US Artificial Intelligence (AI) in Medical Billing Market - Forecasts From 2025 To 2030

Description

US AI in Medical Billing Market Size:

US Artificial Intelligence (AI) in Medical Billing Market is anticipated to expand at a high CAGR over the forecast period.

To learn more about this report, request a free sample copy

Medical billing in the United States grapples with entrenched inefficiencies that erode provider margins and delay reimbursements. Each year, hospitals and clinics process billions of claims, where errors in coding alone trigger a major issue of false claims. This friction stems from fragmented electronic health records, evolving payer rules, and a shrinking pool of certified coders. Artificial intelligence intervenes precisely here, parsing vast datasets to automate code assignment and flag discrepancies before submission.

________________________________________________________________

US AI in Medical Billing Market Analysis:

Growth Drivers

Payer-driven denial surges propel demand for AI billing solutions as providers seek to neutralize automated scrutiny. The Kodiak Solution's research data from 2024 shows denial rates climbing to 12% industry-wide, fueled by insurers' AI tools that parse claims for anomalies in milliseconds. This asymmetry payers armed with algorithms, providers reliant on manual audits, creates acute pressure, spurring hospitals to invest in AI for pre-submission validation. AI predictive models flag maximum at-risk claims, slashing rework and reclaiming billions in annual revenue for mid-sized systems. Demand intensifies because these tools directly convert denied dollars into collected funds, turning defensive necessity into offensive growth.

Exploding volumes of unstructured clinical data further catalyze AI uptake, overwhelming traditional coding workflows. According to the American Hospital Association, millions of Americans visit hospitals & healthcare centers annually, with the majority being community-based. Hence, with healthcare providers facing such a high frequency of annual encounters, they prioritize AI platforms that unlock hidden revenue from overlooked documentation, directly elevating market pull as data proliferation outstrips labor supply. Additionally, labor constraints in specialized coding roles seal the drivers' synergy, and AI fills gaps left by such vacancy rates in health information management.

Challenges and Opportunities

HIPAA's stringent data safeguards impose compliance headwinds that temper AI deployment, curtailing demand until vendors prove ironclad protections. The HHS Security Rule mandates encryption and audit trails for electronic protected health information, yet AI models trained on sensitive claims data risk breaches if not de-identified rigorously. This constraint forces developers to invest in federated learning, processing data locally without central aggregation—elevating costs and slowing rollout, though it ultimately fortifies demand among risk-averse institutions. According to the Identity Theft Resource Centre's research study, in 1H of 2025, a total of 1,1732 data compromises affecting 165,745,452 people were reported in the USA, with the healthcare sector being the second most targeted sector after the financial sector.

Algorithmic bias emerges as another demand dampener, skewing billing outcomes and inviting regulatory backlash. AI systems inheriting disparities from historical datasets overrode procedures for certain demographics, thereby inflating denials. According to the University of Medical Center El Paso's research study, the US hospitals lose nearly USD 262 billion each year as the cost of denial, which marks nearly 10% of claims paid out. Hence, this inequity not only hampers equitable revenue capture but also heightens scrutiny under emerging state laws, like those in California mandating bias audits. Additionally, payer AI escalation in denial automation presents a dual-edged challenge, intensifying adversarial dynamics that paradoxically heighten counter-demand.

Reimbursement ambiguity for AI outputs unlocks substantial opportunity, as federal pilots clarify pathways to monetize efficiencies. This clarity catalyzes demand, particularly for fraud detection modules that recover claims lost to errors. Skill evolution offers another avenue: AHIMA guidelines reposition coders as AI overseers, mitigating job displacement fears and broadening talent pools.

Supply Chain Analysis

The US AI medical billing ecosystem centers on software development hubs in California and Massachusetts, where firms like Oracle and Epic concentrate engineering talent. These nodes produce core algorithms, drawing from domestic data centers for training on de-identified claims datasets. Logistical complexities arise in data ingestion as electronic health records from disparate vendors require API harmonization, thereby delaying integrations and inflating costs for non-standardized feeds. Recent reciprocal tariffs, escalated in 2025 against Chinese semiconductors, ripple through hardware underpinnings, hiking server expenses for AI inference. Mitigation hinges on domestic fabrication incentives under the CHIPS and Science Act.

Government Regulations

________________________________________________________________

US AI in Medical Billing Market Segment Analysis:

By Technology: Natural Language Processing (NLP)

Unstructured clinical documentation, comprising the majority of electronic health records, drives NLP demand in medical billing by enabling code extraction from narrative notes. PMC studies demonstrate NLP achieving 92% accuracy in CPT code generation, far surpassing manual methods. This precision counters the current ongoing surge in documentation volumes, further fueled by post-telehealth expansion, where coders lag behind a million annual encounters.

Providers prioritize NLP-embedded platforms to minimize their coding errors, directly inflating segment revenue as hospitals integrate tools like those that parse operative reports for modifier assignments. Regulatory alignment under CMS value-based models further propels uptake, requiring narrative-linked billing for outcome reimbursements.

By End-User: Hospitals and Clinics

High denial exposure in inpatient billing, averaging a considerable share of claims, fuels AI demand among hospitals and clinics, where predictive tools preempt payer rejections. Hence, this segment's scale, handling the majority of US claims, amplifies pull as AI analyzes historical patterns to flag discrepancies, cutting appeals and easing administrative loads that consume 15 hours weekly per coder. The growing emphasis on workflow management, followed by efforts to remove vulnerabilities that slow the overall claim roll-out, further exemplifies how operational imperatives convert AI from optional to essential.

________________________________________________________________

US AI in Medical Billing Market Competitive Environment and Analysis:

The landscape features entrenched EHR giants alongside specialized revenue cycle vendors, with market share split between integrated platforms and standalone AI tools.

Waystar

Waystar positions itself as a denial-prevention specialist, with its "Denial Prevention + Recovery" platform integrating machine learning to predict rejections pre-submission, which assists in ensuring less revenue loss. The company has invested in advancing its offerings, for instance the April 2025, the company announced new innovations that updated its "AltitudeAI" payment software, thereby enhancing the accuracy of healthcare payments.

Oracle Health

Oracle Health asserts dominance in enterprise billing via Cerner integrations, deploying AI agents that streamline billions in administrative tasks. September 2025 announcement unveiled payer-provider collaboration tools to reconcile claims in real-time, reducing disputes. Hence, Oracle's cloud-native approach targets large systems, which makes its solution more feasible for large healthcare centers.

Recent Market Developments

• November 4th, 2025: Athenahealth Inc. launched its "AI-Native Clinical Encounter," which assists in providing relevant insights for clinicians, draft documentation, and orders based on their conversation with patients. The software has transformed EHR from a record system to a collaboration assistant.
• September 2025: Waystar unveiled next-generation AI denial prevention innovations at its user conference, introducing predictive agents that analyze claims patterns to avert 90% of automated payer rejections, enhancing revenue cycle speed for providers

________________________________________________________________

US AI in Medical Billing Market Scope:

US Artificial Intelligence (AI) in Medical Billing Market Segmentation:

• By Technology
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Generative AI
  • Others
• By Deployment
  • Cloud-Based
  • On-Premise
• By Application
  • Automated Billing & Documentation
  • Revenue Cycle Management
  • Claim Processing
  • Fraud Detection
  • Others
• By End-User
  • Hospitals & Clinics
  • Ambulatory Surgical Centers
  • Others

Table Of Contents

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter's Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. US ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY TECHNOLOGY

5.1. Introduction

5.2. Machine Learning (ML)

5.3. Natural Language Processing (NLP)

5.4. Generative AI

5.5. Others

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

6.1. Introduction

6.2. Cloud-Based

6.3. On-Premise

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

7.1. Introduction

7.2. Automated Billing & Documentation

7.3. Revenue Cycle Management

7.4. Claim Processing

7.5. Fraud Detection

7.6. Others

8. US ARTIFICIAL INTELLIGENCE (AI) IN MEDICAL BILLING MARKET BY END-USER

8.1. Introduction

8.2. Hospitals & Clinics

8.3. Ambualtory Surgical Centers

8.4. 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

10.2. NXGN Management, LLC.

10.3. Oracle Corporation

10.4. Epic Systems Corporation

10.5. Athenahealth, Inc.

10.6. Veradigm LLC

10.7. eClinicalWorks

10.8. GE Healthcare (General Electric Company)

10.9. Tebra Technologies Inc.

10.10. CodaMetrix

11. APPENDIX

11.1. Currency

11.2. Assumptions

11.3. Base and Forecast Years Timeline

11.4. Key benefits for the stakeholders

11.5. Research Methodology

11.6. Abbreviations

LIST OF FIGURES

LIST OF TABLES

Companies Profiled

Waystar

NXGN Management, LLC.

Oracle Corporation

Epic Systems Corporation

Athenahealth, Inc.

Veradigm LLC

eClinicalWorks

GE Healthcare (General Electric Company)

Tebra Technologies Inc.

CodaMetrix

Related Reports