医疗保健互操作性：创新和增长机会

Healthcare Interoperability: Technology Innovation and Growth Opportunities

出版日期: 2023年07月31日 | 出版商:

Frost & Sullivan | 英文 46 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

通过基于人工智能的 API 创新更好地管理和分析医疗数据

医疗保健互操作性，即不同医疗保健系统和应用程序无缝交换和使用患者健康数据的能力，对于高度准确和个性化的医疗至关重要。数据互操作性对于先进、准确和个性化的医疗保健至关重要。然而，由于医疗保健数据的复杂性以及不同医疗保健组织使用的众多系统和标准，实现互操作性一直是一个重大挑战。随着人工智能（AI）和机器学习（ML）技术的出现，利用基于人工智能的API来促进不同系统和组织之间医疗数据的交换和利用，实现互操作性，我可以。

Frost & Sullivan 的这项研究探索了实现医疗保健互操作性的关键技术进步，包括自然语言处理 (NLP)、生成式 AI 和对话式 AI。这些应用程序将非结构化医疗信息转换为符合 FHIR（快速医疗互操作性资源）和 HL7（健康七级）标准的结构化数字文本。这种结构化数据可以由多个医疗保健系统解释，并无缝集成到常用的电子健康记录 (EHR) 中。这些技术促进安全高效的数据共享、同意管理和分析，最终改善医疗保健结果。

该研究还强调了伙伴关係与合作在推进互操作性倡议方面的重要性。它还讨论了实施互操作性解决方案时的挑战和注意事项，包括数据隐私、安全性和法规遵从性。

此外，对行业趋势和新增长机会的分析可以深入了解医疗保健互操作性的增长和创新潜力。它旨在使医疗保健组织、技术提供商和政策制定者能够利用互操作性的力量来改变医疗保健服务、加强患者护理并做出数据驱动的决策。

提高业务价值和用户利益：从基本互操作性到结构互操作性再到语义互操作性
行业领先的参与公司：认识到 HI 的好处
HL7协议（例如FHIR）：大多数采用的HI标准用于结构互操作性
FHIR：使医疗保健提供者能够实时访问患者信息
FHIR：无缝连接不同的卫生系统、设备、应用程序和相关人员
HCC 帮助组织克服 HI 挑战
分析范围
新技术细分
增长促进因素
生长抑製剂

基于 AI 的 API 支持 HI

基于 AI/ML 的医疗应用的增长提高了 HI
基于人工智能的 API 将医疗信息转换并重新转换为结构化、全面的数字文本：挑战和好处
基于人工智能的 API 将医疗信息转换和重组为结构化、全面的数字文本以供跨系统使用：应用领域和示例
Google：Cloud NLP-based Healthcare Engine
微软：与Azure的OpenAI服务和Epic的EHR平台集成
将 Nuance 的数字参与解决方案与 Microsoft 的数字联络中心平台集成
区域见解：实施医疗保健互操作性的行业
未来基于人工智能的 API 将更加依赖于平台和设备，从而减少现有解决方案带来的限制

创新生态系统

协作在医疗保健组织中部署基于人工智能的可互操作平台，以实现有效的医疗保健服务
大幅增加资金，创建真正可互操作的解决方案，以提供“基于价值的护理”

充满增长机会的宇宙

增长机会一：全渠道数据共享和分析
增长机会 2：AI-API 与区块链设备和 IoMT 连接器的融合
增长机会 3：HCC 实现更好的互操作性

下一步

下一步
为什么选择 Frost & Sullivan？为什么选择现在？
免责声明

简介目录

Product Code: DAA9

AI-based API Innovation is Improving Healthcare Data Management and Analysis.

Healthcare interoperability, the ability of different healthcare systems and applications to seamlessly exchange and use patient health data, is critical to advanced, precise, and personalized healthcare. Data interoperability is crucial to advanced, precise, and personalized healthcare; however, achieving interoperability has been a significant challenge due to the complex nature of healthcare data and the multitude of systems and standards used across different healthcare organizations. With the emergence of artificial intelligence (AI) and machine learning (ML) technologies, AI-based APIs can be leveraged to enable interoperability by facilitating the exchange and use of healthcare data between different systems and organizations.

This Frost & Sullivan study explores key technological advancements that enable healthcare interoperability, including Natural Language Processing (NLP), Generative AI, and Conversational AI. These applications convert unstructured medical information into structured digital text that adheres to Fast Healthcare Interoperability Resources (FHIR) and Health Level Seven (HL7) standards. This structured data can be interpreted by multiple health systems and seamlessly integrated into commonly used electronic health records (EHRs). These technologies facilitate secure and efficient data sharing, consent management, and analysis, ultimately improving healthcare outcomes.

The study also highlights the importance of partnerships and collaborations in driving interoperability initiatives. It discusses the challenges and considerations of implementing interoperability solutions, such as data privacy, security, and regulatory compliance.

In addition, the study provides insight into the potential for growth and innovation in healthcare interoperability by analyzing industry trends and emerging growth opportunities. It aims to guide healthcare organizations, technology providers, and policymakers in harnessing the power of interoperability to transform healthcare delivery, enhance patient care, and enable data-driven decision-making.

Strategic Imperatives

Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
The Strategic Imperative 8™
The Impact of the Top 3 Strategic Imperatives on the Healthcare Interoperability (HI) Industry
Growth Opportunities Fuel the Growth Pipeline Engine™
Research Methodology

Growth Opportunity Analysis

Business Value and User Benefits Increase from Foundational to Structural to Semantic Interoperability
Top-tier Industry Participants Recognize HI's Benefits
HL7 Protocols (FHIR, for example) are the Most Adopted HI Standards for Structural Interoperability
FHIR Empowers Healthcare Providers with Real-time Access to Patient Information
FHIR Seamlessly Connects Different Healthcare Systems or Devices, Applications, and Stakeholders
HCC Helps Organizations Overcome HI Challenges
Scope of Analysis
Emerging Technology Segmentation
Growth Drivers
Growth Restraints

AI-based APIs Enabling HI

The Growth of AI/ML-based Healthcare Applications is Improving HI
AI-based APIs Convert and Rearrange Medical Information into Structured Comprehensive Digital Text that can be Used across Systems: Challenges and Benefits
AI-based APIs Convert and Rearrange Medical Information into Structured Comprehensive Digital Text That can be Used across Systems: Applications and Examples
Google Cloud NLP-based Healthcare Engine
Microsoft Azure's OpenAI Service's Integration with Epic's EHR Platform
Nuance's Digital Engagement Solutions' Integration with Microsoft's Digital Contact Center Platform
Regional Insight: Industry Implementing Healthcare Interoperability
Future AI-based APIs will be More Platform and Device-Agonistic, Reducing the Limitations Imposed by Existing Solutions

Innovation Ecosystem

Collaborations to Deploy Generative AI-based Interoperable Platforms across Healthcare Organizations for Effective Healthcare Delivery
Significant Rise in Funding to Create Truly Interoperable Solutions for the Provision of Value-based Care

Growth Opportunity Universe

Growth Opportunity 1: Omnichannel Data Sharing and Analytics
Growth Opportunity 2: Convergence of AI-API with Blockchain Devices and IoMT Connectors
Growth Opportunity 3: HCC to Improve Interoperability