Extending the Capabilities and Reach of EMERSE in Support of Cancer Research

扩展 EMERSE 支持癌症研究的能力和范围

• 批准号: 10740137
• 负责人: David Alan Hanauer
• 金额: $ 85.65万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740137
• 关键词: Address; Administrator; Behavioral; Cancer Center; Clinical; Clinical Research; Communities; Computer software; Computerized Medical Record; Consensus; Country; Data; Development; Diagnosis; Diagnostic Neoplasm Staging; Disease Progression; Electronic Health Record; Exclusion; Family; Feedback; Funding; Future; Genomics; Goals; Grant; Hand; Human Resources; Information Retrieval; Information Retrieval Systems; Institution; Interview; Knowledge; Lead; Leadership; Link; Malignant Neoplasms; Maps; Measures; Michigan; Mothers; Multi-Institutional Clinical Trial; Names; Natural Language Processing; Ontology; Outcome; Pain; Pathology Report; Patients; Peer Review; Perception; Phenotype; Process; Recording of previous events; Recurrence; Research; Research Personnel; Review Literature; Running; Scanning; Secure; Security; Semantics; Services; Site; Social Network; Structure; Surveys; System; Targeted Research; Technology; Testing; Text; Toxic effect; Trust; Tumor Markers; Uncertainty; Unified Medical Language System; Universities; Work; anticancer research; application programming interface; base; cohort; data standards; data visualization; design; electronic health record system; empowerment; experience; health determinants; improved; indexing; individual patient; innovation; malignant breast neoplasm; meetings; novel; operation; optical character recognition; programs; rare cancer; search engine; social; social health determinants; software systems; systems research; text searching; timeline; tool; translational cancer research; treatment response; unstructured data

PROJECT SUMMARY The free text notes in electronic health records (EHRs) contain details vital to cancer research that often are found nowhere else in the EHR. Such details include social and behavioral determinants of health, family histories, cancer staging, tumor markers, disease progression, response to treatment, overall outcomes, and more. Utilizing the free text (also called unstructured data) can be challenging, and researchers need tools to help them leverage those data in a meaningful way. The electronic medical record search engine (EMERSE) has been in operation, and under continuous development, for 17 years and was designed to help cancer researchers meaningfully use unstructured EHR data. EMERSE started as simple information retrieval system but has since gained many features for supporting clinical research. It is distinguished from other text-processing research tools, in part, by its ease of use and other enterprise software features such as maintaining audit logs and providing administrator functions. With the support of an ITCR grant that is nearing completion, EMERSE has grown from a tool used at only one site (University of Michigan) to one that is running, or being implemented, at multiple cancer centers and other academic research institutions across the country. EMERSE also now supports the capability of searching across a network of instances to securely obtain obfuscated patient counts at other sites, which can be valuable for identifying rare cancer cohorts. The innovations in this proposal include: (1) Building new functionality into EMERSE to expand its research capabilities such as timeline data visualizations, self-service data extraction workflows from templated notes, support for optical character recognition (OCR), and integration with other ITCR tools such as DeepPhe through an application programming interface (API); (2) Incorporating natural language processing (NLP) into EMERSE including named entity recognition (mapped to the Unified Medical Language System, UMLS), negation (e.g., “patient denies pain”), uncertainty (e.g., “possibility of recurrence”), and experiencer (e.g, “breast cancer in her mother”); (3) Expanding the EMERSE network in breadth, capability, security, and trust by partnering with external sites to collectively develop a consensus and a viable approach for broadly enabling this novel network technology; and (4) Continuing to evaluate EMERSE with a focus on networking, security, and scientific outcomes by conducting additional user-centered studies to develop a deeper understanding of how EMERSE is being, or could be, used with a goal of continuous improvement. The EMERSE team routinely receives feedback from users and site administrators and the proposed work is highly responsive to our growing user base and will enable research that is currently impractical, if not impossible, for many researchers to accomplish. The enhancements and knowledge gained from this effort will make EMERSE even more powerful and capable of supporting a wide range of clinical and translational cancer research for a growing user base across the nation.

项目摘要 电子健康记录（EHR）中的自由文本注释包含对癌症研究至关重要的细节，这些细节通常 在电子病历里找不到这些细节包括健康的社会和行为决定因素，家庭， 病史、癌症分期、肿瘤标志物、疾病进展、治疗反应、总体结局，以及 更多.利用自由文本（也称为非结构化数据）可能具有挑战性，研究人员需要工具来 帮助他们以有意义的方式利用这些数据。电子病历搜索引擎（EMERSE） 已经运行了17年，并在不断发展，旨在帮助癌症 研究人员有意义地使用非结构化的EHR数据。EMERSE最初是一个简单的信息检索系统 但此后获得了许多支持临床研究的功能。它区别于其他文本处理 研究工具，部分原因是其易用性和其他企业软件功能，如维护审计日志 并提供管理员功能。在即将完成的ITCR赠款的支持下， 已经从仅在一个站点（密歇根大学）使用的工具发展到正在运行或正在实施的工具， 在全国多个癌症中心和其他学术研究机构。如今， 支持在实例网络中搜索的能力，以安全地获取模糊的患者计数 在其他网站，这可能是有价值的，以确定罕见的癌症队列。该提案的创新之处包括： (1)在EMERSE中构建新功能，以扩展其研究功能，例如时间轴数据 可视化，从模板化注释中自助数据提取工作流，支持光学字符 识别（OCR），并通过应用程序编程与其他ITCR工具（如DeepPhe）集成 接口（API）;（2）将自然语言处理（NLP）扩展到包含命名实体的EMERSE中 识别（映射到统一医学语言系统，UMLS），否定（例如，“患者否认疼痛”）， 不确定性（例如，（3）扩大（例如，“复发的可能性”）和经验者（例如，“她母亲的乳腺癌”）; EMERSE网络的广度、能力、安全性和信任度，通过与外部站点合作， 发展共识和可行的方法，以广泛启用这种新的网络技术;以及（4） 继续评估EMERSE，重点关注网络、安全和科学成果， 额外的以用户为中心的研究，以更深入地了解EMERSE正在或可能如何使用 以持续改进为目标。EMERSE团队定期收到用户和研究中心的反馈 管理员和拟议的工作是高度响应我们不断增长的用户群，并将使研究 这对许多研究人员来说，目前是不切实际的，如果不是不可能的话。增强功能和 从这项工作中获得的知识将使EMERSE更加强大，能够支持广泛的 一系列的临床和转化癌症研究，为全国不断增长的用户群。