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引用次数: 0
摘要
在不断发展的医疗保健领域,人工智能(AI)的集成已成为一股变革力量。分析模型(AMs)是这个领域中使用的人工智能的一个子集。人工智能用于提供新颖的预测功能,有助于提高医疗保健的质量和结果。FHIR背后的组织——FHIR快速医疗互操作性资源(FHIR Fast Healthcare Interoperability Resources,简称FHIR)——提出的目标是实现健康记录系统之间电子处理数据的电子健康交换。为了构建一个健壮的框架,通过该框架,医疗保健提供者可以交换此类信息,FHIR即时消息传递(IM)被开发为健康记录系统的API。用于开发IM的框架被称为资源描述框架规范版本。本研究深入研究了人工智能分析模型与快速医疗保健互操作性资源(FHIR)数据标准之间的共生关系,旨在为医疗保健行业的互操作性和数据驱动决策开辟新的维度。随着医疗保健系统继续努力应对数据孤岛和信息交换效率低下的挑战,全面探索人工智能如何弥合这些差距变得非常重要。利用FHIR标准作为坚实的基础,阐明人工智能在利用患者数据方面的潜力变得非常重要。它们还促进了医疗保健利益相关者之间的无缝数据交换,同时还为临床医生提供了可操作的见解。
How AI Analytical Models Can Use FHIR (Fast Healthcare Interoperability Resources) Data
In the ever-evolving landscape of healthcare, the integration of artificial intelligence (AI) has emerged as a transformative force. Analytical Models (AMs) is a subset of AI used in this space. AMs are used to provide novel predictive functionality that can help advance the quality and outcomes of healthcare. The goal advanced by the organization behind FHIR, FHIR Fast Healthcare Interoperability Resources (FHIR), is to enable electronic health exchange of electronically processed data between health record systems. In order to build a robust framework through which healthcare providers can exchange such information, FHIR Instant Messaging (IM) was developed as an API for health record systems. The framework used to develop IM is known as the Resource Description Framework Specification Versions. This study delves into the symbiotic relationship between AI analytical models and the Fast Healthcare Interoperability Resources (FHIR) data standard, aiming to unlock new dimensions of interoperability and data-driven decision-making within the healthcare sector. With the healthcare systems continuing to grapple with the challenges of siloed data and inefficiencies in information exchange, it becomes important to have a comprehensive exploration of how AI can bridge these gaps. Leveraging the FHIR standard as a robust foundation, it becomes significant to elucidate the potential of AI in harnessing patient data. They are also facilitating seamless data exchange among healthcare stakeholders while also empowering clinicians with actionable insights.