Automation in healthcare: Leveraging computer-assisted coding and AI to transform efficiency and accuracy
September 16, 2025 | Roxana Safari
The healthcare industry is undergoing a significant transformation driven by the integration of advanced automation technologies. Among these, computer-assisted coding (CAC) stands out as a prime example of rules-based classical artificial intelligence (AI), while machine learning and generative AI represent the forefront of hybrid approaches. Together, these technologies are revolutionizing healthcare by reducing costs, improving accuracy, increasing productivity and decreasing denials.
CAC: The power of rules-based AI
CAC systems leverage rules-based classical AI to enhance the coding process. These systems use predefined rules and logic to analyze clinical documentation and suggest appropriate diagnosis and procedure codes. This method ensures that coding is consistent, accurate and compliant with the latest regulations. The automation speeds up the coding process and reduces the likelihood of errors and rework, which are common in manual coding.
Hybrid AI approaches: Combining rules-based technology with machine learning and generative AI
While rules-based AI provides a solid foundation, integrating machine learning and generative AI takes automation to the next level. These technologies analyze vast amounts of data to identify rules and patterns and make predictions, continuously improving their accuracy over time.
Benefits of AI in healthcare automation include:
- Cost reduction: AI-driven automation reduces the need for contract coders and minimizes manual processes, leading to significant cost savings.
- Improved accuracy: AI systems provide pre-validated coding suggestions, reducing the risk of human error and ensuring compliance with complex payer guidelines.
- Increased productivity: Automation speeds up the coding process, allowing healthcare providers to handle more claims and improve revenue cycle management.
- Decreased denials: AI technology helps identify and correct documentation errors, reducing delays in payment and improving denial management.
The future of healthcare automation
As AI technologies continue to evolve, their integration into healthcare will become even more seamless and impactful. The hybrid approach, combining the strengths of rules-based AI, machine learning and generative AI promises to address the industry's most pressing challenges. By leveraging these technologies, healthcare providers can enhance efficiency, improve patient care and navigate the complexities of modern healthcare with greater ease.
The automation of healthcare through advanced AI technologies is not just a trend, but a necessity for the future. The capabilities of CAC systems to auto-suggest complete diagnosis codes and the hybrid approach of combining machine learning and generative AI are paving the way for a more efficient, accurate and productive healthcare system.
Roxana Safari, CCS, BA, is a product owner at Solventum.
