Administrative Supplement: Enhance the Utility of Data Available through the Childhood Cancer Data Initiative (CCDI) Ecosystem

项目摘要/摘要 国家癌症研究所(NCI)儿童癌症数据倡议(CCDI)正在构建一个生态系统，以收集 所有儿童癌症患者的数据和学习。这其中的一个重要组成部分是增加 可与提交给CCDI的其他关键数据整合的纵向临床数据的可用性。这个 目前获取这些类型数据的标准做法是通过临床研究手动提取 协调器，这很耗时，而且容易受到人为错误的影响。电子健康记录(EHR)具有 有可能扩大流入CCDI生态系统的数据量。然而，他们也提出了几个 由于缺乏结构和标准化而带来的挑战。这项研究的目的是将两者结合起来 以前开发的获取数据的方法，以便将干净的临床相关数据提供给 中纪委。第一个包ExtractEHR从EHR中提取数据，并利用一系列后处理 编码以将原始的EHR数据转换为临床相关的、人类可理解的关于童年的数据 癌症经历。第二种方法使用基于快速医疗互操作性资源(FHIR) 方法为更具可伸缩性的工作流程和临床数据交换提供技术框架。 本研究旨在将ExtractEHR与FHIR框架开发相结合，以使 CCDI可获得的临床数据。该提案将通过以下方式确定持续捐款的可扩展方式 实现以下两个具体目标。第一个目标是提取和处理EHR数据，为临床提供 CCDI生态系统中有分子数据的患者的治疗和结果的背景。要实现 这个目标，ExtractEHR将提取以前同意儿童脑瘤网络的患者的数据 (CBTN)协议在两家大型儿童医院，费城儿童医院(CHOP)和 亚特兰大儿童保健(CHOA)。提取的数据将被处理以提供与临床相关的 治疗和结果数据将被转移到CCDI。这将证明该方案的可行性和 通过向CCDI提供一组EHR数据来促进协作和 与CCDI团队讨论要优化和/或扩展的工作流程或数据的哪些方面。这个 第二个目标是开发基于FHIR的ExtractEHR版本，并确定其可伸缩性和在 与获得有临床意义的数据所需的后处理工作流程交互。我们会作出努力 在CHOP中将ExtractEHR功能导出到FHIR框架中，并在Choa测试此过程。 这项研究将创建管道，从清理和处理的EHR数据中提供细粒度数据，从而提供 纳入CCDI的患者的临床背景，并将确定基于FHIR的方法是否可以用作 一种可扩展的方法，用于使用ExtractEHR确定要包括在CCDI生态系统中的数据。

PROJECT SUMMARY/ABSTRACT The National Cancer Institute (NCI) Childhood Cancer Data Initiative (CCDI) is building an ecosystem to gather data and learn from all patients with childhood cancer. An important component of this is increasing the availability of longitudinal clinical data that can be integrated with other crucial data submitted to the CCDI. The current standard practice for acquiring these types of data is manual abstraction by a clinical research coordinator, which is time consuming and subject to human error. Electronic health records (EHRs) have the potential to expand the amount of data flowing into the CCDI ecosystem. However, they also present several challenges due to lack of structure and standardization. This objective of this study is to combine two previously developed approaches to obtaining data in order to provision clean, clinically-relevant data to the CCDI. The first package, ExtractEHR, extracts data from the EHR, and utilizes a series of post-processing coding to transform the raw EHR data into clinically relevant, human understandable data about the childhood cancer experience. The second approach uses Fast Healthcare Interoperability Resources (FHIR)-based methods to provide a technical framework for more scalable workflows and interchange of clinical data. This study aims to combine ExtractEHR with the FHIR framework development to enable the expansion of clinical data available to CCDI. The proposal will determine scalable ways for ongoing contributions by achieving the following two specific aims. The first aims is to extract and process EHR data to provide clinical context regarding treatment and outcomes for patients with molecular data in the CCDI ecosystem. To achieve this goal, ExtractEHR will be extract data on patients previously consented to Children’s Brain Tumor Network (CBTN) protocols at two large children’s hospitals, the Children’s Hospital of Philadelphia (CHOP) and Children’s Healthcare of Atlanta (CHOA). Extracted data will be processed to provide clinically-relevant treatment and outcomes data that will be transferred to the CCDI. This will demonstrate the feasibility and operability of a seamless workflow by delivering a set of EHR data to CCDI to facilitate collaboration and discussion with CCDI teams on which aspects of the workflow or data to optimize and/or expand on. The second aim is to develop a FHIR-based version of ExtractEHR and determine its scalability and success in interacting with the post-processing workflow needed to obtain clinically meaningful data. Efforts will be made to export ExtractEHR functionality into a FHIR framework at CHOP and to test this process at CHOA. This study will create pipelines that provide granular data from cleaned and processed EHR data that offers clinical context for patients included in the CCDI and will determine if FHIR-based approaches can be used as a scalable method for using ExtractEHR to ascertain data to be included in the CCDI ecosystem.