醫療保健互操作性：創新和成長機會

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個工作天內

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簡介目錄

透過基於人工智慧的 API 創新更佳管理和分析醫療資料

醫療保健互操作性，即不同醫療保健系統和應用程式無縫交換和使用患者健康資料的能力，對於高度準確和個性化的醫療非常重要。資料互操作性對於先進、準確和個性化的醫療保健非常重要。然而，由於醫療保健資料的複雜性以及不同醫療保健組織使用的眾多系統和標準，實現互操作性一直是一個重大挑戰。隨著人工智慧（AI）和機器學習（ML）技術的出現，利用基於人工智慧的API來促進不同系統和組織之間醫療資料的交換和利用，實現互操作性，我可以。

Frost & Sullivan 的這項研究探索了實現醫療保健互操作性的關鍵技術進步，包括自然語言處理 (NLP)、生成式 AI 和對話式 AI。這些應用程式將非結構化醫療資訊轉換為符合 FHIR（快速醫療互操作性資源）和 HL7（健康七級）標準的結構化數位文本。這種結構化資料可以由多個醫療保健系統解釋，並無縫整合到常用的電子健康記錄 (EHR) 中。這些技術促進安全高效的資料共享、同意管理和分析，最終改善醫療保健結果。

本研究還強調了夥伴關係與合作在推進互操作性舉措方面的重要性。它還討論了實施互操作性解決方案時的挑戰和注意事項，包括資料隱私、安全性和法規遵從性。

此外，對行業趨勢和新成長機會的分析可以深入了解醫療保健互操作性的成長和創新潛力。它目的是使醫療保健組織、技術提供商和政策制定者能夠利用互操作性的力量來改變醫療保健服務、加強患者護理並做出資料驅動的決策。

提高業務價值和用戶利益：從基本互操作性到結構互操作性再到語義互操作性
行業領先的參與公司：認知到 HI 的好處
HL7協議（例如FHIR）：大多數採用的HI標準用於結構互操作性
FHIR：使醫療保健提供者能夠即時存取患者資訊
FHIR：無縫連接不同的衛生系統、設備、應用程式和相關人員
HCC 幫助組織克服 HI 挑戰
分析範圍
新技術細分
成長促進因素
生長抑製劑

基於 AI 的 API 支持 HI

基於 AI/ML 的醫療應用的成長提高了 HI
基於人工智慧的 API 將醫療資訊轉換並重新轉換為結構化、全面的數位文本：挑戰和好處
基於人工智慧的 API 將醫療資訊轉換和重組為結構化、全面的數位文本以供跨系統使用：應用領域和示例
Google：Cloud NLP-based Healthcare Engine
微軟：與Azure的OpenAI服務和Epic的EHR平台整合
將 Nuance 的數位參與解決方案與 Microsoft 的數位聯絡中心平台整合
區域見解：實施醫療保健互操作性的行業
未來基於人工智慧的 API 將更加依賴於平台和設備，減少現有解決方案帶來的限制

創新生態系統

協作在醫療保健組織中部署基於人工智慧的可互操作平台，以實現有效的醫療保健服務
大幅增加資金，創建真正可互操作的解決方案，以提供“基於價值的護理”

充滿成長機會的宇宙

成長機會1：全管道資料共享和分析
成長機會2：AI-API 與區塊鏈設備和 IoMT 連接器的融合
成長機會3：HCC 實現更好的互操作性

下一步

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簡介目錄

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