Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio,USA.
Critical Care Transport, Cleveland Clinic, Cleveland, Ohio,USA.
J Am Med Inform Assoc. 2020 Oct 1;27(10):1520-1528. doi: 10.1093/jamia/ocaa176.
Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system.
The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching.
431 090 total mappings were generated in the Transport Data Repository, consisting of 69 010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped.
The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format.
The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.
接受医疗转运的患者是一个很少被研究的人群，这是因为在多个领域和来源中汇集数据以捕获患者护理的整个过程存在挑战。为了便于访问和二次使用转运患者数据，我们开发了转运数据存储库，该存储库结合了我们的医疗系统中来自 3 个独立领域和许多来源的数据。
该存储库是一个关系数据库，以统一医学语言系统唯一概念标识符为基础，以整合、映射和标准化数据到通用数据模型中。主要数据领域包括发送和接收医院就诊、医疗转运记录和自定义医院转运日志数据。开发了一个 4 步映射过程：1）自动源代码匹配，2）精确文本匹配，3）模糊匹配和 4）手动匹配。
在转运数据存储库中生成了 431090 个总映射，包括 69010 个唯一概念，其中 77%的数据是自动映射的。转运源数据的映射结果明显较低，只有 8%的数据实体自动映射，而大量（43%）的数据仍未映射。
多步映射过程导致大部分数据自动映射。转运医疗记录数据匹配不佳是由于第三方供应商的数据是在非标准化格式中生成和存储的。
开发和实施的多步映射过程对于将来自多个领域和来源的电子健康数据规范化到通用数据模型中以支持数据的二次使用是必要的。
