Department of Digital Health, Samsung Advanced Institute for Health Science & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
HD Junction, Inc, Seoul, Republic of Korea.
J Med Internet Res. 2022 Jul 27;24(7):e37928. doi: 10.2196/37928.
A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms.
In this paper, we introduce the common data model-based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS.
CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients' information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system's modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions' CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine.
We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians' clinical decisions about optimum resource allocation by predicting a patient's acuity and prognosis during triage.
We successfully developed a common data model-based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.
临床决策支持系统（CDSS）被认为是一种提高临床疗效和安全性的技术。然而，它的全部潜力尚未得到充分发挥，主要原因是临床数据标准和不可互操作的平台。
本文介绍了基于通用数据模型的智能算法网络环境（CANE）平台，该平台支持 CDSS 的实施和部署。
CDSS 推理引擎通常表示为 R 或 Python 对象，并部署到 CANE 平台中，并转换为 C# 对象。当临床医生在电子健康记录（EHR）系统中请求基于 CANE 的决策支持时，患者的信息将转换为健康水平 7 快速医疗互操作性资源（FHIR）格式，并传输到医院防火墙内的 CANE 服务器。在接收到必要的数据后，CANE 系统的模块执行以下任务：（1）预处理模块将 FHIR 转换为特定推理引擎所需的输入数据，（2）推理引擎模块运行目标算法，（3）集成模块与其他机构的 CANE 系统进行通信，请求并传输摘要报告以辅助决策支持，（4）通过集成摘要报告和推理引擎计算的结果创建用户界面。
我们开发了一个 CANE 系统，使得在与 EHR 系统集成时，系统中实现的任何算法都可以通过 RESTful 应用程序编程接口直接调用。我们在 CANE 系统中开发和部署了 8 种算法。使用基于知识的算法，医生可以筛选出易患败血症的患者，并使用 CANE 系统获得败血症患者的治疗指南。此外，使用基于非知识的算法，CANE 系统通过预测分诊期间患者的危急程度和预后，支持急诊医生对最佳资源分配的临床决策。
我们成功开发了一个基于通用数据模型的平台，该平台符合医学信息学标准，并可以使用 R 或 Python 来辅助人工智能模型部署。
