The healthcare RCM (Revenue Cycle Management) industry is an ever-evolving space driven by technological advances. Only a few years ago, medical coders used to rely on huge manuals and knowledgebase to check and assign codes. They had to meticulously go through multiple pages online to look for specific medical codes. And every time there was an update in the medical codes or coding guidelines, they would have to work twice as hard to catch up.
Fast forward to 2025
Artificial Intelligence (AI) is transforming medical coding and billing by automating and streamlining processes. And in the coming years, it will continue to disrupt the healthcare RCM industry even more. According to a recent study by The Business Research Company, the AI in medical coding market size will grow to $5.29 billion in 2029 at a compound annual growth rate (CAGR) of 14%.
But is AI in medical coding useful for your facility? Let’s find out.
This blog will dig into the shortcomings of traditional coding methods and how AI is proving to be just the missing puzzle piece. Let’s go through the specific applications of AI in medical coding, examining the benefits, challenges, and future implications of this technology.
The Current State of Medical Coding
The importance of medical coding in the healthcare industry cannot be overstated. By converting patient data into standard alphanumerical codes, it ensures healthcare providers are accurately reimbursed for the services rendered.
Sounds simple, right?
But it isn’t. Healthcare providers constantly struggle with evolving regulations, complex compliance requirements, and ever-changing coding guidelines. These challenges make accurate medical coding and securing proper reimbursement far more challenging than it seems.
The effect?
- 18% increase in initial claim denials since 2020
- 65% of denied claims are never submitted
- 90% of claim denials are preventable with a proper process
The Cause?
At the core of these problems lies human error – an inevitable result of overworked coding teams facing increasingly complex payer requirements. When medical coders, often handling hundreds of claims on a daily basis, make even minor mistakes, the financial impact can be disastrous. A recent report says healthcare providers collectively lose approximately $262 billion annually due to denied claims.
Common challenges in traditional medical coding
First and foremost, manual coding is too slow.
Typically, traditional manual medical coding takes five to seven days from claim generation to submission.
Once submitted for insurance, claims are reviewed and validated. Claims containing errors are denied and sent back for review, further stretching the process for too long.
But, the pace of coding isn’t the only disadvantage. Some other challenges include:
- High Risk of Errors: Human errors like an oversight or misinterpretation of a patient chart can lead to claims denials and financial losses for healthcare providers.
- Complexity of Medical Terminology: In the constantly changing space of RCM, medical coders have to stay informed about the latest codes and guidelines, adding pressure on the coders.
- Workload and Fatigue: The repetitive nature of medical coding can result in burnout among coders.
How is AI Transforming Medical Coding?
AI is transforming the medical coding landscape by automating repetitive tasks, enhancing accuracy, and reducing processing time. AI-driven medical coding works through the following mechanisms:
- Extracting Codes
AI can read and interpret medical records, identifying important terms related to diagnoses and procedures. This helps in accurately mapping these terms to relevant medical codes. - Pattern Recognition
AI in medical coding analyzes huge datasets to detect patterns in coding. With time, AI systems become more efficient, learning from past errors and boosting accuracy. Medical coding tools like Gemini from Aideo-Tech utilize NLP, Machine Learning, and Generative AI to ingest clinical data and map language to ICD-10 and CPT codes in both a fully automated and decision-support manner. - Automated Code Suggestions
AI tools assist coders by suggesting the most relevant medical codes based on the patient’s medical history. This ultimately reduces mistakes and speeds up the coding process.For instance, an AI medical coding tool analyzing a patient’s medical chart and treatment plan might suggest “CPT Code 45380” for a colonoscopy with biopsy, significantly lessening manual effort and improving turnaround times for billing. - Real-time Coding Assistance
AI can readily integrate with Electronic Health Records (EHRs) to offer real-time coding recommendations as providers document patient encounters, minimizing post-visit coding delays.An example of this is an AI tool that might detect a missing modifier in a surgical code and prompt the coder to correct it, reducing the risk of claim rejection. - Compliance and Quality Control
AI systems constantly monitor coding practices to ensure compliance with the latest guidelines and detect fraudulent claims. Any deviation from predefined norms triggers automated alerts, notifying relevant stakeholders of potential compliance issues.
Benefits of Artificial Intelligence in Medical Coding
The adoption of AI in medical coding offers multiple advantages, including:
- Improved Accuracy: AI-powered tools like Gemini by Aideo-tech reduce the chance of human errors, ensuring that correct codes are assigned based on patient records.
- Increased Efficiency: AI-driven coding reduces the time required for medical coding by automating repetitive tasks. This way, medical coders can focus on more complex cases.
- Cost Reduction: By minimizing errors and claim denials, AI helps healthcare providers save costs related to rejected claims and administrative overhead.
- Better Compliance: One major benefit of using AI tools for medical coding is they comply with healthcare data protection regulations like the Health Insurance Portability and Accountability ACT (HIPAA). Such compliance ensures that patient data is handled responsibly and privacy is maintained.
AI: Redefining the Future of Medical Coding
From the looks of it, the role of Artificial Intelligence in medical coding is just beginning to unfold. In the near future, the adoption of AI in medical coding is set to grow exponentially. However, the success of these medical coding solutions relies heavily on skilled medical coders who provide oversight and expertise.
Why is human involvement still crucial?
While AI excels in pattern recognition and streamlining administrative processes, human oversight ensures that the essence of patient care and complex medical cases are not lost.
After all, AI is not here to replace human coders; it is meant to complement them.
Aideo—Your Partner in AI-Powered Medical Coding
For healthcare providers looking to stop claim denials and recapture millions of dollars lost in revenue, Aideo could be your ultimate partner. Our solution, Gemini, is an AI-driven autonomous coding solution that empowers and complements medical coders with workflow efficiencies, artificial intelligence, and supplemental coding services to create scalable and repeatable coding efficiency.
It does so by utilizing AI and ML algorithms to analyze vast amounts of medical data and identify patterns that human coders might miss. Additionally, Aideo’s AI-powered solution is constantly learning and improving with every passing day by leveraging the constructive feedback from the human coders.
What are you waiting for? Get in touch for a free guided demo!
FAQs
- How does AI improve medical coding?
It does so by automating repetitive tasks, reducing the burden on human coders. AI also enhances coding accuracy as AI algorithms can process huge amounts of data to identify patterns and ensure codes are assigned correctly. Moreover, automated systems can analyze and code medical records in a fraction of the time it takes for a human coder, leading to quicker billing and reimbursement cycles. - Can AI fully replace human medical coders?
AI is unlikely to fully replace human medical coders because even though AI can manage to do routine tasks on its own, human intervention is still required for complex cases.