Ayaz Muhammad, Pasha Muhammad Fermi, Le Tham Yu, Alahmadi Tahani Jaser, Abdullah Nik Nailah Binti, Alhababi Zaid Ali
Malaysia School of Information Technology, Monash University, Jalan Lagoon Selatan Bandar Sunway, Subang Jaya 47500, Selangor, Malaysia.
Department of Information System, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Healthcare (Basel). 2023 Jan 30;11(3):390. doi: 10.3390/healthcare11030390.
Although Health Level Seven (HL 7) message standards (v2, v3, Clinical Document Architecture (CDA)) have been commonly adopted, there are still issues associated with them, especially the semantic interoperability issues and lack of support for smart devices (e.g., smartphones, fitness trackers, and smartwatches), etc. In addition, healthcare organizations in many countries are still using proprietary electronic health record (EHR) message formats, making it challenging to convert to other data formats-particularly the latest HL7 Fast Health Interoperability Resources (FHIR) data standard. The FHIR is based on modern web technologies such as HTTP, XML, and JSON and would be capable of overcoming the shortcomings of the previous standards and supporting modern smart devices. Therefore, the FHIR standard could help the healthcare industry to avail the latest technologies benefits and improve data interoperability. The data representation and mapping from the legacy data standards (i.e., HL7 v2 and EHR) to the FHIR is necessary for the healthcare sector. However, direct data mapping or conversion from the traditional data standards to the FHIR data standard is challenging because of the nature and formats of the data. Therefore, in this article, we propose a framework that aims to convert proprietary EHR messages into the HL7 v2 format and apply an unsupervised clustering approach using the DBSCAN (density-based spatial clustering of applications with noise) algorithm to automatically group a variety of these HL7 v2 messages regardless of their semantic origins. The proposed framework's implementation lays the groundwork to provide a generic mapping model with multi-point and multi-format data conversion input into the FHIR. Our experimental results show the proposed framework's ability to automatically cluster various HL7 v2 message formats and provide analytic insight behind them.
尽管健康级别七（HL 7）消息标准（v2、v3、临床文档架构（CDA））已被广泛采用，但仍存在与之相关的问题，尤其是语义互操作性问题以及对智能设备（如智能手机、健身追踪器和智能手表等）缺乏支持等。此外，许多国家的医疗保健机构仍在使用专有的电子健康记录（EHR）消息格式，这使得转换为其他数据格式具有挑战性，尤其是转换为最新的HL7快速健康互操作性资源（FHIR）数据标准。FHIR基于HTTP、XML和JSON等现代网络技术，能够克服先前标准的缺点并支持现代智能设备。因此，FHIR标准可以帮助医疗保健行业利用最新技术的优势并提高数据互操作性。对于医疗保健部门而言，将传统数据标准（即HL7 v2和EHR）的数据表示和映射到FHIR是必要的。然而，由于数据的性质和格式，从传统数据标准直接进行数据映射或转换为FHIR数据标准具有挑战性。因此，在本文中，我们提出了一个框架，旨在将专有的EHR消息转换为HL7 v2格式，并应用基于密度的空间聚类应用噪声（DBSCAN）算法的无监督聚类方法来自动对各种HL7 v2消息进行分组，而不考虑它们的语义来源。所提出框架的实现为提供一个通用的映射模型奠定了基础，该模型可将多点和多格式的数据转换输入到FHIR中。我们的实验结果表明了所提出框架自动聚类各种HL7 v2消息格式并提供其背后分析见解的能力。
