In 2024, coding-related denials surged by 126%, one of the largest increases in the past three years. At the same time, the U.S. healthcare system is facing a 30% shortage of medical coders, putting the financial performance of health systems at risk.
Medical coding is critical to healthcare organizations because it directly influences reimbursement accuracy, cash flow, and compliance with regulations—errors or delays in coding can quickly translate into significant financial disruption. Yet coding is increasingly complex due to frequent regulatory updates from CMS and commercial payers, expanding documentation requirements tied to value-based care, and detailed clinical data needed for accurate code selection.
This evolving complexity puts substantial pressure on clinicians to provide exhaustive documentation and on coders to interpret that documentation precisely. These demands lead to inefficiencies, inconsistent results, and increased administrative overhead, especially when coding workflows rely on manual processes (more than half of health systems are not utilizing AI tools) stretched beyond their limits.
Enter AI medical coding: a transformative technology that leverages natural language processing (NLP) and machine learning to automatically interpret clinical documentation, extract relevant clinical concepts, and map them to the most appropriate billing codes in real time; all while integrating directly with the EHRs via APIs. These tools can also proactively surface key details such as missing documentation, potential coding inaccuracies, or incomplete patient histories, presenting them to coders and clinicians through intuitive nudges and dashboards before, during, and after the patient encounter.
This article explores how AI medical coding is transforming clinical documentation and RCM, and what to look for in a next-generation solution.
The Increasing Complexity of Medical Coding
Medical coding has long served as the foundation of healthcare reimbursement, compliance, and reporting. Since the introduction of systems like the International Classification of Diseases (ICD), Current Procedural Terminology (CPT), and Healthcare Common Procedure Coding System (HCPCS), coders have been tasked with interpreting and coding clinical encounters based on documentation.
Clinical documentation has become increasingly complex due to factors such as the adoption of value-based care, which requires detailed reporting on patient outcomes, and quality initiatives that mandate more comprehensive clinical information. Additionally, payer requirements have become stricter, with insurers frequently updating coding policies and enforcing specific documentation criteria to justify reimbursement.
It’s no surprise that manual coding methods have struggled to keep pace as coders must continuously adapt to changing guidelines from entities like CMS and insurers, while navigating tight deadlines, ranging from 24 hours to just a few days after patient discharge, depending on payer contracts. A 2023 study found that 56% of medical coders failed coding accuracy audits, underscoring the widespread difficulty coders face in meeting these evolving requirements.
The transition to electronic health records was meant to streamline documentation and simplify coding workflows, but in many cases, it introduced new complexities. Instead of reducing the burden, EHRs often increase the volume of documentation and fragment the data needed for coding, creating more friction for both providers and revenue cycle teams.
These specific challenges have driven healthcare organizations to seek new approaches, including AI medical coding solutions, that can help coders and clinicians manage complexity, improve coding accuracy, and reduce reimbursement delays.
How AI is Revolutionizing Medical Coding
AI medical coding is about fundamentally rethinking how clinical data is interpreted, structured, and used to support accurate, timely reimbursement. That shift in perspective opens the door to entirely new capabilities that go far beyond code suggestion.
Improve Accuracy and Reduce Denials
AI tools can analyze clinical documentation, including physician notes, discharge summaries, lab results, and imaging reports, to identify key terms and clinical details. By automatically mapping these details to appropriate ICD-10 and CPT codes, AI significantly reduces the time coders spend manually searching for and assigning codes. This automated extraction helps reduce the selection and input of incorrect codes while simultaneously decreasing the overall time required to complete each chart, enabling coding teams to better manage high chart volumes.
Accurate coding is essential for reducing claim denials, and many denials can be traced back to issues like incomplete documentation, mismatched codes, or missing clinical details. AI helps address these issues by flagging potential gaps in documentation, verifying that selected codes align with payer policies, and prompting for clarification when needed. In fact, Commure Ambient AI customers have seen an average 25%+ reduction in denials. This kind of proactive support can reduce the risk of preventable denials and improve overall coding reliability.
Speed Up the Revenue Cycle
In many organizations, coding happens hours or even days after a patient visit, especially when teams rely on manual processes. Coders often need to review long notes, search for relevant clinical details, and double-check documentation before submitting claims. This review process takes time, and when coders are managing high volumes, it can lead to backlogs that delay reimbursement.
AI medical coding shortens that turnaround by analyzing clinical notes as they’re written and suggesting relevant codes directly within the workflow. Instead of waiting for a chart to be finalized and routed to a coder, the system extracts diagnoses and procedures in real time, flagging potential issues or missing information before the chart moves to billing.
This kind of support helps teams submit cleaner claims faster (Commure Ambient AI customers average 43 seconds to close a clinical note). It doesn’t eliminate the need for review, but it reduces the time spent on routine cases and minimizes errors that cause delays. As a result, coders can focus on complex encounters that still require human judgment, while more straightforward cases move through the process more efficiently.
Support the Full Encounter
Ambient AI tools extend the benefits of AI medical coding by assisting at every stage of the patient visit. Before the appointment, they pull relevant details of the patient’s record (such as prior diagnoses, recent test results, or care gaps) from the EHR and present them in a concise view to help clinicians prepare.
During the patient visit, ambient AI captures the conversation between clinician and patient, identifies key clinical elements like symptoms, assessments, and treatment plans, and structures that information in a draft note format. This reduces the need for manual note-taking and helps ensure important details aren’t missed.
After the visit, the system generates a draft of the clinical note and recommends billing codes based on the captured content. These recommendations are aligned with payer requirements and can be reviewed and edited by the clinician or coding staff before submission.
What sets ambient AI apart is its ability to tie together the entire encounter, from pre-visit data to real-time capture and post-visit coding, within a single, continuous workflow. This helps reduce duplication, supports consistency across documentation and coding, and gives teams a clearer picture of each visit from start to finish.
Key Considerations When Choosing an AI Medical Coding Provider
AI medical coding platforms vary widely in both capability and impact. Some are limited to code suggestions, while others offer deeper workflow integration, documentation support, and audit-ready reporting. When evaluating vendors, healthcare organziations should seek out tools that not only automate tasks but also meaningfully reduce errors, improve throughput, and fit your team’s real-world workflows. Here are a few factors to prioritize:
- Coding accuracy and audit trail: Ask for validated accuracy metrics, ideally backed by real customer results or third-party testing. The platform should allow coders or auditors to see how each code was assigned and flag potential discrepancies.
- Integration with your existing systems: The solution should connect to your EHR and billing systems through established standards or custom APIs. Avoid platforms that require duplicate data entry or disrupt existing workflows.
- Capacity to scale with your organization: Whether you’re coding 100 charts a day or 10,000, the system should maintain performance under load. If you’re growing, make sure the vendor can scale infrastructure and support accordingly.
- User experience for coders and clinicians: Look for a system that fits naturally into existing workflows, minimizes disruption, and doesn’t add unnecessary steps. Features like in-line suggestions, simple editing tools, and onboarding guides can make adoption smoother.
- Implementation and long-term support: Evaluate what onboarding looks like including training, configuration, and timeline. Post-launch, confirm the vendor provides a clear support structure, regular updates, and responsiveness to your team’s evolving needs.
- Security and compliance: In addition to HIPAA compliance, look for industry-standard certifications like SOC 2 or HITRUST. The vendor should be transparent about how data is stored, accessed, and protected.
AI Medical Coding is a Must-Have in 2025
Increased documentation demands, understaffed coding teams, and stricter claim scrutiny by payers continue to make traditional medical coding a challenge for healthcare organizations. These pressures often lead to delays, denials, and added administrative overhead.
AI medical coding offers a practical way to reduce that burden. By supporting documentation and code selection throughout the entire clinical encounter, these tools can help teams submit cleaner claims faster, without overhauling existing workflows.
Leading health systems like Dignity Health are investing in solutions such as Commure Ambient AI that go beyond basic automation to focus on impact, integration, and usability. Ready to see what this looks like at your organization?
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