The North America AI In Revenue Cycle Management Market is expected to witness market growth of 23.3% CAGR during the forecast period (2025-2032).
The US market dominated the North America AI In Revenue Cycle Management Market by country in 2024, and is expected to continue to be a dominant market till 2032; thereby, achieving a market value of $39.70 billion by 2032. The Canada market is experiencing a CAGR of 26% during 2025-2032. Additionally, the Mexico market is expected to exhibit a CAGR of 24.9% during 2025-2032.
Artificial Intelligence (AI) has rapidly emerged as a transformative force across various sectors of the global economy, with the healthcare industry experiencing particularly profound change. Among the critical domains within healthcare, Revenue Cycle Management (RCM) stands out as an area where AI’s potential to drive efficiency, accuracy, and financial sustainability is increasingly evident. The integration of AI into RCM is reshaping how healthcare providers, payers, and associated stakeholders manage the complex web of administrative and financial processes required to track patient care episodes, from registration and appointment scheduling to final payment of a balance.
Revenue Cycle Management encompasses the comprehensive process of capturing, managing, and collecting patient service revenue, a cycle that begins at the time a patient schedules an appointment and concludes when the healthcare provider receives full payment for services rendered. Traditionally, RCM has been fraught with challenges, including manual data entry errors, fragmented workflows, claim denials, coding inaccuracies, delayed payments, and regulatory compliance complexities.
The United States is the world’s leading market for artificial intelligence in revenue cycle management, owing to its vast and complex healthcare ecosystem. The U.S. healthcare sector encompasses thousands of hospitals, clinics, payers, and physician groups, all operating under a multi-payer insurance model with a heavy administrative load. Rising healthcare costs, frequent coding updates, and mounting regulatory pressures have made the adoption of AI-powered RCM solutions a top priority for providers seeking to improve both profitability and patient care.
Canada’s publicly funded healthcare system is increasingly turning to artificial intelligence in revenue cycle management to address operational inefficiencies and meet rising expectations for service delivery. The country’s universal health coverage model, while eliminating the complexities of multi-payer billing, still faces challenges related to resource allocation, administrative burdens, and the integration of disparate provincial healthcare systems.
Mexico’s healthcare system is marked by a mix of public and private providers, with significant variation in quality, efficiency, and financial management across the sector. In recent years, the adoption of artificial intelligence in revenue cycle management has begun to accelerate as hospitals, insurers, and clinics recognize the benefits of automation in reducing administrative burdens and improving revenue collection.
The US market dominated the North America AI In Revenue Cycle Management Market by country in 2024, and is expected to continue to be a dominant market till 2032; thereby, achieving a market value of $39.70 billion by 2032. The Canada market is experiencing a CAGR of 26% during 2025-2032. Additionally, the Mexico market is expected to exhibit a CAGR of 24.9% during 2025-2032.
Artificial Intelligence (AI) has rapidly emerged as a transformative force across various sectors of the global economy, with the healthcare industry experiencing particularly profound change. Among the critical domains within healthcare, Revenue Cycle Management (RCM) stands out as an area where AI’s potential to drive efficiency, accuracy, and financial sustainability is increasingly evident. The integration of AI into RCM is reshaping how healthcare providers, payers, and associated stakeholders manage the complex web of administrative and financial processes required to track patient care episodes, from registration and appointment scheduling to final payment of a balance.
Revenue Cycle Management encompasses the comprehensive process of capturing, managing, and collecting patient service revenue, a cycle that begins at the time a patient schedules an appointment and concludes when the healthcare provider receives full payment for services rendered. Traditionally, RCM has been fraught with challenges, including manual data entry errors, fragmented workflows, claim denials, coding inaccuracies, delayed payments, and regulatory compliance complexities.
The United States is the world’s leading market for artificial intelligence in revenue cycle management, owing to its vast and complex healthcare ecosystem. The U.S. healthcare sector encompasses thousands of hospitals, clinics, payers, and physician groups, all operating under a multi-payer insurance model with a heavy administrative load. Rising healthcare costs, frequent coding updates, and mounting regulatory pressures have made the adoption of AI-powered RCM solutions a top priority for providers seeking to improve both profitability and patient care.
Canada’s publicly funded healthcare system is increasingly turning to artificial intelligence in revenue cycle management to address operational inefficiencies and meet rising expectations for service delivery. The country’s universal health coverage model, while eliminating the complexities of multi-payer billing, still faces challenges related to resource allocation, administrative burdens, and the integration of disparate provincial healthcare systems.
Mexico’s healthcare system is marked by a mix of public and private providers, with significant variation in quality, efficiency, and financial management across the sector. In recent years, the adoption of artificial intelligence in revenue cycle management has begun to accelerate as hospitals, insurers, and clinics recognize the benefits of automation in reducing administrative burdens and improving revenue collection.
List of Key Companies Profiled
- R1 RCM, Inc. (TowerBrook Capital Partners L.P.)
- Athenahealth, Inc. (Bain Capital, LP.)
- McKesson Corporation
- Oracle Corporation
- Veradigm, Inc.
- eClinicalWorks LLC
- CareCloud, Inc.
- Infinx, Inc.
- UnitedHealth Group, Inc. (Optum, Inc.)
- Experian Information Solutions, Inc. (Experian plc)
Market Report Segmentation
By Type
- Integrated
- Standalone
By Delivery Mode
- Cloud-based
- Web-based
- On-premise
By End Use
- Physician Back Offices
- Hospitals
- Diagnostic Laboratories
- Other End Use
By Application
- Claims Management
- Medical Coding & Charge Capture
- Financial Analytics & KPI Monitoring
- Payment Posting & Remittance
- Other Application
By Product
- Software
- Services
By Country
- US
- Canada
- Mexico
- Rest of North America
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. Value Chain Analysis of AI In Revenue Cycle Management Market
Chapter 6. Key Customer Criteria - AI In Revenue Cycle Management Market
Chapter 7. North America AI In Revenue Cycle Management Market by Type
Chapter 8. North America AI In Revenue Cycle Management Market by Delivery Mode
Chapter 9. North America AI In Revenue Cycle Management Market by End Use
Chapter 10. North America AI In Revenue Cycle Management Market by Application
Chapter 11. North America AI In Revenue Cycle Management Market by Product
Chapter 12. North America AI In Revenue Cycle Management Market by Country
Chapter 13. Company Profiles
Companies Mentioned
- R1 RCM, Inc. (TowerBrook Capital Partners L.P.)
- Athenahealth, Inc. (Bain Capital, LP.)
- McKesson Corporation
- Oracle Corporation
- Veradigm, Inc.
- eClinicalWorks LLC
- CareCloud, Inc.
- Infinx, Inc.
- UnitedHealth Group, Inc. (Optum, Inc.)
- Experian Information Solutions, Inc. (Experian plc)