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醫療編碼市場中的人工智慧 - 全球行業規模、佔有率、趨勢、機會和預測，按組件、最終用途、地區和競爭細分，2020-2030 年

AI In Medical Coding Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By End Use, By Region and Competition, 2020-2030F
出版日期: | 出版商: TechSci Research | 英文 182 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2024 年全球醫療編碼人工智慧市場價值為 24.5 億美元，預計到 2030 年將達到 42.3 億美元，預測期內複合年成長率為 9.48%。全球醫療編碼人工智慧市場主要受到醫療管理對自動化和效率日益成長的需求的推動。人工智慧技術，尤其是機器學習和自然語言處理 (NLP)，正在融入醫療編碼中，以簡化流程、減少錯誤並提高準確性。醫療資料量的不斷成長以及編碼系統的複雜性使得手動編碼變得越來越耗時且容易出錯，從而推動了對人工智慧解決方案的需求。法規遵循和向基於價值的護理模式的轉變需要準確、有效的編碼才能進行正確的報銷和報告。人工智慧驅動的醫療編碼自動化提高了營運效率，降低了管理成本，並支持醫療保健組織適應不斷變化的法規和標準，從而推動市場成長。

市場概況
預測期 2026-2030
2024 年市場規模 24.5 億美元
2030 年市場規模 42.3 億美元
2025-2030 年複合年成長率 9.48％
成長最快的領域 外包
最大的市場 北美洲

主要市場促進因素

醫療保健領域對自動化的需求不斷增加

主要市場促進因素

高品質訓練資料的可用性有限

主要市場趨勢

更重視基於價值的護理

目錄

第 1 章：產品概述

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：顧客之聲

第5章：全球醫療編碼人工智慧市場展望

  • 市場規模和預測
    • 按價值
  • 市場佔有率和預測
    • 按組件（內部和外包）
    • 按最終用途（醫療保健提供者、醫療帳單、公司和付款人）
    • 按地區
    • 按公司分類（2024）
  • 市場地圖

第 6 章：北美醫療編碼人工智慧市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 北美：國家分析
    • 加拿大
    • 墨西哥

第 7 章：歐洲醫療編碼人工智慧市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 歐洲：國家分析
    • 英國
    • 義大利
    • 法國
    • 西班牙

第 8 章：亞太地區醫療編碼人工智慧市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 亞太地區：國家分析
    • 印度
    • 日本
    • 韓國
    • 澳洲

第 9 章：南美洲醫療編碼人工智慧市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • 南美洲：國家分析
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲醫療編碼人工智慧市場展望

  • 市場規模和預測
  • 市場佔有率和預測
  • MEA：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國

第 11 章：市場動態

  • 驅動程式
  • 挑戰

第 12 章：市場趨勢與發展

  • 合併與收購（如有）
  • 產品發布（如果有）
  • 最新動態

第 13 章：波特五力分析

  • 產業競爭
  • 新進入者的潛力
  • 供應商的力量
  • 顧客的力量
  • 替代產品的威脅

第 14 章：競爭格局

  • 3M Company
  • Nuance Communications, Inc.
  • MedsIT Nexus Inc.
  • Optum, Inc.
  • Oracle Corporation
  • Olive Technologies, Inc.
  • Medicodio Inc.
  • Fathom, Inc.
  • Wolters Kluwer NV
  • Medisys Data Solutions Inc.

第 15 章：策略建議

第16章 關於出版商,免責事項

簡介目錄
Product Code: 27539

Global AI In Medical Coding Market was valued at USD 2.45 Billion in 2024 and is expected to reach USD 4.23 Billion by 2030 with a CAGR of 9.48% during the forecast period. The Global AI in Medical Coding Market is primarily driven by the increasing need for automation and efficiency in healthcare administration. AI technologies, particularly machine learning and natural language processing (NLP), are being integrated into medical coding to streamline the process, reduce errors, and enhance accuracy. The growing volume of medical data, along with the complexity of coding systems, has made manual coding increasingly time-consuming and prone to mistakes, driving the demand for AI-powered solutions. Regulatory compliance and the shift towards value-based care models necessitate accurate and efficient coding for proper reimbursement and reporting. The AI-driven automation of medical coding improves operational efficiency, reduces administrative costs, and supports healthcare organizations in adapting to evolving regulations and standards, fueling market growth.

Market Overview
Forecast Period2026-2030
Market Size 2024USD 2.45 Billion
Market Size 2030USD 4.23 Billion
CAGR 2025-20309.48%
Fastest Growing SegmentOutsourced
Largest MarketNorth America

Key Market Drivers

Increasing Demand for Automation in Healthcare

The increasing need for automation in healthcare is one of the primary drivers behind the growth of the Global AI in Medical Coding Market. As healthcare systems become more complex, managing the volume of patient data, clinical documents, and medical records has become a daunting task. Medical coding, the process of translating healthcare diagnoses, procedures, medical services, and equipment into universally recognized alphanumeric codes, is a crucial part of this workflow. Traditionally, this process has been manual, time-consuming, and prone to human error, which can lead to costly mistakes, delayed reimbursements, and compliance issues. In March 2021, Athenahealth introduced its Medical Coding Solution, an EHR-based coding tool designed to reduce the coding workload for clinicians, ultimately helping to alleviate clinician burnout.

With the adoption of electronic health records (EHRs) and the expansion of regulatory requirements, the volume of coding has significantly increased, and traditional methods can no longer keep up. Manual medical coding involves not just identifying the correct codes, but also interpreting complex medical terminology, which varies by region, healthcare system, and clinical context. AI technologies, particularly machine learning and natural language processing (NLP), are increasingly being employed to automate these tasks, significantly improving both speed and accuracy.

Key Market Drivers

Limited Availability of High-Quality Training Data

For AI algorithms to be effective in medical coding, they require large amounts of high-quality training data. AI systems, particularly machine learning models, are trained on annotated datasets to learn patterns and relationships between medical conditions, treatments, and their respective codes. However, the availability of large, diverse, and accurately annotated datasets in the healthcare sector remains a challenge.

Key Market Trends

Increasing Focus on Value-Based Care

The shift towards value-based care is a significant driver in the Global AI in medical coding market. Under the value-based care model, healthcare providers are reimbursed based on patient outcomes rather than the volume of services provided. This model places a greater emphasis on accurate documentation and coding, as reimbursement is directly tied to the correct coding of diagnoses and procedures. In March 2023, Clinion, a leading healthcare technology company, introduced an AI-driven medical coding solution tailored specifically for clinical trials. This innovative service enhances the efficiency, accuracy, and speed of medical coding in clinical research. Using advanced AI algorithms, the system rapidly processes and analyzes large volumes of clinical trial data, extracting relevant information and assigning the correct codes. This significantly reduces the time and effort needed for coding tasks.

Accurate coding is essential for healthcare providers to receive appropriate reimbursement under value-based care models. AI can help ensure that codes are assigned correctly and comprehensively, enabling providers to demonstrate the quality of care delivered to patients. AI-powered coding systems can help identify areas for improvement in care delivery by analyzing coding patterns and patient outcomes, allowing healthcare providers to align their practices with value-based care objectives. As the adoption of value-based care increases, healthcare providers will rely more heavily on AI to optimize coding accuracy, reduce errors, and ensure that they are properly reimbursed for the care they provide. This shift will further drive the demand for AI in medical coding solutions.

Key Market Players

  • 3M Company
  • Nuance Communications, Inc.
  • MedsIT Nexus Inc.
  • Optum, Inc.
  • Oracle Corporation
  • Olive Technologies, Inc.
  • Medicodio Inc.
  • Fathom, Inc.
  • Wolters Kluwer N.V.
  • Medisys Data Solutions Inc.

Report Scope:

In this report, the Global AI In Medical Coding Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

AI In Medical Coding Market, By Component:

  • In-House
  • Outsourced

AI In Medical Coding Market, By End Use:

  • Healthcare Providers
  • Medical Billing
  • Companies
  • Payers

AI In Medical Coding Market, By Region:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia-Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global AI In Medical Coding Market.

Available Customizations:

Global AI In Medical Coding market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validations
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global AI In Medical Coding Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Component (In-House and Outsourced)
    • 5.2.2. By End Use (Healthcare Providers, Medical Billing, Companies, and Payers)
    • 5.2.3. By Region
    • 5.2.4. By Company (2024)
  • 5.3. Market Map

6. North America AI in Medical Coding Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Component
    • 6.2.2. By End Use
    • 6.2.3. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States AI in Medical Coding Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Component
        • 6.3.1.2.2. By End Use
    • 6.3.2. Canada AI in Medical Coding Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Component
        • 6.3.2.2.2. By End Use
    • 6.3.3. Mexico AI in Medical Coding Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Component
        • 6.3.3.2.2. By End Use

7. Europe AI in Medical Coding Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Component
    • 7.2.2. By End Use
    • 7.2.3. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany AI in Medical Coding Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Component
        • 7.3.1.2.2. By End Use
    • 7.3.2. United Kingdom AI in Medical Coding Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Component
        • 7.3.2.2.2. By End Use
    • 7.3.3. Italy AI in Medical Coding Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Component
        • 7.3.3.2.2. By End Use
    • 7.3.4. France AI in Medical Coding Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Component
        • 7.3.4.2.2. By End Use
    • 7.3.5. Spain AI in Medical Coding Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Component
        • 7.3.5.2.2. By End Use

8. Asia-Pacific AI in Medical Coding Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Component
    • 8.2.2. By End Use
    • 8.2.3. By Country
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China AI in Medical Coding Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Component
        • 8.3.1.2.2. By End Use
    • 8.3.2. India AI in Medical Coding Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Component
        • 8.3.2.2.2. By End Use
    • 8.3.3. Japan AI in Medical Coding Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Component
        • 8.3.3.2.2. By End Use
    • 8.3.4. South Korea AI in Medical Coding Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Component
        • 8.3.4.2.2. By End Use
    • 8.3.5. Australia AI in Medical Coding Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Component
        • 8.3.5.2.2. By End Use

9. South America AI in Medical Coding Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Component
    • 9.2.2. By End Use
    • 9.2.3. By Country
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil AI in Medical Coding Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Component
        • 9.3.1.2.2. By End Use
    • 9.3.2. Argentina AI in Medical Coding Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Component
        • 9.3.2.2.2. By End Use
    • 9.3.3. Colombia AI in Medical Coding Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Component
        • 9.3.3.2.2. By End Use

10. Middle East and Africa AI in Medical Coding Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Component
    • 10.2.2. By End Use
    • 10.2.3. By Country
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa AI in Medical Coding Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Component
        • 10.3.1.2.2. By End Use
    • 10.3.2. Saudi Arabia AI in Medical Coding Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Component
        • 10.3.2.2.2. By End Use
    • 10.3.3. UAE AI in Medical Coding Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Component
        • 10.3.3.2.2. By End Use

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Porter's Five Forces Analysis

  • 13.1. Competition in the Industry
  • 13.2. Potential of New Entrants
  • 13.3. Power of Suppliers
  • 13.4. Power of Customers
  • 13.5. Threat of Substitute Products

14. Competitive Landscape

  • 14.1. 3M Company
    • 14.1.1. Business Overview
    • 14.1.2. Company Snapshot
    • 14.1.3. Products & Services
    • 14.1.4. Financials (As Reported)
    • 14.1.5. Recent Developments
    • 14.1.6. Key Personnel Details
    • 14.1.7. SWOT Analysis
  • 14.2. Nuance Communications, Inc.
  • 14.3. MedsIT Nexus Inc.
  • 14.4. Optum, Inc.
  • 14.5. Oracle Corporation
  • 14.6. Olive Technologies, Inc.
  • 14.7. Medicodio Inc.
  • 14.8. Fathom, Inc.
  • 14.9. Wolters Kluwer N.V.
  • 14.10. Medisys Data Solutions Inc.

15. Strategic Recommendations

16. About Us & Disclaimer