# Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

arXiv: https://arxiv.org/abs/2412.18043 · HTML: https://mlsanity.com/paper/2412.18043/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 21 of 21)

- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding](https://mlsanity.com/paper/2203.01515/) (2022)
- [PLM-ICD: Automatic ICD Coding with Pretrained Language Models](https://mlsanity.com/paper/2207.05289/) (2022)
- [Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://mlsanity.com/paper/2304.10909/) (2023)
- [A Label Attention Model for ICD Coding from Clinical Text](https://mlsanity.com/paper/2007.06351/) (2020)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining](https://mlsanity.com/paper/2006.03685/) (2020)
- [Towards Automated ICD Coding Using Deep Learning](https://mlsanity.com/paper/1711.04075/) (2017)
- [Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification](https://mlsanity.com/paper/1909.04176/) (2019)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [MDACE: MIMIC Documents Annotated with Code Evidence](https://mlsanity.com/paper/2307.03859/) (2023)
- [Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset](https://mlsanity.com/paper/2006.07332/) (2020)
- [Condensed Memory Networks for Clinical Diagnostic Inferencing](https://mlsanity.com/paper/1612.01848/) (2016)
- [Description-based Label Attention Classifier for Explainable ICD-9 Classification](https://mlsanity.com/paper/2109.12026/) (2021)
- [CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification](https://mlsanity.com/paper/2109.04853/) (2021)
- [A Two-Stage Decoder for Efficient ICD Coding](https://mlsanity.com/paper/2306.00005/) (2023)
- [Clinical Concept Extraction for Document-Level Coding](https://mlsanity.com/paper/1906.03380/) (2019)
- [TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding](https://mlsanity.com/paper/2305.18576/) (2023)
- [Multitask Recalibrated Aggregation Network for Medical Code Prediction](https://mlsanity.com/paper/2104.00952/) (2021)

## Built on by (most cited first, 5 of 5)

- [From Documents to Spans: Span-Centric Learning for LLM-based Explainable Medical Coding](https://mlsanity.com/paper/2603.15270/) (2026)
- [Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes](https://mlsanity.com/paper/2605.17755/) (2026)
- [Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://mlsanity.com/paper/2605.17775/) (2026)
- [Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral](https://mlsanity.com/paper/2605.31512/) (2026)
- [A decision-basis contract for auditable LLM-assisted medical billing verification: deterministic rules, verbatim evidence, and fail-closed abstention](https://mlsanity.com/paper/2609.12156/) (2026)

## Abstract (arXiv)

Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical contexts. For example, evaluations that focus on the top 50 most common codes are an oversimplification, as there are thousands of codes used in practice. This position paper aims to align AI coding research more closely with practical challenges of clinical coding. Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods. Additionally, we propose new AI-based methods beyond automated coding, suggesting alternative approaches to assist clinical coders in their workflows.