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The study explains data-centric artificial intelligence (AI) for nurses, shifting from model-centric AI. This approach enhances nurse involvement in designing AI technologies and managing electronic health records data.

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Area of Science:

  • Nursing Informatics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Electronic health records generate vast healthcare data, offering opportunities for AI and machine learning (ML).
  • While AI/ML applications in nursing are studied, the shift towards data-centric AI is not widely understood in the discipline.
  • Most ML implementations traditionally use a model-centric strategy.

Purpose of the Study:

  • To elucidate the data-centric machine learning (ML) concept for nursing.
  • To differentiate between data-centric and model-centric ML approaches.
  • To highlight the advantages of data-centric ML for nurses' engagement in technology design and data usage.

Main Methods:

  • Utilized the Norris Concept Clarification method.
  • Explored the origins of data-centric ML in data and computer science.
  • Differentiated data-centric vs. model-centric ML, including the ML operation life cycle.
  • Explained the benefits of data-centric ML for nursing.

Main Results:

  • Identified a fundamental industry pivot from model-centric to data-centric AI.
  • Clarified the differences between the two ML approaches.
  • Highlighted the importance of nurses' engagement in AI technology design and electronic health record data management.

Conclusions:

  • Nurses need to understand data-centric AI due to its growing importance.
  • This approach empowers nurses in technological design and proper data utilization.
  • The shift to data-centric AI offers significant advantages for the nursing discipline.