Global AI in Medical Coding Market Size Study & Forecast, by Component (In-house, Outsourced), and Regional Analysis, 2023-2030

October 2023 | 200 pages | ID: G28BF0883891EN
Bizwit Research & Consulting LLP

Abstracts

Global AI in Medical Coding Market is valued at approximately USD 2.06 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 13.7% over the forecast period 2023-2030. Artificial intelligence (AI) in medical coding refers to the incorporation of AI technology into the process of medical coding. Medical coding is the process of standardizing medical diagnoses, treatments, and services into codes. These codes are necessary for data analysis, billing, and reimbursement in the healthcare sector. AI technologies are being increasingly utilized in medical coding to automate and enhance various aspects of the coding process, leading to increased accuracy, efficiency, and cost-effectiveness. Accordingly, the increase in demand for a standardized language to reduce insurance claim fraud and misinterpretations, and the rising focus on increasing the effectiveness of hospital billing and coding operations are primarily attributed to the global market expansion. Additionally, the rapid penetration of efficient healthcare solutions, coupled with the growing volume of healthcare data is augmenting the growth of the AI in medical coding market during the estimated period.

The rapid shift towards remote work and telehealth services has propelled the demand for these services, allowing remote coders to access and analyze medical records effectively. AI algorithms have been essential in accelerating the coding process, rapidly retrieving pertinent data, and lightening the workload for human programmers. According to Statista, in 2019, the telemedicine sector was estimated to value around USD 49.9 billion around the world. Also, it is anticipated to grow and is expected to reach about USD 277.9 billion by the year 2025. Moreover, the growing advancements in Natural Language Processing (NLP), as well as the rising integration with Electronic Health Records (EHRs) present various lucrative opportunities over the forecasting years. However, the growing inefficiency in medical billing and revenue cycle management, along with increasing privacy concerns are challenging the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global AI in Medical Coding Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the growing burden of chronic diseases in various countries and the expansion of healthcare infrastructure. Also, various key players in the market are increasingly launching innovative solutions and services to maintain a competitive edge further contributing to the regional market expansion. Whereas, Asia Pacific is expected to grow at the highest CAGR over the forecasting years. The regional market is expanding due to increasing expenditures on healthcare, rising initiatives by the government, increased burden of chronic illnesses, and outsourcing opportunities. APAC has developed into a robust market for medical coding because of the increasing emphasis on enhancing healthcare systems, adoption of advanced technologies such as AI, and establishment of new healthcare facilities.

Major market players included in this report are:

International Business Machines (IBM) Corporation

Fathom, Inc.

Epic Systems Corporation

Clinion

BUDDI.AI

Cerner Corporation

CodaMetrix

Nuance Communications, Inc

aidйo technologies, LLC

Optum, Inc.

Recent Developments in the Market:

• In May 2023, Codametrix and Henry Ford Health launched their joint Autonomous Bedside Pro (ABP) medical coding system. The ABP technology enables doctors to record clinical documentation in real-time, which is subsequently examined by AI algorithms to provide precise legal codes. This novel technology decreases coding backlogs, increases productivity, and improves coding accuracy by removing the need for manual coding and optimizing workflow.
• In March 2023, Clinion, a healthcare technology business, unveiled a clinical trial medical coding system driven by AI. The solution improves clinical trial medical coding's effectiveness, precision, and speed. Advanced AI algorithms are capable of rapidly analyzing and interpreting a high number of clinical trial data, extracting pertinent information, and assigning suitable codes, which substantially decreases the time and effort needed for coding.

Global AI in Medical Coding Market Report Scope:

• Historical Data – 2020 - 2021
• Base Year for Estimation – 2022
• Forecast period - 2023-2030
• Report Coverage - Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
• Segments Covered - Component, Region
• Regional Scope - North America; Europe; Asia Pacific; Latin America; Middle East & Africa
• Customization Scope - Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

In-house

Outsourced

By Region:

North America

U.S.

Canada

Europe

UK

Germany

France

Spain

Italy

ROE

Asia Pacific

China

India

Japan

Australia

South Korea

RoAPAC

Latin America

Brazil

Mexico

Middle East & Africa

Saudi Arabia

South Africa

Rest of Middle East & Africa

Contents

CHAPTER 1. EXECUTIVE SUMMARY

1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
  1.2.1. AI in Medical Coding Market, by Region, 2020-2030 (USD Billion)
  1.2.2. AI in Medical Coding Market, by Component, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption

CHAPTER 2. GLOBAL AI IN MEDICAL CODING MARKET DEFINITION AND SCOPE

2.1. Objective of the Study
2.2. Market Definition & Scope
  2.2.1. Industry Evolution
  2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates

CHAPTER 3. GLOBAL AI IN MEDICAL CODING MARKET DYNAMICS

3.1. AI in Medical Coding Market Impact Analysis (2020-2030)
  3.1.1. Market Drivers
    3.1.1.1. Increasing volume of healthcare data
    3.1.1.2. Rapid shift towards remote work and telehealth services
  3.1.2. Market Challenges
    3.1.2.1. Growing inefficiency in medical billing and revenue cycle management
    3.1.2.2. Regulatory and privacy concerns
  3.1.3. Market Opportunities
    3.1.3.1. Growing advancements in Natural Language Processing (NLP)
    3.1.3.2. Rising integration with Electronic Health Records (EHRs)

CHAPTER 4. GLOBAL AI IN MEDICAL CODING MARKET INDUSTRY ANALYSIS

4.1. Porter’s 5 Force Model
  4.1.1. Bargaining Power of Suppliers
  4.1.2. Bargaining Power of Buyers
  4.1.3. Threat of New Entrants
  4.1.4. Threat of Substitutes
  4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
  4.3.1. Political
  4.3.2. Economical
  4.3.3. Social
  4.3.4. Technological
  4.3.5. Environmental
  4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion

CHAPTER 5. GLOBAL AI IN MEDICAL CODING MARKET, BY COMPONENT

5.1. Market Snapshot
5.2. Global AI in Medical Coding Market by Component, Performance - Potential Analysis
5.3. Global AI in Medical Coding Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. AI in Medical Coding Market, Sub Segment Analysis
  5.4.1. In-house
  5.4.2. Outsourced

CHAPTER 6. GLOBAL AI IN MEDICAL CODING MARKET, REGIONAL ANALYSIS

6.1. Top Leading Countries
6.2. Top Emerging Countries
6.3. AI in Medical Coding Market, Regional Market Snapshot
6.4. North America AI in Medical Coding Market
  6.4.1. U.S. AI in Medical Coding Market
    6.4.1.1. Component breakdown estimates & forecasts, 2020-2030
  6.4.2. Canada AI in Medical Coding Market
6.5. Europe AI in Medical Coding Market Snapshot
  6.5.1. U.K. AI in Medical Coding Market
  6.5.2. Germany AI in Medical Coding Market
  6.5.3. France AI in Medical Coding Market
  6.5.4. Spain AI in Medical Coding Market
  6.5.5. Italy AI in Medical Coding Market
  6.5.6. Rest of Europe AI in Medical Coding Market
6.6. Asia-Pacific AI in Medical Coding Market Snapshot
  6.6.1. China AI in Medical Coding Market
  6.6.2. India AI in Medical Coding Market
  6.6.3. Japan AI in Medical Coding Market
  6.6.4. Australia AI in Medical Coding Market
  6.6.5. South Korea AI in Medical Coding Market
  6.6.6. Rest of Asia Pacific AI in Medical Coding Market
6.7. Latin America AI in Medical Coding Market Snapshot
  6.7.1. Brazil AI in Medical Coding Market
  6.7.2. Mexico AI in Medical Coding Market
6.8. Middle East & Africa AI in Medical Coding Market
  6.8.1. Saudi Arabia AI in Medical Coding Market
  6.8.2. South Africa AI in Medical Coding Market
  6.8.3. Rest of Middle East & Africa AI in Medical Coding Market

CHAPTER 7. COMPETITIVE INTELLIGENCE

7.1. Key Company SWOT Analysis
  7.1.1. Company
  7.1.2. Company
  7.1.3. Company
7.2. Top Market Strategies
7.3. Company Profiles
  7.3.1. International Business Machines (IBM) Corporation
    7.3.1.1. Key Information
    7.3.1.2. Overview
    7.3.1.3. Financial (Subject to Data Availability)
    7.3.1.4. Product Summary
    7.3.1.5. Recent Developments
  7.3.2. Fathom, Inc.
  7.3.3. Epic Systems Corporation
  7.3.4. Clinion
  7.3.5. BUDDI.AI
  7.3.6. Cerner Corporation
  7.3.7. CodaMetrix
  7.3.8. Nuance Communications, Inc
  7.3.9. aidйo technologies, LLC
  7.3.10. Optum, Inc.

CHAPTER 8. RESEARCH PROCESS

8.1. Research Process
  8.1.1. Data Mining
  8.1.2. Analysis
  8.1.3. Market Estimation
  8.1.4. Validation
  8.1.5. Publishing
8.2. Research Attributes
8.3. Research Assumption