一种基于使用OpenEHR电子健康记录的临床研究患者筛查工具：开发研究。

A Patient-Screening Tool for Clinical Research Based on Electronic Health Records Using OpenEHR: Development Study.

作者信息

Li Mengyang, Cai Hailing, Nan Shan, Li Jialin, Lu Xudong, Duan Huilong

机构信息

College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Key Laboratory for Biomedical Engineering, Ministry of Education, Hangzhou, Zhejiang, China.

出版信息

JMIR Med Inform. 2021 Oct 21;9(10):e33192. doi: 10.2196/33192.

DOI:10.2196/33192

PMID:34673526

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8569542/

Abstract

BACKGROUND

The widespread adoption of electronic health records (EHRs) has facilitated the secondary use of EHR data for clinical research. However, screening eligible patients from EHRs is a challenging task. The concepts in eligibility criteria are not completely matched with EHRs, especially derived concepts. The lack of high-level expression of Structured Query Language (SQL) makes it difficult and time consuming to express them. The openEHR Expression Language (EL) as a domain-specific language based on clinical information models shows promise to represent complex eligibility criteria.

OBJECTIVE

The study aims to develop a patient-screening tool based on EHRs for clinical research using openEHR to solve concept mismatch and improve query performance.

METHODS

A patient-screening tool based on EHRs using openEHR was proposed. It uses the advantages of information models and EL in openEHR to provide high-level expressions and improve query performance. First, openEHR archetypes and templates were chosen to define concepts called simple concepts directly from EHRs. Second, openEHR EL was used to generate derived concepts by combining simple concepts and constraints. Third, a hierarchical index corresponding to archetypes in Elasticsearch (ES) was generated to improve query performance for subqueries and join queries related to the derived concepts. Finally, we realized a patient-screening tool for clinical research.

RESULTS

In total, 500 sentences randomly selected from 4691 eligibility criteria in 389 clinical trials on stroke from the Chinese Clinical Trial Registry (ChiCTR) were evaluated. An openEHR-based clinical data repository (CDR) in a grade A tertiary hospital in China was considered as an experimental environment. Based on these, 589 medical concepts were found in the 500 sentences. Of them, 513 (87.1%) concepts could be represented, while the others could not be, because of a lack of information models and coarse-grained requirements. In addition, our case study on 6 queries demonstrated that our tool shows better query performance among 4 cases (66.67%).

CONCLUSIONS

We developed a patient-screening tool using openEHR. It not only helps solve concept mismatch but also improves query performance to reduce the burden on researchers. In addition, we demonstrated a promising solution for secondary use of EHR data using openEHR, which can be referenced by other researchers.

摘要

背景

电子健康记录（EHR）的广泛采用促进了EHR数据在临床研究中的二次利用。然而，从EHR中筛选符合条件的患者是一项具有挑战性的任务。资格标准中的概念与EHR并不完全匹配，尤其是派生概念。结构化查询语言（SQL）缺乏高级表达式，使得表达这些概念既困难又耗时。开放EHR表达式语言（EL）作为一种基于临床信息模型的领域特定语言，有望表示复杂的资格标准。

目的

本研究旨在开发一种基于EHR的患者筛选工具，用于临床研究，使用开放EHR来解决概念不匹配问题并提高查询性能。

方法

提出了一种基于EHR并使用开放EHR的患者筛选工具。它利用开放EHR中信息模型和EL的优势来提供高级表达式并提高查询性能。首先，选择开放EHR原型和模板直接从EHR中定义称为简单概念的概念。其次，使用开放EHR EL通过组合简单概念和约束来生成派生概念。第三，在Elasticsearch（ES）中生成与原型相对应的分层索引，以提高与派生概念相关的子查询和连接查询的查询性能。最后，我们实现了一个用于临床研究的患者筛选工具。

结果

总共对从中国临床试验注册中心（ChiCTR）的389项中风临床试验的4691条资格标准中随机选择的500个句子进行了评估。将中国一家三级甲等医院中基于开放EHR的临床数据存储库（CDR）作为实验环境。基于此，在500个句子中发现了589个医学概念。其中，513个（87.1%）概念可以表示，而其他概念由于缺乏信息模型和粗粒度要求而无法表示。此外，我们对6个查询的案例研究表明，我们的工具在4个案例（66.67%）中表现出更好的查询性能。