Search Results (2,014)

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

Background/Objectives: The rapid expansion of social media platforms has profoundly changed the way adolescents access reproductive health information. While digital environments increase accessibility to contraceptive content, they also facilitate the dissemination of misinformation, potentially influencing both contraceptive literacy and psychological well-being. The present study aimed to evaluate the relationship between sources of contraceptive information, contraceptive misinformation endorsement, contraceptive knowledge, and mental health indicators among Romanian adolescents and young adults. Methods: A cross-sectional observational study was conducted in a cohort of 210 Romanian adolescents and young adults. Participants completed a structured self-administered questionnaire assessing demographic characteristics, contraceptive information sources, digital health behaviors, contraceptive misconceptions, and contraceptive knowledge. Anxiety and depressive symptoms were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) scales. Correlation analyses and multivariable logistic regression models were performed to identify factors associated with poor contraceptive knowledge and moderate-to-severe anxiety. Results: Social media represented the primary source of contraceptive information for 58.1% of participants. Individuals relying predominantly on social media demonstrated significantly lower contraceptive knowledge questionnaire (CKQ) scores compared to those obtaining information from healthcare professionals (5.9 ± 1.8 vs. 8.1 ± 1.7, p < 0.001). Contraceptive misinformation endorsement was inversely correlated with CKQ scores (r = −0.44, p < 0.001) and positively associated with anxiety (r = 0.47, p < 0.001) and depressive symptoms (r = 0.41, p < 0.001). In multivariable analyses, primary reliance on social media (OR 2.21, 95% CI 1.12–4.34, p = 0.022) and low digital health literacy (OR 2.94, 95% CI 1.51–5.71, p = 0.001) were independently associated with poor contraceptive knowledge. Higher misinformation endorsement, infertility-related fears, and high social media exposure were independently associated with moderate-to-severe anxiety. Conclusions: Contraceptive misinformation endorsement was associated with lower contraceptive literacy and poorer psychological outcomes among adolescents and young adults. These findings highlight the growing importance of digital health literacy. However, given the cross-sectional design, the observed relationships should be interpreted as associations rather than causal effects, and longitudinal studies are required to clarify their directionality. Full article

Background/Objectives: Health literacy (HL) is recognized as an important social determinant of health. It supports healthy behaviors and effective health management throughout one’s life. For children and adolescents, developing HL influences their well-being, development, and ability to make informed health decisions. Nurses are strategically positioned to promote HL from an early age. To our knowledge, no prior synthesis has specifically examined nurse-led HL interventions targeting pediatric populations, highlighting the originality and relevance of this scoping review. The purpose of this review was to map and characterize nursing interventions aimed at improving HL outcomes in children and adolescents. Methods: A scoping review was conducted according to the Joanna Briggs Institute methodology, using a three-step search strategy, and reported in accordance with the PRISMA-ScR guidelines. Searches were conducted in MEDLINE, CINAHL, Scopus, Web of Science, and ProQuest with no date restriction, including studies published in Portuguese, English, or Spanish. Studies involving children and adolescents (ages 0–18) in any healthcare or community setting were eligible. Data on intervention characteristics and HL outcomes were extracted and analyzed descriptively, and no critical appraisal of the included sources was conducted. Results: A total of 44 studies were included. Interventions were predominantly school-based and focused on adolescents (n = 26), with a clear gap in early childhood (n = 2). Studies of early childhood primarily used storytelling and reading activities, whereas interventions targeting older children and adolescents more often employed participatory educational strategies, group-based approaches and digital platforms. The most frequently addressed topics were chronic disease management (n = 12), mental health (n = 7), and nutrition (n = 5). HL domains mainly focused on healthcare and health promotion, with fewer studies addressing disease prevention. Most interventions were conducted in school settings (n = 24), highlighting this context over those in primary care, community, and hospital settings. Conclusions: The results revealed nursing interventions used to promote HL, particularly in the management of chronic diseases, mental health and nutrition. However, the existing body of research is still limited. Key gaps include the absence of standardized measurement tools and the scarcity of longitudinal studies evaluating long-term outcomes. These limitations constrain the comparability and generalizability of findings, highlighting the necessity of more rigorous, methodologically robust research to support evidence-based practices. This scoping review comprehensively maps nurse-led interventions that promote HL among children and adolescents, identifying key priorities to guide future research in this area. Full article

The rapid integration of artificial intelligence (AI) into mental healthcare presents opportunities and ethical challenges, particularly for complex conditions like obsessive–compulsive disorder (OCD). In this perspective, we argue for a Dual Imperative: establishing safety architectures for AI-powered therapeutic tools to prevent algorithmic sycophancy (symptom accommodation), while mandating explainable AI (XAI) in prognostic models to ensure clinical auditability. In therapeutics, we propose a Guardian Angel architecture that utilizes patient-specific fear hierarchies and linguistic stance detection to distinguish compulsive reassurance-seeking from legitimate patient questions. This approach transforms potential therapeutic ruptures into opportunities for distress tolerance via the Digital Ulysses Pact, a patient-authorized, algorithmically enforced response prevention protocol. In diagnostics, we address the black box problem in precision psychiatry. We argue that as AI evolves from detection to high-stakes treatment selection, safety and accountability become a prerequisite for clinical application. Although distinct in implementation, these architectures form an integrated framework for aligning therapeutic and diagnostic AI. These architectures are not parallel tracks but a unified ecosystem: A patient’s XAI-audited profile can inform the Guardian Angel’s configuration, while the longitudinal data gathered during therapy enriches diagnostic precision. Grounded in ethical principles and best practices in OCD, this suggests a path toward AI that is auditable in its diagnostic logic, firm in its therapeutic boundaries, and enforceable through emerging regulatory frameworks. Full article

Background: Cervical cancer remains a significant health issue in Indonesia, where structural barriers, fragmented information, and sociocultural norms continue to hinder timely diagnosis and treatment. Families play a central role throughout the illness journey, yet their perspectives are often overlooked in the development of digital patient navigation systems. This study explored family experiences, caregiving challenges, and expectations for a family-centered digital navigation model, DIVA.ID, by integrating Digital Health frameworks and Family Systems Theory. Methods: A qualitative descriptive approach was employed through semi-structured, in-depth interviews with 18 purposively selected family caregivers of women with cervical cancer at a major referral hospital in West Java. Participants were selected because they were directly involved in daily care, treatment decisions, logistical support, or emotional assistance. Interviews were conducted between August and October 2025 and continued until thematic saturation was reached, as indicated by repetition of categories and the absence of new major codes in the final interviews. Data were analyzed using inductive–deductive content analysis guided by Elo and Kyngäs, with five researchers conducting independent coding, iterative code comparison, consensus meetings, and theoretical mapping. Results: Four main themes emerged: (1) family involvement in decision-making, including collective discussion, shifting authority roles, and patient autonomy; (2) caregiver burden, involving physical exhaustion, psychological distress, social restriction, stigma, financial pressure, and employment disruption; (3) psycho-spiritual coping mechanisms, including emotional sharing, prayer, crying, patience, and surrender to God; and (4) digital healthcare needs, covering BPJS guidance, treatment information, scheduling, communication pathways, shelter support, and mental–spiritual support. Mapping these themes to Digital Health frameworks and Family Systems Theory clarified how DIVA.ID could translate family experiences into practical navigation functions. Conclusions: This study provides empirical foundations for a culturally sensitive, family-centered digital navigation model in Indonesia. Rather than demonstrating effectiveness, the findings identify design requirements for DIVA.ID that should be tested in subsequent feasibility, usability, and intervention studies. Full article

Background: Digital twins and patient-specific computational models are emerging technologies in healthcare, enabling predictive, personalized, and adaptive interventions. Their integration with artificial intelligence (AI) facilitates the simulation of clinical scenarios, optimization of treatment strategies, and advancement of precision medicine. Despite growing interest, the evidence base is still evolving, highlighting the need for a comprehensive synthesis to identify current trends, applications, and gaps. Methods: A narrative review was conducted using PubMed, Web of Science, and Scopus to identify relevant literature on digital twins in healthcare. Priority was given to systematic reviews and meta-analyses in the selection process. From this process, 28 studies were selected for in-depth analysis, and their findings were complemented by primary research and conceptual, and synthesized evidence to capture emerging trends and real-world applications. Results and Discussion: The analysis revealed that digital twins are increasingly applied for patient-specific monitoring, predictive simulations, and adaptive interventions. Integration with AI enhances their ability to model complex clinical scenarios and support precision medicine. While the selected systematic reviews provide consolidated evidence of established applications, the complementary analysis indicates that these studies actively contribute to stabilizing clinical evidence, consolidating knowledge, and enabling the development of more robust patient-specific strategies. Conclusions: Digital twins are progressively shaping patient-centered healthcare by combining AI-driven simulations with clinical insights. Current research is not only consolidating existing evidence but also exploring novel applications, underscoring the potential of digital twins to enhance precision medicine. Further studies are required to fully integrate these technologies into routine clinical practice. Full article

Aim: To evaluate the status of the nutritional management of diet-treated IEM in Brazil from the perspectives of healthcare professionals, patients, and families. Methods: Data were collected through two nationwide digital questionnaires administered to healthcare professionals involved in dietary management (n = 37) and to patients and caregivers (n = 278), addressing professional training, workload, access to resources, treatment adherence, and socioeconomic factors. Results: Healthcare professionals from 20 out of the 26 Brazilian states participated, most of them female (81%) and dietitians (81%). Although more than half had over 10 years of experience, 59% considered their training insufficient to work with IEM. Only 19% reported exclusive dedication to the field, and 54% were the sole professional responsible for dietary prescriptions at their center. Weekly workload dedicated to IEM varied widely. Among the patients and families, phenylketonuria (60.4%) and glycogen storage disease (25.9%) were the most frequent conditions. Higher educational level and longer time since diagnosis were associated with a better understanding of dietary management (p < 0.05). Among patients on protein-restricted diets, most reported regular use of protein substitutes, although 92% reported poor palatability and 36% reported supply problems. Access to special low-protein foods was limited, and over half of the families reported some level of food insecurity. Conclusions: Significant systemic, logistical, and socioeconomic barriers to optimal dietary management of IEM persist in Brazil, highlighting the need for strengthened public policies, professional training, and equitable access to dietary resources. Full article

Urban healthcare systems are fundamentally constrained by the mismatch between static resource configurations and dynamically evolving patient demand. Under the tiered healthcare system, traditional static planning methods struggle to capture the complexity and randomness of patient flows. While recent reinforcement learning (RL) approaches enable adaptive decision-making, they suffer from dimensionality explosion and unstable convergence due to massive action spaces and delayed spatiotemporal credit assignment in city-scale environments. To address this gap, we propose Twin–Game: a digital twin-driven hierarchical reinforcement learning (HRL) framework that formulates adaptive healthcare resource optimization as a “Twin Game” between a simulation-based game environment (Strategic Sandbox) and a hierarchical decision policy. First, we construct the “first twin”—an offline digital twin that serves as the Strategic Sandbox parameterized with Wuhan’s observed facility, population, and transportation data, while patient arrivals and disease profiles are generated synthetically under documented assumptions because individual-level clinical flow data are not publicly available. This environment integrates a dynamic gravity model with a two-way referral mechanism to represent the nonlinear coupling between hospital attractiveness, crowding levels, and patient choice behaviors. Second, we build the “second twin”—an Option-based HRL policy. The Manager (Macro-level Strategic Layer) uses a Deep Q-Network (DQN) for discrete spatial attention allocation; the Worker (Micro-level Execution Layer) uses Proximal Policy Optimization (PPO) for continuous, fine-grained controls such as bed expansion ratios and personnel scheduling. The two twins interact in a closed-loop game, performing strategy search and game evolution under complex constraints to optimize allocation. Experimental results from the Wuhan case indicate that the Twin–Game framework outperforms static baselines and single-layer RL in reducing average travel times, enhancing resource utilization, and improving tiered diagnosis and treatment within the simulation setting. The results should be interpreted as simulation-based decision-support evidence rather than direct clinical validation. This study provides a data-driven, game-theoretic decision support tool for building resilient urban healthcare systems. Full article

Background/Objectives: Maternal mortality remains disproportionately high in low- and middle-income countries, where ineffective referral systems and a lack of infrastructure contribute to delays in emergency obstetric care. In sub-Saharan Africa, referrals are largely conducted via paper, often resulting in lost documents and limited follow-up. Mobile health (mHealth) offers a promising solution by enabling real-time, bidirectional communication. This study aimed to examine how the Mobile Obstetric Referral Emergency System (MORES), a WhatsApp-based referral platform piloted in 20 rural health facilities and two district hospitals in Bong County, Libera, influences healthcare providers’ communication, collaboration, and relationships. Methods: A mixed-methods design was used. Ninety one (N = 91) providers completed demographic and Trust and Teamwork surveys. Of the 91 providers, 35 providers from rural health facilities and 56 providers from district hospitals participated in a 10-question survey and individual interviews. Results: Survey results indicated high levels of mutual respect, confidence, and teamwork perceived by both the rural health facility and district hospital providers. Qualitative data further expanded on the quantitative results showing the MORES intervention enhanced the timeliness and accuracy of referrals, supported problem-solving between facilities, and fostered shared goals, mutual respect, and knowledge exchange. Conclusions: Providers perceived the MORES to be associated with increased collaboration and continuity of care, as well as a feasible, low-cost, and sustainable intervention to improve obstetric referral systems in low-resource settings. Full article

Background/Objectives: Survivors of sexual violence and domestic violence/intimate partner violence (IPV) often require support beyond the immediate emergency encounter; however, post-acute care remains inconsistently defined, unevenly organised or conceptualised, and fragmented across service systems. This scoping review mapped international post-acute follow-up, care, assistance, and support pathways, with particular attention to organisational models, continuity mechanisms, loss to follow-up after first access, and implications for the Italian context. Methods: We conducted an international health-services scoping review of post-acute follow-up, care, assistance, and support interventions for survivors of sexual violence and domestic violence/IPV. Searches were performed in PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, APA PsycINFO via EBSCOhost, and CINAHL via EBSCOhost. Eligible studies were published from 2013 onward and had to describe an identifiable post-acute component beyond the initial emergency, forensic, or first-contact phase. The review followed a Population–Concept–Context framework and was reported in accordance with PRISMA-ScR. Results: Forty-four studies were included in the core synthesis, comprising 16 studies on sexual violence/sexual assault, 27 on domestic violence/IPV, and one mixed domestic, family, and sexual violence outreach model. The sexual violence literature clustered around early trauma-focused interventions, sexual assault care centre pathways, medical follow-up, follow-up attendance, and digital continuity tools. The IPV literature was broader and included psychotherapy, advocacy and case-management models, housing-first and trauma-informed stabilisation approaches, nurse-led and clinic-based services, outreach and safety-contact programmes, digital interventions, and programmes for system-involved survivors. Across both fields, the pathways most consistently described as supporting continuity combined structured re-contact, coordinated support, and multi-component responses over time. Conclusions: The mapped literature supports conceptualising post-acute responses to sexual violence and domestic violence/IPV as continuity pathways that extend beyond first contact and link healthcare, psychological, advocacy, and social supports. Systems may be better positioned to support continuity when they provide structured follow-up, warm handoffs, coordinated navigation, and context-sensitive recovery models. These findings point to provisional, evidence-informed organisational questions for strengthening post-acute pathways, including in Italy, particularly around structured re-contact, warm handoffs, survivor navigation, and integration between healthcare, anti-violence, psychological, and territorial social-support services. Full article

Background/Objectives: Digital technologies and data-driven approaches are rapidly transforming healthcare practice and enabling more personalized and preventive care. As personal health data becomes increasingly embedded in healthcare systems, understanding how future healthcare professionals interpret these developments is essential for shaping responsive health education. This study aims to identify the factors that students in health-related programs consider important regarding the increasing use of personal health data in healthcare. Methods: An exploratory qualitative focus group study was conducted between March 2024 and July 2025 across five higher education institutions in Australia, Canada, and the Netherlands. Seven focus groups were conducted with forty students from health-related programs, including nursing, public health, occupational therapy, and social work. Participants discussed the use of personal health data in healthcare and reflected on short fictional future scenarios designed to stimulate discussion about possible developments in data-driven healthcare. Data were analyzed using reflexive thematic analysis using ATLAS.ti. Results: Three overarching domains were identified: (1) personalization and prevention, (2) data quality and ethical considerations, and (3) organizational implications and conditions. Students described personal health data as a powerful tool for personalization, prevention, and informed decision-making. At the same time, they raised concerns about data reliability, overreliance on automated systems, patient anxiety, potential dehumanization of care, privacy risks, and emerging inequalities related to access to and representation within data systems. Overall, students appeared neither purely techno-optimistic nor technophobic, but articulated nuanced ethical, cultural, and professional tensions surrounding data-driven care. Conclusions: Preparing future healthcare professionals for data-driven healthcare requires integrating critical data literacy, ethical reflection, interdisciplinary collaboration and opportunities to critically engage with the societal and professional implications of data-driven technologies into health professional education, while ensuring that organizational conditions support the responsible use of personal health data. Full article

The increasing demand of digital technologies and their integration with wearable health devices provides an efficient trigger for next-generation wearable healthcare devices for long-term physiological monitoring. The advancement of energy harvesting mechanism, nanomaterial-based sensor fabrication and their integration with digital technologies have emerged as a promising solution for transforming future of digital health. This study provides a comprehensive summary and framework for wearable self-powered electronic devices, enabling continuous, battery-free health monitoring and advancing the development of sustainable, next-generation digital healthcare systems. This review paper presents a broad and detailed overview of current technologies and sensors advancement in developing low-power wearable, self-powered electronic devices suitable for healthcare applications. The importance and reliable use of key energy harvesting approaches including triboelectric, piezoelectric, thermoelectric, and photovoltaic approaches are systematically presented which focused on development of energy efficient wearable devices. This review further examines the low-power circuit design strategies for flexible electronics focusing personalized healthcare monitoring. Current challenges and limitations related to advanced manufacturing of wearable health devices focusing on large-scale deployment are also analyzed. Finally, the key future research directions are outlined for advancing a next-generation intelligent digital health system. Full article

Persons with Disabilities (PWDs) face persistent barriers to healthcare access, welfare services, and timely medical assistance, particularly where hospital information is fragmented across institutions. In Thailand, these challenges are exacerbated by heterogeneous Hospital Information Systems (HISs) across provincial, district, and sub-district hospitals. This study presents the design, implementation, and evaluation of an integrated mobile application and a hybrid Hospital Information Exchange (HIE) system to enhance healthcare accessibility and service coordination for PWDs. The platform integrates a user-centered mobile application (iOS and Android) with a hybrid data exchange architecture (MedEx Hybrid) combining an application programming interface (API) and Message Queuing Telemetry Transport (MQTT). This enables real-time and on-demand data exchange while accommodating hospitals with limited infrastructure. Key functionalities include disability registration, emergency medical service (1669) integration, appointment management, rights notification, service location mapping, teleconsultation, and peer communication. Deployment across 159 hospitals nationwide demonstrates system scalability and interoperability. The system supports secure access to electronic medical records and enables emergency responders to retrieve patient information during SOS events, improving continuity of care. Findings confirm the feasibility of the proposed system and its potential to support inclusive digital health and national healthcare interoperability. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in clinical decision-support systems, yet its adoption in low- and middle-income countries, including Jordan, remains limited and underexplored. Understanding how healthcare professionals perceive AI-assisted clinical decision-making is essential for safe and contextually appropriate implementation. This study explored healthcare professionals’ perceptions of AI-assisted clinical decision-making in Jordan, with particular attention to trust, accuracy, accountability, professional judgement, digital literacy, and health-system readiness. Medication-related safety and prescribing concerns were examined as secondary cross-cutting issues where they emerged from participants’ accounts. Methods: A qualitative study was conducted using semi-structured, in-depth interviews with 22 purposively sampled healthcare professionals from public, private, and university-affiliated healthcare institutions in Amman, Irbid, and Zarqa. Participants included physicians, nurses, pharmacists, and allied health professionals with varied specialties and levels of seniority. Data were analysed using Braun and Clarke’s reflexive thematic analysis. Member checking, peer debriefing, reflexive memos, and audit trails were used to enhance trustworthiness, and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ). Results: Eight overarching themes were identified: conditional trust in AI-assisted clinical decision-making; concerns regarding accuracy and confident algorithmic errors; accountability and professional responsibility; AI as an adjunct rather than a substitute for clinical judgement; the influence of experience, specialty, and digital literacy on AI acceptance; Jordanian health-system readiness; privacy, confidentiality, and algorithmic bias; and training requirements for safe AI use. Medication-related safety emerged as a cross-cutting concern, particularly in relation to dosing, polypharmacy, drug–drug and drug–herb interactions, and the risk of over-reliance on AI-generated recommendations. Conclusions: Healthcare professionals in Jordan expressed cautious but constructive views toward AI-assisted clinical decision-making. AI was perceived as potentially useful when used to support, rather than replace, professional judgement. Participants’ accounts suggest that safe implementation depends on local validation, clear accountability frameworks, ethical data governance, interprofessional training, and careful consideration of medication-safety expertise where AI tools influence prescribing or therapeutic decisions. These findings highlight the importance of context-sensitive AI governance strategies that support trustworthy, accountable, and professionally supervised AI adoption in healthcare. Full article

South Africa’s public hospital system faces mounting pressure from ageing infrastructure, rising patient demand, and constrained maintenance budgets. While significant investment has been directed toward the construction of new healthcare facilities, the diffusion and adoption of advanced technologies within operations and maintenance (O&M) remain uneven and underdeveloped. This misalignment limits the long-term performance, safety, and sustainability of hospital assets. This study investigates technological diffusion within the O&M environment of a newly commissioned 500-bed regional hospital in Durban, KwaZulu-Natal. A qualitative single-case study approach was adopted, drawing on semi-structured interviews with 14 stakeholders across project delivery and facility management functions. Data were analysed thematically to identify systemic patterns and operational constraints. Findings reveal a persistent reliance on manual, reactive maintenance practices, with minimal integration of digital tools, including building management systems, predictive maintenance technologies, and real-time monitoring platforms. Key barriers include unclear institutional roles, inadequate handover processes, limited technical capacity, and the absence of strategic leadership to drive innovation. A critical disconnect was also identified between managerial expectations and operational realities. The study argues that technological adoption in hospital O&M is not merely a technical challenge but an institutional one. It recommends targeted capacity development, structured transition frameworks, and stronger governance mechanisms to enable sustainable digital integration. Full article