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Automated clinical coding: what, why, and where we are?

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Automated clinical coding aims to enhance efficiency and accuracy in healthcare data management. Integrating knowledge-based methods with deep learning is crucial for explainability and consistency in AI-driven coding systems.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Linguistics

Background:

  • Clinical coding transforms patient health records into structured data for statistical analysis.
  • This process is cognitive, time-consuming, and requires high consistency.
  • Automation offers potential improvements in efficiency and accuracy.

Purpose of the Study:

  • To introduce automated clinical coding and explore its challenges.
  • To identify gaps between current deep learning approaches and real-world needs for explainability and consistency.
  • To discuss the integration of knowledge-based methods into AI for clinical coding.

Main Methods:

  • Literature review on Artificial Intelligence (AI) and Natural Language Processing (NLP) for clinical coding.
  • Analysis of project experience from late 2019 to early 2022.
  • Discussions with clinical coding experts in Scotland and the UK.

Main Results:

  • Current deep learning methods for clinical coding lack explainability and consistency.
  • Knowledge-based methods are needed to complement deep learning for real-world application.
  • Technical and organizational challenges hinder the development and deployment of AI-based automated coding systems.

Conclusions:

  • Automated clinical coding is a promising but challenging AI application.
  • Incorporating explainable, knowledge-based reasoning into AI is essential for clinical coding.
  • Collaboration with clinical coders is vital for developing and deploying effective AI systems in the next five years and beyond.