By Kacie Geretz, RCM Solutions Manager
April 15, 2025
Key Takeaway: Manual medical coding creates significant financial strain throughout the revenue cycle, with error rates reaching 40% in some specialties and a worsening 30% coder shortage nationwide. Autonomous medical coding technology addresses these challenges by processing thousands of charts in minutes with 95%+ accuracy, reducing A/R days by 3-5 days, and slashing DNFB metrics by half while allowing staff to focus on higher-complexity tasks.
A/R: Accounts Receivable
CAC: Computer-Assisted Coding
CPT: Current Procedural Terminology
DNFB: Discharged Not Final Billed
E/M: Evaluation and Management
HCPCS: Healthcare Common Procedure Coding System
HIPAA: Health Insurance Portability and Accountability Act
ICD-10-CM: International Classification of Diseases, 10th Revision, Clinical Modification
NLP: Natural Language Processing
RCM: Revenue Cycle Management
The claims processing lifecycle encompasses multiple stages, from patient registration to final payment posting, with medical coding representing a critical early phase where several challenges can arise:
Processing capacity bottlenecks: Traditional medical coding requires human coders to review documentation and assign appropriate codes, a process that can take up to 15-20 minutes per chart in certain specialties. The American Medical Association (AMA) estimated a 30% shortage of medical coders in 2023, with projections showing this shortage growing by 8% by 2029, further constraining this crucial claims processing step (1).
Coding accuracy and compliance concerns: The AMA estimates that up to 12% of medical claims are submitted with inaccurate codes. These inaccuracies cascade through subsequent claims processing steps, leading to denials and rework that delay final reimbursement and cost the healthcare industry approximately $36 billion annually in lost revenue, denied claims, and potential fines, according to a report by the AMA.
Documentation-coding misalignment: Inconsistencies between clinical documentation and assigned codes create compliance risks and lead to significant revenue leakage throughout the revenue cycle. These misalignments compromise claim quality before they even reach the submission stage, often resulting in costly denials that require extensive manual intervention from your already hard-working coding teams.
Autonomous medical coding, the only technology capable of true medical coding automation, leverages sophisticated AI to transform the traditionally manual coding phase of claims processing. While the underlying technology behind solutions varies by vendor, the high-level approach is consistent:
Integration into existing workflows: Unlike CAC software that requires human validation, autonomous coding solutions operate in the background, independently processing patient encounters and sending successfully coded charts directly to billing with zero human intervention.
Multi-layered AI approach: Most autonomous coding solutions employ advanced technologies, including NLP, machine learning, and deep learning, to analyze clinical documentation accurately and comprehensively.
Automated code application: Using AI, rules-based engines, or some combination of these approaches, autonomous coding solutions assign the appropriate medical codes (ICD-10-CM, CPT, HCPCS, E/M, etc.) and relevant modifiers based on the extracted clinical information.
This technology represents a significant advancement over traditional coding methods and earlier automation attempts, enabling healthcare organizations to process thousands of charts in minutes rather than days while maintaining the high accuracy standards required for successful claims processing. For more information, check out Nym’s Introductory Guide to Autonomous Medical Coding.
When the medical coding phase is optimized through automation, the entire claims processing workflow benefits:
Accelerated end-to-end timeline: By addressing bottlenecks in the coding phase, AI claims processing can reduce the overall claims submission timeline.
Reduced errors and denials: Medical coding automation via autonomous medical coding solutions significantly reduces error rates in the coding stage, which leads to fewer denials and rework in subsequent claims processing steps (2).
Optimized resource allocation: By automating routine coding tasks, organizations can redirect human expertise to other crucial stages of the claims processing workflow, including complex cases and denial management (3).
Improved financial performance: Healthcare facilities have reported reductions in A/R days by an average of 3-5 days after implementing autonomous coding solutions, demonstrating how optimizing the coding phase accelerates the entire claims payment cycle.
Real-world results demonstrate this comprehensive impact. For example, Inova, the top health system in Northern Virginia, reduced weekly DNFB by 50% and reallocated medical coders to higher-complexity departments after implementing Nym’s autonomous medical coding engine. Read the Inova Case Study.
Organizations looking to improve their claims processing through AI claims processing should establish a strategic approach:
Workflow assessment: Analyze your complete claims processing workflow to identify how coding bottlenecks affect downstream processes.
Phased implementation: Start by addressing medical coding automation in high-volume, lower-complexity specialties to measure performance improvements across the claims processing lifecycle.
Performance monitoring: Establish metrics that track not only coding accuracy but also the impact on overall claims processing timelines.
Staff transition planning: Develop a plan for evolving coder roles to focus on complex cases and other high-value aspects of the claims processing workflow (3).
AI claims processing improves efficiency by addressing the traditionally manual coding phase, which often creates significant bottlenecks. Medical coding automation technology such as autonomous medical coding can process thousands of charts in under an hour with accuracy rates exceeding 95%, allowing the subsequent steps in claims processing to proceed without delays (3).
Common challenges include integration with existing systems across the claims processing lifecycle, developing comprehensive audit protocols, and managing workforce transition as coding roles evolve within the broader revenue cycle team.
Leading autonomous coding solutions maintain HIPAA compliance through robust security protocols, including data encryption, access controls, and comprehensive audit trails that protect patient information throughout all stages of claims processing (4).
Find out how Nym can streamline your organization's claims processing and improve revenue cycle performance by requesting a demo of Nym's autonomous medical coding engine.
HFMA. Why is autonomous coding having a moment in healthcare? Retrieved March 21, 2025, from https://www.hfma.org/technology/why-autonomous-coding-is-having-a-moment-in-healthcare/
UTSA. How AI is Revolutionizing Medical Billing and Coding. Retrieved March 21, 2025, from https://www.utsa.edu/pace/news/ai-in-medical-billing-and-coding.html
Journal of AHIMA. Updating HIPAA Security to Respond to Artificial Intelligence. Retrieved March 21, 2025, from https://journal.ahima.org/page/updating-hipaa-security-to-respond-to-artificial-intelligence
American Medical Association. Addressing another health care shortage: medical coders. Retrieved March 21, 2025, from https://www.ama-assn.org/about/leadership/addressing-another-health-care-shortage-medical-coders
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