How Access Healthcare combined automation coding technology with certified coding expertise to improve accuracy, efficiency, and scale
When an autonomous coding platform could no longer meet the needs of a growing multi-site organization, Access Healthcare stepped in with a different approach. Working with senior leadership, Access migrated affected care centers to a human-in-the-loop autonomous coding model that combines technology with certified coding expertise.
The transition addressed gaps in the existing workflow while creating a more scalable approach to coding accuracy and throughput. Work throughput improved 50%. Scrubs and denials decreased 78% overall, while coding-specific denials fell 82%.
The Challenge
The organization had adopted autonomous coding technology to reduce manual errors, accelerate coding, and create greater consistency across multiple care centers. As the technology encountered real-world clinical workflows, two limitations emerged.
First, script-driven logic could not consistently accommodate variations in provider documentation or patient dynamics, leading to missed CPT codes and other capture opportunities. Second, the platform only processed encounter notes closed and signed on the same day. When providers completed documentation the following morning, those records fell outside the workflow and remained untouched.
The organization needed a coding model that could preserve the speed and consistency of automation while adapting to provider behavior, payer requirements, and the complexities of clinical documentation.
The Solution
Access Healthcare worked with the organization’s senior leadership to migrate the affected care centers to its autonomous coding platform. Rather than asking people and workflows to conform to the technology, Access designed the model around the expertise of the coders doing the work.
An existing integration with the practice management platform supported a clean cutover on a set calendar date, with no parallel-run period and no backlog left behind. From day one, the platform pulled signed records electronically regardless of date of service or date signed, immediately addressing the previous same-day limitation.
Each record routes through the autonomous coding engine before reaching a certified coder with proposed codes attached. When a coder modifies a recommendation, the coder documents why. When a recommendation is accepted, the coder validates that the engine interpreted the clinical documentation correctly. In both cases, the feedback is captured to continuously improve the model.
A rules engine applies customer-specific business rules by provider, location, and payer, layered on top of standard clean coding rules, including Correct Coding Initiative edits and payer-specific requirements. Instead of requiring coders to remember an expanding set of rules across locations and payers, the technology applies them before a coder opens the record.
The result is a model in which technology handles repeatable complexity while coders focus their expertise where it adds the most value: interpreting the clinical documentation and confirming coding accuracy.
The Results
The migration produced measurable improvements across the coding operation. Work throughput improved 50%. Scrubs and denials decreased 78% overall, and coding-specific denials fell 82%.
The model remains human-in-the-loop by design. Coders still review the work, but their role has shifted. Rather than tracking which payer requires what at each location, they can focus their attention on the clinical note, their specialty knowledge, and whether the proposed codes accurately reflect the care delivered.
Reducing the number of business rules coders must carry from memory also reduces opportunities for rules to be missed during review. At the same time, coder decisions continue feeding the autonomous coding engine, allowing the technology to improve as the model operates.
The Bottom Line
Autonomous coding delivers greater value when technology and coding expertise work as one operating model. For this organization, the transition meant addressing immediate workflow gaps while building a foundation capable of supporting greater volume, consistency, and accuracy.
Access Healthcare combined autonomous coding technology, customer-specific rules, and certified coding expertise to create a model built around the realities of clinical workflows. The result was higher throughput, fewer denials, and a coding operation positioned to scale as the organization grows.