Healthcare Technology Research Articles

Health literacy in the evolving era of personalised medicine and digital health

Abstract Health literacy is pivotal for advancing public health, empowering individuals to grasp health information and make informed decisions. However, health literacy levels are often influenced by social and economic factors, with lower levels correlating with reduced disease-specific knowledge and underutilization of preventive health services. As healthcare technology rapidly evolves, particularly with the advent of personalised medicine (PM) and novel genetic testing methods, there’s an increasing demand for heightened health literacy to effectively navigate these transformative innovations and optimize health outcomes. The European ExACT project aims to assess the understanding of PM and related technologies across the EU. In this context, through a survey conducted in several European countries, including Italy, France, Spain, Poland, the Netherlands, Romania, Hungary, and Germany, demographic data and attitudes towards PM, genetic testing, health portals, and big data were gathered. Logistic regression analysis identified factors influencing knowledge and attitudes. Among 6581 respondents, the results highlight a broad awareness of genetic testing (81%) and health portals (54%) yet a lesser familiarity with PM (48%) and big data (37%). Education level, age, and gender influenced awareness, with Hungary showing significant awareness across all topics (OR 2.09 95%CI 1.46-2.98), Spain excelling in big data (OR 1.88 95%CI 1.55-2.27), and Poland leading in genetic testing knowledge (OR 2.68 95%CI 1.82-3.96). Differences between countries may indicate successful policies and factors that can be replicated. Overall, the study emphasizes the importance of enhancing health literacy and sheds light on the predicting role of sociodemographic factors. It underscores the significance of bolstering health literacy to effectively navigate the advancements in PM and digital health, improving health outcomes in light of these evolving healthcare landscapes. Key messages • In Europe, awareness of personalised medicine, health portals and big data is lacking. Educational initiatives are crucial for effective navigation of innovations, considering regional differences. • Social and economic factors affect health literacy levels, impacting disease-specific knowledge and healthcare utilization.

The Use of Mobile Health Care Among Medical Professionals in the Sichuan-Chongqing Region: Cross-Sectional Survey Study.

The emergence and integration of mobile health care technology have fundamentally transformed the health care industry, providing unprecedented opportunities to improve health care services and professional practice. Despite its immense potential, the adoption of mobile health care technology among health care professionals remains uneven, particularly in resource-limited regions. This study aims to explore the use and influencing factors of mobile health care among health care professionals in the Sichuan-Chongqing region of China and make recommendations. Convenience sampling was used in a cross-sectional study conducted from November 8 to November 14, 2023, to survey frontline clinical health care professionals at 5 district-level secondary public hospitals in the Sichuan-Chongqing region. A web-based questionnaire was used to investigate the use of mobile health care and its influencing factors among the participants. Descriptive analysis and logistic regression analysis were used in the study. A total of 550 valid questionnaires were completed. Among the surveyed health care professionals, only 18.7% (103/550) used mobile health care, with a satisfaction rate of only 50.5% (52/103). Around 81.3% (447/550) did not use any form of mobile health care. The age group of 30-39 years was found to be a significant factor influencing the use of mobile health care by health care professionals (P=.03). The main reasons for not using mobile health care among health care professionals were lack of appropriate technical training and support (266/447, 59.5%), lack of suitable management-specific apps (204/447, 45.6%), and concerns about increased workload (180/447, 40.3%). There were significant differences in the single-factor analysis of the reasons for the nonuse of mobile health care among health care professionals from different specialties (P=.04). Logistic regression analysis indicated that age was the only significant factor influencing the use of mobile health care by health care professionals (P=.04). The utilization rate of mobile health care among health care professionals in the Sichuan-Chongqing region is low. Age is a significant factor that influences whether health care professionals use mobile health care. Providing appropriate technical training and support may help improve the enthusiasm of health care professionals in using mobile health care.

Transitioning Perspectives in Digital Health Through Phenomenology Integration.

The evolution of digital health, from its early days as eHealth to its current expansive scope, reflects a significant transformation in health care delivery and management. This transition underscores the integration of digital technologies across the health continuum from prevention and diagnosis to treatment and rehabilitation. The emergence of digital health has introduced innovative solutions but also posed challenges, particularly in aligning technological advancements with health needs, human experiences, and ethical considerations. This position paper aims to explore the integration of phenomenology in digital health, advocating for a paradigm that emphasizes the centrality of human experience in the design and implementation of digital health solutions. It specifically seeks to address challenges related to relevance for individuals who "speak" different languages, ensuring long-term use, addressing digital and health literacy, coordinating various sources, and navigating ethical issues in the rapidly evolving digital health landscape. Drawing upon years of research and practical experience in communication technologies and health, this paper uses a reflective approach to examine the intersection of digital health and phenomenology. It reviews the historical development of digital health, identifies the challenges faced during its evolution, and discusses the potential of phenomenological methods to enhance user-centered design and ethical practices in digital health. The integration of phenomenology into digital health facilitates a deeper understanding of user experiences, enabling the development of more responsive and ethical digital health solutions. Participatory design models, informed by phenomenological perspectives, offer a pathway to bridge the gap between technological innovation and human-centric health care. The paper highlights successful practices in digital health development, including mobile apps for vaccination decision-making and platforms for managing chronic conditions, illustrating the benefits of a phenomenological approach. Transitioning perspectives in digital health through phenomenology integration represents a critical step toward realizing the full potential of digital technologies in health care.

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

BackgroundThe global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.PurposeThis review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety.MethodsWe deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.ResultsWe report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.ConclusionsOur findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Exploring the feasibility of using a bedside device to help prevent nighttime falls.

Older people living in the community are at risk of preventable nighttime falls. Technology can support falls interventions but there is a lack of research into this area. A new bedside device called Bide senses movement and changes in light levels and plays a prerecorded message encouraging the user to follow falls prevention advice when trying to mobilise at night. To test the feasibility of using the Bide device in a community health service. Healthcare workers were trained in the use of the Bide device and asked to identify potential participants. The research team subsequently recruited participants, provided them with a device and explained how to use it. After four weeks, participants took part in face-to-face semi-structured interviews regarding the acceptability of the device. Participants' fear of falling was calculated before and after the intervention using the Falls Efficacy Scale-International. The Bide device appeared to increase patients' confidence to mobilise at night and no adverse events were reported from its use. The device was generally found to be acceptable and easy to use, although one participant found it irritating. Healthcare workers flagged up very few potential participants to the research team, which may have been due to workload pressures and a disconnect between healthcare workers and technology. It appears feasible to study the use of the Bide device for nighttime falls prevention in the community. To enhance recruitment, a member of the research team may be embedded in the clinical team with the aim of proactively identifying potential participants.

Policies, Progress, and Prospects for Internet Telemedicine in China

ABSTRACT Around the world, the adoption of digital technologies in health care has accelerated considerably in the wake of the COVID-19 pandemic. Prior to the emergence of the pandemic, China had already embarked on a private sector, technology enterprise-led creation of an innovative internet health care ecosystem, which has dramatically transformed China’s health care landscape. In this article, we describe the evolution of China’s internet health market, focusing on internet telemedicine. We trace its early origins with the establishment of information networks in the 1980s, to the emergence of a fast-growing internet health market in the 2010s that leveraged the capabilities of technology enterprises in e-commerce, logistics, and payment systems. Private health care platforms have played a central role throughout this transformative process. The supplyside of the market has a unique public-private mix structure, with the coexistence of public and private internet hospitals, and with the majority of telemedicine services provided by public hospital doctors working on private platforms in a dual practice capacity. We conclude with a discussion of the prospects of internet telemedicine, including how it should be optimized and harnessed to improve China’s health system. Finally, we set out areas where more research is needed.

The role of machine learning in the improvement of physician-nurse relationships when the management of burn patients: A narrative review

In recent years, machine learning (ML) has emerged as a transformative technology in healthcare, providing significant advancements in patient care and management. Burn care, which necessitates comprehensive and coordinated efforts due to the severe and multifaceted nature of burn injuries, particularly benefits from ML's capabilities. This literature review investigates how ML enhances the collaboration between physicians and nurses in managing burn patients. In the present study, significant findings show that ML's predictive analytics can predict patient outcomes and complications, helping with proactive care strategies. ML-driven decision support systems offer real-time, evidence-based recommendations, ensuring consistent care approaches. In addition, ML-powered virtual simulations improve training and comprehension of roles, while advanced electronic health records (EHR) systems streamline documentation and information sharing. Continuous quality improvement is supported by ML's data-driven insights, leading to improved patient monitoring and management. Ultimately, integrating ML in burn care significantly improves physician-nurse collaboration, resulting in better patient outcomes. This includes reduced infection rates and shorter hospital stays. This highlights the vital role of ML in transforming healthcare delivery and professional collaboration in managing complex conditions like burn injuries.

Optimizing Heart Disease Diagnosis: Feature Selection Techniques for Enhanced Machine Learning Model Performance

Heart disease is a growing global concern, affecting people across various age groups and genders. Detecting heart failure early is crucial, and ongoing research leverages advancements in healthcare technology, machine learning, imaging techniques, and data science to analyze vast datasets for this purpose. However, not all data attributes contribute equally to diagnosing heart disease, and the inclusion of irrelevant features can increase resource demands and potentially lead to inaccurate predictions with fatal consequences. This study focuses on feature extraction and reduction techniques to identify the most critical attributes for heart disease diagnosis, balancing resource efficiency with diagnostic accuracy. Using a dataset from the UCI repository, which includes both continuous and categorical features, we standardize the data and split it into training and testing sets in an 80:20 ratio. We then apply feature selection techniques to machine learning models such as K-nearest neighbor, decision tree classifier, SVM, logistic regression, and random forest. The models' predictive performance is evaluated using confusion matrices and ROC curves, demonstrating the impact of feature selection on diagnostic accuracy.

Integration of Health Care Modules in Current Health Care Management Systems in India

Health care beyond worldwide boundaries is a critical problem, particularly when the population differs in terms of geographic position, culture, habits, socioeconomic background, medical strategy, and variations in disease burden over time. For countries like India, with a significant percentage of high-end medical experts as well as support and other health care systems, the development and integration of comprehensive and functionally versatile health care management systems are among the most important clinical problems addressed. The central dogma of a country that facilitates adopting a conglomeration will create a dedicated population to provide tailor-made health care in health shield, health protection, and health care delivery; a nation that takes care of a nation. Until now, it has given us E-Health, and offerings have been fine-tuned in research literature. In India, the health care domain is currently breaking the boundaries of knowledge workers in clinical and research domains working with the help of these workhorses (Fletcher-Brown et al. 2021). Many e-governance and public health reform programs in India are under consideration, and many of them are under implementation. This research focuses more on the integration of health care technology relevant to the paper topic within clinical setups, passive provision, and utilization of services for administrative purposes technologically implemented (Das et al. 2023).

Cost of epilepsy in the Russian Federation

Background. Epilepsy underdiagnosis in Russia, population aging, innovative drugs and expensive treatment regimens, the introduction of additional and alternative methods such as neurosurgery, annually increase the cost for treatment of epilepsy patients. In this regard, an issue of maximizing benefits for the largest number of the population becomes relevant. Pharmacoeconomical research within the assessment of healthcare technologies helps to choose the most economically and medically effective treatment strategy among numerous alternatives.Objective: analysis of epilepsy-associated costs in the Russian Federation, aimed at supporting decision-making processes in the field of health policy and financing.Material and methods. The empirical study was based on collecting the data on a continuous sampling of 384 epilepsy outpatient records from subjects aged 19 to 79 years for the period 2019–2022. The issues related to all costs of epilepsy were discussed. While planning, conducting and analyzing these studies, the principles of strengthening the reporting of observational studies in epidemiology (STROBE) were taken into account.Results. The total annual per-patient expenditure on epilepsy treatment comprised a fifth of gross domestic product per capita. Indirect costs exceed direct cost savings. It was found out that the economic damage caused to society, family, and individual related to epilepsy is three times higher than the direct costs.Conclusion. The results obtained are consistent with other publications in which unemployment among epilepsy patients is one of the major cost-determining factors and illustrates a critical need to maintain employment for such patients or to find suitable alternative work through professional retraining.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

Utilizing Blockchain Technology for Healthcare and Biomedical Research: A Review

Utilizing Blockchain Technology for Healthcare and Biomedical Research: A Review

Eco-physiological approach to assessing the functional state of workers in geological prospecting expeditions during meridian movement between mid-latitudes and the Arctic region

BACKGROUND: The current challenge lies in defining the unique characteristics of compensatory and adaptive mechanisms formation, as well as the development of healthcare technologies for various forms of work in Arctic region conditions. AIM: To study associations between climate patterns and physiological responses to daily fluctuations in meteorological factors. Specifically, we aimed to investigate how these factors impact the cardiovascular system and psychophysiological well-being of workers during geological prospecting expeditions along the meridian travel between Tyumen (57° 09’ N) and Yamal (71° 11ʹ N). MATERIAL AND METHODS: A total of 115 men aged 20–40 years in mid-latitude conditions (baseline data) and during occupational relocation to the Arctic region comprised the sample. The examination included recording key indicators of the cardiovascular system (heart rate, blood pressure, mean arterial pressure, pulse pressure), ECG in standard unipolar and augmented leads, and psychophysiological assessments (evaluation of trait and state anxiety, stress diagnostics, regulation of self-control, and emotional lability). Additionally, to calculate the natural and climatic contrast of regions in Western Siberia, meteorological data and geomagnetic activity indicators were analyzed based on eight daily measurements over 12 months across 11 years using a unified system of meteorological station indicators in Tyumen and Kharasavey. RESULTS: The cardiovascular system experiences desynchronosis during meridian movement from middle latitudes to the Arctic region. This condition is influenced by the interregional climatic contrast and the differences in daily rhythm characteristics of meteorological data between the initial and final destinations. The interaction within the system of meteorological rhythms plays an important role in these variations. CONCLUSION: The established associations within the ‘meteorhythms–biorhythms’ system underscore the necessity for developing regional norms for chronophysiological parameters of a healthy individual under specific environmental and climatic conditions. It also highlights the importance of dynamic monitoring of deviations in these parameters during occupational relocations between mid-latitudes and the Arctic region across different seasons. This approach is essential for the effective monitoring of maladaptive disorders and for predicting the potential development of pathologies.

Special Issue on Robotics and Mechatronics Technology for Sports, Exercise, and Health Care (Part 2)

Robotics, mechatronics, and digital technologies are advancing significantly in sports, exercise, and healthcare, and offer innovative solutions that shape the future of these fields. In Part 2 of this special issue, we examine independent research and cutting-edge developments that address new challenges and provide unique contributions to these domains. Technological advancements in assistive devices and rehabilitation offer new solutions for improving the quality of life of individuals with physical challenges. Researchers of wearable assistive devices and prosthetics continue to devise methods to enhance mobility and support physical functions, thereby benefiting both users and healthcare providers. Sports and motion analysis remains a key focus area, where innovative technologies are employed to analyze and improve athletic performance. By capturing and evaluating detailed movement data, these technologies can help athletes refine their techniques and achieve greater precision in their sports, thus ultimately resulting in enhanced performance and injury prevention. As in Part 1, the studies presented in this issue demonstrate the practical applications of cutting-edge technologies and offer insights into their future potential. We hope that these contributions will inspire researchers and practitioners alike and provide a foundation for continued advancements in these exciting and impactful fields.

Prevention and Management of Restenosis After Coronary Interventional Therapy

Percutaneous coronary intervention (PCI) is one of the key methods for treating coronary heart disease, yet post-procedural restenosis remains a significant clinical challenge. Restenosis refers to the re-narrowing of the coronary artery after PCI due to factors such as neointimal hyperplasia and smooth muscle cell proliferation, which adversely affects blood flow restoration and patient prognosis. To address this issue, this study systematically explores strategies for the prevention and management of restenosis. Effective preventive measures are discussed in terms of pharmacological prevention, stent selection, optimization of procedural techniques, and lifestyle interventions. Additionally, the article analyzes pharmacological treatment options and re-intervention strategies for patients experiencing restenosis. The findings indicate that personalized treatment plans, optimized stent selection, rigorous postoperative management, and long-term monitoring are key to reducing the incidence of restenosis. Future research should further investigate the application of genetic testing and smart healthcare technologies in the management of restenosis.

Elderly care and health monitoring using smart healthcare technology: An improved routing scheme for wireless body area networks

AbstractHypertensive patients need regular checkups and constant monitoring for taking time critical decisions by the medical experts. Unfortunately, it is hard to maintain uninterrupted patient health surveillance due to limited medical staff resulting in an increasing mortality rate annually. Thanks to recent developments in wireless sensor networking, we can monitor constantly and efficiently diverse parameters of a network. Similarly, Wireless Body Area Networks (WBANs) have become a well‐known sub‐branch of Wireless Sensor Networks. Such sensor networks can be leveraged for patient health monitoring, minimising the medical staff workload. Wireless Body Area Networks require tiny sensor nodes with limited battery power. Therefore, it is always desirable to design effective routing schemes that can enhance network lifetime, and reduce packet drop ratio. In this paper, we re‐simulate and explain in detail the results of a selected published journal article for WBANs and provide some modifications to improve the network's overall performance. Based on these amendments, the modified protocol successfully extends the operational time of the network than the original. Our performance evaluation parameters are dead nodes, throughput, residual energy, and path loss versus the number of rounds. These analyses support effective solutions that improve network performance and data delivery ratio.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

Breathable and Stretchable Epidermal Electronics for Health Management: Recent Advances and Challenges.

Advanced epidermal electronic devices, capable of real-time monitoring of physical, physiological, and biochemical signals and administering appropriate therapeutics, are revolutionizing personalized healthcare technology. However, conventional portable electronic devices are predominantly constructed from impermeable and rigid materials, which thus leads to the mechanical and biochemical disparities between the devices and human tissues, resulting in skin irritation, tissue damage, compromised signal-to-noise ratio (SNR), and limited operational lifespans. To address these limitations, a new generation of wearable on-skin electronics built on stretchable and porous substrates has emerged. These substrates offer significant advantages including breathability, conformability, biocompatibility, and mechanical robustness, thus providing solutions for the aforementioned challenges. However, given their diverse nature and varying application scenarios, the careful selection and engineering of suitable substrates is paramount when developing high-performance on-skin electronics tailored to specific applications. This comprehensive review begins with an overview of various stretchable porous substrates, specifically focusing on their fundamental design principles, fabrication processes, and practical applications. Subsequently, a concise comparison of various methods is offered to fabricate epidermal electronics by applying these porous substrates. Following these, the latest advancements and applications of these electronics are highlighted. Finally, the current challenges are summarized and potential future directions in this dynamic field are explored.

Patient Preferences Concerning Humanoid Features in Healthcare Robots

In this paper, I argue that patient preferences concerning human physical attributes associated with race, culture, and gender should be excluded from public healthcare robot design. On one hand, healthcare should be (objective, universal) needs oriented. On the other hand, patient well-being (the aim of healthcare) is, in concrete ways, tied to preferences, as is patient satisfaction (a core WHO value). The shift toward patient-centered healthcare places patient preferences into the spotlight. Accordingly, the design of healthcare technology cannot simply disregard patient preferences, even those which are potentially morally problematic. A method for handling these at the design level is thus imperative. By way of uncontroversial starting points, I argue that the priority of the public healthcare system is the fulfillment of patients’ therapeutic needs, among which certain potentially morally problematic preferences may be counted. There are further ethical considerations, however, which, taken together, suggest that the potential benefits of upholding these preferences are outweighed by the potential harms.

The influence of artificial intelligence within health-related risk work: a critical framework and lines of empirical inquiry

In this editorial we highlight the need for empirical studies into the growing use of artificial intelligence (AI) technology in healthcare and social work settings, especially studies which are theoretically informed by critical social science studies of risk and uncertainty. In setting out the importance of interpretative and critical traditions for research into such AI-oriented forms of risk work, we propose three important conceptual lines of inquiry which empirical studies might follow. First, we sketch ways in which the enactment of AI in healthcare work may be changing how risk is handled amid professional decision-making, and creating new categories of patient/service-user. Patients may be evaluated as being at lower or higher risk depending, respectively, upon their engagement or non-engagement with AI-technologies. These questions of (non-)engagement lead us to consider, second, the trust and distrust dynamics around AI-technologies, exploring the potential inequalities that can emerge as a result of (non) engagement. We then consider drivers of this technological embrace in terms of hope and magical thinking in technological-imaginaries, connecting these cultural tendencies to broader structures of ideology and political-economic interests. We conclude this editorial with a plea to social scientists to be cautious to avoid both techno-optimistic narratives and alarmist warnings regarding the implications of artificial intelligence (AI). Instead, we argue that our focus should be a theoretically informed and detailed examining of how expectations (pertaining to risk, trust, and hope) materialise in practice, particularly in the daily experiences of those who develop and enact AI technologies in care settings.