Search Results (1,483)

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called HITH, where virtual care takes precedence to address the multifaceted needs of an increasingly aging population grappling with a substantial burden of chronic disease. HITH programs have the potential to significantly reduce hospital bed occupancy, enabling hospitals to better manage the ever-increasing demand for inpatient care. Although many health providers and hospitals have established their own HITH programs, there is a lack of research that provides healthcare executives and HITH program managers with management models and frameworks for such initiatives. There is also a lack of research that provides strategies for improving HITH management in the health sector. To fill this gap, the current study ran a systematic literature review to explore state-of-the-art with regard to this topic. Out of 2631 articles in the pool of this systematic review, 20 articles were deemed to meet the eligibility criteria for the study. After analyzing these studies, nine management models were extracted, which were then categorized into three categories, namely, governance models, general models, and virtual models. Moreover, this study found 23 strategies and categorized them into five groups, namely, referral support, external support, care model support, technical support, and clinical team support. Finally, implications of findings for practitioners are carefully provided. These findings provide healthcare executives and HITH managers with practical frameworks for selecting appropriate management models and implementing evidence-based strategies to optimize program effectiveness, reduce costs, and improve patient outcomes while addressing the growing demand for home-based care. Full article

This paper presents a case study of the Local Care Ecosystems developed by the provincial government of Gipuzkoa (Basque Country, Spain) to strengthen coordination between social services, health services, and community-based initiatives at the municipal level. The initiative seeks to personalize care, enhance service integration, and support community-based care with the overarching goal of improving the quality of life for older adults living at home. These ecosystems incorporate social, institutional, and technological innovations aimed at supporting individuals who are frail or vulnerable throughout the care cycle. At present, 18 Local Care Ecosystems are active, providing services to 1202 people over the age of 65 and 167 families. The model addresses a growing global challenge linked to population aging, which has led to increasing demand for care and support services that are often fragmented, under-resourced, and constrained by outdated regulatory frameworks. These structural issues can compromise both the quality and efficiency of care for dependent individuals. Based on the findings, the paper offers policy recommendations to support the transfer and adaptation of this model, with the aim of improving the well-being of older adults who wish to remain in their own homes. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article

Self-medication is a subject of global importance. If practiced responsibly, self-medication represents a part of self-care or positive care of an individual or a community in promoting their own health. However, today’s practices of self-medication are often inappropriate and irresponsible, and as such appear all over the world. Inappropriate self-medication can be connected with possible serious health risks and consequences. Therefore, it represents a global health issue. It can even generate additional health problems, which will eventually become a burden to healthcare systems and can induce significant costs, which also raises socioeconomic concerns. Hence, self-medication attracts the attention of researchers and practitioners globally in efforts to clarify the current status and define feasible measures that should be implemented to address this issue. This narrative review aims to give an overview of the situation in the field of self-medication globally, including current practices and attitudes, as well as implications for actions needed to improve this problem. A PubMed/MEDLINE search was conducted for articles published in the period from 1995 up to March 2025 using keywords “self-medication” or “selfmedication” alone or in combinations with terms related to specific subthemes related to self-medication, such as COVID-19, antimicrobials, healthcare professionals, and storing habits of medicines at home. Studies were included if self-medication was their main focus. Publications that only mentioned self-medication in different contexts, but not as their main focus, were excluded. Considering the outcomes of research on self-medication in various contexts, increasing awareness of responsible self-medication through education and informing, together with surveillance of particular medicines and populations, could lead to more appropriate and beneficial self-medication in the future. Full article

As global populations age, there is increasing demand for effective, person-centered healthcare solutions that support older adults to age in place. Home healthcare has emerged as a crucial strategy to address the complex health and social needs of older adults while reducing reliance on institutional care. This umbrella review aimed to synthesize evidence from existing systematic reviews and meta-analyses on home healthcare services and interventions targeting older adults. A comprehensive search was conducted across five databases and gray literature sources, including Google Scholar, for reviews published between 2000 and 2025. The review followed the Joanna Briggs Institute methodology and PRISMA statement. Twenty reviews met the inclusion criteria, encompassing a total of over 3.1 million participants. Interventions were grouped into four categories: integrated and multidisciplinary care, preventive and supportive home visits, technological and digital interventions, and physical, transitional, and environmental support. Results indicated that many interventions led to improved health outcomes, including enhanced functional ability, reduced hospital readmissions, and increased satisfaction. However, effectiveness varies depending on the intervention type, delivery model, and population. Challenges such as caregiver burden, digital exclusion, and implementation in diverse settings were noted. This review highlights the promise of home healthcare interventions and underscores the need for context-sensitive, equitable, and scalable models to support aging populations. Full article

Background: Stigma associated with tuberculosis (TB) continues to undermine patient well-being, treatment adherence, and public health goals and objectives. This study aims to systematically review the literature to identify and synthesize TB stigma reduction interventions published between 2015 and 2025. Methods: Following the PRISMA guidelines, we conducted a comprehensive literature search across PubMed, Scopus, Science Direct, ProQuest, and Google Scholar. Eligible studies included those with qualitative, quantitative, and mixed-methods designs that focused on interventions related to TB-related stigma. We categorized the studies into three groups: (1) intervention development studies, (2) TB treatment programs with stigma reduction outcomes, (3) stigma-specific interventions. Data extraction and quality appraisal were conducted independently by two reviewers using the Mixed Methods Appraisal Tool (MMAT). Results: A total of 15 studies met the inclusion criteria. Five studies focused on co-developing stigma interventions, which incorporated multi-level and multicomponent strategies targeting internalized, enacted, anticipated, and intersectional stigma. Two studies assessed TB treatment-related interventions (e.g., home-based care, digital adherence tools) with incidental stigma reduction effects. The remaining seven studies implemented stigma-targeted interventions, including educational programs, video-based therapy, peer-led support, and anti-self-stigma toolkits. Interventions addressed stigma across individual, interpersonal, institutional, community, and policy levels. Conclusions: This review highlights the evolution and diversification of TB stigma interventions over the past decade. While earlier interventions emphasized education and support, recent strategies increasingly integrate peer leadership, digital platforms, and socio-ecological frameworks. The findings underscore the need for comprehensive, contextually grounded interventions that reflect the lived experiences of people affected by TB. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Continuous monitoring of respiratory patterns is essential for disease diagnosis and daily health care. Contact medical devices enable reliable respiratory monitoring, but can cause discomfort and are limited in some settings. Radar offers a noncontact respiration measurement method for continuous, real-time, high-precision monitoring. However, it is difficult for a single radar to characterize the coordination of chest and abdominal movements during measured breathing. Moreover, motion interference during prolonged measurements can seriously affect accuracy. This study proposes a dual radar system with customized narrow-beam antennas and signals to measure the chest and abdomen separately, and an adaptive dynamic time warping (DTW) algorithm is used to effectively suppress motion interference. The system is capable of reconstructing respiratory waveforms of the chest and abdomen, and robustly extracting various respiratory parameters via motion interference. Experiments on 35 healthy subjects, 2 patients with chronic obstructive pulmonary disease (COPD), and 1 patient with heart failure showed a high correlation between radar and respiratory belt signals, with correlation coefficients of 0.92 for both the chest and abdomen, a root mean square error of 0.80 bpm for the respiratory rate, and a mean absolute error of 3.4° for the thoracoabdominal phase angle. This system provides a noncontact method for prolonged respiratory monitoring, measurement of chest and abdominal asynchrony and apnea detection, showing promise for applications in respiratory disorder detection and home monitoring. Full article

Background/Objectives: Breastmilk offers numerous benefits for the health and development of preterm infants, while prolonged hospitalization may impair neurodevelopment. At our institution, the implementation of enhanced family-centered care (FCC) has enabled earlier discharge of preterm infants. This study aimed to assess the impact of early discharge on breastfeeding and breastmilk provision. Methods: This analysis is based on data from a prospective single-center longitudinal cohort study conducted from October 2020 to November 2023, involving six consecutive cohorts (one baseline and five intervention cohorts; n = 184). FCC was progressively enhanced across cohorts. The primary outcome of the main study was postmenstrual age (PMA) at discharge. In this secondary analysis, breastfeeding and breastmilk provision were assessed at four time points: 4 weeks postnatal age, at discharge, 4 weeks post-discharge, and at 3 months PMA. Results: From baseline to intervention cohort 5, the PMA at discharge declined significantly from 37.8 ± 2.1 to 35.7 ± 0.91 weeks (p = 0.03), while the percentage of infants necessitating home nasogastric tube feeding increased from 6.3% to 66.7% (p < 0.01). The proportion of breastmilk of daily feeding volume remained unchanged at 4 weeks postnatal age (0.66 ± 0.42 vs. 0.9 ± 0.28) and at discharge (0.6 ± 0.45 vs. 0.79 ± 0.36). At 4 weeks post-discharge, 65.8% vs. 62.5% of the infants were on partial or exclusive breastmilk (p = 0.91) feeding. Similarly, the percentage of exclusively breastfed infants at 4 weeks post-discharge (23.7% vs. 19.8%) and at 3 months PMA (20% vs. 28.6%) did not differ significantly between baseline and intervention cohort 5. Conclusions: Early discharge did not reduce breastmilk supply or exclusive breastfeeding. However, the persistently low rate of exclusive breastfeeding post-discharge highlights the need for additional support strategies during and after hospitalization. Full article

Aim: To explore the experiences, beliefs, and values of patients who participated in a two-arm randomized clinical trial assessing a nurse-led intervention program for chronic pain self-management, which demonstrated positive effects on pain reduction, depression, and anxiety, and on health-related quality of life 24 months after completion of the program. Design: Descriptive phenomenological qualitative study. Methods: Patients were recruited via telephone, informed about the study, and invited to participate in an individual interview at a place of their choice (hospital or home). All interviews were audiotaped, and an inductive thematic analysis was performed. Results: Seven interviews were carried out between both groups. Six emerging categories were found: effective relationship with the healthcare system, learning to live with pain, family and social support, behaviors regarding pain, resources for self-management, and concomitant determinants. Conclusions: Patients report key aspects that help us to understand the impact of this type of nurse-led group intervention: the intrinsic therapeutic effect of participating in the program itself, the ability to learn to live with pain, the importance of family and social support, the modification of pain-related behaviors, and the identification of resources for self-care. The findings highlight the need for gender-sensitive, individualized care approaches to chronic pain, addressing stigma and social context. Expanding community-based programs and supporting caregivers is essential, as is further research into gender roles, family dynamics, and work-related factors. Full article

Background/Objectives: This study evaluates the effects of a personalized exercise program on symptoms (pain, fatigue, sleep, cognitive function, physical function), resilience, and health-related quality of life (HRQOL) and compares the effectiveness of in-person versus telehealth delivery. Methods: A secondary data analysis was conducted on two 12-week randomized control pilot studies for solid tumor cancer survivors. One study involved in-person home visits with telephone follow-ups. The second utilized weekly exercise recommendations via a smartphone app. Both studies had control participants who received the standard care. Symptoms, resilience, and HRQOL were measured at baseline and after 12 weeks. Paired t-tests were conducted for intervention effects and ANCOVA for group differences, adjusting for age and education. Results: The analysis included 75 program completers: 15 in-person (iHBE), 38 telehealth (TEHE), and 22 who received standard care. Those receiving exercise interventions reported improvements in physical (t = 3.0, p < 0.01) and mental fatigability (t = 3.1, p < 0.01) at program completion compared to baseline. Comparing the mean changes between participants receiving exercise interventions in-person and via telehealth, there were no significant differences between the two delivery methods except perceived visuo-perceptual cognitive difficulty (F = 3.55, p = 0.027), where telehealth showed a slight advantage. Conclusions: The study provides initial evidence of the effectiveness of a telehealth personalized exercise on fatigability and cognitive difficulty, suggesting it is a potential viable alternative to in-person intervention. Further research with a larger cohort is essential to ascertain the effects of these interventional modalities on cancer-related health outcomes. Full article

This study aimed to examine the 6-month hospital readmission rate for Transition Care Program (TCP) clients and its association with community goal attainment. This was a single-site retrospective cohort study of TCP clients admitted from 2014 to 2019. Goals were set at TCP entry and coded as goals ‘within the home’ or ‘in the community’. Hospital readmissions were tracked using electronic health records. Logistic regression, area under the curve, and number needed to treat were the primary analyses performed. Of 747 (66.8% female and 33.2% male) client episodes, 164 (22%) resulted in a hospital readmission. Clients who were not readmitted to hospital set and achieved a higher number of community-based goals (1.08 vs. 0.8, p = 0.01 and 0.8 vs. 0.6, p = 0.001). Utilising a logistic regression model, each additional community goal achieved was associated with a 30% reduction in risk of readmission to the hospital (OR: 0.69, 95%CI: 0.5–0.8; p = 0.002), adjusted for age, sex, MBI change, number of home goals achieved, hospital length of stay and number of comorbidities. Achieving community-based goals can reduce the risk of hospital readmission by 30% after adjusting for demographic and clinical variables. Full article

Background: Older adults living in aged care are at risk of poor health outcomes due to influenza, pneumococcal disease, and herpes zoster infections. Despite these conditions being vaccine-preventable, little is known about vaccine uptake rates in the residential elderly care setting in Australia. Methods: This was a retrospective cohort study examining the medical records of residents of 31 aged care homes in Australia (n = 1108). Data were extracted from medical records for the period March 2023 to September 2023. The proportion of residents vaccinated against influenza, pneumococcal disease, and herpes zoster was calculated. Univariate and multivariate logistic regressions were used to identify possible demographic and other characteristics associated with the vaccination uptake. Results: This study included 1108 residents. Two-thirds (68%) were female, and the median age was 87 years. All residents had one or more comorbidities. Most (92.6%) had received an influenza vaccine within the prior two years, but only 38.3% had received a pneumococcal vaccine, and 16.8% had received herpes zoster vaccination. In all models, receipt of the other vaccines was a significant predictor for vaccine administration. The other factor associated with influenza vaccination was non-consumption of alcohol and younger age for herpes zoster vaccination. Conclusions: While there is a high uptake of influenza vaccines, there is a low uptake of both pneumococcal and herpes zoster vaccines in residents of aged care facilities. Further research into the barriers and enablers of vaccine uptake should be undertaken, with the goal of increasing the vaccination uptake in this vulnerable population. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article