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Potential uses of AI for perioperative nursing handoffs: a qualitative study
Christopher Ryan King1, Ayanna Shambe1,2, Joanna Abraham1,3
1Department of Anesthesiology, Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.
JAMIA Open
|March 20, 2023
Summary
Artificial intelligence (AI) can improve postoperative nursing handoffs by identifying patient risks and enhancing situational awareness. AI tools may overcome communication barriers, but user experience and information overload are potential challenges.
Area of Science:
- Nursing Informatics
- Artificial Intelligence in Healthcare
- Patient Safety
Background:
- Effective nursing handoff and situational awareness are crucial for postoperative patient safety.
- Current research lacks definition on artificial intelligence (AI) roles in supporting these nursing functions.
- Understanding AI's potential in nursing handoff and patient assessment is vital.
Purpose of the Study:
- To explore how artificial intelligence (AI) can support nurses during postoperative patient handoff and assessment.
- To identify potential roles and barriers for AI implementation in nursing communication.
- To gather nurse perspectives on AI integration in clinical workflows.
Main Methods:
- Semistructured interviews were conducted with eleven nurses.
- A mixed inductive-deductive thematic analysis was employed to identify key themes.
- The analysis focused on AI's potential roles in postoperative nursing.
Main Results:
- Five major themes emerged regarding AI's role: guiding care decisions, addressing handoff barriers, augmenting decision-making, and potential user-experience challenges.
- AI could identify elevated patient risks, triggering re-evaluation and discussion.
- AI-identified data may enhance nursing assessments and team communication.
Conclusions:
- AI tools, if well-designed, can improve postoperative nursing handoff communication by highlighting patient-specific risks.
- AI has the potential to enhance nurse situational awareness and patient care decision-making.
- Barriers such as electronic health record usability and information overload need consideration for successful AI adoption.
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