析和确定多种决定因素之间的相互关系，并发现可能最终有助于改善患者护理和人口健康的潜在意想不到的关系。电子健康记录（EHR）系统的日益普及有可能加强收集和获取有关个人终身健康状况和卫生保健的广泛信息，以支持生物医学、行为和社会科学以及公共卫生研究等一系列“次要用途”。传统上，临床医生在临床记录中记录个人的健康史，包括“社会史”部分的社会和行为因素以及“家族史”部分的家族因素。虽然一些电子病历系统有专门的模块，用于以结构化或半结构化格式收集社会和家族史，

DESCRIPTION (provided by applicant): The importance of understanding interactions among social, behavioral, environmental, and genetic factors and their relationship to health has led to greater interest in studying these determinants of disease in the biomedical research community. While some knowledge exists regarding contributions of specific determinants such as socioeconomic status, educational background, tobacco and alcohol use, and genetic susceptibility to particular diseases or conditions, enhanced methods are needed to analyze and ascertain interrelationships among multiple determinants and to discover potentially unexpected relationships that may ultimately contribute to improving patient care and population health. The increased adoption of electronic health record (EHR) systems has the potential for enhanced collection and access to a wide range of information about an individual's lifetime health status and health care to support a range of "secondary uses" such as biomedical, behavioral and social science, and public health research. Traditionally, clinicians document an individual's health history in clinical notes, including social and behavioral factors within the "social histor" section and familial factors in the "family history" section. While some EHR systems have specific modules for collecting social and family history in structured or semi-structured formats, a large amount of this information is recorded primarily in narrative format, thus necessitating the need for automated methods to facilitate the extraction and integration of social, behavioral, and familial factors for subsequent uses. Once extracted, knowledge acquisition and discovery methods can be applied to both confirm known relationships relative to specific diseases or conditions as well as to potentially discover new relationships. We hypothesize that advanced computational methods can transform social, behavioral, and familial factors from the EHR into a rich longitudinal resource for generating knowledge regarding various determinants of health including their temporal progression, severity, and relationship to health conditions. Towards this goal, the specific aims are to: (1) develop comprehensive information models and natural language processing (NLP) techniques to represent, extract, and integrate social, behavioral, and familial factors from social and family history information in the EHR, (2) adapt and extend data mining techniques to identify non-temporal and temporal relationships among these factors and diseases, and (3) evaluate and validate known and candidate new relationships for specific conditions (pediatric asthma and epilepsy). This multi-site proposal will involve a transdisciplinary team of investigators from the University of Vermont and University of Minnesota, use of EHR data from both institutions, and collaborative development and evaluation of the NLP and data mining techniques. Ultimately, this work has the potential to provide a generalizable approach for supporting and enhancing existing knowledge regarding the interactions among social, behavioral, and familial factors and diseases.