한글목차

의료 코딩 분야 AI 세계 시장 규모는 2024년 24억 5,000만 달러로 평가되었고, 예측 기간 동안 9.48%의 연평균 복합 성장률(CAGR)로 2030년에는 42억 3,000만 달러에 달할 것으로 예상됩니다.

세계의 의료 코딩 분야 AI 시장은 의료 관리의 자동화와 효율성에 대한 요구가 증가함에 따라 AI 기술, 특히 머신러닝과 자연어 처리(NLP)가 의료 코딩에 통합되어 프로세스를 간소화하고 오류를 줄이며 정확성을 높이고 있습니다. 의료 데이터 증가와 코딩 시스템의 복잡성으로 인해 수작업 코딩은 시간이 오래 걸리고 오류가 발생하기 쉬워 AI를 활용한 솔루션에 대한 수요가 증가하고 있습니다. 규제 준수와 가치 기반 진료 모델로의 전환은 적절한 상환 및 보고를 위해 정확하고 효율적인 코딩을 필요로 하며, AI를 통한 의료 코딩 자동화는 업무 효율성을 개선하고, 관리 비용을 절감하며, 의료 기관이 진화하는 규제 및 표준에 적응할 수 있도록 지원하고, 시장 성장을 가속할 수 있도록 지원합니다. 성장을 가속합니다.

시장 성장 촉진요인

헬스케어 분야의 자동화 수요 증가

시장 성장 촉진요인

양질의 트레이닝 데이터 가용성 제한

주요 시장 동향

가치 기반 케어에 대한 관심 증가

목차

제1장 개요

제2장 조사 방법

제3장 주요 요약

제4장 고객의 소리

제5장 세계의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
  • 금액별
• 시장 점유율과 예측
  • 컴포넌트별(사내 및 외주)
  • 최종 용도별(의료 제공자, 의료비 청구, 기업, 지불자)
  • 지역별
  • 기업별(2024년)
• 시장 맵

제6장 북미의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
• 시장 점유율과 예측
• 북미 : 국가별 분석
  • 캐나다
  • 멕시코

제7장 유럽의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
• 시장 점유율과 예측
• 유럽 : 국가별 분석
  • 영국
  • 이탈리아
  • 프랑스
  • 스페인

제8장 아시아태평양의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
• 시장 점유율과 예측
• 아시아태평양 : 국가별 분석
  • 인도
  • 일본
  • 한국
  • 호주

제9장 남미의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
• 시장 점유율과 예측
• 남미 : 국가별 분석
  • 아르헨티나
  • 콜롬비아

제10장 중동 및 아프리카의 의료 코딩 분야 AI 시장 전망

• 시장 규모와 예측
• 시장 점유율과 예측
• 중동 및 아프리카 : 국가별 분석
  • 사우디아라비아
  • 아랍에미리트(UAE)

제11장 시장 역학

• 성장 촉진요인
• 과제

제12장 시장 동향과 발전

• 인수합병(M&A)(있는 경우)
• 제품 발매(있는 경우)
• 최근 동향

제13장 Porter의 Five Forces 분석

• 업계내 경쟁
• 신규 참여 가능성
• 공급업체의 힘
• 고객의 힘
• 대체품의 위협

제14장 경쟁 구도

• 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.

제15장 전략적 제안

제16장 리서치사에 대해 & 면책사항

영문목차

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