mlsanityData to 7 Oct 2026

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

arXiv 2412.18043 ↗First posted 23 Dec 2024cs.CL cs.AIcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 67 references that are in the map. First posted on arXiv 23 Dec 2024.

Builds on

21 of 67 references.

References · 21

  1. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 231
  2. 2019ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Networkcited by 73
  3. 2018Joint Embedding of Words and Labels for Text Classificationcited by 69
  4. 2022Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Codingcited by 52
  5. 2022PLM-ICD: Automatic ICD Coding with Pretrained Language Modelscited by 49
  6. 2023Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Studycited by 48
  7. 2020A Label Attention Model for ICD Coding from Clinical Textcited by 47
  8. 2022Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Codingcited by 40
  9. 2020BERT-XML: Large Scale Automated ICD Coding Using BERT Pretrainingcited by 37
  10. 2017Towards Automated ICD Coding Using Deep Learningcited by 35
  11. 2019Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classificationcited by 35
  12. 2021Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machinescited by 23
9 more
  1. 2023MDACE: MIMIC Documents Annotated with Code Evidencecited by 19
  2. 2020Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Datasetcited by 15
  3. 2016Condensed Memory Networks for Clinical Diagnostic Inferencingcited by 14
  4. 2021Description-based Label Attention Classifier for Explainable ICD-9 Classificationcited by 12
  5. 2021CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classificationcited by 9
  6. 2023A Two-Stage Decoder for Efficient ICD Codingcited by 8
  7. 2019Clinical Concept Extraction for Document-Level Codingcited by 7
  8. 2021Multitask Recalibrated Aggregation Network for Medical Code Predictioncited by 5
  9. 2023TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Codingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026From Documents to Spans: Scalable Supervision for Evidence-Based ICD Coding with LLMscited by 0
  2. 2026Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codescited by 0
  3. 2026Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scalecited by 0
  4. 2026Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferralcited by 0
  5. 2026A decision-basis contract for auditable LLM-assisted medical billing verification: deterministic rules, verbatim evidence, and fail-closed abstentioncited by 0