Clinical Decision‐Making Among Community Nurses: A Comparative Human and Generative AI ‐Assisted Qualitative Analysis

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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Publication Details

Journal
Nursing Open
Published
2026-11-01
DOI
https://doi.org/10.1002/nop2.71007
Primary Topic
Clinical Reasoning and Diagnostic Skills
Type
article
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article

Clinical Decision‐Making Among Community Nurses: A Comparative Human and Generative AI ‐Assisted Qualitative Analysis

Mor Saban, Chedva Levin, Dor Zafar
7 links
Nursing Open
Clinical Reasoning and Diagnostic Skills
article

Clinical Decision‐Making Among Community Nurses: A Comparative Human and Generative AI ‐Assisted Qualitative Analysis

Mor Saban, Chedva Levin, Dor Zafar
article en

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.

Nursing OpenVol. 13(11)
Tel Aviv University (IL), Jerusalem College of Technology (IL), Sheba Medical Center (IL)
Good health and well-being
Openalex Percentile: Top 8%
Clinical Reasoning and Diagnostic Skills
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