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Healthcare staff largely favor AI-driven patient flow systems in the postanesthesia care unit (PACU). These machine learning (ML) systems can improve efficiency and patient care, but require strong leadership and clear communication for successful adoption.

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Area of Science:

  • Healthcare technology adoption
  • Artificial intelligence in medicine
  • Patient flow management

Background:

  • Healthcare systems face resource limitations, necessitating innovative technological solutions.
  • The postanesthesia care unit (PACU) presents unique challenges for patient flow management.
  • Digital patient flow systems utilizing machine learning (ML) predictions offer potential improvements.

Purpose of the Study:

  • To investigate staff needs and expectations for an AI-based digital patient flow system in the PACU.
  • To explore the potential benefits and drawbacks of ML-driven patient flow management.
  • To assess the alignment of proposed system features with the technology acceptance model (TAM2).

Main Methods:

  • Qualitative study involving interviews with 20 healthcare professionals (nurse managers and staff).
  • Data analyzed using reflexive thematic analysis (familiarization, coding, theme generation).
  • Themes evaluated against the modified technology acceptance model (TAM2).

Main Results:

  • Staff anticipate ML systems will enhance PACU throughput, length of stay predictions, and patient flow overview.
  • Potential for increased patient interaction time due to improved efficiency.
  • Concerns identified regarding patient confidentiality and varied staff receptiveness to new technology.

Conclusions:

  • Respondents showed general favorability towards implementing the proposed ML system in the PACU.
  • Nurse managers play a critical role in patient workflow, safety, and successful digitization.
  • Effective leadership and communication strategies are crucial for system implementation.