by 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.
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 |
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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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