Abstract

Objective

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.

Methods

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.

Results

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.

Discussion

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.

Conclusion

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.

中文翻译：

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使用 UMLS 进行电子健康数据标准化和数据库设计。

摘要

客观的

接受医疗转移的患者代表了 1 个患者群体，由于在跨多个域和来源聚合数据方面存在挑战，而这些数据是捕获整个患者护理过程所必需的，因此这些患者群体仍然很少被研究。为了促进对运输患者数据的访问和二次使用，我们开发了运输数据存储库，该存储库将来自 3 个不同领域的数据和我们卫生系统内的许多来源结合在一起。

方法

存储库是由统一医学语言系统唯一概念标识符锚定的关系数据库，用于将数据集成、映射和标准化为通用数据模型。主要数据域包括发送和接收医院遭遇、医疗运输记录和自定义医院运输日志数据。开发了 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，以及 4) 手动匹配。

结果

运输数据存储库中总共生成了 431 090 个映射，包括 69 010 个独特的概念，其中 77% 的数据被自动映射。传输源数据产生的映射结果显着降低，只有 8% 的数据实体自动映射，还有大量 (43%) 未映射。

讨论

多步映射过程导致大部分数据被自动映射。运输病历数据匹配不良是由于第三方供应商数据以非标准化格式生成和存储。

结论

开发和实施的多步骤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为通用数据模型是必要的，以支持数据的二次使用。