MedDCR: Learning to Design Agentic Workflows for Medical Coding

TL;DR

MedDCR introduces a learning-based framework for designing adaptable, interpretable workflows for medical coding, outperforming existing methods by capturing real-world variability and enhancing system reliability.

Contribution

MedDCR systematically learns workflow designs through a closed-loop process, integrating feedback and memory to improve medical coding automation.

Findings

Outperforms state-of-the-art baselines on benchmark datasets.

Produces interpretable and adaptable workflows.

Enhances reliability and trustworthiness of automated coding systems.

Abstract

Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems.