By Kacie Geretz, RCM Solutions Manager
April 11, 2025
Key Takeaway: Medical billing errors represent a significant financial and administrative burden across the healthcare ecosystem, with Americans losing an estimated $88 billion annually due to these errors (1). For healthcare organizations, billing errors create substantial revenue cycle challenges, from compliance concerns and payer audits to claim denials and delayed payments. As revenue cycle leaders face increasing pressure to maximize accuracy while managing staffing shortages, AI solutions are emerging as critical tools for reducing errors and improving billing accuracy.
Medical billing errors can occur at multiple points throughout the revenue cycle for various reasons:
Provider documentation lacking essential details or containing ambiguous terminology prevents accurate code assignment, resulting in potential revenue loss or compliance issues. These documentation deficiencies represent the root cause of approximately 40% of medical billing errors, creating a significant impact on healthcare organizations' financial performance (2).
Human data entry introduces transposition errors, missing digits, and code selection mistakes that directly impact claim accuracy and processing timelines. These avoidable errors contribute significantly to the $262 billion wasted annually in healthcare administrative costs, making them a critical area for quality improvement initiatives (3).
Submitting claims without thorough insurance verification creates payment delays and increases costly resubmission workflows. These verification failures account for approximately 25% of initial claim rejections, representing one of the most preventable sources of revenue cycle inefficiency (5).
Human data entry introduces transposition errors, missing digits, and code selection mistakes that directly impact claim accuracy and processing timelines. These avoidable errors contribute significantly to the $262 billion wasted annually in healthcare administrative costs (3), making them a critical area for quality improvement initiatives.
Advanced AI technologies, including machine learning and natural language processing (NLP), can significantly reduce billing errors through several mechanisms:
Advanced clinical documentation technologies analyze patterns in real time, flagging deficiencies before claims submission and suggesting improvements to clinical accuracy. These systems identify documentation gaps that human reviewers miss, reducing the 40% error rate associated with documentation issues (6).
Modern data extraction systems eliminate repetitive manual entry by automatically transferring information from documentation to billing systems with precision. Healthcare organizations have documented reductions in administrative waste, with UCHealth reporting 94% accuracy in automated extraction compared to 61% with manual processes (7).
The most sophisticated medical coding technology available today, autonomous medical coding solutions analyze and translate patient information in medical records into the appropriate medical codes with remarkable accuracy and consistency. For example, Geisinger Health leveraged Nym’s autonomous medical coding engine to reduce its coding-related denials rate to under 0.1%. Read the Geisinger Case Study.
Intelligent verification platforms perform eligibility checks across multiple payers simultaneously, identifying coverage details before services and reducing denials. These systems detect policy limitations that might otherwise be missed, addressing the 25% of rejections from eligibility verification failures. Johns Hopkins Medicine implemented a verification platform that reduced eligibility-related denials by 35% (8).
AI solutions, including technologies like autonomous medical coding, are transforming revenue cycle management by significantly reducing billing errors, accelerating payment cycles, and decreasing administrative burdens. As with any technology handling protected health information, implementing these solutions with robust security protocols is essential. When properly deployed, AI creates value across the healthcare ecosystem - from improved margins for healthcare organizations to more accurate billing for patients.
Request a demo with Nym to see how our medical coding engine streamlines processes and helps reduce errors.
While AI may not eliminate medical billing errors completely, it significantly reduces them by allowing for more accurate data capture and earlier identification of coding mistakes. Autonomous medical coding technology, like Nym's solution, has helped organizations reduce coding-related denials to under 0.1%. Read the Geisinger Case Study.
Possible risks include misuse of data and security issues, like unauthorized access and data breaches. Stringent security measures and regular audits can mitigate these risks while ensuring AI solutions maintain compliance with healthcare regulations.
Patients experience fewer billing errors, more transparent cost estimates, and faster insurance verification when AI is applied to revenue cycle processes. These improvements lead to reduced financial stress, clearer billing statements, and fewer surprise charges after receiving care.
Consumer Financial Protection Bureau. CFPB Estimates $88 Billion in Medical Bills on Credit Reports. Retrieved March 21, 2025, from https://www.consumerfinance.gov/about-us/newsroom/cfpb-estimates-88-billion-in-medical-bills-on-credit-reports/
AARP Healthcare. Medical Billing and Coding Errors Statistics. Retrieved March 21, 2025, from https://www.aarp.org/health/medicare-insurance/info-2022/medical-billing-errors-statistics.html
JAMA Network. Healthcare Administrative Waste Report. Retrieved March 21, 2025, from https://jamanetwork.com/journals/jama/article-abstract/2785479
Centers for Disease Control and Prevention. ICD-10-CM Official Guidelines. Retrieved March 21, 2025, from https://www.cdc.gov/nchs/icd/icd10cm.htm
Medical Group Management Association. Eligibility and Benefits Verification. Retrieved March 21, 2025, from https://www.mgma.com/resources/revenue-cycle/eligibility-verification-best-practices
Healthcare IT News. 3M and MModal launch new AI-powered CDI tool. Retrieved March 21, 2025, from https://www.healthcareitnews.com/news/3m-and-mmodal-launch-new-ai-powered-cdi-tool
Becker's Hospital Review. How UCHealth realized financial savings, FTE efficiencies with authorization automation. Retrieved March 21, 2025, from https://www.beckershospitalreview.com/finance/how-uchealth-realized-financial-savings-fte-efficiencies-with-authorization-automation/
Business Wire. Experian Health Acquires Wave HDC, Immediately Enabling Real-Time, Single-Inquiry Insurance Discovery/Verification at the Point of Patient Registration. Retrieved March 21, 2025, from https://www.businesswire.com/news/home/20231130987932/en/Experian-Health-Acquires-Wave-HDC-Immediately-Enabling-Real-Time-Single-Inquiry-Insurance-DiscoveryVerification-at-the-Point-of-Patient-Registration
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