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How Can Transformer Models Shape Future Healthcare: A Qualitative Study.

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Transformer models offer potential for healthcare automation. Experts identified 8 key use cases, including documentation, decision support, and patient education, to enhance clinical outcomes.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Healthcare Informatics

Background:

  • Transformer models have advanced natural language processing and machine translation.
  • Emerging models like GPT-3 show potential for automating complex tasks.
  • Healthcare faces time-consuming tasks that could benefit from AI automation to improve clinical outcomes.

Purpose of the Study:

  • To summarize potential future use cases of transformer models in healthcare.
  • To gather expert opinions on the application of transformer models in clinical settings.

Main Methods:

  • A survey was conducted among healthcare experts.
  • Expert responses were analyzed using an adapted thematic analysis.
  • 28 expert responses were collected and analyzed.

Main Results:

  • Eight distinct categories of use cases for transformer models in healthcare were identified.
  • Identified use cases include: documentation and clinical coding, workflow and healthcare services, decision support, knowledge management, interaction support, patient education, health management, and public health monitoring.

Conclusions:

  • Transformer models present significant opportunities for innovation in healthcare.
  • Further research and development are needed to test and implement these identified use cases.
  • The application of transformer models can potentially improve clinical outcomes and healthcare efficiency.