Hui Duan

Background: Generative AI chat systems are increasingly used for everyday information seeking, but plausible errors and omissions can mislead users when outputs are accepted without scrutiny. Interface-level safety cues may help users calibrate trust and engage in verification; yet, evidence in older Chinese adults remains limited. Objective: This study aimed to test whether adding a safety user interface (UI) bundle to a generative AI chat interface increases verification intention among older Chinese adults and to examine selected secondary outcomes, including reliance intention, trust calibration, perceived trustworthiness, comprehension, usability/readability, cognitive load, and a behavioral proxy of verification. Methods: We conducted a cross-sectional survey with an embedded randomized UI vignette experiment between May 22, 2025, and September 3, 2025. Chinese adults aged ≥60 years were recruited through community sites, outpatient clinic waiting areas, and WeChat (Tencent Holdings Ltd) groups, and randomized 1:1 to view screenshots of a baseline chat UI or a safety UI bundle containing generic source-label cues, and an uncertainty and verification nudge. Each participant completed 2 scenarios (service/travel decision and general well-being related to sleep/fatigue), followed by measures of verification intention (primary), reliance intention, trust calibration index, comprehension (0‐8), perceived trustworthiness, usability/readability, cognitive load (0‐10), manipulation checks, and a behavioral proxy (expanding optional “source information”). Analyses used intention-to-treat regression models with covariate adjustment. Results: Of 214 consenting respondents who started the survey, 200 were included in the analysis (100 per arm). The safety UI bundle increased verification intention (mean 4.72, SD 0.63 vs 4.41, SD 0.59 on a 7-point scale; adjusted β=0.293, 95% CI 0.128-0.457; P <.001). Reliance intention did not increase (mean 4.97, SD 0.54 vs 5.03, SD 0.58; adjusted β=−0.105, 95% CI −0.239 to 0.029; P =.13). Trust calibration improved (trust calibration index: mean −0.29, SD 1.43 vs 0.29, SD 1.43; adjusted β=−0.567, 95% CI −1.005 to −0.129; P=.01). Expansion of optional source information was numerically higher, although the adjusted CI included the null (42% vs 27%; adjusted odds ratio [OR]=1.76, 95% CI 0.95-3.27; P =.07). Comprehension remained high and similar across arms (mean 6.33, SD 1.14 vs 6.32, SD 1.08; adjusted β=−0.132, 95% CI −0.428 to 0.163; P =.38). Perceived trustworthiness was modestly lower in the Safety UI arm (mean 5.20, SD 0.61 vs 5.39, SD 0.66; adjusted β=−0.199, 95% CI −0.382 to −0.016; P =.03). Usability/readability was unchanged, and cognitive load did not increase. Manipulation checks indicated higher cue recognition in the Safety UI arm. Conclusions: In a randomized static-vignette survey of older Chinese adults, a brief safety UI bundle was associated with higher verification intention and a trust calibration index consistent with lower overreliance risk, without detectable reductions in comprehension or usability/readability. Because the intervention was tested as a bundle using screenshots and generic source labels, findings should be interpreted as evidence for a practical interface-level strategy rather than proof that any single cue caused the observed effects.

Medical disputes are a significant challenge in healthcare settings worldwide, with hospital administrators playing a pivotal role in managing the conflicts. In China, despite a reported decline, medical disputes continue to raise public and governmental concerns due to factors such as medical accessibility, patient expectations, and staff competency. This study aimed to explore the lived experiences and perspectives of Chinese hospital administrators directly engaged in medical dispute management, to understand the challenges they face and inform the development of targeted training and support systems. Using a qualitative phenomenological approach, we conducted semi-structured, in-depth interviews with 13 hospital administrators from two major regional comprehensive hospitals in Beijing. Participants were selected through purposive sampling and met criteria including active engagement in managing medical disputes and adequate communication skills. Data were analyzed using Colaizzi’s thematic analysis method to extract significant themes from the participants’ experiences. Administrators reported four interconnected challenges: how gender shapes authority and perceived safety in disputes; the struggle to maintain an impartial, ‘outsider’ stance; wide variability in the emotional toll of conflict; and a pronounced need for structured, ongoing dispute-management training. The key themes revealed included (1) Gender Dynamics in Conflict Management: female administrators faced challenges in authority recognition and preferred having male colleagues present during intense disputes for perceived safety and effectiveness; (2) Outsider Perspective in Conflict Management: maintaining an impartial and detached stance was crucial for effective mediation, but administrators varied in their ability to achieve this mindset; (3) Variability in Psychological Experiences: administrators reported diverse emotional reactions and psychological impacts. Some struggled to detach emotionally and lacked formal support systems; (4) Need for Specific Training: there was a clear demand for structured, ongoing training programs tailored to the unique challenges of medical dispute management. Chinese hospital administrators face complex challenges including emotional strain, mixed-gender preference in intense encounters, and insufficient training and support. Addressing the issues is essential for enhancing administrators’ effectiveness in conflict resolution, improving patient care outcomes, and promoting organizational efficiency. Medical institutions should develop comprehensive training programs and establish support systems for administrators’ well-being.

Background: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. Objective: This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. Methods: A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants’ experiences and perspectives. Results: Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs and institutional support to enhance usability and confidence. Conclusions: Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors.

BACKGROUND Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, the adoption of such tools among hospital administrators remains understudied, particularly at the individual level. OBJECTIVE This study aims to explore factors influencing the adoption and use of LLM AI tools among hospital administrators in China, focusing on enablers, barriers, and practical applications in daily administrative tasks. METHODS A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 hospital administrators across 3 tertiary hospitals in Beijing, Shenzhen, and Chengdu from June 2024 to August 2024. The Colaizzi method was used for thematic analysis to identify patterns in participants’ experiences and perspectives. RESULTS Adoption of LLM AI tools was generally low, with significant site-specific variations. Participants with higher technological familiarity and positive early experiences reported more frequent use, while barriers such as mistrust in tool accuracy, limited prompting skills, and insufficient training hindered broader adoption. Tools were primarily used for document drafting, with limited exploration of advanced functionalities. Participants strongly emphasized the need for structured training programs and institutional support to enhance usability and confidence. CONCLUSIONS Familiarity with technology, positive early experiences, and openness to innovation may facilitate adoption, while barriers such as limited knowledge, mistrust in tool accuracy, and insufficient prompting skills can hinder broader use. LLM AI tools are now primarily used for basic tasks such as document drafting, with limited application to more advanced functionalities due to a lack of training and confidence. Structured tutorials and institutional support are needed to enhance usability and integration. Targeted training programs, combined with organizational strategies to build trust and improve accessibility, could enhance adoption rates and broaden tool use. Future quantitative investigations should validate the adoption rate and influencing factors.