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Joshua Au Yeung1,2, Anthony Shek3, Thomas Searle3

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Summary
This summary is machine-generated.

This study introduces a clinical natural language processing (NLP) service in the UK

Keywords:
BioinformaticsElectronic health recordsLarge language modelsMachine learningNatural language processing

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Integrating machine learning into hospital settings is crucial for aligning models with clinical workflows and real-world data.
  • Digital transformation in non-healthcare sectors has seen the development of integrated data labeling and quality management services.

Purpose of the Study:

  • To describe the development and implementation of a novel clinical NLP service within the UK's National Health Service.
  • To detail the creation of clinical NLP resources and an implementation framework for distilling expert knowledge into NLP models.

Main Methods:

  • Development of parallel harmonized platforms for clinical NLP service implementation.
  • Distillation of expert clinical knowledge into NLP models.
  • Utilizing named entity recognition (NER) for clinical and operational use-cases.

Main Results:

  • Amassed over 26,086 annotations covering 556 SNOMED CT concepts from secondary care specialties.
  • Successfully delivered numerous clinical and operational use-cases through an integrated language modeling service.
  • Demonstrated efficiency improvements in healthcare delivery and enabled downstream data-driven technologies.

Conclusions:

  • The developed clinical NLP service offers a scalable solution for integrating AI into healthcare.
  • NLP services are poised to become an integral component of healthcare provider operations.
  • This work provides a foundational framework for future clinical NLP implementations.