医療コーディングにおけるAI市場- 世界の産業規模、シェア、動向、機会、予測、コンポーネント別、エンドユース別、地域別、競合別、2020～2030年

医療コーディングにおけるAIの世界市場規模は2024年に24億5,000万米ドルで、予測期間中のCAGRは9.48%で2030年には42億3,000万米ドルに達すると予測されています。

医療コーディングにおけるAIの世界市場は、ヘルスケア管理における自動化と効率化のニーズの高まりが主な要因です。AI技術、特に機械学習と自然言語処理（NLP）は、プロセスを合理化し、エラーを減らし、精度を高めるために医療コーディングに統合されつつあります。医療データの増大とコーディングシステムの複雑化により、手作業によるコーディングはますます時間がかかり、ミスも発生しやすくなっており、AIを活用したソリューションへの需要が高まっています。規制コンプライアンスと価値ベースのケアモデルへのシフトは、適切な償還と報告のために正確で効率的なコーディングを必要とします。AIによる医療コーディングの自動化は、業務効率を改善し、管理コストを削減し、医療機関が進化する規制や標準に適応するのをサポートし、市場の成長を促進します。

市場促進要因

ヘルスケアにおける自動化需要の増加

市場促進要因

質の高いトレーニングデータの入手可能性の制限

主な市場動向

バリューベースケアへの注目の高まり

目次

第1章 概要

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 顧客の声

第5章 世界の医療コーディングにおけるAI市場展望

• 市場規模・予測
  • 金額別
• 市場シェア・予測
  • コンポーネント別（社内および外注）
  • エンドユース別（ヘルスケア提供者、医療費請求、企業、支払者）
  • 地域別
  • 企業別（2024）
• 市場マップ

第6章 北米の医療コーディングにおけるAI市場展望

• 市場規模・予測
• 市場シェア・予測
• 北米：国別分析
  • 米国
  • カナダ
  • メキシコ

第7章 欧州の医療コーディングにおけるAI市場展望

• 市場規模・予測
• 市場シェア・予測
• 欧州：国別分析
  • ドイツ
  • 英国
  • イタリア
  • フランス
  • スペイン

第8章 アジア太平洋地域の医療コーディングにおけるAI市場展望

• 市場規模・予測
• 市場シェア・予測
• アジア太平洋地域：国別分析
  • 中国
  • インド
  • 日本
  • 韓国
  • オーストラリア

第9章 南米の医療コーディングにおけるAI市場展望

• 市場規模・予測
• 市場シェア・予測
• 南米：国別分析
  • ブラジル
  • アルゼンチン
  • コロンビア

第10章 中東・アフリカの医療コーディングにおけるAI市場展望

• 市場規模・予測
• 市場シェア・予測
• 中東・アフリカ：国別分析
  • 南アフリカ
  • サウジアラビア
  • アラブ首長国連邦

第11章 市場力学

• 促進要因
• 課題

第12章 市場動向と発展

• 合併と買収（ある場合）
• 製品の発売（ある場合）
• 最近の動向

第13章 ポーターのファイブフォース分析

• 業界内の競合
• 新規参入の可能性
• サプライヤーの力
• 顧客の力
• 代替品の脅威

第14章 競合情勢

• 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.

第15章 戦略的提言

第16章 調査会社について・免責事項

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.

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