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Development of Optical Character Recognition-Based Quality Control Process of Paper-Based Consent Forms.

Juyoung Lee1, Meehee Lee1, Hye Young Nam1

  • 1Division of Biobank, National Biobank of Korea, National Institute of Health, Chungju Chungbuk-do, Korea.

Biopreservation and Biobanking
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Summary

This study developed an AI-powered optical character recognition (OCR) tool to automate the processing of paper-based consent forms, improving biobanking efficiency and data quality for consent management.

Keywords:
artificial intelligence (AI)handwriting recognitioninformed consentoptical character recognition (OCR)quality management system (QMS)

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Area of Science:

  • Biobanking and Data Management
  • Artificial Intelligence in Healthcare
  • Digital Transformation in Research

Background:

  • Efficient consent management is crucial for ethical and legal biobanking.
  • Traditional paper-based consent forms present challenges in legibility and scalability.
  • Automating the reading and quality control of consent forms is needed.

Purpose of the Study:

  • To automate the reading and quality control of paper-based consent forms.
  • To develop a reliable optical character recognition (OCR) model for handwritten Korean consent documents.
  • To enhance the digital transformation of traditional biobanking processes.

Main Methods:

  • Optimized a proprietary optical character recognition (OCR) model for handwritten Korean characters.
  • Generated and utilized synthetic standard and nonstandard consent documents for training.
  • Evaluated the model's performance on 3,790 pages of consent forms in routine biobanking.

Main Results:

  • The optimized OCR model achieved 88.94% accuracy on standard forms and 91.88% on nonstandard forms.
  • Applied to routine biobanking, the model demonstrated 91.25% accuracy and an F1-score of 0.91.
  • The model exhibited high performance and excellent generalization capabilities for consent data.

Conclusions:

  • Developed a highly efficient and reliable AI-based OCR tool for paper-based consent management.
  • This approach facilitates the digital transformation of traditional biobanking.
  • The optimized OCR model enhances the processing of consent documents in biobanks.