Aug 2026· Nursing Open· Vol 13· 0 citations· 20 references
Medicine
TL;DR
While artificial intelligence produced more comprehensive information, it generated clinically unsafe content and scored significantly lower than nurses in empathy and readability, and healthcare organizations should establish protocols requiring nurse verification of all artificial intelligence-generated discharge content.
Abstract
ABSTRACT Aim To compare the multidimensional performance of discharge instructions generated by generative AI (GPT‐4) versus those created by clinical registered nurses across three dimensions—accuracy, empathy and readability—and to explore the impact of patient. Design A prospective, double‐blind, vignette‐based cross‐sectional study. Methods Five standardized multidisciplinary discharge scenarios were constructed. Discharge instructions were generated independently by five registered nurses and GPT‐4. Fifteen clinical experts conducted blinded assessments of accuracy, while 38 patients conducted blinded assessments of empathy and readability. Objective text features were extracted using natural language processing. Paired t‐tests or Wilcoxon signed‐rank tests were used to compare differences between groups, and a generalized linear mixed model was constructed to analyse factors influencing the acceptability of AI‐generated text. Results AI outperformed nurses in information comprehensiveness, but experts identified safety risks in AI‐generated texts, whereas no such issues were found in nurse‐produced texts. Nurses significantly outperformed AI in both empathy and readability, and objective NLP analysis confirmed that AI‐generated texts exhibited higher syntactic complexity and terminology density. The generalized linear mixed model indicated that advancing age and lower educational attainment were associated with reduced acceptance of AI‐generated texts. These findings derive from standardized vignettes under controlled experimental conditions and require further validation in real clinical settings. Conclusion Generative AI offers value as a drafting aid for ensuring information completeness in discharge instructions; however, its safety risks, empathy deficits, and linguistic complexity currently limit its standalone application. AI may be better positioned as an assistive tool for nurses rather than an independent communication tool, and its deployment should address potential digital divide issues among patient populations. Implications for the Profession and/or Patient Care These findings support positioning artificial intelligence as an information completeness tool requiring mandatory nurse review before patient delivery. Nurses remain essential for ensuring clinical safety, providing empathetic communication, and adapting language complexity to individual patient needs. Healthcare organizations should establish protocols requiring nurse verification of all artificial intelligence‐generated discharge content, with particular attention to medication dosages and contraindications. The identification of a digital divide necessitates that deployment strategies include health literacy assessment and tiered delivery approaches to prevent technological advances from exacerbating health communication inequalities among vulnerable populations. Impact What problem did the study address? ○ Generative artificial intelligence tools are increasingly proposed for clinical documentation, yet limited evidence exists comparing their discharge instruction quality against registered nurses—the professionals primarily responsible for discharge education—across multiple dimensions relevant to patient safety and comprehension. What were the main findings? ○ While artificial intelligence produced more comprehensive information, it generated clinically unsafe content and scored significantly lower than nurses in empathy and readability. Older and less‐educated patients showed reduced acceptance of artificial intelligence‐generated text, indicating a potential digital divide. Where and on whom will the research have an impact? ○ These findings inform nursing practice, healthcare informatics policy, and equitable care delivery globally, particularly regarding the safe integration of artificial intelligence tools into nurse‐led discharge education workflows for diverse patient populations. Reporting Method This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and incorporated principles from the Decision Support Systems Evaluation Guideline for Artificial Intelligence in Healthcare framework. No Patient or Public Contribution Patients and the public were not involved in the design, conduct, reporting or dissemination of this research. Trial and Protocol Registration This study is an observational cross‐sectional study and does not require clinical trial registration.
ABSTRACT Aim This qualitative study aimed to explore how nurses reason ethically about potential digital ethical risks arising from the routine use of digital systems in clinical practice and the strategies they propose to address these risks in response to vignette‐based scenarios. Background Digital systems shape nursing documentation, monitoring, communication, education, and decision‐making. Although they support care, digital ethical risks may arise when digital outputs are accepted without clinical verification, patients are not adequately informed, or data protection is weak. Design A vignette‐elicited qualitative design was used. Methods The study was conducted in September 2025 with 16 nurses working in three public hospitals in Central Anatolia, Türkiye. Data were generated through face‐to‐face interviews using seven vignette‐based scenarios and analyzed using Braun and Clarke's reflexive thematic analysis. Results Three main themes were generated through the analysis of nurses’ ethical reasoning in response to the vignette‐based scenarios: (i) nurses’ purposes for using digital systems, (ii) digital ethical risks in nursing, and (iii) strategies to prevent digital ethical risks. The findings within Themes 2 and 3 were organized into four subthemes—nonmaleficence and beneficence, autonomy and respect for persons, justice and equity, privacy and confidentiality—and further structured across three researcher‐developed categories: nurse, digital system, and organization. Conclusions Participants’ interpretations of the vignette‐based scenarios highlighted potential digital ethical risks that may require nurses to combine clinical judgment with ethical reasoning when using digital systems. Implications for Nursing Participants’ responses may inform digital ethics education by highlighting the importance of verifying digital data through bedside assessment, questioning potentially unsafe digital outputs, protecting confidentiality, obtaining meaningful consent, and recognizing potential risks in documentation, alarms, communication, and data sharing. Implications for Health Policy Participant‐identified priorities may inform institutional consideration of protocols for documentation, alarm management, mobile communication, image sharing, access control, patient information, accessibility, and accountability in digital nursing care.
This study examined empathy and compassion fatigue among staff nurses in selected public hospitals in Iloilo, Philippines, as a basis for an enhanced workplace action plan. A quantitative descriptive-correlational research design was employed involving staff nurses assigned to wards, emergency rooms, delivery rooms, and operating rooms. Standardized instruments, including the Toronto Empathy Questionnaire (TEQ), Jefferson Scale of Empathy (JSE), and the Professional Quality of Life Scale (ProQOL-V), were used to collect data. Data were analyzed using descriptive statistics, an independent samples t-test, one-way ANOVA, and Pearson’s r at a 0.05 level of significance. Findings revealed that the nursing workforce was predominantly female, early- to mid-career, and mostly assigned to high-demand clinical areas with standard 8-hour shifts. Overall, nurses demonstrated a high level of empathy, with very high cognitive empathy and compassionate care, while affective empathy was moderate. This suggests strong cognitive understanding of patients’ experiences and consistent delivery of compassionate care, accompanied by a balanced level of emotional engagement. In terms of compassion fatigue, nurses generally experienced low levels of burnout and secondary traumatic stress, indicating effective coping mechanisms and psychological resilience despite demanding work conditions. However, a considerable proportion of respondents reported moderate levels on both dimensions, suggesting ongoing occupational emotional strain that warrants attention. Correlation analysis showed that cognitive empathy and compassionate care were significantly and negatively associated with burnout and secondary traumatic stress, indicating that these dimensions function as protective factors against compassion fatigue. In contrast, affective empathy showed no significant relationship with either burnout or secondary traumatic stress. Comparative analyses revealed that empathy and compassion fatigue were generally not significantly influenced by sex, shift schedule, years of experience, or area of assignment. Age had a partial influence on affective empathy and compassionate care, while burnout varied with years of experience and area of assignment. Secondary traumatic stress remained consistent across all groups. The study concludes that nurses exhibit high empathy and low compassion fatigue, with outcomes largely shaped by professional and organizational factors rather than demographic characteristics. These findings inform the development of a targeted workplace action plan to strengthen cognitive empathy, sustain compassionate care, and mitigate burnout through organizational and psychosocial support interventions.
Unknown authors· International Journal of Nur...· 0 citations
OBJECTIVE
To evaluate healthcare professionals' attitudes, knowledge and clinical preparedness in caring for lesbian, gay, bisexual, transgender, queer/questioning; the + denoting inclusion of all identities (LGBTQ+) children and young people across international healthcare settings.
DESIGN
Mixed-methods study incorporating quantitative self-assessment of clinical competency using the LGBT-Development of Clinical Skills Scale (LGBT-DOCSS) and qualitative free-text responses exploring educational needs and perceived barriers to providing inclusive care, hosted on the Don't Forget The Bubbles platform.
PARTICIPANTS
771 healthcare professionals; 770 included after removal of one duplicate.
MAIN OUTCOME MEASURES
Self-reported attitudes, knowledge and clinical preparedness using LGBT-DOCSS composite scores and qualitative coding of free-text responses examining contextual drivers of clinicians' confidence and practice.
RESULTS
Respondents were largely experienced clinicians (66% with ≥10 years in healthcare). Mean total LGBT-DOCSS Score was 5.42/7 (SD 0.80). Attitudinal scores were high (mean 6.54, SD 0.88), while basic knowledge (5.40, SD 1.26) and clinical preparedness in particular (4.31, SD 1.29) were lower. Education-related preparedness items scored the lowest, particularly regarding transgender and gender-diverse care (2.92, SD 1.69). Qualitative analysis identified two themes: barriers to inclusive care, which encompassed structural constraints, cultural norms and emotional or psychological barriers; and desire for competence, which encompassed recognition of knowledge gaps, need for transgender-specific training and desire for practical, lived-experience-informed education.
CONCLUSIONS
Healthcare professionals report positive attitudes but limited knowledge and preparedness. Clinicians are motivated to provide inclusive care but system-level support, structured education and skills-based training are required to translate intent into competent, inclusive clinical practice.
Rebecca Singer, Tamsin Gannon, Agnes Higgins et al.· Archives of Disease in Child...· 0 citations
ABSTRACT Background and Aims Shared decision‐making (SDM) is increasingly recognized as a core element of patient‐centered dental care, promoting collaboration between clinicians and patients to ensure that treatment decisions reflect both clinical evidence and individual preferences. However, few patient decision aids (PDAs) tailored to dental contexts have been systematically developed or evaluated. This study aimed to develop a dental‐specific PDA and explore patients’ perceptions of SDM following PDA‐assisted consultations. Methods A mixed‐methods design was employed in Taiwan, combining a quantitative survey (n = 46) using the 9‐item Shared Decision‐Making Questionnaire (SDM‐Q‐9) and qualitative focus group interviews (n = 11). Quantitative analyses included t‐tests and ANOVA to examine demographic and treatment‐related variations, while qualitative data were analyzed using thematic analysis with open, axial, and selective coding procedures derived from grounded theory approaches. Results Patients reported high overall SDM‐Q‐9 scores (M = 5.88, SD = 0.27; however, this mean approaches the scale maximum of 6.0, suggesting a potential ceiling effect that may have limited score discrimination). Significant variations were observed by gender (p < 0.05), marital status (p < 0.001), and treatment type (p < 0.05), suggesting that demographic and clinical contexts were associated with differences in perceived SDM. Focus group findings identified three modes of participation: tool‐driven, passive acceptance, and integrative reflective participation. Patients emphasized the importance of clear communication, supportive provider interaction, and contextual readiness in shaping their decision‐making experiences. Conclusion Patients reported high levels of perceived SDM following PDA‐assisted consultations, and qualitative findings suggested that the PDA was viewed positively as a tool to support communication and treatment discussions. The study contributes theoretically by extending transformative learning theory into dental decision‐making, practically by providing tailored strategies for PDA deployment, and at the policy level by highlighting dentistry as a favorable setting for SDM implementation due to its multi‐visit treatment structure.
Ching-Wen Liu, Hsiao-Hui Jung· Health Science Reports· 0 citations
AIM
To provide an early-stage integrative synthesis of shared and scenario-specific ethical risks of Conversational Artificial Intelligence in nursing triage and patient education, and to synthesize governance directions and limitations discussed in the current literature.
DESIGN
A systematic integrative review following the Whittemore-Knafl framework and PRISMA guidelines.
METHODS
Eight databases were searched. Two researchers independently conducted screening, data extraction, and thematic coding, followed by inductive synthesis. Quality appraisal used design-appropriate tools according to article type. Ethical risks were analysed within and across scenarios. Registered in PROSPERO (CRD420251079144).
RESULTS
Nine articles were included (four on nursing triage; five on patient education), comprising two empirical studies, four reviews, two randomized controlled trial protocols, and one debate paper. Both scenarios shared six common ethical challenges: data privacy and security, over-reliance and deskilling, training-data bias and stigma reproduction, lack of empathy and emotional interaction capability, algorithmic black box and insufficient interpretability, and blurred accountability and regulatory gaps. Nursing triage presented additional risks including assessment inaccuracy, contextual misunderstanding, lack of personalization, superficially plausible misguidance and insufficient clinician trust. Patient education revealed four distinct issues: misleading information, digital accessibility gaps, cross-cultural and multilingual adaptation, and fairness and health inequality. Six shared governance directions were synthesized-human oversight and manual review, improvement of legal policies and industry standards, enhanced transparency and interpretability, continuous algorithm optimization and scientific validation, the human-machine balance principle, and capacity building for healthcare professionals. The literature also suggested scenario-specific reinforcements for triage and education.
CONCLUSION
Ethical risks of Conversational Artificial Intelligence in nursing show both common and scenario-dependent patterns. Given the limited and heterogeneous evidence base, the identified governance directions should be viewed as preliminary pathways requiring further validation.
IMPACT
This review offers evidence-informed ethical insights and scenario-based governance references to support safe, equitable and human-centred application of Conversational Artificial Intelligence in nursing practice.
PATIENT OR PUBLIC CONTRIBUTION
Not applicable.
Yucheng Cao, Yang Tang, Lili Deng et al.· Journal of Advanced Nursing· 0 citations
Background: Empathy is essential for supportive nursing work environments, yet little is known about Generation Z (Gen Z) nurses’ experiences of empathy from colleagues and nurse managers. Aim: To examine Gen Z nurses’ experiences of empathy in workplace relationships with colleagues and nurse managers and the individual, generational, and institutional factors shaping these experiences. Methods: A qualitative descriptive study design was employed. Data were collected between October 2025 and March 2026 through semi-structured, in-depth interviews with 20 Gen Z nurses working in diverse clinical settings in Turkey. Participants were recruited using purposive and snowball sampling. Interviews were conducted via Zoom, audio-recorded with consent, and transcribed verbatim. Data were analyzed using Braun and Clarke’s reflexive thematic analysis framework. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist guided the study reporting. Results: Four main themes were identified: (1) perceptions of empathy among Gen Z nurses, (2) empathy experiences in workplace relationships, (3) factors influencing empathy experiences, and (4) expectations for building an empathic work environment. Empathy was experienced through feeling understood, non-judgmental listening, and emotional and practical support, particularly in peer relationships. Hierarchical communication, perceived intergenerational differences, workload, and time pressure were key factors influencing workplace empathy experiences. Conclusions: Participants described empathy as a relational and context-dependent experience embedded in everyday workplace interactions. Their accounts suggest that supportive, non-hierarchical relationships may facilitate empathic engagement, whereas rigid communication styles and heavy workloads may act as barriers. These findings represent the experiences and interpretations of the Gen Z nurses interviewed and should not be read as objective comparisons between generations. Organizational strategies that support empathic communication and psychologically safe teamwork may help strengthen team cohesion and nurse retention.
Aytolan Yıldırım, A. Aydın, Merve Ertunç Soycan et al.· Healthcare· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.