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市场调查报告书
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1970985

医疗编码人工智慧市场－全球产业规模、份额、趋势、机会、预测：按组件、最终用途、地区和竞争对手划分，2021-2031年

AI In Medical Coding Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By End Use, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 185 Pages | 商品交期: 2-3个工作天内

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简介目录

全球医疗编码人工智慧市场预计将从 2025 年的 24.5 亿美元成长到 2031 年的 42.2 亿美元，复合年增长率为 9.49%。

在这个领域，包括自然语言处理和机器学习在内的人工智慧技术被用于自动将医疗文件转换为标准化的字母数字代码，以用于计费和诊断。推动这一市场成长的关键因素包括医疗保健数据的激增以及医疗服务提供者迫切需要最大限度地减少人为错误造成的计费错误。此外，全球范围内熟练的医疗编码员长期短缺，以及透过降低管理成本来简化收入週期管理的需求，都在加速这些技术的应用。

市场概览
预测期 2027-2031
市场规模：2025年 24.5亿美元
市场规模：2031年 42.2亿美元
复合年增长率：2026-2031年 9.49%
成长最快的细分市场 外包
最大的市场 北美洲

根据美国医学会 (AMA) 的一项调查，到 2025 年，31% 的医生表示将使用人工智慧 (AI)，尤其是在建立医疗记录和分配计费代码方面。儘管 AI 的应用正在不断增长，但市场在数据准确性和 AI 生成的错误所带来的责任风险方面仍然面临重大挑战。演算法可能出现「幻觉」以及对复杂临床细微差别的误解，这可能需要持续的人工监督，使实施过程更加复杂，并阻碍医疗机构完全依赖自主编码解决方案。

市场驱动因素

减少理赔拒付和提高支付准确性是推动医疗编码领域人工智慧应用的当务之急。医疗机构越来越多地应用机器学习演算法，在提交理赔申请前，根据复杂的支付方规则审核临床文檔，从而避免因人为疏忽造成的收入损失。随着监管标准的不断演变和支付方审核流程的日益严格，理赔拒付率不断上升，因此，这种转变至关重要，也使得能够主动识别差异的工具的需求日益增长。 Experian Healthcare 于 2024 年 6 月发布的《2024 年理赔状况报告》显示，73% 的医疗服务提供者报告理赔拒付率上升，凸显了采用自动化解决方案以确保编码准确性和合规性的紧迫性。

同时，熟练的医疗编码员长期短缺以及数据量的不断增长，推动了对营运效率提升的需求。各机构正将大量重复性的医疗记录处理工作外包给自动化编码平台，将人力资源集中在复杂病例，并减轻可能导致员工倦怠的行政负担。这些技术能够快速处理大量资料集，显着缩短计费週期，从而变革收入週期管理。例如，Fathom公司在2024年2月发布的新闻稿中宣布，其人工智慧技术在急诊医疗案例中实现了90%的自动化率，证明了这些工具在工作量管理方面的有效性。此外，大量资金筹措正用于扩展这些解决方案。 2024年，CodaMetrix公司获得了4,000万美元的B轮资金筹措，用于进一步开发其自动化医疗编码平台。

市场挑战

数据准确性方面的重大挑战以及人工智慧生成错误可能引发的责任问题，正直接阻碍着全球医疗编码人工智慧市场的成长。医疗机构对全面采用自主编码解决方案持谨慎态度，因为演算法的「幻觉」和对复杂临床细微差别的误解可能导致严重的计费差异和法律后果。这种可靠性的缺失迫使医疗机构持续进行人工监督以检验人工智慧的输出，这与降低管理和营运成本的主要目标相反。因此，人工检验的需求降低了投资报酬率，并减缓了人工智慧在医疗系统中的普及速度。

根据医疗集团管理协会 (MGMA) 的数据，到 2025 年，44% 的医疗机构领导者表示，在已实施人工智慧工具的机构中，该技术并未减轻员工的工作量。这项数据凸显了准确性问题对营运的影响。由于需要持续的人工干预来纠正和检验人工智慧产生的数据，机构无法真正享受到自动化所承诺的效率提升。这种未能减轻行政负担的情况，是人工智慧在医疗编码领域广泛应用的主要障碍。

市场趋势

生成式人工智慧与大规模语言模型（LLM）的融合代表着技术能力的根本性转变，它超越了基本的关键字提取，发展到对非结构化临床记录进行深入的脉络理解。与传统的基于规则的系统不同，这些先进的模型能够分析医生观察、出院小结和手术报告，自主产生准确的编码分配，同时也能总结复杂的病历供检验审查。这一趋势弥合了编码中的解读鸿沟，使得对传统演算法经常错误分类的细微临床数据进行精确处理成为可能。业界对此技术飞跃充满信心。根据Akasa于2024年10月发布的报告《医疗编码中生成式人工智慧的潜力－收入週期管理者的视角》，65%的受访医疗系统收入周期管理者相信，生成式人工智慧将对其医疗编码业务产生重大影响。

除了其生成能力外，人工智慧在风险调整编码准确性方面的应用正在重塑基于价值的医疗保健策略，因为它能够发现那些在人工流程中经常被忽视的慢性疾病。在该模式下，演算法会回顾性和主动性地审核患者记录，以识别未记录的层级疾病分类（HCC），从而确保根据患者病情的严重程度向健康保险计划支付适当的报销。这种应用不同于单纯的索赔拒付预防或基于交易的索赔接受，而是侧重于收入健康和人群健康数据的长期品质。这一趋势的具体影响在营运成果中显而易见。根据 RISE Health 于 2024 年 11 月发表的报导《编码的十字路口：揭示下一代风险调整人工智慧》，将深度学习人工智慧应用于风险调整审查的健康保险计画的 ICD 覆盖率提高了 27%，风险评分准确性和财务绩效也得到了直接提升。

目录

第一章概述

第二章：调查方法

第三章执行摘要

第四章：客户心声

第五章：人工智慧在医疗编码领域的全球市场展望

  • 市场规模及预测
    • 按金额
  • 市占率及预测
    • 按组件（内部组件、外包组件）
    • 依最终使用者（医疗服务提供者、医疗计费机构、企业、支付方）划分
    • 按地区
    • 按公司（2025 年）
  • 市场地图

第六章：北美医疗编码人工智慧市场展望

  • 市场规模及预测
  • 市占率及预测
  • 北美洲：国别分析
    • 我们
    • 加拿大
    • 墨西哥

第七章：欧洲医疗编码人工智慧市场展望

  • 市场规模及预测
  • 市占率及预测
  • 欧洲：国别分析
    • 德国
    • 法国
    • 英国
    • 义大利
    • 西班牙

第八章：亚太地区医疗编码人工智慧市场展望

  • 市场规模及预测
  • 市占率及预测
  • 亚太地区：国别分析
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳洲

第九章：中东和非洲医疗编码人工智慧市场展望

  • 市场规模及预测
  • 市占率及预测
  • 中东与非洲：国别分析
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 南非

第十章：南美洲医疗编码人工智慧市场展望

  • 市场规模及预测
  • 市占率及预测
  • 南美洲：国别分析
    • 巴西
    • 哥伦比亚
    • 阿根廷

第十一章 市场动态

  • 促进因素
  • 任务

第十二章 市场趋势与发展

  • 併购
  • 产品发布
  • 近期趋势

第十三章：全球医疗编码人工智慧市场：SWOT分析

第十四章：波特五力分析

  • 产业竞争
  • 新进入者的潜力
  • 供应商的议价能力
  • 顾客权力
  • 替代品的威胁

第十五章 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.

第十六章 策略建议

第十七章：关于研究公司及免责声明

简介目录
Product Code: 27539

The Global AI In Medical Coding Market is projected to expand from USD 2.45 Billion in 2025 to USD 4.22 Billion by 2031, reflecting a compound annual growth rate of 9.49%. This sector involves utilizing artificial intelligence technologies, including natural language processing and machine learning, to automatically convert medical documentation into standardized alphanumeric codes for billing and diagnostic purposes. The primary factors driving this market's growth include the surging volume of healthcare data and the critical need for providers to minimize claim denials resulting from human error. Additionally, the adoption of these technologies is being accelerated by a persistent global shortage of skilled medical coders and the necessity to streamline revenue cycle management by lowering administrative operational costs.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 2.45 Billion
Market Size 2031USD 4.22 Billion
CAGR 2026-20319.49%
Fastest Growing SegmentOutsourced
Largest MarketNorth America

According to the American Medical Association, 31% of physicians reported in 2025 that they were using AI specifically for documenting medical charts and billing codes. Despite this increasing adoption, the market faces significant obstacles regarding data accuracy and liability risks associated with AI-generated errors. The risk of algorithmic "hallucinations" or the misinterpretation of complex clinical nuances requires continuous human oversight, which can complicate the implementation process and discourage organizations from fully relying on autonomous coding solutions.

Market Driver

The urgent need to mitigate claim denials and improve payment accuracy acts as a primary catalyst for the adoption of AI in medical coding. Healthcare providers are increasingly applying machine learning algorithms to audit clinical documentation against intricate payer rules prior to claim submission, thereby preventing revenue leakage associated with human oversight. This shift is critical as denial rates rise due to evolving regulatory standards and stricter payer adjudication processes, necessitating tools that can preemptively identify discrepancies. In the 'State of Claims 2024' report by Experian Health from June 2024, 73% of healthcare providers indicated that claim denials are increasing, highlighting the urgent need for automated solutions that ensure coding precision and compliance.

Simultaneously, there is an escalating demand for operational efficiency to address the chronic shortage of skilled medical coders and increasing data volumes. Organizations are deploying autonomous coding platforms to handle high-volume, repetitive charts, allowing human staff to focus on complex cases and reducing the administrative burden that leads to workforce burnout. The capability of these technologies to process vast datasets rapidly is transforming revenue cycle management by significantly shortening billing cycles. For example, a February 2024 press release from Fathom noted that their AI technology achieved a 90% automation rate for emergency medicine encounters, demonstrating the capacity of these tools to manage workload volume. Furthermore, the financial commitment to scaling these solutions is evident; CodaMetrix secured $40 million in Series B funding in 2024 to further develop its autonomous medical coding platform.

Market Challenge

The significant challenge of data accuracy and the potential for liability arising from AI-generated errors is directly hampering the growth of the Global AI In Medical Coding Market. Healthcare organizations are hesitant to fully integrate autonomous coding solutions because algorithmic hallucinations or the misinterpretation of complex clinical nuances can lead to severe billing discrepancies and legal repercussions. This lack of reliability forces providers to maintain continuous human oversight to validate AI outputs, which counteracts the primary objective of reducing administrative operational costs. Consequently, the necessity for manual verification diminishes the return on investment and slows the speed of implementation across health systems.

According to the Medical Group Management Association, 44% of medical practice leaders using AI tools reported in 2025 that the technology had not reduced their staff workload. This statistic underscores the operational impact of the accuracy challenge, as the persistent need for human intervention to correct or verify AI-generated data prevents organizations from realizing the efficiency gains promised by automation. This failure to alleviate the administrative burden creates a significant barrier to the widespread adoption of AI in the medical coding sector.

Market Trends

The Integration of Generative AI and Large Language Models (LLMs) represents a fundamental shift in technical capability, moving beyond basic keyword extraction to the deep contextual understanding of unstructured clinical narratives. Unlike earlier rule-based systems, these advanced models analyze physician notes, discharge summaries, and operative reports to autonomously generate accurate code assignments while simultaneously summarizing complex medical histories for validator review. This trend addresses the interpretative gap in coding, allowing for the precise handling of nuanced clinical data that traditional algorithms often misclassify. The industry confidence in this technological leap is substantial; according to the 'Revenue cycle leaders see gen AI's medical coding potential' report by Akasa in October 2024, 65% of surveyed health system revenue cycle leaders believe that generative AI will have a substantial effect on their medical coding operations.

Concurrent with generative capabilities, the Utilization of AI for Risk Adjustment Coding Accuracy is reshaping value-based care strategies by uncovering chronic conditions that manual processes frequently overlook. In this model, algorithms retrospectively and prospectively audit patient charts to identify undocumented Hierarchical Condition Categories (HCCs), ensuring that health plans receive appropriate reimbursement commensurate with patient acuity. This application is distinct from simple denial prevention as it focuses on revenue integrity and long-term population health data quality rather than transactional claim acceptance. The tangible impact of this trend is evident in operational outcomes; according to the 'Coding at a crossroads: Unpacking the next generation of AI for risk adjustment' article by RISE Health in November 2024, a health plan implementing deep learning AI for risk adjustment reviews achieved a 27% increase in ICD capture, directly improving their risk score accuracy and financial performance.

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 & Validation
  • 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, Outsourced)
    • 5.2.2. By End Use (Healthcare Providers, Medical Billing, Companies, Payers)
    • 5.2.3. By Region
    • 5.2.4. By Company (2025)
  • 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. France 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. United Kingdom 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. Italy 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. Middle East & Africa 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. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia 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. UAE 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. South Africa 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. South America 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. South America: Country Analysis
    • 10.3.1. Brazil 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. Colombia 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. Argentina 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. Global AI In Medical Coding Market: SWOT Analysis

14. Porter's Five Forces Analysis

  • 14.1. Competition in the Industry
  • 14.2. Potential of New Entrants
  • 14.3. Power of Suppliers
  • 14.4. Power of Customers
  • 14.5. Threat of Substitute Products

15. Competitive Landscape

  • 15.1. 3M Company
    • 15.1.1. Business Overview
    • 15.1.2. Products & Services
    • 15.1.3. Recent Developments
    • 15.1.4. Key Personnel
    • 15.1.5. SWOT Analysis
  • 15.2. Nuance Communications, Inc.
  • 15.3. MedsIT Nexus Inc.
  • 15.4. Optum, Inc.
  • 15.5. Oracle Corporation
  • 15.6. Olive Technologies, Inc.
  • 15.7. Medicodio Inc.
  • 15.8. Fathom, Inc.
  • 15.9. Wolters Kluwer N.V.
  • 15.10. Medisys Data Solutions Inc.

16. Strategic Recommendations

17. About Us & Disclaimer