Beyond the black box: Building confidence in autonomous coding

Healthcare leaders are under constant pressure to improve the revenue cycle operations within their facilities. With a retiring workforce, tighter budgets, and increasingly complex regulations, autonomous coding has arrived as an innovation that yields strong results. Yet, not all automation is created equal. 

A recent conversation with a health information management director made a lasting impression: “I don’t need a machine that just reads orders. I need technology that understands the patient’s story.” That distinction is critical. Many AI solutions act as “black boxes” — data goes in, results come out, but the reasoning remains unclear. This lack of transparency introduces risk. When audits occur, we must be able to defend coding decisions with confidence. True automation should be fast and reliable, but also transparent and auditable. 
 

The problem with context-free coding 

Many tools are designed to code from orders alone — prioritizing speed at the expense of context and detail. Every clinical document tells part of a patient’s unique story. When automation ignores the complexities of the full clinical encounter, there is a risk for compliance issues, revenue loss, and final codes that don’t reflect the full spectrum of care that was delivered. 
 

Moving from black box to transparent automation 

The future of autonomous coding calls for more than just artificial intelligence (AI). It calls for transparency, clinical expertise, and shared control. The most effective AI isn’t magic — it’s mentorship and knowing where to look for the data. By blending expertise from data scientists and seasoned coding analysts, we can teach technology not only the “how” but the “why” behind complex coding decision. This approach helps ensure autonomous coding matches the precision of expert humans, capturing the details and fully compliant results. Take the case of certain radiology coding. Effective autonomous coding solutions code not just from the order, but from the interpretation because that’s where the real story lies. 

Every health system has its own operational processes and coding guidelines. Rather than relying on black box solutions, organizations should be able to set their own confidence thresholds and automation criteria, automating straightforward cases and routing the more complex cases for expert human review.  
 

Empowering, not replacing, the coding workforce 

Concerns exist that automation might replace people. In reality, it can empower teams to focus on what matters most. When routine, high-volume visits are handled by automation, skilled coders have more time to concentrate on complex cases that truly need their expertise. This shift reduces burnout, improves job satisfaction, and ensures human talent is applied where it delivers the greatest value.  Additionally, there are increasing opportunities within revenue cycle including outpatient CDI, utilization management and revenue integrity, that many health systems are already starting to staff. 

The workforce shortage in healthcare won’t disappear overnight and regulatory demands will only continue to grow.  Keeping in mind that ICD-11 will be a factor in the coming years, thriving in this environment means adopting technologies that use AI technology coupled with human expertise, and that provide transparency from end to end. Leaders should embrace solutions that move beyond the black box — prioritizing speed, accuracy, compliance and consistency in every coding decision.

Brinton Frisby, autonomous coding business director at Solventum.