Clinical Decision-Making Among Community Nurses: A Comparative Human and Generative AI-Assisted Qualitative Analysis.
5 affiliations
- 1Faculty of School of Life and Health Sciences, Nursing Department, The Jerusalem College of Technology-Lev Academic Center, Jerusalem, Israel.
- 2The Department of Vascular Surgery, The Chaim Sheba Medical Center, Ramat Gan, Tel Aviv, Israel.
- 3Faculty of Medical & Health Sciences, The Maurice and Gabriela Goldschleger School of Dental Medicine, Tel Aviv University, Israel.
- 4Department of Nursing, School of Health Professions, Gray Faculty of Medical & Health Sciences, Tel Aviv University.
- 5The Gertner Institute for Epidemiology and Health Policy Research, The Chaim Sheba Medical Center, Ramat Gan, Tel Aviv, Israel.
Abstract
Aim
To explore how community nurses describe clinical decision-making across simulated cases and to examine how themes and quotations from human thematic analysis compare with those from generative artificial intelligence-assisted analysis.Design
Qualitative descriptive study using simulated clinical cases and semi-structured debriefing interviews.Methods
Between May and July 2024, 68 academic nurses with at least 1 year of community nursing experience were recruited in Israel. Each nurse responded to four simulated cases and a debriefing interview. Three researchers analysed transcripts thematically. Separately, Claude Projects (Claude 3.5 Sonnet) analysed the same de-identified Hebrew corpus without access to the human themes. The researchers then compared corresponding themes and illustrative quotations.Results
Six themes described nurses' decision-making: perceived control over treatment management; challenges in responding to scenarios; consultation with professional colleagues; use of non-human support resources; training and experience and promotion of patient health and well-being. Both analyses identified corresponding themes and sometimes selected identical or related quotations. The comparison was not a test of agreement, reliability or validity.Conclusion
Community nurses described decision-making as a context-dependent process shaped by experience, perceived control, consultation, protocols and a holistic orientation to health promotion. Generative artificial intelligence may assist in organizing qualitative material but requires transparent procedures and human oversight. The study does not establish that AI can reproduce or substitute for human qualitative interpretation.Implications for the profession and/or patient care
Generative AI tools must remain subordinate to professional nursing judgement in community practice; AI-assisted qualitative analysis requires transparent, human-verified procedures.Impact
The findings characterize nurses' accounts of decision-making in simulated community cases. The study did not evaluate clinical effectiveness, implementation or outcomes.Reporting method
The study is reported in accordance with the Consolidated Criteria for Reporting Qualitative Research.Patient or public contribution
No patient or public contribution. Community nurses participated as research participants.
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BibTeX
@article{levin2026clinical,
title = {{Clinical Decision-Making Among Community Nurses: A Comparative Human and Generative AI-Assisted Qualitative Analysis.}},
author = {Chedva Levin and Dor Zafar and Mor Saban},
journal = {Nursing open},
year = {2026},
month = nov,
doi = {10.1002/nop2.71007},
url = {https://europepmc.org/article/MED/42967829},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Clinical Decision-Making Among Community Nurses: A Comparative Human and Generative AI-Assisted Qualitative Analysis.
AU - Chedva Levin
AU - Dor Zafar
AU - Mor Saban
PY - 2026
DA - 2026/11/01
JO - Nursing open
DO - 10.1002/nop2.71007
UR - https://europepmc.org/article/MED/42967829
AB - Aim To explore how community nurses describe clinical decision-making across simulated cases and to examine how themes and quotations from human thematic analysis compare with those from generative artificial intelligence-assisted analysis.Design Qualitative descriptive study using simulated clinical cases and semi-structured debriefing interviews.Methods Between May and July 2024, 68 academic nurses with at least 1 year of community nursing experience were recruited in Israel. Each nurse responded to four simulated cases and a debriefing interview. Three researchers analysed transcripts thematically. Separately, Claude Projects (Claude 3.5 Sonnet) analysed the same de-identified Hebrew corpus without access to the human themes. The researchers then compared corresponding themes and illustrative quotations.Results Six themes described nurses' decision-making: perceived control over treatment management; challenges in responding to scenarios; consultation with professional colleagues; use of non-human support resources; training and experience and promotion of patient health and well-being. Both analyses identified corresponding themes and sometimes selected identical or related quotations. The comparison was not a test of agreement, reliability or validity.Conclusion Community nurses described decision-making as a context-dependent process shaped by experience, perceived control, consultation, protocols and a holistic orientation to health promotion. Generative artificial intelligence may assist in organizing qualitative material but requires transparent procedures and human oversight. The study does not establish that AI can reproduce or substitute for human qualitative interpretation.Implications for the profession and/or patient care Generative AI tools must remain subordinate to professional nursing judgement in community practice; AI-assisted qualitative analysis requires transparent, human-verified procedures.Impact The findings characterize nurses' accounts of decision-making in simulated community cases. The study did not evaluate clinical effectiveness, implementation or outcomes.Reporting method The study is reported in accordance with the Consolidated Criteria for Reporting Qualitative Research.Patient or public contribution No patient or public contribution. Community nurses participated as research participants.
SN - 2054-1058
ER -