ArticleJAMIA open2023
Potential uses of AI for perioperative nursing handoffs: a qualitative study.
Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare-A Systematic Review of Qualitative Studies.Journal of advanced nursing · 2026Pooled it
- Reimagining nursing practice in the era of AI: a qualitative systematic review and meta-synthesis of nurses' lived experiences.Journal of health, population, and nutrition · 2026Pooled it
- Artificial Intelligence Anxiety and Patient Safety Attitudes Among Operating Room Professionals: A Descriptive Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2025Article
- Application of artificial intelligence in nursing practice: a qualitative study of Jordanian nurses' perspectives.BMC nursing · 2025Article
- Elevating surgical outcomes: a comprehensive analysis of innovative nursing practices in intraoperative care across diverse specialties.Frontiers in medicine · 2025Review
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Objective: Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context. Materials and Methods: Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing. Results: Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment. Discussion and Conclusion: Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.