Objectives To explore the postsurgery lived experiences of patients with colorectal cancer with a permanent ostomy for informing initiatives to improve patient care and future quantitative research. Design A descriptive qualitative phenomenological study. Setting We conducted this study in the Colorectal Cancer Division at the Cancer Center of West China Hospital, Sichuan University, a premier institution renowned for its comprehensive cancer care and research. Participants 12 patients who had undergone surgeries for colorectal cancer with a permanent ostomy. Results Our interviews revealed profound adjustments in the lives of colorectal cancer survivors living with permanent ostomies. Participants articulated a transition to a ‘new normal’, characterised by extensive daily life adjustments, psychological adaptation and ongoing management challenges. Key themes identified included the adaptation to a reshaped daily routine and altered perceptions of quality of life. Many faced substantial challenges in stoma care, requiring significant learning and adaptation. Psychological adaptations were marked by a redefinition of body autonomy and personal identity, alongside a recalibration of social interactions and privacy. The need for robust professional guidance and a comprehensive social support system was universally emphasised. Conclusions Patients with colorectal cancer with permanent ostomies navigate significant changes in their lives postsurgery, undertaking a complex process of reconstructing and adapting to a new normalcy. They recalibrate their physical, psychological and social well-being, demonstrating resilience and adaptability in the face of these challenges. Their pervasive expression of needs for information and support may reflect gaps in the existing patient education and support measures and communication strategies. Healthcare professionals and policy-makers should adopt a patient-centred approach acknowledging the multifaceted nature of postsurgery recovery and adaptation by fostering open communication, tailoring personalised education and facilitating supportive community networks.

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.

Objective: To determine the level and influencing factors of informal caregiver burden in gynaecological oncology inpatients receiving chemotherapy. Methods: This cross-sectional study enrolled gynaecological oncology patients and their informal caregivers between May 2018 and November 2018 and measured the caregivers’ burden using the Caregiver Burden Inventory. The influencing factors were evaluated with univariate regression analysis and multivariate linear stepwise regression analysis. Results: A total of 138 patients and their informal caregivers completed the questionnaire. The mean ± SD total informal caregiver burden score was 53.18 ± 10.97. The highest mean ± SD score was recorded in the dimension of time-dependent burden (14.28 ± 2.74), followed by developmental burden (13.65 ± 2.15), physical burden (10.52 ± 2.07), social burden (7.61 ± 2.58) and emotional burden (7.12 ± 1.43). Multivariate analysis showed that the informal caregiver’s sex, relationship to the patient, daily duration of care, presence of chronic health problems and the duration of the patient’s disease were factors influencing the level of caregiver burden. Conclusions: The informal caregivers of gynaecological cancer patients hospitalized for chemotherapy experience a moderate level of burden. Nursing measures should be considered to reduce informal caregiver burden and improve the quality of lives of both patients and their caregivers.

Background: Myocardial infarction (MI) remains a leading cause of morbidity and mortality worldwide. Although postsurgical cardiac interventions have improved survival rates, effective management during recovery remains challenging. Traditional informational support systems often provide generic guidance that does not account for individualized medical histories or psychosocial factors. Recently, artificial intelligence (AI)–based large language models (LLM) tools have emerged as promising interventions to deliver personalized health information to post-MI patients. Objective: We aim to explore the user experiences and perceptions of an AI-based LLM tool (iflyhealth) with integrated personal health record functionality in post-MI care, assess how patients and their family members engaged with the tool during recovery, identify the perceived benefits and challenges of using the technology, and to understand the factors promoting or hindering continued use. Methods: A purposive sample of 20 participants (12 users and 8 nonusers) who underwent MI surgery within the previous 6 months was recruited between July and August 2024. Data were collected through semistructured, face-to-face interviews conducted in a private setting, using an interview guide to address participants’ first impressions, usage patterns, and reasons for adoption or nonadoption of the iflyhealth app. The interviews were audio-recorded, transcribed verbatim, and analyzed using Colaizzi method. Results: Four key themes revealed included: (1) participants’ experiences varied based on digital literacy, prior exposure to health technologies, and individual recovery needs; (2) users appreciated the app’s enhanced accessibility to professional health information, personalized advice tailored to their clinical conditions, and the tool’s responsiveness to health status changes; (3) challenges such as difficulties with digital literacy, usability concerns, and data privacy issues were significant barriers; and (4) nonusers and those who discontinued use primarily cited complexity of the interface and perceived limited relevance of the advice as major deterrents. Conclusions: iflyhealth, an LLM AI app with a built-in personal health record functionality, shows significant potential in assisting post-MI patients. The main benefits reported by iflyhealth users include improved access to personalized health information and an enhanced ability to respond to changing health conditions. However, challenges such as digital literacy, usability, and privacy and security concerns persist. Overcoming the barriers may further enhance the use of the iflyhealth app, which can play an important role in patient-centered, personalized post-MI management.

Background: The rapid evolution of generative artificial intelligence (GenAI) is transforming health care globally. In China, hospitals are rapidly embracing digital transformation. Senior leaders are pivotal in influencing and deciding the adoption of GenAI technologies in professional settings. However, evidence on their perceptions and attitudes and how they evolve over time is lacking. Objective: This study aims to investigate senior Chinese hospital leaders’ perceptions relating to GenAI and their attitudes toward adopting GenAI technologies in professional settings and to document how their perspectives evolve. Methods: A longitudinal, descriptive qualitative study was conducted using a phenomenological approach. Senior leaders, including hospital directors/deputies and department heads/deputies, from 3 tertiary hospitals across the Hebei, Guangdong, and Sichuan provinces were purposively sampled. Data were collected through semistructured telephone interviews at two time points (baseline and 6 months later). Interview transcriptions were analyzed using Colaizzi’s method to extract themes related to knowledge acquisition, attitudinal shifts, and evolving barriers and facilitators to GenAI adoption. Results: A total of 32 participants were interviewed in August 2024 and February 2025, including 11 (34.4%) participants from Hebei, 12 (37.5%) from Sichuan, and 9 (28.1%) from Guangdong. Their roles included 8 (25%) hospital directors/deputies and 24 (75%) department heads/deputies. The analysis of the interview transcriptions revealed three primary themes. First, participants’ understanding of GenAI improved markedly over time as they engaged with diverse information sources and gained practical experience. Second, despite widespread support for personal use, senior leaders shifted from initial reluctance to formal institutional adoption. Third, initial obstacles, such as limited technical literacy and resource constraint concerns, diminished over time, while new drivers, including peer influence and technological breakthroughs such as DeepSeek, emerged to catalyze adoption. Conclusions: Senior Chinese hospital leaders’ perceptions and attitudes toward GenAI have evolved significantly over time. Improved knowledge through diverse information channels has enhanced their comprehension and shifted their stance from cautious personal experimentation toward formal institutional adoption. The dynamic interplay between diminishing obstacles and emerging catalysts, notably the influence of peer practices and technological breakthroughs such as DeepSeek, underscores the potential for transformative change in health care management. Targeted educational initiatives, pilot projects, and robust policy frameworks are needed to facilitate GenAI integration.

Background: Chatbots driven by large language model artificial intelligence (AI) have emerged as potential tools to enhance health information access for patients with cancer. However, their integration into patient education raises concerns among oncologists. Limited literature has examined the perceptions and attitudes of oncologists in terms of endorsing AI-driven chatbots for health information. Objective: This study aims to explore the perceptions and attitudes of Chinese oncologists toward endorsing AI-driven chatbots to patients with cancer. Methods: In this phenomenological qualitative study, we purposively sampled oncologists from 4 hospitals in Southwest and East China and conducted semistructured interviews with 24 participants between November 19, 2024, and December 21, 2024. The data saturation principle was observed to determine the end point of data collection. Data were analyzed using the Colaizzi method. Results: The participants were aged 42.0 (range 29‐53) years on average, including 9 (37%) female and 15 (62%) male participants. The participants had an average of 8.8 (range 1‐25) years in oncology. Of the participants, 7 (29%) had recommended AI chatbots to patients. Three key themes were revealed from analysis of interview transcriptions, including perceived benefits, significant concerns, and impacts on doctor-patient dynamics. Benefits included enhanced accessibility and potential support for chronic condition management. Concerns centered on liability, misinformation, lack of personalization, privacy and data security risks, and patient readiness and education. Oncologists stressed a dual impact of AI chatbots on doctor-patient dynamics, recognizing the potential for improved communication and risks of trust erosion due to overreliance on AI. Conclusions: While recognizing the potential of AI-driven chatbots to enhance accessibility of health information and chronic disease management, Chinese oncologists report significant concerns, including liability, misinformation, lack of personalization, privacy and data security risks, and patient readiness. Addressing the challenges requires comprehensive solutions, such as clear policies and guidelines, rigorous testing and validation, institutional endorsement, and robust patient and provider education. Future efforts should focus on resolving the barriers while leveraging the strengths of AI technology to support patient-centered care in a safe, effective, and ethical manner.