Medical coding, where standardized codes are assigned to diseases and procedures in encounter notes and other clinical documentation, is an area of healthcare provider operations that’s ready for disruption. When done manually, these workflows are time-consuming and prone to errors, but mistakes can lead to claim denials that delay payments and threaten cash flow. According to the American Hospital Association, as many as 15% of claims are denied on first submission, with many of these denials linked to coding errors. Provider organizations spend nearly $20 billion annually on adjudicating denied claims with payers.
Because of their repetitive nature and strict reliance on rule-based logic, these highly structured workflows are among the most automation-ready functions in healthcare today. Despite interest from providers, though, full-scale adoption of autonomous or computer-assisted coding (CAC) has yet to be achieved across the industry. There are many reasons for this lag in adoption, but the high costs of the technology and skepticism about inaccuracies are chief among them. A number of medical coding companies offer custom-tailored proprietary solutions, but these require significant upfront investments, are slow to deploy and can be challenging for end users to adopt. Other vendors deliver integrated platforms, but these often give providers limited control and flexibility while locking them into a single vendor’s ecosystem. Neither of these approaches is ideal.
What’s needed instead is a solution that applies technologies like large language models (LLMs) and natural language processing (NLP) alongside the expertise of human coders. This approach balances the efficiencies that AI can create with the accuracy that human oversight guarantees. It offers provider organizations the best of both worlds, leveraging automation to accelerate processing, save time and human effort, and support scalability. However, it also reduces error rates, with quality reviews ensuring that code assignment is uniform, consistent, and in line with the latest regulatory guidelines.
By combining the best aspects of human expertise and machine speed, this approach will enable hospitals, health systems and other provider organizations to overcome the roadblocks that have prevented them from modernizing this critical business process.
Let’s take a closer look at some of these obstacles and how advances in autonomous coding technology, combined with human oversight, are enabling leading healthcare providers to overcome them.
Low Coding Accuracy Negates Time Savings
When autonomous coding systems are inaccurate, the consequences can include delayed reimbursements, regulatory penalties, and even impacts on patient care. Yet many of the platforms available today still rely on outdated optical character recognition (OCR) technology, which often misreads handwritten notes or scanned charts. When these systems can’t deliver consistently accurate results across all inputs—including non-digital ones—end users need to check their output regularly, erasing the time savings they’d hoped to achieve when they implemented them.
Some autonomous coding solutions struggle to interpret nuanced clinical documentation, such as physician notes from multiple specialists, medication lists from various providers and multimodal data (like lab and imaging results). Without extensive model training on healthcare-specific data, the solution may choose semantically plausible but clinically incorrect codes, leading to low coding accuracy and a greater need for manual intervention.
High inaccuracy rates also erode human trust in AI-powered systems. Healthcare providers tend to expect expert-level precision from the tools they use in their profession. When LLMs and generative AI models fail to meet these expectations, providers hesitate to adopt them.
Lengthy Ramp-up Time Reduces ROI
Implementing an autonomous coding solution can be an extended, complex process. Simpler systems may go live in as little as three months, but larger organizations with less standardized coding practices can take a year or more to fully integrate a solution into production.
Data integration is often the biggest bottleneck. If the provider organization has already digitized its medical charts, patient progress notes and other key documentation, and it has consolidated this data within a central digital data exchange platform, model training can proceed much more quickly. Provider organizations that have already integrated automation across their revenue cycle management (RCM) workflows will likely already have the needed data infrastructure in place.
By nature, medical coding is a data-intensive workflow. AI systems can achieve optimal accuracy only if they’re trained on high-quality data. Poor data hygiene or disconnected systems can lead to flawed outputs, creating a lengthy learning curve before the solution achieves acceptable accuracy.
Potential for Error Creates Regulatory Risk
Upcoding—when a provider uses a billing code for a more complex service or more severe diagnosis than was actually delivered—can result in significant regulatory penalties, exclusion from Medicare/Medicaid programs and even criminal charges. The American Medical Association reports that one psychiatrist was recently fined $400,000 and permanently barred from taking part in Medicare and Medicaid because he billed for 30- to 60-minute face-to-face sessions when he was in fact only meeting with patients for 15-minute medication checks. Even if done accidentally by GenAI, upcoding can trigger audits or result in fines from regulators.
What’s more, AI-driven autonomous coding tools with black-box behavior may not be able to provide the documentation that auditors require. If the coding system doesn’t provide complete transparency into how it is assigning codes, limited explainability can reduce trust among the system’s users. It also adds compliance risk.
How the RightMedical Coding Service Partner Can Help
In a comprehensive outsourced medical coding as-service solution, certified medical coders with deep expertise in code assignment, regulatory requirements and payer submission processes will work together with AI and machine learning to handle the entire coding process from end to end. The provider can translate your organization’s EHR data into HL7 format, allowing it to be onboarded onto the platform for processing by the algorithms. Approximately 80–85% of transactions can be auto-coded, but human reviewers will oversee the entire process, reviewing individual transactions when needed and keeping all of them available for audit purposes.
