Search Results (889)

Background/Objectives: The growing number of older adults with complex healthcare needs increases demand for homecare services, while a shrinking workforce often lacks skills for advanced tasks. Digital health is seen as a promising tool to address these challenges. This study explored Swedish homecare professionals’ perceptions of the potential use of digital twins in daily work. Methods: Four focus group discussions were conducted with 31 homecare professionals; two groups each in one urban/rural and one rural municipality. Data were analyzed using inductive content analysis. Results: Three main themes emerged: (i) Perceptions of digital twins as support for older adults, including needs-based, individualized care and proactive support enabling preventive measures; (ii) Perceptions of digital twins as support for professionals, including a better work environment through streamlined tasks and flows and enhanced planning and assessment; and (iii) Concerns about digital twins, focusing on ethical and social issues and limited understanding, which were related to monitoring aspects, the importance of physical visits, and how the technology works. Conclusions: Digital twins are perceived by professionals to have the potential to improve homecare services by supporting both older adults and professionals; however, further research is needed to address concerns and practical implications. Full article

Background and Objectives: Academic point-of-care (POC) manufacturing enables the in-hospital design and production of patient-specific medical devices within certified environments, integrating clinical practice, engineering, and translational research. This model represents a new academic ecosystem that accelerates innovation while maintaining compliance with medical device regulations. Gregorio Marañón University Hospital has established one of the first ISO 13485-certified academic manufacturing facilities in Spain, providing on-site production of anatomical models, surgical guides, and custom implants for oral and maxillofacial surgery. This study presents a retrospective review of all devices produced between April 2017 and September 2025, analyzing their typology, materials, production parameters, and clinical applications. Materials and Methods: A descriptive, retrospective study was conducted on 442 3D-printed medical devices fabricated for oral and maxillofacial surgical cases. Recorded variables included device classification, indication, printing technology, material type, sterilization method, working and printing times, and clinical utility. Image segmentation and design were performed using 3D Slicer and Meshmixer. Manufacturing used fused deposition modeling (FDM) and stereolithography (SLA) technologies with PLA and biocompatible resin (Biomed Clear V1). Data were analyzed descriptively. Results: During the eight-year period, 442 devices were manufactured. Biomodels constituted the majority (approximately 68%), followed by surgical guides (20%) and patient-specific implants (7%). Trauma and oncology were the leading clinical indications, representing 45% and 33% of all devices, respectively. The orbital region was the most frequent anatomical site. FDM accounted for 63% of the printing technologies used, and PLA was the predominant material. The mean working time per device was 3.4 h and mean printing time 12.6 h. Most devices were applied to preoperative planning (59%) or intraoperative use (35%). Conclusions: Academic POC manufacturing offers a sustainable, clinically integrated model for translating digital workflows and additive manufacturing into daily surgical practice. The eight-year experience of Gregorio Marañón University Hospital demonstrates how academic production units can enhance surgical precision, accelerate innovation, and ensure regulatory compliance while promoting education and translational research in healthcare. Full article

Background: Medication adherence and persistence in treating chronic diseases present as a continuous challenge for healthcare providers in long-term management. The most frequent reasons that several diseases are poorly controlled in the population include suboptimal drug adherence and discontinuation of therapies. One main issue why physicians cannot detect patients with poor adherence is that they have relatively limited time and tools to do so. Aim: To review the critical role of medication adherence in the management of chronic diseases by addressing the following: what medication adherence is; its critical role; factors and strategies influencing it; challenges and consequences of poor adherence; patients at risk; present and future strategies in place to detect and improve adherence; implications for public health and health value creation for patients; key analytical frameworks for understanding it; determinants; how adherence improves health; the role of healthcare professionals and technological innovations; implications of medication adherence; adherence as a key area for exploring the psychological mechanisms underlying patient behavior; and patient adherence as a major social and public health challenge. Finally, this review considers strengths, limitations, recommendations, and future value. Methodology: The following databases were used to carry out the review: PubMed, Scopus, Google Scholar, and ScienceDirect. The following themes were combined in the search: what adherence is, why it is critical, why adherence occurs, and how to improve adherence. The following search terms were used: what adherence is and critical, why and adherence and occurs, and how and to improve adherence. Results: Under the theme of why adherence is critical, five sub-themes were reviewed; four sub-themes were reviewed under the theme of why adherence occurs; and five sub-themes were reviewed under the theme of how to improve adherence. Conclusions: Strategies to enhance medication adherence involve a comprehensive approach that includes patient education, streamlined treatment plans, digital tools, and effective communication from healthcare professionals. Full article

Objectives: The purpose of the current research is to examine and analyze the attitudes of healthcare service users towards the integration of remote medical services into ophthalmology in Bulgaria, including teleglaucoma. Methods: A cross-sectional survey study was conducted among 902 healthcare users during the period from May 2023 until December 2024. Descriptive statistics, parametric, and non-parametric tests for hypothesis testing were used. Results: The present study outlined predominantly positive attitudes towards the use of telemedicine services in ophthalmology, with 69.6% of respondents reporting a positive overall opinion in the final assessment. The greatest support was observed during remote consultations with a familiar doctor (77.4%) and during continuous follow-up of eye conditions (55.2%). Willingness to use such services was lower in emergencies or when contacting an unfamiliar specialist. A significant correlation was established between socio-demographic characteristics and attitudes—respondents with greater education levels (p = 0.006), men, and younger participants were more positive towards telemedicine (p < 0.05). The high level of awareness about glaucoma, particularly among those with university-level education, served as a positive prerequisite for the implementation of teleophthalmology services related to its monitoring. Mobile applications and digital solutions were evaluated as beneficial means of facilitating communication and increasing adherence to treatment. Regarding the use of artificial intelligence, certain skepticism and insufficient awareness levels were observed, which required additional efforts to increase trust and digital literacy among users. Conclusions: The implementation of telemedicine services into ophthalmology has potential but outlines the necessity of considering the individual attitudes of applying coherent quality and safety standards and of directed awareness campaigns, especially towards the groups of lower technological and healthcare literacy. Full article

Climate change is the greatest health threat of the 21st century, with healthcare contributing approximately 4–5% of global greenhouse gas emissions. Decarbonising healthcare, the deliberate reduction of emissions across all healthcare activities, is essential to reduce the health sector’s environmental impact while maintaining equitable, high-quality care. Preparing future health professionals for sustainable, low-carbon practice is increasingly recognised as critical; however, education on healthcare decarbonisation remains inconsistent and weakly embedded in curricula. This scoping review mapped existing educational resources for pre-registration health profession students. Following the JBI methodology, six databases (Scopus, Web of Science, MEDLINE, CINAHL, PsycINFO, and GreenFILE) were searched in April 2025 (updated in October 2025). Data were thematically analysed. In total, 32 studies met inclusion criteria, comprising 17 mixed-methods, 11 quantitative, and 4 qualitative designs. Most interventions were multimodal, addressing sustainability or climate change through simulation, digital, formal, or didactic methods. Knowledge and attitudes were the most frequently evaluated outcomes. Thematic analysis identified knowledge and awareness, attitudes and emotional responses, behavioural intent and action, identity formation through collaborative learning, and barriers to decarbonisation. Findings suggest that blended, interactive, and technology-enhanced education improves knowledge, attitudes, and identity, but sustained impact requires longitudinal, skills-based, and policy-aligned interventions to drive meaningful healthcare decarbonisation action. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article

This study investigates how digital transformation in healthcare shapes sustainable customer value by analyzing the role of digital quality and its influence on satisfaction and loyalty within an AI-based diabetes prognostic service. Drawing on system, information, and service quality as core dimensions of digital quality, the study examines their direct effects on satisfaction and their contribution to loyalty formation relative to traditional service factors. Using survey data collected from over 1000 users of a digital healthcare platform equipped with an AI-driven diabetes prognostic algorithm, 800 valid responses were analyzed through PLS-SEM in SmartPLS 4.0. The results show that both traditional service attributes and digital quality significantly enhance customer satisfaction, which in turn promotes loyalty. However, digital quality does not strengthen the satisfaction–loyalty linkage, indicating that its value lies in establishing baseline trust and usability rather than amplifying loyalty outcomes. Environmental uncertainty—captured as technological and market uncertainty—also positively affects loyalty. This study contributes to digital healthcare research by providing empirical evidence from an AI-based long-term prognostic service and clarifying that digital quality operates as a foundational hygiene factor essential for sustainable customer value, rather than as a competitive differentiator. Full article

The growing economic pressures on healthcare systems have heightened the need for effective and sustainable cost management strategies. This study presents a PRISMA-based systematic review of 210 peer-reviewed articles published between 1974 and 2024, retrieved from the Scopus and Web of Science databases. Following a structured selection and screening process, the articles were analysed to identify dominant cost control tools, contextual applications, and methodological trends across diverse health systems. The findings highlight a strong prevalence of Activity-Based Costing (ABC), Diagnosis-Related Groups (DRG), and benchmarking practices, predominantly in public hospital settings. However, significant thematic gaps remain, particularly concerning low-income countries, interdisciplinary integration, and the evaluation of digital technologies for financial optimisation. This review provides a comprehensive thematic synthesis of international research, consolidating knowledge in healthcare cost management and offering evidence-based recommendations to guide future empirical research, policy design, and strategic planning in health finance. Full article

As new technologies, such as electronic medical records (EMRs), are introduced into healthcare services, we need to consider how they may be incorporated into simulated environments, so as to maintain and enhance authenticity and learning opportunities. While EMRs have revolutionised clinical practice, many education settings continue to rely on paper-based documentation in simulation, creating a widening gap between educational environments and real-world clinical workflows. This disconnect limits learners’ ability to engage authentically with the tools and resources that underpin contemporary healthcare, impeding the transfer of knowledge to the clinical environment. This practice-based commentary draws on institutional experience from a large, multi-disciplinary simulation-based education facility that explored approaches to integrating EMRs into simulation-based education. It describes the decision points and efforts made to integrate an EMR into simulation-based education and concludes that while genuine EMR systems increase fidelity, their technical rigidity and data governance constraints reduce authenticity. To overcome this, Adelaide Health Simulation adopted an academic EMR (AEMR), a purpose-built digital platform designed for education. The AEMR maintains the functional realism of clinical systems while offering the pedagogical flexibility required to control data, timelines, and learner interactions. Drawing on this experience, this commentary highlights how authenticity in simulation-based education is best achieved not through technological replication alone, but through deliberate use of technologies that align with clinical realities while supporting flexible, learner-centred design. Purpose-built AEMRs exemplify how digital tools can enhance both fidelity and authenticity, fostering higher-order thinking, clinical reasoning, and digital fluency essential for safe and effective contemporary healthcare practice. Here, we argue that advancing simulation-based education in parallel with health service innovations is required if we want to adequately prepare learners for contemporary clinical practice. Full article

Background: S3-level clinical practice guidelines represent the highest standard of evidence-based healthcare, integrating systematic reviews, formal evidence grading, and structured expert consensus. In periodontology, current S3-level guidelines provide robust recommendations for the management of stage I–III periodontitis. However, increasing clinical complexity, emerging diagnostic technologies, and the need for patient-centred and implementation-oriented care highlight important gaps that warrant further methodological refinement. Objective: This review aims to critically appraise the conceptual foundations, strengths, and limitations of existing S3-level periodontal guidelines and to propose a structured roadmap for the development of next-generation S3 guidance. Methods: A narrative and methodological review was conducted focusing on key European S3-level guidelines in periodontology and endodontics, with emphasis on guideline methodology, evidence grading, outcome prioritization, and consensus processes. Results: Current S3-level periodontal guidelines demonstrate strong methodological rigor but show limited coverage of stage IV periodontitis, peri-implant diseases, and endo–perio lesions. In addition, emerging domains such as biomarker-based diagnostics, artificial intelligence-assisted decision support, and implementation science are not yet systematically integrated. Conclusions: Future S3-level periodontal guidelines should incorporate clinical complexity, patient-reported outcomes, precision diagnostics, digital technologies, and real-world implementation strategies to enhance personalization, transparency, and clinical impact. Full article

For individuals with mental illness who experience multidimensional marginalization, the risks of encountering discrimination and receiving inadequate care are compounded. Artificial intelligence (AI) systems have propelled the provision of mental healthcare through the creation of digital mental health applications (DMHAs). DMHAs can be trained to identify specific markers of distress and resilience by incorporating community knowledge in machine learning algorithms. However, DMHAs that use rule-based systems and large language models (LLMs) may generate algorithmic bias. At-risk populations face challenges in accessing culturally and linguistically competent care, often exacerbating existing inequities. Creating equitable solutions in digital mental health requires AI training models that adequately represent the complex realities of marginalized people. This narrative review analyzes the current literature on digital mental health through an intersectional framework. Using an intersectional framework considers the nuanced experiences of individuals whose identities lie at the intersection of multiple stigmatized social groups. By assessing the disproportionate mental health challenges faced by these individuals, we highlight several culturally responsive strategies to improve community outcomes. Culturally responsive strategies include digital mental health technologies that incorporate the lived experience of individuals with intersecting identities while reducing the incidence of bias, harm, and exclusion. Full article

Background/Objectives: Digital transformation is increasingly essential for healthcare organizations to improve operational efficiency and service quality. However, in developing countries such as Indonesia, many lower-middle-class hospitals lag due to limited financial, human, and infrastructural resources. This study examines how environmental dynamism—comprising regulatory changes, market pressures, and technological shifts—affects the digital capabilities of these hospitals. Methods: A quantitative, cross-sectional survey was conducted in Class C and D hospitals across Indonesia. Respondents included hospital directors, deputy directors, and IT heads. Data were collected through structured questionnaires measuring environmental dynamism and digital capability using a six-point Likert scale. Reliability testing yielded Cronbach’s alpha values above 0.96 for both constructs. Correlation analysis was performed to examine the relationship between environmental dynamism and digital capability. Results: Findings reveal a weak positive correlation (r = 0.1816) between environmental dynamism and digital capability. Although external factors such as policy regulations and technological competition encourage digital adoption, hospitals with limited internal resources struggle to translate these pressures into sustainable transformation. Key challenges include low ICT budgets, inconsistent staff training, and insufficient infrastructure. Conclusions: The results suggest that environmental change alone cannot drive digital readiness without internal capacity development. To foster resilient digital healthcare ecosystems, policy interventions should integrate regulatory frameworks with practical support programs that strengthen resources, leadership, and human capital in lower-middle-class hospitals. Full article

Asthma is a common chronic disease in children, contributing to significant morbidity and healthcare utilization worldwide. The integration of artificial intelligence (AI) and machine learning (ML) into pediatric asthma care is rapidly advancing, offering new opportunities for early diagnosis, risk stratification, and personalized management. AI-driven tools can analyze complex clinical, genetic, and environmental data to identify asthma phenotypes and endotypes, predict exacerbations, and support timely interventions. In pediatric populations, these technologies enable non-invasive diagnostic approaches, remote monitoring through wearable devices, and improved medication adherence via smart inhalers and digital health platforms. Despite these advances, challenges remain, including the need for pediatric-specific datasets, transparency in AI decision-making, and careful attention to data privacy and equity. The integration of AI in pediatric asthma care and into the clinical decision system can offer personalized treatment plans, reducing the burden of the disease both for patients and health professionals. This is a narrative review on the applications of AI and ML in pediatric asthma care. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives. Full article