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Healthcare

Healthcare is an international, scientific, peer-reviewed, open access journal on health care systems, industry, technology, policy, and regulation, and is published semimonthly online by MDPI.
The European Medical Association (EMA), Ocular Wellness & Nutrition Society (OWNS) and Italian Society of Nephrology Nurses (SIAN) are affiliated with Healthcare and their members receive discounts on the article processing charges.
Indexed in PubMed | Quartile Ranking JCR - Q2 (Health Policy and Services | Health Care Sciences and Services)

All Articles (14,926)

Background/Objectives: Vast amounts of textual data are generated by healthcare organizations every year. Traditional content analysis is time-intensive, error-prone, and potentially biased. This study demonstrates how freely available large language model (LLM) artificial intelligence (AI) tools can efficiently and effectively analyze qualitative healthcare data and uncover insights missed by traditional manual analysis. Interview data from chief nursing officers (CNOs) at top-performing academic medical centers were analyzed to identify factors contributing to their operational and patient quality success. Methods: Semi-structured interviews were conducted with CNOs from top-performing academic medical centers that achieved top-decile quality measures while using resources most efficiently. Interview transcripts were analyzed using a mix of traditional text mining in LSA and Gemini 2.5. The capability of four freely available AI platforms—Gemini 2.5, Scholar AI 5.1, Copilot’s Chat, and Claude’s Sonnet 4.5—was also reviewed. Results: LLM AI analysis identified ten primary factors, comprising twenty-four subtopics, that characterized successful hospital performance. Notably, AI analysis identified a theoretical connection that manual analysis had missed, revealing how the identified framework aligned with Donabedian’s seminal structure, process, outcomes quality model. The AI analysis reduced the required time from weeks to nearly instantaneous. Conclusions: LLM AI tools offer a transformative approach to unlocking insight from the analysis of qualitative textual data in healthcare settings. These tools can provide rapid insight that is accessible to personnel with minimal text-mining expertise and offer a practical solution for healthcare organizations to unlock insight hidden in the vast amounts of textual data they hold.

19 January 2026

First Graphical Representation of the Factors. (Additional or alternative images appear in the chat session, which is accessible from https://gemini.google.com/share/d66028e6b36a (accessed on 11 January 2026).

Background/Objectives: Cross-border retirement migration has become a global trend. However, this population from Hong Kong, with a unique status, offers valuable opportunities for multidimensional empirical research. This paper aims to apply a Stress and Coping Theory–based model to verify the presence of reverse culture shock (RCS) among them and explore how social support and its appraisal are associated with loneliness. It further examines indirect associations involving secondary appraisal within the appraisal structure. Methods: We recruited 210 Hong Kong seniors (aged ≥65) who had relocated to mainland China and had ever returned and surveyed them using validated scales. Results: Robust regression results revealed that higher levels of RCS were associated with higher levels of loneliness. Compared to social support (β = −0.04, p = 0.278), its appraisal had a significant negative association with loneliness (β = −0.09, p < 0.05). Mediation analysis demonstrated a significant indirect association involving social support appraisal, with variation across duration since the last return. Conclusions: With the resumption of normal cross-border travel after COVID-19, RCS is associated with subjective well-being among older returnees. Support appraisal shows a stronger association with loneliness, although this association varies by temporal context. We further propose that within the appraisal structure, secondary appraisal may be implicated in indirect associations linking primary appraisal to emotional outcomes, and that these associations vary by temporal context.

19 January 2026

Background/Objectives: Smartphone-based leisure reservation platforms increasingly shape how individuals participate in leisure, yet little is known about how such technology-mediated engagement influences users’ awareness of multidimensional health benefits. The purpose of this study is to investigate how regular users of smartphone-based leisure reservation platforms perceive multidimensional health benefits associated with their leisure activities. Methods: Based on a constructivist/interpretivist approach, this study applied Interpretative Phenomenological Analysis (IPA). Ten participants with at least one year of platform use completed semi-structured interviews. Data were analyzed through iterative coding and theme development, with trustworthiness ensured through member checking, peer debriefing, and triangulation. Results: Participants reported three dimensions of health awareness. (1) App-enabled accessibility as a catalyst for physical health awareness (i.e., physical health benefits) included improved vitality and increased motivation to maintain exercise routines. (2) App-based planning and anticipation in supporting mental well-being (i.e., mental health benefits) involved stress reduction, emotional recovery, enjoyment, and heightened self-care awareness. (3) Platform-mediated social encounters and the construction of social health (i.e., social health benefits) reflected expanded social networks, strengthened interpersonal relationships, and a greater sense of belonging fostered through shared leisure experiences. Conclusions: Smartphone-based leisure platforms play a meaningful role in enhancing users’ awareness of multidimensional health benefits. By improving accessibility, diversifying leisure options, and facilitating social interaction, these platforms support holistic well-being. The findings contribute to understanding technology-mediated leisure and offer practical implications for designing digital leisure services that promote physical, mental, and social health.

19 January 2026

Background: Cardiovascular-Kidney-Metabolic (CKM) syndrome involves interconnected cardiovascular, renal, and metabolic conditions. The dose–response relationship between physical activity and bidirectional CKM stage transitions remains unclear. Methods: Using data from the China Health and Retirement Longitudinal Study (CHARLS), cross-sectional analysis pooled 14,310 observations from 10,868 participants. Logistic regression with clustered robust standard errors accounted for intra-individual correlation. Longitudinal analysis (n = 3442) employed continuous-time multi-state Markov models with a 5-state structure (Stages 0–4). To evaluate physical activity effects, stages were regrouped into low-risk (Stages 0–2) and high-risk states (Stages 3–4) using a 2 × 2 transition intensity matrix. Physical activity was measured in MET-min/week and categorized into quartiles (Q1–Q4). Results: Compared with Q1, Q2, Q3, and Q4 were associated with 43.1%, 52.5%, and 53.1% lower risk of high-risk CKM stages, respectively. RCS analysis demonstrated nonlinear dose–response relationships between physical activity and CKM stage progression. Subgroup analyses showed more pronounced protective effects in older adults and single individuals. During 4-year follow-up, 31.6% experienced progression and 6.8% showed improvement. Stage 4 acted as a complete absorbing state without any reversal. Transition intensity analysis revealed that transitions between adjacent stages were notably higher than cross-stage transitions. The Q4 physical activity level significantly reduced transitions from low-risk to high-risk states (HR = 0.598, 95% CI: 0.459–0.777) and promoted transitions from high-risk to low-risk states (HR = 2.995, 95% CI: 1.257–7.134). Conclusions: Moderate-to-high physical activity effectively reduces CKM progression risk and promotes improvement, providing evidence for CKM prevention and management.

19 January 2026

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Healthcare - ISSN 2227-9032