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Global AI in Medical Coding Market to reach USD 5.75 billion by the end of 2030.

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

Product Code: HLSHIT-80185146
Publish Date: 30-10-2023
Page: 200

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
aidéo technologies, LLC
Optum, Inc.

Recent Developments in the Market:
Ø 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.
Ø 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:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü 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

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 1
7.1.2. Company 2
7.1.3. Company 3
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. aidéo 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

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Data Collection:
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Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
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