May 21, 2024
ICT

AI in Medical Coding Market Size to Garner USD 7.15 Billion by 2032

The global AI in medical coding market size was estimated to be around US$ 2.06 billion in 2022. It is projected to reach US$ 7.15 billion by 2032, indicating a CAGR of 13.3% from 2023 to 2032.

AI in Medical Coding Market Size 2023 To 2032

Key Takeaways

  • By region, North America contributed more than 32% of revenue share in the AI in medical coding market in 2022. Whereas Asia-Pacific is estimated to witness the fastest CAGR between 2023 and 2032.
  • By component, the outsourced segment has held the largest market share of 68% in 2022. On the other hand, the in-house segment is anticipated to grow at a remarkable CAGR of 20.8% between 2023 and 2032.
  • By end user, the healthcare providers segment generated over 45% of market share in 2022. Whereas the payers segment is expected to expand at the fastest CAGR over the projected period.

The market research report on the AI in medical coding market provides a comprehensive analysis of various key aspects. It includes the definition, classification, and application of AI in medical coding products. The report examines the development trends, competitive landscape, and industrial chain structure within the industry. Furthermore, it presents an overview of the industry, analyzes national policies and planning, and offers insights into the latest market dynamics and opportunities at a global level.

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AI in Medical Coding Market Scope

Report Coverage Details
Growth Rate from 2023 to 2032 CAGR of 13.3%
Market Size in 2023 USD 2.32 Billion
Market Size by 2032 USD 7.15 Billion
Largest Market North America
Base Year 2022
Forecast Period 2023 to 2032
Segments Covered By Component and By End User
Regions Covered North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa

Read More: 3D Food Printing Market Size to Garner USD 1,238.99 Million by 2032

The report presents the volume and value-based market size for the base year 2022 and forecasts the market’s growth between 2023 and 2032. It estimates market numbers based on product form and application, providing size and forecast for each application segment in both global and regional markets.

Focusing on the global AI in medical coding market, the report highlights its status, future forecasts, growth opportunities, key market players, and key market regions such as the United States, Europe, and China. The study aims to present the development of the AI in medical coding market by considering factors like Year-on-Year (Y-o-Y) growth, in addition to Compound Annual Growth Rate (CAGR). This approach enables a better understanding of market certainty and the identification of lucrative opportunities.

Regarding production, the report investigates the capacity, production, value, ex-factory price, growth rate, and market share of major manufacturers, regions, and product types. On the consumption side, the report focuses on the regional consumption of AI in medical coding products across different countries and applications.

Buyers of the report gain access to verified market figures, including global market size in terms of revenue and volume. The report provides reliable estimations and calculations for global revenue and volume by product type from 2023 to 2032. It also includes accurate figures for production capacity and production by region during the same period.

The research includes product parameters, production processes, cost structures, and data classified by region, technology, and application. Furthermore, it conducts SWOT analysis and investment feasibility studies for new projects.

This in-depth research report offers valuable insights into the AI in medical coding market. It employs an objective and fair approach to analyze industry trends, supporting customer competition analysis, development planning, and investment decision-making. The project received support and assistance from technicians and marketing personnel across various links in the industry chain.

The competitive landscape section of the report provides detailed information on AI in medical coding market competitors. It includes company overviews, financials, revenue generation, market potential, research and development investments, new market initiatives, global presence, production sites, production capacities, strengths and weaknesses, product launches, product range, and application dominance. However, the data points provided only focus on the companies’ activities related to the AI in medical coding market.

Prominent players in the market are expected to face tough competition from new entrants. Key players are targeting acquisitions of startup companies to maintain their dominance. The report

Reasons to Purchase this Report:

  • Comprehensive market segmentation analysis incorporating qualitative and quantitative research, considering the impact of economic and policy factors.
  • In-depth regional and country-level analysis, examining the demand and supply dynamics that influence market growth.
  • Market size in USD million and volume in million units provided for each segment and sub-segment.
  • Detailed competitive landscape, including market share of major players, recent projects, and strategies implemented over the past five years.
  • Comprehensive company profiles encompassing product offerings, key financial information, recent developments, SWOT analysis, and employed strategies by major market players.

AI In Medical Coding Market Players

  • 3M Health Information Systems
  • Optum360
  • M*Modal
  • TruCode
  • Cerner Corporation
  • nThrive
  • Dolbey Systems, Inc.
  • Streamline Health
  • M-scribe Technologies
  • Talix
  • HRS Coding
  • Saince
  • Flash Code
  • ZyDoc
  • Aviacode

Market Segmentations

By Component

  • In-house
  • Outsourced

By End User

  • Healthcare providers
  • Medical billing companies
  • Payers

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East and Africa

TABLE OF CONTENT

Chapter 1. Introduction

1.1. Research Objective

1.2. Scope of the Study

1.3. Definition

Chapter 2. Research Methodology (Premium Insights)

2.1. Research Approach

2.2. Data Sources

2.3. Assumptions & Limitations

Chapter 3. Executive Summary

3.1. Market Snapshot

Chapter 4. Market Variables and Scope 

4.1. Introduction

4.2. Market Classification and Scope

4.3. Industry Value Chain Analysis

4.3.1. Raw Material Procurement Analysis

4.3.2. Sales and Distribution Channel Analysis

4.3.3. Downstream Buyer Analysis

Chapter 5. COVID 19 Impact on AI in Medical Coding Market 

5.1. COVID-19 Landscape: AI in Medical Coding Industry Impact

5.2. COVID 19 – Impact Assessment for the Industry

5.3. COVID 19 Impact: Global Major Government Policy

5.4. Market Trends and Opportunities in the COVID-19 Landscape

Chapter 6. Market Dynamics Analysis and Trends

6.1. Market Dynamics

6.1.1. Market Drivers

6.1.2. Market Restraints

6.1.3. Market Opportunities

6.2. Porter’s Five Forces Analysis

6.2.1. Bargaining power of suppliers

6.2.2. Bargaining power of buyers

6.2.3. Threat of substitute

6.2.4. Threat of new entrants

6.2.5. Degree of competition

Chapter 7. Competitive Landscape

7.1.1. Company Market Share/Positioning Analysis

7.1.2. Key Strategies Adopted by Players

7.1.3. Vendor Landscape

7.1.3.1. List of Suppliers

7.1.3.2. List of Buyers

Chapter 8. Global AI in Medical Coding Market, By Component

8.1. AI in Medical Coding Market, By Component, 2023-2032

8.1.1. In-house

8.1.1.1. Market Revenue and Forecast (2020-2032)

8.1.2. Outsourced

8.1.2.1. Market Revenue and Forecast (2020-2032)

Chapter 9. Global AI in Medical Coding Market, By End User

9.1. AI in Medical Coding Market, By End User, 2023-2032

9.1.1. Healthcare providers

9.1.1.1. Market Revenue and Forecast (2020-2032)

9.1.2. Medical billing companies

9.1.2.1. Market Revenue and Forecast (2020-2032)

9.1.3. Payers

9.1.3.1. Market Revenue and Forecast (2020-2032)

Chapter 10. Global AI in Medical Coding Market, Regional Estimates and Trend Forecast

10.1. North America

10.1.1. Market Revenue and Forecast, By Component (2020-2032)

10.1.2. Market Revenue and Forecast, By End User (2020-2032)

10.1.3. U.S.

10.1.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.1.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.1.4. Rest of North America

10.1.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.1.4.2. Market Revenue and Forecast, By End User (2020-2032)

10.2. Europe

10.2.1. Market Revenue and Forecast, By Component (2020-2032)

10.2.2. Market Revenue and Forecast, By End User (2020-2032)

10.2.3. UK

10.2.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.2.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.2.4. Germany

10.2.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.2.4.2. Market Revenue and Forecast, By End User (2020-2032)

10.2.5. France

10.2.5.1. Market Revenue and Forecast, By Component (2020-2032)

10.2.5.2. Market Revenue and Forecast, By End User (2020-2032)

10.2.6. Rest of Europe

10.2.6.1. Market Revenue and Forecast, By Component (2020-2032)

10.2.6.2. Market Revenue and Forecast, By End User (2020-2032)

10.3. APAC

10.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.3.3. India

10.3.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.3.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.3.4. China

10.3.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.3.4.2. Market Revenue and Forecast, By End User (2020-2032)

10.3.5. Japan

10.3.5.1. Market Revenue and Forecast, By Component (2020-2032)

10.3.5.2. Market Revenue and Forecast, By End User (2020-2032)

10.3.6. Rest of APAC

10.3.6.1. Market Revenue and Forecast, By Component (2020-2032)

10.3.6.2. Market Revenue and Forecast, By End User (2020-2032)

10.4. MEA

10.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.4.2. Market Revenue and Forecast, By End User (2020-2032)

10.4.3. GCC

10.4.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.4.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.4.4. North Africa

10.4.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.4.4.2. Market Revenue and Forecast, By End User (2020-2032)

10.4.5. South Africa

10.4.5.1. Market Revenue and Forecast, By Component (2020-2032)

10.4.5.2. Market Revenue and Forecast, By End User (2020-2032)

10.4.6. Rest of MEA

10.4.6.1. Market Revenue and Forecast, By Component (2020-2032)

10.4.6.2. Market Revenue and Forecast, By End User (2020-2032)

10.5. Latin America

10.5.1. Market Revenue and Forecast, By Component (2020-2032)

10.5.2. Market Revenue and Forecast, By End User (2020-2032)

10.5.3. Brazil

10.5.3.1. Market Revenue and Forecast, By Component (2020-2032)

10.5.3.2. Market Revenue and Forecast, By End User (2020-2032)

10.5.4. Rest of LATAM

10.5.4.1. Market Revenue and Forecast, By Component (2020-2032)

10.5.4.2. Market Revenue and Forecast, By End User (2020-2032)

Chapter 11. Company Profiles

11.1. 3M Health Information Systems

11.1.1. Company Overview

11.1.2. Product Offerings

11.1.3. Financial Performance

11.1.4. Recent Initiatives

11.2. Optum360

11.2.1. Company Overview

11.2.2. Product Offerings

11.2.3. Financial Performance

11.2.4. Recent Initiatives

11.3. M*Modal

11.3.1. Company Overview

11.3.2. Product Offerings

11.3.3. Financial Performance

11.3.4. Recent Initiatives

11.4. TruCode

11.4.1. Company Overview

11.4.2. Product Offerings

11.4.3. Financial Performance

11.4.4. Recent Initiatives

11.5. Cerner Corporation

11.5.1. Company Overview

11.5.2. Product Offerings

11.5.3. Financial Performance

11.5.4. Recent Initiatives

11.6. nThrive

11.6.1. Company Overview

11.6.2. Product Offerings

11.6.3. Financial Performance

11.6.4. Recent Initiatives

11.7. Dolbey Systems, Inc.

11.7.1. Company Overview

11.7.2. Product Offerings

11.7.3. Financial Performance

11.7.4. Recent Initiatives

11.8. Streamline Health

11.8.1. Company Overview

11.8.2. Product Offerings

11.8.3. Financial Performance

11.8.4. Recent Initiatives

11.9. M-scribe Technologies

11.9.1. Company Overview

11.9.2. Product Offerings

11.9.3. Financial Performance

11.9.4. Recent Initiatives

11.10. Talix

11.10.1. Company Overview

11.10.2. Product Offerings

11.10.3. Financial Performance

11.10.4. Recent Initiatives

Chapter 12. Research Methodology

12.1. Primary Research

12.2. Secondary Research

12.3. Assumptions

Chapter 13. Appendix

13.1. About Us

13.2. Glossary of Terms

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Prathamesh

I have completed my education in Bachelors in Computer Application. A focused learner having a keen interest in the field of digital marketing, SEO, SMM, and Google Analytics enthusiastic to learn new things along with building leadership skills.

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