For years, automation has supported coders in bridging clinical care and financial reimbursement. Today, we are in a new era in medical coding automation fueled by artificial intelligence (AI) and increasingly sophisticated technology tools.
Today’s Automation Landscape
Medical coding automation solutions have existed for more than two decades. Early offerings were rule-based, often relying on robotic process automation (RPA) to perform repetitive actions with minimal variation. In the mid-2010s, Computer-Assisted Coding (CAC) gained momentum, offering suggestions to coders based on structured data.
Today, the range of solutions includes:
- Assisted coding tools that help coders accelerate documentation review and code assignment.
- Semi-autonomous coding that can pre-code certain chart types or specialties with human review.
- Autonomous coding, where AI fully processes charts and sends them direct to bill without human involvement.
While exciting, true autonomy is currently limited in scope. Due to the nuanced nature of clinical data, most specialties are too complex for full automation. Current success stories tend to be in high-volume outpatient settings like radiology and emergency departments, where documentation and procedures are more standardized. For most services, human oversight is still critical to ensure coding accuracy and compliance with payer rules.
This reality means that a hybrid approach is the most effective model for many healthcare organizations. Technology can handle simpler, repetitive coding tasks, while human experts focus on complex decisions, edge cases, and quality assurance. This hybrid strategy allows hospitals and health systems to achieve greater speed and consistency without compromising clinical and financial accuracy.
When documentation lacks specificity, medical coders must interpret intent, which increases the risk of denials or compliance issues. Automation can help bridge these gaps by flagging documentation deficiencies in real time, and the best results come when Clinical Documentation Integrity (CDI) and coding automation work in tandem. A unified platform that integrates coding, CDI, and analytics ensures that all departments are aligned and that data flows seamlessly from point of care to final billing.
How Is Coding Automation Measured?
Whether coding is done manually or with AI support, performance metrics remain consistent and should form the backbone of return on investment (ROI) measurement for automation initiatives. Regardless of the coding model, healthcare leaders should track:
- Turnaround Time: How quickly charts are coded (commonly within 24–48 hours).
- Reimbursement Accuracy: Are all earned dollars captured through correct coding?
- Quality and Compliance: Is accuracy consistently at or above 95%?
High-performing AI tools require clean data. Healthcare systems often house data across silos, whether it is electronic health records (EHRs), laboratory information systems, radiology systems, pharmacy systems, or billing platforms. Poor quality or inconsistent data weakens automation outcomes.
Success with coding automation depends on how well the healthcare organization can:
- Centralize and normalize data inputs across platforms such as EHRs, diagnostic clinical data, and billing systems.
- Ensure clean, accurate clinical documentation to ensure structured, well-organized data.
- Maintain interoperability between coding, CDI, and billing systems.
Strategic Roadmap to Automation
Implementing medical coding automation isn’t a one-size-fits-all effort. Building a roadmap involves the following steps:
- Discovery: Map your current coding processes. Identify where inputs come from and where bottlenecks occur.
- Clean and Organize Data: Your systems must provide high-quality, integrated data.
- Pilot with Focus: Begin with a single specialty where documentation is structured and coding is repetitive. Analyze the results, refine the process, then scale.
- Adopt a Hybrid Mindset: Automate what’s simple and let your human experts focus on the more complex challenges.
- Measure: Use KPIs to validate progress. Focus on ROI and outcomes and move on if a solution is not delivering value.
The Future: Human-AI Collaboration at Scale
As AI accelerates, the potential for autonomous coding will expand, and so will the need for expert human oversight. The future isn’t about choosing between machines or people. It’s about designing intelligent systems that work in harmony to improve quality, speed, and reimbursement. Watch this video for more insights into medical coding , AI, and automation in healthcare.
Conrad Coopersmith
