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Nurses developed intuitive software for pain recognition, enabling accurate data labeling for machine learning models. This collaboration ensures technology meets clinical needs and builds trust for adoption in patient care.

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Area of Science:

  • Nursing Informatics
  • Machine Learning in Healthcare
  • Human-Computer Interaction

Background:

  • Nurses are crucial for acute pain management, especially in non-verbal patients.
  • Nurses' expertise is vital for developing machine learning (ML) models for pain detection.
  • A pain recognition automated monitoring system (PRAMS) requires nurse input for effective clinical decision support software (CDSS).

Purpose of the Study:

  • To create an intuitive and efficient data labeling software, Human-to-Artificial Intelligence (H2AI).
  • To facilitate nurse engagement in the development of ML-based healthcare tools.

Main Methods:

  • The Human-centered Design for Embedded Machine Learning Solutions (HCDe-MLS) model was employed.
  • Nurses utilized H2AI software for data labeling tasks.
  • Video pre-processing utilized OpenCV, and MobileFaceNet assisted with landmark placement.

Main Results:

  • H2AI software was developed, enabling 6 nurses to label 139 videos (3189 images).
  • The system tracked inter-rater reliability and stored labeled data for ML model training.
  • Average labeling time per image was 75 seconds.

Conclusions:

  • Nurse engagement in CDSS development is critical for user-centered design.
  • Involving nurses ensures technology aligns with clinical priorities and decision-making processes.
  • Active participation fosters trust and promotes technology adoption in nursing practice.