Cancer Protocols and Data Standards, College of American Pathologists, Northfield, IL.
Department of Pathology/Microbiology, University of Nebraska Medical Center, Omaha, NE.
JCO Clin Cancer Inform. 2021 Feb;5:194-201. doi: 10.1200/CCI.20.00103.
Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC's primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data's lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic-SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.
缺乏互操作性是医疗信息学面临的最大挑战之一。最近的互操作性努力主要集中在数据传输上,通常忽略了数据捕获标准化。结构化数据捕获(SDC)是一个开源技术框架,可在护理点实现标准化和结构化数据的捕获和交换,并通过互操作数据输入表单(DEF)进行交换。SDC 的一些主要用例涉及复杂的肿瘤学数据,如解剖病理学、生物标志物和临床肿瘤学数据收集和报告。其互操作性目标是在数据的整个生命周期中保持捕获数据的语义、上下文和结构完整性。SDC 文档采用可扩展标记语言(XML)编写,因此是计算机可读的,但技术上是不可知的——任何 EHR 供应商或注册表都可以实施 SDC。任何具有 SDC 功能的系统都可以将 SDC XML 文件呈现为 DEF,接收和解析 SDC 传输,并使用响应数据完整地将原始 SDC 表单重新生成为 DEF 或概要报告。因此,SDC 能够为患者护理、临床试验、癌症监测和公共卫生需求、临床研究和可计算护理指南提供互操作的数据捕获和交换。当 SDC 数据元素映射到标准术语时,SDC 捕获的肿瘤学数据的可用性会得到增强。例如,SDC 到系统命名医学临床术语(SNOMED CT)的映射可以将 SDC 数据与其他相关数据集进行聚合,并允许根据 SNOMED CT 概念属性和描述逻辑进行高级查询和分组。SDC 支持使用单独的映射文件或作为嵌入 SDC 文档中的术语代码的术语映射。