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Optimising the paradigms of human AI collaborative clinical coding.

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This study introduces CliniCoCo, a human-in-the-loop framework for automated clinical coding (ACC). It enhances efficiency by integrating human coders with deep learning, reducing coding time and improving accuracy in electronic medical records.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Documentation Improvement

Background:

  • Manual clinical coding is time-consuming and prone to errors.
  • Automated clinical coding (ACC) offers a potential solution but requires human oversight for real-world application.
  • Integrating human expertise with ACC systems is crucial for maximizing efficiency and accuracy.

Purpose of the Study:

  • To propose and evaluate CliniCoCo, a novel human-in-the-loop (HITL) framework for effective collaboration between ACC systems and human coders.
  • To optimize annotation workloads and improve the efficiency of the coding process using deep learning.
  • To assess the impact of CliniCoCo on coding time, accuracy, and the performance of professional coders.

Main Methods:

  • Developed a human-in-the-loop (HITL) framework named CliniCoCo, leveraging deep learning for automated clinical coding (ACC).
  • Implemented collaborative strategies across annotation, training, and user interaction stages within the framework.
  • Conducted experiments using real-world electronic medical record (EMR) datasets from Chinese hospitals.

Main Results:

  • CliniCoCo achieved F1 scores of 0.80-0.84 with optimized annotation workloads.
  • The system demonstrated the ability to halve annotation requirements for EMRs with 30% mistaken codes, with a minimal 0.01 F1 decrease.
  • Human evaluations showed CliniCoCo reduced coding time by 40% and improved correction rates for EMR mistakes, including a threefold improvement in identifying missing codes.
  • Professional coders' performance improved significantly, with F1 scores boosted to over 0.93 from 0.72.

Conclusions:

  • The CliniCoCo framework effectively integrates automated clinical coding with human expertise, enhancing efficiency and accuracy in EMR processing.
  • HITL approaches, like CliniCoCo, are vital for optimizing ACC systems in practical healthcare settings.
  • CliniCoCo offers a significant advancement in clinical coding, reducing workload and improving data quality for better healthcare outcomes.