Hana Lee

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).

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.

Although scientific progress has enabled South Koreans to live longer, it has also amplified the incidence of chronic illnesses, leaving terminal patients and their caregivers struggling with significant physical and emotional burdens. We analyzed public perceptions of palliative care and hospice using sentiment analysis and topic modeling of 12,156 YouTube comments. Neutral comments were the most prevalent (57.8%), followed by negative (23.0%) and positive (19.2%) comments. Key themes included religious beliefs about death, pain management, family caregiving, and ethical considerations regarding dignified death. The findings highlight the diverse emotional and cultural perceptions of palliative care and hospice and reveal barriers such as stigma and misinformation. These insights can inform educational initiatives and policies to improve public understanding and acceptance of end-of-life care.

Aim: To identify the factors affecting the length of stay (LOS) and discharge destination (DD) of home health care (HHC) patients in South Korea.Methods: A retrospective cross‐sectional study was conducted using the electronic health records of 1769 patients from a hospital in South Korea. Data were collected from January 2013 to December 2022. We categorized the independent variables into patient context, structure, and process factors following a modification of Donabedian's model. Hierarchical and multinomial logistic regression analyses were used.Results: The mean length of stay was 26.41 days. Patients were discharged to the following locations: 35.0% continued HHC, 21.0% died, 19% were discharged to their homes, 17.0% were admitted, and 8.0% were sent to other locations. Patients' sex, type of insurance coverage, and primary caregiver as well as the number of nurse visits, HHC admission route, and type of nursing service were predictors of their LOS. Operation history, a high Charlson comorbidity index, the type of insurance coverage, HHC admission route, and certain nursing care services were associated with admission and death as the DD.Conclusions: Process variables (e.g., number of nurse visits, HHC admission route, type of nursing services) have a considerable influence on determining the LOS and DD of HHC patients. This result provides new insights into the use of HHC services and care transitions out of the hospital for patients living in their home, offering evidence to reduce unnecessary readmissions and ensure more effective and efficient HHC.

The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community‐dwelling adults with a high risk for prediabetes based on prediabetes‐related symptoms and to identify their characteristics, which might be factors associated with prediabetes. We analyzed homecare nursing records (n = 26,840) of 1628 patients aged over 20 years. Using a natural language processing algorithm, we classified each nursing episode as either low‐risk or high‐risk for prediabetes based on the detected number and category of prediabetes‐symptom words. To identify differences between the risk groups, we employed t‐tests, chi‐square tests, and data visualization. Risk factors for prediabetes were identified using multiple logistic regression models with generalized estimating equations. A total of 3270 episodes (12.18%) were classified as potentially high‐risk for prediabetes. There were significant differences in the personal, social, and clinical factors between groups. Results revealed that female sex, age, cancer coverage as part of homecare insurance coverage, and family caregivers were significantly associated with an increased risk of prediabetes. Although prediabetes is not a life‐threatening disease, uncontrolled blood glucose can cause unfavorable outcomes for other major diseases. Thus, medical professionals should consider the associated symptoms and risk factors of prediabetes. Moreover, the proposed algorithm may support the detection of individuals at a high risk for prediabetes. Implementing this approach could facilitate proactive monitoring and early intervention, leading to reduced healthcare expenses and better health outcomes for community‐dwelling adults.

This study aimed to identify the characteristics of home health care (HHC) patients, main diagnoses, service type, home visit patterns, and primary caregiver type from a major tertiary hospital in South Korea to provide basic data for tailored nursing care. We used Electronic Health Records (EHRs) data of 1776 patients, from 2011 to 2020, from a premier tertiary hospital in South Korea and conducted secondary data analysis using descriptive statistics, a chi-squared test, and an independent samples t-test. Patients’ mean age (i.e., 50.9% men and 49.1% women) was 74.50 ± 15.48 years. Further, admission to HHC via the outpatient and inpatient department comprised 1047 (59.0%) and 729 patients (41.0%), respectively. Based on the Korean Standard Classification of Diseases, neoplasms (49.9%) were identified as the most common main diagnosis, followed by diseases of the nervous system (10.3%) and musculoskeletal system and connective tissue disease (6.2%). An average of 2.2 homecare nursing services were provided per visit, with lab sampling (43.9%), other intravenous injections (31.0%), and ventilator care (23.0%) being the most frequent services. The total HHC service period for patients via the outpatient department was 76.88 days longer (292.24 vs 215.36 days) than those from the inpatient department. Younger patients were cared for by parents and siblings and older patients by spouses, offspring, and formal caregivers. It is pivotal to identify general characteristics of HHC patients through long-term monitoring and retrospective analysis to provide high-quality and tailored interventions to them.