AI in Medical Coding: Enhancing Accuracy, Efficiency, and Patient Outcomes

Artificial intelligence (AI) has the potential to make value-based medicine more accurate and efficient by simplifying clinical review. Medical coding is an important but time-consuming process highly susceptible to human error. Accuracy is crucial. Mistakes can skew reimbursements, and missed diagnoses can impact care.

Fortunately, AI solutions can surface clinical insights, including potential undiagnosed hierarchical condition codes (HCCs), by combing through mountains of unstructured data and notes from across a patient’s care journey, as well as medical records in provider systems. The result is a more accurate, quantifiable picture of patient health status. Proactive clinical insights can also give providers an opportunity to intervene earlier, ideally preventing avoidable complications and hospitalizations. 

AI solutions that integrate clinical insights into clinical workflows transform the coding process from a retrospective manual review of lengthy charts into an intuitive, care experience for providers. AI highlights relevant information and suggests suspected diagnoses, making clinical review an exercise in verification and validation rather than a time-consuming search through medical records. This not only improves accuracy, but also significantly enhances efficiency, allowing for faster and more precise coding decisions that directly support value-based care initiatives.

AI is revolutionizing medical coding in several ways, transforming how healthcare providers and insurers approach this critical task.

Enhancing coding accuracy

One of the most significant impacts of AI in medical coding is its ability to improve diagnostic and coding accuracy. This improvement is particularly evident in specialized fields like nephrology, where AI has demonstrated up to 99% accuracy in predicting diagnosis codes in simulated cases.

The power of AI lies in its ability to process vast amounts of data and identify patterns far faster than people could do it by themselves. AI -powered solutions for risk adjustment have helped coders increase the productivity of clinical review by 3X, with HCC discovery accuracy rates of more than 95% on real-world clinical data.

‍Streamlining workflows and improving efficiency

By guiding clinicians to relevant insights, AI makes medical record review an exercise in verification and validation rather than a time-consuming search through clinical evidence. A real-world example of business impact created by AI comes from Reveleer's work with a large Blue Cross Blue Shield plan. By implementing AI-powered clinical review, the health plan accelerated coding volumes by 3X while also increasing their value per chart by 40%.

Improving CPT code accuracy and chart reviews

Beyond diagnosis codes, AI is also proving valuable in assigning procedure codes. A study using AI neural network models to predict the accuracy of current procedural terminology (CPT) codes from pathology reports achieved 97.5% coding accuracy. This capability for retrospective chart reviews is crucial for maintaining coding accuracy and compliance.

Driving value-based care outcomes

The impact of more accurate clinical coding using AI extends beyond administrative efficiency. In value-based care, precise coding ensures that providers serving high-risk patients can allocate resources appropriately and deliver optimal care. AI models trained on patient data can identify potential care gaps, supporting clinical decision-making and improving quality performance.

Additionally, more accurate and efficient coding has far-reaching effects. When diagnoses are captured at the point of care, patient RAF scores are more accurate, which ensures that reimbursements for care are also accurate. AI-powered solutions also help organizations better compete for quality incentives.

By leveraging AI, payers and providers are enhancing the accuracy of patient data and clinical decision-making. Ultimately, this contributes to a more effective, patient-centered healthcare system. As artificial intelligence continues to advance, it will become an indispensable assistant to clinicians at the point-of-care because of it. This technology goes beyond improving the efficiency of processes like medical coding and clinical review.