Kyungmi Woo

Background: Place of death is a key indicator of quality in end‐of‐life care, reflecting how well care aligns with patients' values and preferences. Advance care planning (ACP) has been proposed to support goal‐concordant care, yet evidence regarding its association with actual place of death remains inconsistent. Objective: To examine the association between participation in ACP and place of death and to explore whether this association varies according to ACP implementation method. Methods: In this systematic review and meta‐analysis, we searched five databases (PubMed, Embase, PsycInfo, CINAHL, and ProQuest) for studies published in English from inception to September 2025. Studies of adults engaged in ACP and reporting actual place of death were included. Random‐effects meta‐analyses were conducted, with subgroup analyses by documentation‐based or discussion‐based ACP implementation methods. Risk of bias and certainty were assessed using RoB 2, ROBINS‐I V2, and GRADE. Results: Of 4216 records, 13 studies were included. ACP was associated with increased home death and reduced hospital death, with considerable heterogeneity. For home death, discussion‐based implementation showed a significant association, whereas documentation‐based implementation did not. For hospital death, both implementation methods were associated with reduced odds. Linking Evidence to Action: ACP is associated with place of death, but its impact appears to depend on how it is implemented. These findings suggest that ACP should be approached as a communication‐centered process rather than solely as documentation. Clinicians should prioritize facilitated discussions that clarify patient values and preferences, and integrate them into care planning. Future implementation efforts should focus on strengthening communication processes and care coordination to support goal‐concordant end‐of‐life care, particularly in enabling home‐based death. Protocol Registration: PROSPERO (CRD420251236312).

Purpose: Older adults with cardiometabolic diseases (CMDs) often experience medication-related burden (MRB) in the context of long-term use of multiple medications, yet how health literacy and social support relate to this burden remains under-examined. This study aimed to examine the association between health literacy and MRB and the mediating role of perceived social support in this population. Patients and Methods: In this cross-sectional study, 254 community-dwelling older adults (aged ≥ 65 years) with CMDs using ≥ 2 prescription medications were recruited from an online survey panel in South Korea between November 14 and 24, 2025. MRB, health literacy, and perceived social support were assessed using the Living with Medicines Questionnaire version 3, Short-Form Health Literacy Instrument, and Multidimensional Scale of Perceived Social Support, respectively. Multiple regression and mediation analysis (SPSS PROCESS macro, Model 4; 5000 bootstrap samples) were performed, adjusting for 10 covariates. Results: Participants’ mean age was 69.83 years (SD = 3.64), and 55.12% were female. Higher health literacy (B = − 1.305, p < 0.001), higher perceived social support (B = − 0.324, p < 0.001), and good self-rated health (B = − 10.162, p < 0.001) were associated with lower MRB (adjusted R² = 0.394). Social support showed a statistically significant indirect effect in the tested model (indirect effect = − 0.243; 95% bootstrap confidence interval [− 0.418, − 0.098]), accounting for 15.7% of the total effect. Conclusion: Higher health literacy and perceived social support were associated with lower MRB, with social support representing a partial pathway. These findings suggest that efforts to manage MRB may need to prioritize health literacy while also considering supportive social systems. However, given the cross-sectional design and an online panel sample that may underrepresent older adults with limited digital access, frailty, cognitive impairment, or low health literacy, causal inferences cannot be drawn; longitudinal and intervention studies are needed.

BACKGROUND Population aging has contributing to increase in the number of patients with cancer and chronic progressive illnesses, making hospice and palliative care indispensable for improving end of life care and quality of life. Although home healthcare plays a pivotal role in initiating hospice transition discussions, the appropriate timing remains clinically challenging. Existing prediction approaches rely on structured clinical variables that poorly capture the contextual and communicative factors emerging during consultations. Although free-text consultation records may capture such information, no study has examined whether linguistic markers from these consultations predict subsequent hospice transitions. OBJECTIVE This study examined whether clinical and text-derived linguistic markers from the first home healthcare consultation were associated with subsequent hospice transitions, aiming to identify early signals that may inform hospice referral discussions. METHODS This study used prospectively collected data from 37 patient–caregiver pairs receiving home healthcare at a tertiary hospital in South Korea, yielding 149 recorded consultations between December 2024 and November 2025. The consultations were transcribed using Naver CLOVA Speech, de-identified, and corrected using Gemma 3 (12 B), an on-premise large-language model selected by benchmarking five open-weight models against a human expert-corrected gold standard. A hospice terminology dictionary (4 categories, 34 subcategories, and 1327 terms) was used to derive term occurrence and frequency, and clinical variables were extracted from the hospital clinical data warehouse. Only variables from the first consultation served as candidate predictors; the outcome was a subsequent hospice transition, ascertained from electronic medical records. The analyses used Firth logistic regression, receiver-operating characteristic analysis with DeLong tests, and generalized estimating equations. RESULTS Thirty-seven patients underwent 149 consultations and 10 (27%) transitioned to hospice care. The baseline characteristics did not differ between the groups. The presence of Management of Other Systemic Symptoms terms was lower in the transition group and associated with hospice transition in univariate Firth regression (OR=0.08, 95% CI=0.01-0.54; P=.009). In the primary cancer subgroup (n=28), this association remained significant (OR=0.12, 95% CI=0.01-0.83; P=.03), while cancer status was excluded from the multivariable model due to quasi-complete separation. The combined clinical–text models demonstrated modest discrimination (area under the curve=0.70 in the cancer subgroup) with no significant model differences. Generalized estimating equations analysis showed a markedly lower occurrence in the transition group at baseline (OR=0.09, 95% CI=0.02-0.39; P=.002) and a significant time × group interaction (OR=1.67, 95% CI=1.02-2.72; P=.04). CONCLUSIONS Reduced use of symptom management language at the first consultation was associated with a subsequent hospice transition. Because anticipating the timing of hospice transition is challenging, a signal derived from the initial consultation alone may be more clinically applicable than one requiring repeated encounters. Thus, linguistic markers in consultation texts may serve as early low-burden signals for hospice transition.

Home health care (HHC) has become vital amid global aging, rising chronic disease burdens, and shifts toward community-based care. Yet, research priorities remain fragmented. This study aimed to map the evolution of HHC scholarship and identify emerging and underexplored themes. We analyzed 39,548 PubMed-indexed HHC publications (1952-April 2025) using Latent Dirichlet Allocation topic modeling. A hybrid Konstanz Information Miner-Python workflow supported preprocessing, parameter optimization, and topic extraction. Model performance was assessed with perplexity, coherence, and clustering validation. Ten coherent topics were identified, spanning clinical, technological, and population-focused domains. Healthcare resource utilization remained the most consistent and dominant theme. Rapidly expanding areas included telehealth adoption, technology-assisted caregiving, and end-of-life services. Temporal analysis revealed increasing attention to digitally integrated, person-centered models since the 2010s, accelerated by the COVID-19 pandemic. Pediatric care and high-risk population screening were notably underrepresented. This study provides a macro-level mapping of seven decades of HHC research, highlighting evolving priorities and persistent blind spots. Topic modeling offers valuable insights for shaping a more inclusive, evidence-informed research agenda and guiding policy development in home-based care.

Background: This systematic review aimed to evaluate the performance of various telenursing technologies in home health care and to compare whether specific telenursing modalities yield superior clinical outcomes compared with nurse-led telephone support. Methods: Following PRISMA 2020 guidelines, we searched PubMed, EMBASE, CINAHL, Cochrane Library, and ProQuest through April 12, 2024. This study was registered with PROSPERO (CRD42023396450). We included randomized controlled trials published in English since 1997 that compared advanced telenursing technologies, such as telemonitoring and videophones, with nurse-led telephone support for adult patients receiving home health care. Quality was appraised using the Cochrane Risk of Bias 2.0. Due to clinical heterogeneity, a qualitative synthesis was performed. Results: Eight studies were ultimately included. Heart failure was the most targeted condition. Telemonitoring showed the most consistent benefits across studies, including reductions in hospitalizations and length of stay, lowered 1-year mortality, and improvement in glycemic control and self-care behaviors. Videophone-based interventions demonstrated improvement trends in the quality of life and self-care behaviors, focusing predominantly on fundamental nursing and interactive approaches. Discussion: This review highlights that telenursing effectiveness varies by technology and that no single telenursing method can address the diverse, complex needs of home care patients. While telemonitoring showed superior clinical impacts, small sample sizes in several studies may limit the precision of findings. A flexible, multimodal strategy that is tailored to patient characteristics, care goals, and available resources appears most appropriate, and integrated models that combine telemonitoring with videophone or telephone support may enhance continuity, access, and personalization of care.

With the global aging of populations, advance care planning is essential for ensuring dignified end-of-life care that honors patients’ preferences; h

Objectives: This study aimed to identify high‐risk factors for type 2 diabetes and develop a machine learning (ML)–based diabetes prediction model using nursing notes from home health care. Design: Retrospective cohort study. Sample: A total 7896 medical records from 1747 patients aged ≥ 20 years who received home health care at a university hospital in South Korea over 10 years. Measurements: The patient's sociodemographic characteristics, diagnosis codes, and clinical history as well as narrative nursing notes detailing nursing assessments and descriptions of the patient's condition were recorded. Logistic regression was used to identify risk factors, and five machine learning models were constructed using 10‐fold cross‐validation. Results: Female sex, depression, hypertension, hyperlipidemia, nursing services such as dressing and nutritional care, and dysuria were identified as prediabetes‐related symptoms extracted from nursing notes using natural language processing (NLP). The Random Forest model demonstrated the highest predictive performance (AUC = 0.985). Conclusions: Nursing documentation is a valuable resource for early diabetes risk screening. High predictive performance was achieved by integrating structured and unstructured data, prediabetes‐related symptoms extracted from nursing notes. This approach enhances nurses’ ability to deliver timely, personalized interventions and improve preventive care outcomes, with significant benefits for patients receiving home care who require continuous support.

This study explores public perceptions of palliative and hospice care through analysis of user comments on Korean-language YouTube videos. A total of 12,155 comments were collected from 54 videos retrieved using the keywords “palliative care” and “hospice.” The search was conducted between September 11 and October 31, 2024. Sentiment analysis using a Korean-language sentiment lexicon revealed that the majority of comments were neutral (57.8%), followed by negative (23.0%) and positive (19.2%) sentiments. Topic modeling using the Latent Dirichlet Allocation method identified 13 recurring themes: religious beliefs, diagnosis and treatments, pain control, family caregiving, death with dignity, emotional farewell, social support and policy, life reflection, meaning of end-of-life care, hope, appreciation, decision-making, and remembrance and mourning. Nurses play an important role in addressing the public misconceptions and emotional concerns regarding end-of-life care that are frequently expressed on online platforms. Insights from this study can support the development of tailored communication strategies, promote early advance care planning, and inform informatics-based interventions to improve equitable access to hospice and palliative care. These findings also highlight the potential of social media analysis as a tool for understanding public needs and guiding data-informed nursing practice.

The increasing dependence on information and communication technology in nursing is associated with the growing recognition of the role of nursing informatics in enhancing community nursing efficiency. This concept analysis aimed to define and clarify the essence and attributes of community nursing informatics and establish a theoretical foundation for applicability in nursing practice and education. Guided by Walker and Avant’s 8-step concept analysis framework, we identified the definition, key attributes, antecedents, and consequences of community nursing informatics. Based on the analysis, community nursing informatics can be described as a nursing field that examines the collection, classification, storage, retrieval, and dissemination of relevant information to support all facets of community health nursing, including practice, management, research, and education. Its defining attributes are as follows: (1) community-focused; (2) health-oriented; (3) utilization of information technology; (4) data management and analysis; (5) active and autonomous collaboration; and (6) innovativeness. Community nursing informatics improves health outcomes and optimizes community functions through the effective use of information technology. The framework demonstrates that utilizing technology and fostering autonomy and engagement among community members can lead to innovative outcomes in community nursing practice. Accordingly, the findings provide theoretical and practical implications for developing and implementing community nursing informatics in real-world settings.

Generative artificial intelligence (GAI) can be used to support healthcare professionals. An in-depth understanding of how healthcare professionals adopt and use GAI in practice is lacking. We aimed to identify the intention to use GAI in nursing practice, its application areas, and the key factors associated with this intention. This cross-sectional study comprised an online survey with 46 items to assess nurses’ intention to use GAI. Data were collected between March 17 and 28, 2025. The participants were 176 nurses working at a tertiary hospital in Seoul, Korea. Descriptive statistics, Mann–Whitney U tests, Kruskal–Wallis H tests, correlation analyses, multiple regression analyses, and an exploratory subgroup analysis were performed. Overall, 61.4% of nurses reported having no experience with GAI, whereas 38.6% had used it, primarily in educational material development (50.0%) and data analysis (26.5%). Voluntariness (coefficient = 0.358, p < .001), performance expectancy (coefficient = 0.344, p < .001), and GAI literacy (coefficient = 0.192, p = .010) were key factors associated with usage intention; experience with GAI, effort expectancy, social influence, facilitating conditions, and perceived risk were not significantly associated with the intention to use GAI. The subgroup analysis revealed that the influence of key factors was moderated by prior user experience. This study revealed that voluntariness, performance expectancy, and GAI literacy are key drivers of nurses’ intention to use GAI. Notably, their importance differs according to user experience, highlighting the need for tailored strategies to get nurses to use GAI. In the future, the sustained value of GAI in nursing practice should be assessed.