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市场调查报告书
商品编码
1667940

医疗编码市场中的人工智慧 - 全球行业规模、份额、趋势、机会和预测，按组件、最终用途、地区和竞争细分，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个工作天内

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简介目录

2024 年全球医疗编码人工智慧市场价值为 24.5 亿美元，预计到 2030 年将达到 42.3 亿美元，预测期内复合年增长率为 9.48%。全球医疗编码人工智慧市场主要受到医疗管理对自动化和效率日益增长的需求的推动。人工智慧技术，尤其是机器学习和自然语言处理 (NLP)，正在融入医疗编码中，以简化流程、减少错误并提高准确性。医疗资料量的不断增长以及编码系统的复杂性使得手动编码变得越来越耗时且容易出错，从而推动了对人工智慧解决方案的需求。法规遵循和向基于价值的护理模式的转变需要准确、有效的编码才能进行正确的报销和报告。人工智慧驱动的医疗编码自动化提高了营运效率，降低了管理成本，并支持医疗保健组织适应不断变化的法规和标准，从而推动市场成长。

市场概况
预测期 2026-2030
2024 年市场规模 24.5 亿美元
2030 年市场规模 42.3 亿美元
2025-2030 年复合年增长率 9.48％
成长最快的领域 外包
最大的市场 北美洲

主要市场驱动因素

医疗保健领域对自动化的需求不断增加

主要市场驱动因素

高品质训练资料的可用性有限

主要市场趋势

更重视基于价值的护理

目录

第 1 章：产品概述

第 2 章：研究方法

第 3 章：执行摘要

第 4 章：顾客之声

第五章：全球医疗编码人工智慧市场展望

  • 市场规模和预测
    • 按价值
  • 市场占有率和预测
    • 按组件（内部和外包）
    • 按最终用途（医疗保健提供者、医疗帐单、公司和付款人）
    • 按地区
    • 按公司分类（2024）
  • 市场地图

第 6 章：北美医疗编码人工智慧市场展望

  • 市场规模和预测
  • 市场占有率和预测
  • 北美：国家分析
    • 加拿大
    • 墨西哥

第 7 章：欧洲医疗编码人工智慧市场展望

  • 市场规模和预测
  • 市场占有率和预测
  • 欧洲：国家分析
    • 英国
    • 义大利
    • 法国
    • 西班牙

第 8 章：亚太地区医疗编码人工智慧市场展望

  • 市场规模和预测
  • 市场占有率和预测
  • 亚太地区：国家分析
    • 印度
    • 日本
    • 韩国
    • 澳洲

第 9 章：南美洲医疗编码人工智慧市场展望

  • 市场规模和预测
  • 市场占有率和预测
  • 南美洲：国家分析
    • 阿根廷
    • 哥伦比亚

第 10 章：中东和非洲医疗编码人工智慧市场展望

  • 市场规模和预测
  • 市场占有率和预测
  • MEA：国家分析
    • 沙乌地阿拉伯
    • 阿联酋

第 11 章：市场动态

  • 驱动程式
  • 挑战

第 12 章：市场趋势与发展

  • 合併与收购（如有）
  • 产品发布（如果有）
  • 最新动态

第 13 章：波特五力分析

  • 产业竞争
  • 新进入者的潜力
  • 供应商的力量
  • 顾客的力量
  • 替代产品的威胁

第 14 章：竞争格局

  • 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：策略建议

第16章 调査会社について・免责事项

简介目录
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