Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation

• URL: http://arxiv.org/abs/2510.07629v1
• Date: Wed, 08 Oct 2025 23:50:58 GMT
• Title: Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation
• Authors: Zhangdie Yuan, Han-Chin Shing, Mitch Strong, Chaitanya Shivade,
• Abstract summary: We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods.<n>To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component.
• Score: 3.952186976672079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate clinical coding is essential for healthcare documentation, billing, and decision-making. While prior work shows that off-the-shelf LLMs struggle with this task, evaluations based on exact match metrics often overlook errors where predicted codes are hierarchically close but incorrect. Our analysis reveals that such hierarchical misalignments account for a substantial portion of LLM failures. We show that lightweight interventions, including prompt engineering and small-scale fine-tuning, can improve accuracy without the computational overhead of search-based methods. To address hierarchically near-miss errors, we introduce clinical code verification as both a standalone task and a pipeline component. To mitigate the limitations in existing datasets, such as incomplete evidence and inpatient bias in MIMIC, we release an expert double-annotated benchmark of outpatient clinical notes with ICD-10 codes. Our results highlight verification as an effective and reliable step toward improving LLM-based medical coding.

Related papers

• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• A Multi-faceted Analysis of Cognitive Abilities: Evaluating Prompt Methods with Large Language Models on the CONSORT Checklist [1.1731001328350983]
  This study applies a behavioral and metacognitive analytic approach using an expert-validated dataset.<n>We analyze both cognitive adaptation and calibration error using metrics: Expected Error (ECE) and a baseline-normalized Relative Error (RCE)<n>Our results reveal pronounced miscalibration and overconfidence in both models, especially under clinical role-playing conditions.
  arXiv  Detail & Related papers  (2025-10-22T00:15:02Z)
• From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations [45.414878840652115]
  Large language models (LLMs) have demonstrated promising performance on medical benchmarks.<n>However, their ability to perform medical calculations remains underexplored and poorly evaluated.<n>In this work, we revisit medical calculation evaluation with a stronger focus on clinical trustworthiness.
  arXiv  Detail & Related papers  (2025-09-20T09:10:26Z)
• Not What the Doctor Ordered: Surveying LLM-based De-identification and Quantifying Clinical Information Loss [1.514900191663287]
  De-identification in the healthcare setting is an application of NLP where automated algorithms are used to remove personally identifying information of patients (and, sometimes, providers)<n>With the recent rise of generative large language models (LLMs), there has been a corresponding rise in the number of papers that apply LLMs to de-identification.<n>This paper identifies three key limitations in the current literature: inconsistent reporting metrics direct comparisons, the inadequacy of traditional classification metrics in capturing errors, and lack of manual validation of automated metrics which aim to quantify these errors.
  arXiv  Detail & Related papers  (2025-09-17T22:37:15Z)
• CRG Score: A Distribution-Aware Clinical Metric for Radiology Report Generation [6.930435788495898]
  We propose the CRG Score, a metric that evaluates only clinically relevant abnormalities explicitly described in reference reports.<n>By balancing penalties based on label distribution, it enables fairer, more robust evaluation and serves as a clinically aligned reward function.
  arXiv  Detail & Related papers  (2025-05-22T17:02:28Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction [5.7931394318054155]
  We present our approach that achieved top performance in all three subtasks. For the MS dataset, which contains subtle errors, we developed a retrieval-based system. For the UW dataset, reflecting more realistic clinical notes, we created a pipeline of modules to detect, localize, and correct errors.
  arXiv  Detail & Related papers  (2024-04-22T19:31:45Z)
• Surpassing GPT-4 Medical Coding with a Two-Stage Approach [1.7014913888753238]
  GPT-4 LLM predicts an excessive number of ICD codes for medical coding tasks, leading to high recall but low precision. We introduce LLM-codex, a two-stage approach to predict ICD codes that first generates evidence proposals and then employs an LSTM-based verification stage. Our model is the only approach that simultaneously achieves state-of-the-art results in medical coding accuracy, accuracy on rare codes, and sentence-level evidence identification.
  arXiv  Detail & Related papers  (2023-11-22T23:35:13Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? [135.71855998537347]
  We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy. We show that this metric is unreliable, as even vacuous blind classifiers can achieve unrealistically high online accuracy. Existing OCL algorithms can also achieve high online accuracy, but perform poorly in retaining useful information.
  arXiv  Detail & Related papers  (2023-05-16T08:29:33Z)
• Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study [60.56194508762205]
  We reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. We present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models.
  arXiv  Detail & Related papers  (2023-04-21T11:54:44Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.