Artificial Intelligence (AI) In Medical Billing Market Size, Share, Opportunities, And Trends By Deployment Mode (Cloud-Based, On-Premise), By Application (Automated Coding And Documentation, Revenue Cycle Management, Claims Processing, Denial Management, Fraud Detection, Others), By End-User (Hospitals And Clinics, Healthcare Payers, Ambulatory Surgical Centers, Others), And By Geography - Forecasts From 2024 To 2029

Artificial Intelligence (AI) in the medical billing market is expected to grow at a CAGR of 27.56%, reaching a market size of US$12.581 billion in 2029 from US$3.726 billion in 2024.

Medical billing is one of the key components of the revenue cycle of health care because of its complex systems and coding styles and the submission of various bills. Given that tedious manual chores are being abolished, precision enhanced, and costs reduced, the sector is expected to embrace such technologies. Medical billing has undergone changes, including analyzing medical data by AI and, where appropriate, searching for the right medical coding before making the billings and following up on the claims once submitted to ensure effectiveness. The market for AI in medical billing is propelled by the increasing demand for simplicity in operations, cost containment, and better revenue management. Considering the changes in the entire healthcare sector, healthcare providers are searching for newer solutions to enhance their efficiency and profitability. It can be expected that artificial intelligence will completely transform the medical billing landscape.

What are the artificial intelligence (AI) in the medical billing market growth drivers?

• Increasing adoption of Electronic Health Records (EHRs) boosts AI in the medical billing market.

The increasing adoption of electronic health records (EHR) accelerates the prevalence of artificial intelligence in the medical billing sector. EHR stores patients' health records in a computer to enable easy management, storage, access, and even sharing of health records among health providers. As EHRs have comprehensive and up-to-date patient information, algorithms can also use patient data to retrieve the correct billing codes and submit processed claims. Joining AI with EHR systems eases the processes of filing claims as it reduces errors and cuts down on management burdens. Since most healthcare providers worldwide are increasingly embracing an electronic form of maintaining patient records, the scope of AI-enhanced medical billing systems increases, propelling market growth.

• Demand for streamlined healthcare operations enhances AI in the medical billing market growth

The need for optimized healthcare processes is a key driver of AI in the medical billing industry’s expansion. Increasing volumes of patients and complicated billing systems necessitate the need for effective measures in the management of healthcare institutions. Medical billing solutions based on AI can eliminate labor-intensive processes such as claim processing and billing submission and improve revenue cycle management. By adopting AI, health care slashes down the costs associated with billing, improving billing processes and the system's general efficiency. This desire for faster procedures encourages using AI solutions in medical billing, moving the industry forward.

• Automation of manual billing processes is boosting AI in the medical billing market expansion.

In the AI in the medical billing industry, automation of manual billing procedures is a critical growth element. Traditional billing operations, including claim production, billing, and submission, can be time-consuming and error-prone. AI-powered automation automates these operations by utilizing powerful algorithms to extract key information from medical data, assign appropriate billing codes, and manage claim submissions. AI billing aims to shorten the billing process, improve quality, and reduce the manpower needed to engage them in more crucial activities by lowering manual work. This level of automation is productive and cuts costs, and thus, reimbursements are done faster, explaining the need for AI in medical billing.

Artificial intelligence (AI) in medical billing market restraints:

• Rising security concerns are anticipated to impede market growth

Healthcare systems have incorporated the use of AI in medical billing, which is an improvement. However, issues in the market, such as privacy and confidentiality, still affect this application positively. Therefore, AI-based systems that use that data must have very stringent policies, such as HIPAA, to prevent data misuse and establish high levels of security. The dangers posed by breach of systems and loss of data are quite real. As such, any security system for a healthcare facility must be water-tight, as any slight deviation will likely have dire economic and legal consequences for the healthcare service provider. There is also the issue of a patient’s data being exposed, which is damaging and entirely unfavorable to the organization.

What are the key geographical trends shaping the artificial intelligence (AI) in the medical billing market?

• North America is witnessing exponential growth during the forecast period

North America makes large R&D investments, especially in the fields of AI and healthcare. This investment supports the growth of AI applications and innovation in several medical domains, including medical billing. Significant funding is available in the area for new businesses and technology startups. The expansion of AI startups that focus on healthcare, including medical billing solutions, is aided by this financial support. On AI projects, startups, technology companies, and healthcare facilities in North America frequently work together. These partnerships encourage the creation of innovative approaches to medical billing.

Recent developments in the artificial intelligence (AI) in the medical billing market

• In June 2024, Claimocity presented its game-changing AI-powered Charge Capture product, solidifying its position as the leading platform for billing software and revenue cycle management services for inpatient providers. This cutting-edge platform uses AI to dramatically reduce administrative burden (by 85%) and increase revenue (by 6% or more) by streamlining the billing process for acute, facility-based providers.
• In June 2023, Nuance Communications, Inc., a Microsoft Company, and Epic announced the availability of the Nuance Dragon Ambient Experience Express (DAX Express) solution to the Epic community. Expanding on the strategic collaboration between Microsoft, Nuance, and Epic, the integration of DAX Express into Epic workflows serves as a co-pilot for Dragon Medical users. This integration helps to better manage administrative workloads, reduce burnout, increase patient access to care, and enhance healthcare outcomes through clinical documentation that writes itself.

Artificial Intelligence (AI) In Medical Billing Market Scope:

Report Metric	Details
AI in Medical Billing Market Size in 2024	US$3.726 billion
AI in Medical Billing Market Size in 2029	US$12.581 billion
Growth Rate	CAGR of 27.56%
Study Period	2019 to 2029
Historical Data	2019 to 2022
Base Year	2024
Forecast Period	2024 – 2029
Forecast Unit (Value)	USD Billion
Segmentation	Deployment Mode Application End-User Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in AI in Medical Billing Market	Nextgen Healthcare, Inc. Cerner Corporation Mckesson Corporation Epic Systems Corporation Athenahealth, Inc.
Customization Scope	Free report customization with purchase

 

The Artificial Intelligence (AI) in medical billing market is segmented and analyzed as follows:

• By Deployment Mode
  • Cloud-Based
  • On-Premise        
• By Application
  • Automated Billing and Documentation
  • Revenue Cycle Management
  • Claims Processing
  • Denial Management
  • Fraud Detection
  • Others     
• By End-User
  • Hospitals And Clinics
  • Healthcare Payers
  • Ambulatory Surgical Centers
  • Others                 
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain    
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • Japan
    • China
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others

1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base and Forecast Years Timeline

1.8. Key Benefits to the Stakeholder

2. RESEARCH METHODOLOGY  

2.1. Research Design

2.2. Research Processes

3. EXECUTIVE SUMMARY

3.1. Key Findings

3.2. CXO Perspective

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porter’s Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

4.5. Analyst View

5. ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY DEPLOYMENT MODE

5.1. Introduction

5.2. Cloud-Based

5.3. On-Premise 

6. ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY APPLICATION

6.1. Introduction

6.2. Automated Billing and Documentation

6.3. Revenue Cycle Management

6.4. Claims Processing

6.5. Denial Management

6.6. Fraud Detection

6.7. Others

7. ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY END-USER INDUSTRY

7.1. Introduction

7.2. Hospitals And Clinics

7.3. Healthcare Payers

7.4. Ambulatory Surgical Centers

7.5. Others          

8. ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY GEOGRAPHY

8.1. Introduction

8.2. North America

8.2.1. By Deployment Mode

8.2.2. By Application

8.2.3. BY End-User Industry

8.2.4. By Country

8.2.4.1. United States

8.2.4.2. Canada

8.2.4.3. Mexico

8.3. South America

8.3.1. By Deployment Mode

8.3.2. By Application

8.3.3. BY End-User Industry

8.3.4. By Country

8.3.4.1. Brazil

8.3.4.2. Argentina

8.3.4.3. Others

8.4. Europe

8.4.1. By Deployment Mode

8.4.2. By Application

8.4.3. BY End-User Industry

8.4.4. By Country

8.4.4.1. United Kingdom

8.4.4.2. Germany

8.4.4.3. France

8.4.4.4. Italy

8.4.4.5. Spain

8.4.4.6. Others

8.5. Middle East and Africa

8.5.1. By Deployment Mode

8.5.2. By Application

8.5.3. BY End-User Industry

8.5.4. By Country

8.5.4.1. Saudi Arabia

8.5.4.2. UAE

8.5.4.3. Others

8.6. Asia Pacific

8.6.1. By Deployment Mode

8.6.2. By Application

8.6.3. BY End-User Industry

8.6.4. By Country

8.6.4.1. Japan

8.6.4.2. China

8.6.4.3. India

8.6.4.4. South Korea

8.6.4.5. Indonesia

8.6.4.6. Taiwan

8.6.4.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. Waystar (Formerly Known As Zirmed)

10.2. Nextgen Healthcare, Inc.

10.3. Cerner Corporation

10.4. Mckesson Corporation

10.5. Epic Systems Corporation

10.6. Athenahealth, Inc.

10.7. Allscripts Healthcare Solutions, Inc.

10.8. Eclinicalworks Llc

10.9. Ge Healthcare (A Division Of General Electric Company)

10.10. Optum, Inc. (A Subsidiary Of UnitedHealth Group)  

Waystar (Formerly Known As Zirmed)

Nextgen Healthcare, Inc.

Cerner Corporation

Mckesson Corporation

Epic Systems Corporation

Athenahealth, Inc.

Allscripts Healthcare Solutions, Inc.

Eclinicalworks Llc

Ge Healthcare (A Division Of General Electric Company)

Optum, Inc. (A Subsidiary Of UnitedHealth Group)