The Internet of Things: Impact and Implications for Health Care Delivery

Point-of-Care Sensors and Medical Internet-of-Things Technologies to Manage Catheter-Associated Urinary Tract Infections in the Intensive Care Unit

This article examines the current technologies used with indwelling urinary catheters to monitor potential catheter-associated urinary tract infections (CAUTIs) in the intensive care unit. Advancements in medical internet-of-things, artificial intelligence, and machine learning technologies can enable remote monitoring, improving working conditions for critical care nurses, alleviating increasing workloads, reducing direct health care costs, and paving the way for predictive analytics to lower CAUTI rates. The most effective approach to preventing CAUTIs is implementing technologies that facilitate for the early removal of indwelling urinary catheters from patients.

Bridging the gap: a paradigm shift in medical device regulations in India

India is among the top 20 international markets for medical devices. With increasing economic growth, rising life expectancy, and rapid technological advancements, the healthcare needs of the population have surged. This is particularly evident with the rise in chronic diseases, growing healthcare awareness, and the growing demand for personalized care and treatment. Medical devices, which encompass a wide range of machines, instruments, apparatus, and even software, play a vital role in diagnosing, treating, preventing, monitoring, and providing palliative care for various health conditions. Examples include X-ray machines, pacemakers, hip implants, ultrasound machines, glucose metres, prosthetics, and defibrillators. These devices are indispensable in the healthcare continuum across India. However, the development of India's medical device industry faces significant challenges, including limited infrastructure, outdated regulatory frameworks, and an inability to support sustainable growth. The Johnson & Johnson hip implant scandal highlighted the gaps in the existing regulatory system. The current Drugs and Cosmetics Act of 1940 (DCA, 1940) is inadequate in defining medical devices, regulating their import and manufacture, ensuring patient safety, managing adverse event reporting, and holding medical device companies accountable. To address these issues, the Medical Device Rules was introduced by the Government of India in 2017 (MDR, 2017). However, there has been a long-standing need for a more comprehensive legislative framework specifically for medical devices. To this end, the new Drugs, Medical Devices, and Cosmetics Bill of 2022 was proposed to substitute the outdated 1940 Act. This doctrinal research critically examines the shortcomings of the current DCA,1940 vis-à-vis the case study of Johnson & Johnson hip implant issue and provides a comparative analysis of this Act with the newly proposed Drugs, Medical Devices, and Cosmetics Bill of 2022.

Comprehensive analysis of Australia's aged care system to inform policies for a sustainable future

Background

Australia's aging population presents significant socioeconomic challenges, necessitating the aged care sector reforms. Projections indicate continued growth in this demographic, prompting the implementation of various funding mechanisms to support healthy aging. However, notable disparities persist, including care quality concerns, workforce shortages, and safety issues, hindering the sector's ability to meet consumer expectations. Despite recognition of these challenges, no comprehensive overview exists addressing these shortcomings. This study aims to provide a comprehensive analysis of the literature to identify these challenges and inform policy development.

Methods

In this study, a scoping review was conducted, examining primary and secondary sources, including peer-reviewed articles, government reports, and aged care policies. The Scopus database was searched using relevant keywords, and a snowball search technique was employed to identify additional literature. The inclusion criteria were applied, and journal articles were screened for titles and abstracts before full-text analysis. Thematic analysis was conducted on selected literature, and secondary data were from administrative and organizational websites and agencies.

Results

Findings indicate a preference for home-based care among older Australians due to concerns about quality and safety in aged care facilities. While the Australian government has developed reforms and policies to govern the sector, funding remains insufficient to meet the escalating demand for high-quality care. Challenges include workforce shortages, the growing burden of aging, and difficulties in adopting emerging technologies, which impact the quality of care delivered to older Australians.

Conclusion

This paper serves as a resource for policymakers and aged care professionals, informing the development of reforms to address pressing issues in the sector. A comprehensive evaluation of existing knowledge provides a clearer understanding of current and future obstacles ensuring a holistic view and fostering the development of sustainable aged care reforms.

Integrating 6G technology in smart hospitals: challenges and opportunities for enhanced healthcare services

Introduction

The advent of sixth-generation (6G) wireless communication technology promises to transform various sectors, with healthcare-particularly smart hospitals-standing to gain significantly. This study investigates the transformative potential of 6G in healthcare by exploring its architectural foundations and enabling technologies.

Methods

A comprehensive review and analysis were conducted on current technological trends, frameworks, and integration strategies relevant to 6G-enabled healthcare systems. The proposed model integrates key technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, robotics, telemedicine, and advanced data analytics within the context of smart hospitals.

Results

The findings suggest that 6G's ultralow latency, massive device connectivity, and high data throughput can dramatically enhance patient care, real-time monitoring, and hospital operational efficiency. The proposed 6G-based smart hospital model fosters seamless communication between medical devices and systems, enabling intelligent decision-making and optimized resource allocation.

Discussion

Despite the promising benefits, several challenges were identified, including data privacy and security risks, system interoperability, and ethical implications. The study underscores the critical importance of robust regulatory frameworks and standardized protocols to ensure secure and ethical deployment of 6G technologies in healthcare settings.

Conclusion

By providing a forward-looking analysis of the opportunities and challenges associated with 6G-powered smart hospitals, this research offers valuable insights into the evolving landscape of digital healthcare and its potential to redefine patient care and hospital management in the near future.

Translating digital health services for nutrition care management of chronic conditions in outpatient settings: A multi-stakeholder e-Delphi study

AIM

To identify and achieve expert consensus on the most important and feasible strategies to implement digital health services for nutrition care management of chronic conditions in outpatient settings.

METHODS

Determinants and strategies for implementing digital health services for nutrition care management were identified in line with the literature and the Consolidated Framework for Implementation Research. These were presented to team leaders and senior clinicians, as well as dietetic and allied health directors in a two-round e-Delphi process. Consensus was reached when strategies were rated very important/feasible by at least 75% of respondents, calculated by the median, interquartile range and frequency. Following the two survey rounds, a final prioritisation survey was distributed to participants, where participants were asked to prioritise their top strategy for each determinant, which was analysed by frequency calculations.

RESULTS

Twenty participants participated in round one of the survey and 18 completed the final prioritisation round. Following the two rounds, 3 strategies did not meet consensus for importance and 7 strategies did not meet consensus for feasibility out of 25 strategies presented. Nine strategies were prioritised following the survey rounds. Key concepts of the strategies that met consensus and were prioritised related to (i) adhering to quality of care with effective evaluation processes; (ii) providing options for digital health upskilling and support; and (iii) individualising patient care.

CONCLUSION

Team leaders and senior clinicians as well as dietetic and allied health directors have indicated that there are many important digital health strategies yet not all are feasible to implement within current resourcing and systems.

Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era

Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals' inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.

Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

An Interdisciplinary Overview on Ambient Assisted Living Systems for Health Monitoring at Home: Trade-Offs and Challenges

The integration of IoT and Ambient Assisted Living (AAL) enables discreet real-time health monitoring in home environments, offering significant potential for personalized and preventative care. However, challenges persist in balancing privacy, cost, usability, and system reliability. This paper provides an overview of recent advancements in sensor and IoT technologies for assisted living, with a focus on elderly individuals living independently. It categorizes sensor types and technologies that enhance healthcare delivery and explores an interdisciplinary framework encompassing sensing, communication, and decision-making systems. Through this analysis, this paper highlights current applications, identifies emerging challenges, and pinpoints critical areas for future research. This paper aims to inform ongoing discourse and advocate for interdisciplinary approaches in system design to address existing trade-offs and optimize performance.

Enhancing Real-Time Patient Monitoring in Intensive Care Units with Deep Learning and the Internet of Things

The demand for intensive care units (ICUs) is steadily increasing, yet there is a relative shortage of medical staff to meet this need. Intensive care work is inherently heavy and stressful, highlighting the importance of optimizing these units' working conditions and processes. Such optimization is crucial for enhancing work efficiency and elevating the level of diagnosis and treatment provided in ICUs. The intelligent ICU concept represents a novel ward management model that has emerged through advancements in modern science and technology. This includes communication technology, the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics. By leveraging these technologies, the intelligent ICU aims to significantly reduce potential risks associated with human error and improve patient monitoring and treatment outcomes. Deep learning (DL) and IoT technologies have huge potential to revolutionize the surveillance of patients in the ICUs due to the critical and complex nature of their conditions. This article provides an overview of the most recent research and applications of linical data for critically ill patients, with a focus on the execution of AI. In the ICU, seamless and continuous monitoring is critical, as even little delays in patient care decision-making can result in irreparable repercussions or death. This article looks at how modern technologies like DL and the IoT can improve patient monitoring, clinical results, and ICU processes. Furthermore, it investigates the function of wearable and advanced health sensors coupled with IoT networking systems, which enable the secure connection and analysis of various forms of patient data for predictive and remote analysis by medical professionals. By assessing existing patient monitoring systems, outlining the roles of DL and IoT, and analyzing the benefits and limitations of their integration, this study hopes to shed light on the future of ICU patient care and identify opportunities for further research.

Improving consumer trust in digital health: A mixed methods study involving people living with chronic kidney disease

Aim

To explore preferences, experience and trust in digital health in people living with chronic kidney disease (CKD), and tailor these findings towards solutions that may enhance uptake of digital health services.

Methods

Mixed methods study, with cross-sectional survey and individual interviews with adults living with CKD attending specialist appointments at an Australian metropolitan hospital. Descriptive statistics and Wilcoxon matched-pairs test were used for survey responses and thematic analysis of interview transcripts, both reported on a theme-by-theme basis provided an overall understanding of trust in digital healthcare.

Results

Digital health is changing the way health services are provided, and our results demonstrate that despite limited familiarity, participants are open to learn and adapt to existing digital models of care. Limited exposure to technology may undermine trust in digital health, and telehealth can promote improvements in health literacy. Having the choice in healthcare modalities can promote trust, which can arise from trustful relationships with clinicians who demonstrate genuine interest in patient care. Participants expressed more concerns about sharing identity data than health data online and worry about fragmented information among providers. They preferred public health services due to distrust generated by the perceived risk of private sector data commercialisation. Building trust requires increasing awareness of digital health benefits, promoting positive experiences, improving digital literacy and ensuring interoperability and transparency in digital healthcare systems.

Conclusion

People with CKD want to learn and benefit from digital health. Choice and open disclosure on data management and purpose are paramount to building trust.

Internet of Things (IoT)-Driven Fermentation System for Enhanced Cordycepin Production in Cordyceps militaris (Ascomycetes) under Hypoxic Conditions

Cordycepin, known for its tumor-suppressive and antiviral properties, has garnered attention due to its therapeutic and biological potential. Current Cordyceps militaris - based cordycepin production methods involve time-consuming and cost-intensive solid-state fermentation. Using an internet of things (IoT) architecture, we developed an active air-feed regulation fermentation system (AAFRFS) to detect CO2 emitted during C. militaris submerged fermentation. Equipped with a microcontroller unit and proportional-integral-derivative plus pulse-width modulation technology, the AAFRFS also regulates the air supply, inducing hypoxic stress to enhance cordycepin production. Our system uploads all fermentation data to a cloud database. After 14 d of fermentation (volume 5 L) at 3000 ppm metabolic CO2, cordycepin levels exceeded 1.44 g/L (yield: 103.2 mg/L/d). Hypoxic stress promoted earlier cordycepin production. Utilizing big data with an alert mechanism enabled the early detection of microbial contamination within a 12- to 24-h period. Principal component analysis revealed a positive correlation between temperature and CO2 concentration, suggesting that temperature fluctuations likely affect the respiration rate of C. militaris, thereby altering CO2 levels. Our findings may help optimize fermentation strategies not only for C. militaris but also for other fungal strains.

A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring

The analysis of tissue cultures, particularly brain organoids, requires a sophisticated integration and coordination of multiple technologies for monitoring and measuring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Our approach enables continuous, communicative, non-invasive interactions within an Internet of Things (IoT) architecture among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids while measuring their neuronal activity. The organoids are cultured in custom, 3D-printed chambers affixed to commercial microelectrode arrays. Periodic feeding is achieved using programmable microfluidic pumps. We developed a computer vision fluid volume estimator used as feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a set of 7-day studies of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated protocols were validated in maintaining robust neural activity throughout the experiment. The automated system enabled hourly electrophysiology recordings for the 7-day studies. Median neural unit firing rates increased for every sample and dynamic patterns of organoid firing rates were revealed by high-frequency recordings. Surprisingly, feeding did not affect firing rate. Furthermore, performing media exchange during a recording showed no acute effects on firing rate, enabling the use of this automated platform for reagent screening studies.

The role of telemedicine towards improved sustainability in healthcare and societal productivity in Turkey

The healthcare systems of low and middle-income countries suffer from lack of resources that could be remedied by employing novel care strategies such as telemedicine [1]. Here, the hypothetical impact of delivering telemedicine care on environment and society in three busy cardio-vascular clinics in Istanbul, Turkey, is examined. The study exploits demographics, wages, productivity, and patient-specific data to develop a hypothetical telemedicine framework for the Turkish healthcare landscape. Specifically, the distance traveled and travel time to receive care using location of the clinics and patients addresses seeking care are tabulated. Data from August 3, 2015, to January 25, 2023 involves 45,602 unique encounters with 448 unique diagnoses recorded for the patient encounters, where the patients in the top 5% of the most common diagnoses traveled 23.82 ± 96.3 km to reach the clinics. Based on our model, telemedicine care for cardiovascular diseases would have saved 656,258 km if all patients were to take the first visit in person followed by telemedicine visits in lieu of face-to-face care for all visits. The travel-associated carbon footprint and wage losses for in-person care is calculated and exploiting telemedicine could have saved approximately 30% carbon footprint and prevented approximately $503,752.8 wage loss. It is possible that telemedicine could ease the burden on patients, environment, increase access, and prevent the wage losses caused by unnecessary hospital visits.

Healthcare Professionals' Views on the Use of Passive Sensing and Machine Learning Approaches in Secondary Mental Healthcare: A Qualitative Study

INTRODUCTION

Globally, many people experience mental health difficulties, and the current workforce capacity is insufficient to meet this demand, with growth not keeping pace with need. Digital devices that passively collect data and utilise machine learning to generate insights could enhance current mental health practices and help service users manage their mental health. However, little is known about mental healthcare professionals' perspectives on these approaches. This study aims to explore mental health professionals' views on using digital devices to passively collect data and apply machine learning in mental healthcare, as well as the potential barriers and facilitators to their implementation in practice.

METHODS

Qualitative semi-structured interviews were conducted with 15 multidisciplinary staff who work in secondary mental health settings. Interview topics included the use of digital devices for passive sensing, developing machine learning algorithms from this data, the clinician's role, and the barriers and facilitators to their use in practice. Interview data were analysed using reflexive thematic analysis.

RESULTS

Participants noted that digital devices for healthcare can motivate and empower users, but caution is needed to prevent feelings of abandonment and widening inequalities. Passive sensing can enhance assessment objectivity, but it raises concerns about privacy, data storage, consent and data accuracy. Machine learning algorithms may increase awareness of support needs, yet lack context, risking misdiagnosis. Barriers for service users include access, accessibility and the impact of receiving insights from passively collected data. For staff, barriers involve infrastructure and increased workload. Staff support facilitated service users' adoption of digital systems, while for staff, training, ease of use and feeling supported were key enablers.

CONCLUSIONS

Several recommendations have arisen from this study, including ensuring devices are user-friendly and equitably applied in clinical practice. Being with a blended approach to prevent service users from feeling abandoned and provide staff with training and access to technology to enhance uptake.

PATIENT OR PUBLIC CONTRIBUTION

The study design, protocol and topic guide were informed by a lived experience community group that advises on research projects at the authors' affiliation.

Blockchain in Health Information Systems: A Systematic Review

(1) Background: With the increasing digitalization of healthcare systems, data security and privacy have become crucial issues. In parallel, blockchain technology has gradually proven to be an innovative solution to address this challenge, as its ability to provide an immutable and secure record of transactions offers significant promise for healthcare information management. This systematic review aims to explore the applications of blockchain in health information systems, highlighting its advantages and challenges. (2) Methods: The publications chosen to compose this review were collected from six databases, resulting in the initial identification of 4864 studies. Of these, 73 were selected for in-depth analysis. (3) Results: The main results show that blockchain has been used mainly in electronic health records (63%). Furthermore, it was used in the Internet of Medical Things (8.2%) and for data sharing during the COVID-19 pandemic (6.8%). As advantages, greater security, privacy, and data integrity were identified, while the challenges point to the need for standardization and regulatory issues. (4) Conclusions: Despite the difficulties encountered, blockchain has significant potential to improve healthcare data management. However, more research and continued collaboration between those involved are needed to maximize its benefits.

Managing operational resilience during the implementation of digital transformation in healthcare organisational practices

PURPOSE

The aim of this study is to investigate ways in which healthcare organisations can successfully maintain operational resilience within intricate and varied engagements during digital transformation processes.

DESIGN/METHODOLOGY/APPROACH

The present research applied cultural-historical activity theory as the theoretical framework and the ethnographic account as an approach and strategy to interpret and understand the operational resilience of digital transformation tools in daily practices. Fieldwork was based on the research technique of shadowing, whereby the researcher closely accompanied the participants to record their conduct, activities and exchanges.

FINDINGS

Research results propose that effective operational resilience management in the implementation of digital transformation projects is based on (1) identifying and interpreting internal contradictions in everyday interactions as opportunities for capability developments; (2) navigating through multiple sites in fast and improvised movements, which derives in distributed and emergent practices; (3) interplaying between dyadic interactions and networked dependencies, which is achieved through the articulation of varied interests and (4) implementing novel intermediary tools, roles and regulations that facilitate the reduction of disturbances.

ORIGINALITY/VALUE

The propositions of the present study indicate that the management of operational resilience extends beyond conventional adaptive and socio-technical models in healthcare services. The study emphasises the significance of expressing and converting differing interests into mutual advantages. It additionally demonstrates the intricacy of this obstacle, as it entails navigating through uncertain information, concealed interpretations and conflicting interests.

Assessment of sleep patterns in dementia and general population cohorts using passive in-home monitoring technologies

BACKGROUND

Nocturnal disturbances are a common symptom experienced by People Living with Dementia (PLWD), and these often present prior to diagnosis. Whilst sleep anomalies have been frequently reported, most studies have been conducted in lab environments, which are expensive, invasive and not natural sleeping environments. In this study, we investigate the use of in-home nocturnal monitoring technologies, which enable passive data collection, at low cost, in real-world environments, and without requiring a change in routine.

METHODS

Clustering analysis of passively collected sleep data in the natural sleep environment can help identify distinct sub-groups based on sleep patterns. The analysis uses sleep activity data from; (1) the Minder study, collecting in-home data from PLWD and (2) a general population dataset (combined n = 100, >9500 person-nights).

RESULTS

Unsupervised clustering and profiling analysis identifies three distinct clusters. One cluster is predominantly PLWD relative to the two other groups (72% ± 3.22, p = 6.4 × 10-7, p = 1.2 × 10-2) and has the highest mean age (77.96 ± 0.93, p = 6.8 × 10-4 and p = 6.4 × 10-7). This cluster is defined by increases in light and wake after sleep onset (p = 1.5 × 10-22, p = 1.4 × 10-7 and p = 1.7 × 10-22, p = 1.4 × 10-23) and decreases in rapid eye movement (p = 5.5 × 10-12, p = 5.9 × 10-7) and non-rapid eye movement sleep duration (p = 1.7 × 10-4, p = 3.8 × 10-11), in comparison to the general population.

CONCLUSIONS

In line with current clinical knowledge, these results suggest detectable dementia sleep phenotypes, highlighting the potential for using passive digital technologies in PLWD, and for  detecting architectural sleep changes more generally. This study indicates the feasibility of leveraging passive in-home technologies for disease monitoring.

Accuracy of a Smart Diaper System for Nursing Home Residents for Automatically Detecting Voided Volume: Instrument Validation Study

BACKGROUND

Diapers are commonly used by older patients with urination disorders. A smart diaper system (SDS) may be able to estimate the weight of urine comparably to conventional measurements made by weighing diapers.

OBJECTIVE

The aim of the current research is to determine the degree of accuracy of an SDS technology specifically designed for the management of urination routines and the use of incontinence pads in older adults.

METHODS

From January to December 2022, 97 older patients with at least 1 chronic disease from 3 nursing homes were included. In this study, the SDS was used for 1 month per patient after obtaining their consent; all patients originally used traditional diapers in the nursing home. The index test measured the change in electrical resistance of the SDS and the reference test measured the change in actual urine weight. When measuring the actual urine weight, the degree of absorption was directly confirmed with the naked eye because the expression value varied according to pressure changes caused by the user's movement or position. The Pearson correlation was used to determine the correlation between the 2 test methods, the intraclass correlation coefficient (ICC) was used to check the degree of agreement between the evaluators, and the Bland-Altman test was used to confirm whether there was a significant difference between the 2 test methods.

RESULTS

The average age of the 97 participants was 86.48 (SD 6.26) years, with 10 men and 87 women. There were 73 patients (75%) with hypertension, 86 patients (88%) with dementia, and 86 patients (88%) with 2 or more comorbidities, accounting for the majority. The Pearson correlation coefficient and ICC were 0.971 and 0.985 (P<.001). In the Bland-Altman figure, the difference in the mean between the 2 tests was evenly scattered without showing a specific pattern, indicating that the SDS and actual urine weight were very consistent. The difference between the mean of the 2 tests was -0.045 of the standardized mean difference, and all measurements were located within the 95% CI, so this confirms that the 2 test methods are equivalent.

CONCLUSIONS

Our study showed a fairly high correlation coefficient and ICC for all patient groups, which reveals that the 2 tests were very consistent and that the SDS can replace traditional diapers, even in a real clinical setting. This study shows the possibility that heath care professionals could be alerted by the SDS to the need for pad replacement due to incontinence, thus avoiding the development of dermatological complications.

Stakeholder views on addressing challenges to the implementation of social prescribing in the United Kingdom

Objectives

This study aimed to understand the key barriers to successfully implementing Social Prescribing (SP) initiatives from different perspectives.

Methods

An in-depth process evaluation using a multi-method qualitative design was conducted. Qualitative data was collected via semi-structured interviews (N = 23) and Focus Group Discussion (FGD' N = 4). Twenty-three stakeholders took part in the study, including community support providers (n = 7), SP link workers (n = 6), service users (n = 6), NHS employees/referrals, and those who were involved in SP leadership and coordination (n = 4). MAXQDA Version 20.0 was used for management and data analysis.

Results

We identified eight themes representing challenges for a successful implementation of a SP programme. The themes included (i) financial issues and sustainability, (ii) human resources challenges, (iii) partnership working challenges, (iv) inadequate and inconsistent implementation, (v) information system challenges, (vi) referral system issues, (vii) training and knowledge gaps, and (viii) accessibility and privacy concerns.

Conclusion

Study findings provide insight for commissioners, providers, and link workers to guide the delivery of appropriate SP services by identifying a range of factors that hinder the successful implementation of the programme. Future policy, service development, and research should consider tackling these challenges and generating different ideas for potential solutions to address the root causes of problems.

Legal implications for clinicians in cybersecurity incidents: A review

Cybersecurity incidents in healthcare present significant legal implications for clinicians, necessitating careful consideration of technological advancements and regulatory frameworks. This literature examines the healthcare cybersecurity landscape, emphasizing clinicians' challenges, and legal responsibilities. It explores the impact of advanced technologies such as artificial intelligence and quantum computing, highlighting the potential benefits and risks, including biases and ethical dilemmas. The review addresses international regulatory differences, offering a comparative analysis of how various countries handle cybersecurity incidents. This analysis provides insights into best practices and identifies areas for improvement. Practical recommendations are provided, tailored to different healthcare settings, including large hospitals and small clinics, to enhance cybersecurity preparedness. Case studies illustrate real-world scenarios, offering practical guidance for clinicians in managing cybersecurity challenges. The review also identifies critical gaps in the literature, particularly concerning artificial intelligence ethics and international regulatory frameworks, suggesting specific areas for future research. These findings underscore the need for robust cybersecurity policies, comprehensive training for healthcare professionals, and a nuanced understanding of the legal landscape. This review informs policymakers, clinicians, and researchers about the evolving nature of cybersecurity challenges in healthcare, addressing key concerns raised by reviewers and contributing to a comprehensive understanding of the field.

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study

BACKGROUND

Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved, enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semantic interoperability is ontology.

OBJECTIVE

This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients, and disparate heterogeneous infectious disease surveillance.

METHODS

The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, ease of extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible to maximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency questions as requirements for the ontology to achieve the intended goals.

RESULTS

We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring, clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.

CONCLUSIONS

The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management, infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems, providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely manner.

Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

Electrochemical Paper-Based Microfluidics: Harnessing Capillary Flow for Advanced Diagnostics

Electrochemical paper-based microfluidics has attracted much attention due to the promise of transforming point-of-care diagnostics by facilitating quantitative analysis with low-cost and portable analyzers. Such devices harness capillary flow to transport samples and reagents, enabling bioassays to be executed passively. Despite exciting demonstrations of capillary-driven electrochemical tests, conventional methods for fabricating electrodes on paper impede capillary flow, limit fluidic pathways, and constrain accessible device architectures. This account reviews recent developments in paper-based electroanalytical devices and offers perspective by revisiting key milestones in lateral flow tests and paper-based microfluidics engineering. The study highlights the benefits associated with electrochemical sensing and discusses how the detection modality can be leveraged to unlock novel functionalities. Particular focus is given to electrofluidic platforms that embed electrodes into paper for enhanced biosensing applications. Together, these innovations pave the way for diagnostic technologies that offer portability, quantitative analysis, and seamless integration with digital healthcare, all without compromising the simplicity of commercially available rapid diagnostic tests.

Remote Patient Monitoring Is Associated with Improved Outcomes in Hypertension: A Large, Retrospective, Cohort Analysis

Hypertension (HTN) is a chronic condition that requires careful monitoring and management. Blood pressure readings in the clinic and self-reported blood pressure readings are often too intermittent to allow for careful management. Remote patient monitoring is a solution that may have positive impacts on HTN management. Individuals at cardiac and primary care clinics were prescribed a remote patient-monitoring (RPM) program. Patients were sent blood pressure monitors that were enabled to transmit data over cellular networks. We reviewed trends in HTN management retrospectively in patients who had previously been on conventional therapy for a year and participated in RPM for a minimum of 90 days. There were 6595 patients enrolled, and the mean duration on RPM was 289 days. A total of 4370 participants (66.3%) had uncontrolled HTN, and 2476 (37.5%) had stage 2 HTN. After at least 90 days on the RPM program, the number of patients with uncontrolled HTN reduced to 2648 (40.2%, p < 0.01), and the number of patients with stage 2 HTN reduced to 1261 (19.1%, p < 0.01). Systolic blood pressure improved by 7.3 mmHg for all patients and 16.7 mmHg for stage 2 HTN. There was improvement in mean arterial pressure (MAP) in all patients with uncontrolled HTN by 8.5 mmHg (p < 0.0001). RPM is associated with improved HTN control and provides further evidence supporting telehealth programs which can aid in chronic disease management.

Revolutionizing indoor air quality monitoring through IoT innovations: a comprehensive systematic review and bibliometric analysis

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers.

The Use of Internet of Things (IoT) Technology to Promote Children's Oral Health: A Scoping Review

Dental treatments and oral health promotion are now more mobile and versatile thanks to the Internet of Things (IoT)-based healthcare services. This scoping review aims to compile the available data and outline the aims, design, assessment procedures, efficacy, advantages, and disadvantages of the implementation of IoT to improve children's oral health. Articles for this review were gathered from PubMed, Scopus, and Ebscohost databases to identify and construct the keywords and primary research topic. The selected studies were published between 2000 and 2022 and focused on children aged 1 to 18 and/or parents/caregivers of children who received oral health promotion and/or dental disease preventive treatments utilizing the IoT. Each study topic required data extraction. A total of nine papers were included in this review. Two of the nine publications were quasi-experimental, while the remaining six papers were randomized control trials. The nine papers considered in this appraisal have a range of interventions and follow-up periods. Mobile-Health (m-Health), home healthcare, hospital/clinical management, and electronic-Health applications (e-Health) are the most common IoT architecture used as interventions. Three studies assessed oral health knowledge and behavior scores, whereas the bulk of studies (6/7) used m-Health treatments focusing on dental plaque buildup as well as gingival health evaluation to assess oral hygiene. IoT is one of the mediums or instruments that might be used to encourage children's dental health. The studies suggest that the use of IoT could help in improving oral hygiene and oral health, which can further improve children's oral health.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients' plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient's care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

This work aims to utilize Internet of Things (IoT) technology and the Artificial Neural Network - Cellular Automaton (ANN-CA) model to analyze the construction of indicators for territorial spatial planning and urban development suitability assessment. Firstly, the IoT technology is introduced, and its application potential in land planning is explored. Using the IoT technology, various data related to land use are collected, and these data are transmitted and summarized through IoT equipment to form a data base. Based on the collected data, the ANN-CA model and the "dual assessment" concept are employed to establish an indicator system for urban development suitability assessment, encompassing permanent basic farmland, ecological redlines, and current built-up areas. Through the combination of these two models, the future land use situation can be predicted more accurately. The trained model is evaluated, including simulation accuracy, error analysis, Kappa coefficient and other indicators. Compared with the actual data, the accuracy and credibility of the model are verified. Finally, according to the prediction results of the model, the land use situation is analyzed and interpreted to provide decision support for urban planning and development. The research results show that the combination of IoT technology and ANN-CA model can effectively analyze the evolution law of land use and the suitability of urban development. Through the reasonable setting and processing of model parameters and data, people can get high accuracy land use prediction results, which provides important reference and support for urban planning and sustainable development. The suitability for urban development within County M exhibits noticeable spatial disparities, with the central region being more suitable for development while the peripheral regions are relatively less favorable. This work provides valuable guidance for decision-makers and researchers in the field of territorial planning, and promotes orderly urban development and sustainable prosperity.

An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Revolutionizing elderly care: Building a healthier aging society through innovative long-term care systems and assessing the long-term care acceptance model

AIM

With a growing elderly population, the demand for caregivers is increasing in Khon Kaen, Thailand, with approximately 17 000 elderly residents. This growing number of older people and a shortage of caregivers could overload the healthcare system.

METHODS

The present study involved 129 healthcare volunteers (caregivers for questionnaires study) and the collection of health data from 290 elderly residents from northeastern Thailand. After training, the volunteers assessed its usefulness through questionnaires. Tool reliability and statistical hypotheses were tested using stratified regression analysis (hierarchical regression) and multiple regression.

RESULTS

The relative mean scores of perceived usefulness, perceived ease of use, attitude toward usage and behavioral intention to use technology were 4.51, 4.29, 4.44 and 4.41, respectively. In addition, perceived usefulness and user attitudes positively affected volunteers' willingness to use the system.

CONCLUSION

The study was developed from the awareness of enhancing community quality and ecosystem through a long-term care system application. Analyzing external factors can enhance technology's future effectiveness. Geriatr Gerontol Int 2024; 24: 477-485.

Landscape of Digital Technologies Used in the National Health Service in England: Content Analysis

BACKGROUND

In England, digital technologies are exploited to transform the way health and social care is provided and encompass a wide range of hardware devices and software that are used in all aspects of health care. However, little is known about the extent to which health care providers differ in digital health technology capabilities and how this relates to geographical and regional differences in health care capacities and resources.

OBJECTIVE

This paper aims to identify the set of digital technologies that have been deployed by the National Health Services clinical commissioning groups (NHS CCGs) in England. In doing this, we respond to calls to shed light on the internal dynamics and variation in the form of digital capability in England in terms of health service regional differences and health diversity, equity, and inclusion.

METHODS

We collected 135 annual reports that belong to 106 NHS CCGs in England, comprising more than 18,000 pages in total, released from 2020 to 2021. Using this data set, we identified 2163 pages related to digital technologies and labeled them using content analysis. We follow the construct taxonomy used by digital options theory, a theory from the management information systems field analyzing organizational resource investment choices, in classifying observed technologies according to digital themes-inherent design patterns that we identified and explained. We then used a hierarchical clustering method to extract groups of NHS CCGs that implement similar technology themes.

RESULTS

We found 31 technologies from the reports and grouped them into 9 digital themes. The 9 themes were further assigned to 1 of the 3 constructs of digital options theory, the identification of patients' requirements (we identified information portals [76/106], digital health engagement [67/106], and digital inclusion support [45/106]), the development of new work patterns (we identified telehealth [87/106], telemedicine [35/106], and care home technologies [40/106]), the realization of improvements in efficiency and public accessibility (we identified online booking [26/106], online triage [104/106], and digital mental health services [74/106]). The 3 clusters of CCGs are identified based on the 8 themes (Hopkins=0.9914, silhouette=0.186), namely (1) digitally disengaged, (2) digitally engaged, and (3) digital torchbearer.

CONCLUSIONS

Our findings show prominent digital themes within each construct group, namely information portals, telehealth, and online triage, covering people's fundamental health information needs. Almost half of CCGs fell into the digitally disengaged group, and all London CCGs (5/106) belonged to this group. We propose that practitioners should offer specialized assistance to regions with limited digital engagement, emphasizing digital health literacy, inclusion support, and ongoing evaluation, rather than concentrating solely on technical advancements.

Navigating the Digital Landscape: Embracing Innovation, Addressing Challenges, and Prioritizing Patient-Centric Care

In the digital era, healthcare customer feedback plays a pivotal role in shaping the reputation of healthcare organizations. The study explores how digital advancements are integrated into modern healthcare, offering both transformative insights and addressing the challenges they present. It investigates how technologies such as artificial intelligence (AI), digital platforms, and patient feedback systems impact patient care, operational efficiency, and customer satisfaction in healthcare settings. The study emphasizes the importance of balancing both capitalizing on the opportunities presented by innovations and addressing the inherent difficulties associated with digitalization in healthcare, underlining the need for a comprehensive approach to navigating the opportunities and challenges in healthcare digitalization. AI is recognized for its role in reshaping value creation in healthcare, fostering collaboration among stakeholders, and improving patient care. Additionally, the study identifies key areas of research essential for effectively navigating the digital transformation in healthcare, including operational efficiency, patient-centric strategies, and organizational factors. However, along with the potential benefits come challenges, such as the need for regulatory frameworks to validate new technologies and address privacy concerns surrounding patient data. Managing reputation and customer relationships in the digital sphere also emerges as critical for healthcare organizations. In summary, the study underscores the importance of healthcare institutions prioritizing patient-centric care, adopting digital innovations, and adeptly navigating regulatory and ethical challenges. By doing so, they can enhance patient outcomes, and satisfaction, and drive innovation in today's dynamic healthcare landscape.

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10-3 comparing the DigiPen's computed Δt (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment

BACKGROUND

The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment.

METHODS

We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms.

RESULTS

The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model.

CONCLUSIONS

The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

[CamiCon study: Evaluation of a new tool for automated and connected voiding calendar]

INTRODUCTION

A voiding diary (VD) is a key element in the evaluation of patients with overactive bladder (OAB) at initial presentation and during treatment to assess its effectiveness. In order to be clinically relevant, it must be performed over 3 days according to the International Continence Society (ICS). Unfortunately, some patients find it cumbersome. We aimed to evaluate the reliability and patient satisfaction when using a connected tank device.

MATERIAL AND METHOD

We conducted a single-center prospective study including 41 patients. Each patient completed a paper voiding diary and then a diary with Diary Pod® (DP) or inversely depending on the study arm. Data from 34 patients were collected. After completion of both diaries, patients completed a satisfaction questionnaire sent by email via GoogleForm. Study statistics were performed with Jamovi® and Excel® software.

RESULT

Data from 34 patients were analyzed. There was a statically significant difference (P=0.046) between the mean volume calculated from the paper VD and that calculated from the connected VD (DP). There was no statistically significant difference (P=0.112) between the mean number of daytime voids, mean number of nighttime voids (P=0.156), mean water intake (P=0.183) reported on the paper VD and the connected VD. Thirteen (42%) paper VD and 1 connected VD did not include documentation of the presence or absence of urine leakage or urgency. There was no statistically significant difference between the two calendars regarding the presence or absence of urine leakage (P=0.180) and urinary urgency (P=0.564). Eighty-four percent (26/31) preferred the connected tank to the usual method (paper/pen), while 55% (17/31) and 29% (9/31) of the participants respectively answered that the DP was "very definitely" or "definitely" an aid for performing VD. Nevertheless, 39% (12/31) and 55% (17/31) considered its price to be high or fair and only 22% (7/31) were inclined to buy it.

CONCLUSION

This study showed that the Diary connected reservoir Pod® is a reliable and innovative tool for voiding schedules. It facilitates data collection for the majority of patients (83%) and could, through better patient compliance, provide better quality data and help their interpretation by the physician. These factors could encourage the implementation of the connected voiding diary as a diagnostic tool. It would also be used for the assessment of treatment effectiveness in daily clinical practice as well as in research. Its cost remains a major obstacle, judged by 39% of patients to be too high, and could therefore be proposed in specific situations requiring precise data.

Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis

BACKGROUND

Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.

OBJECTIVE

This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.

METHODS

A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.

RESULTS

Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.

CONCLUSIONS

Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.

A New Player in the Game: Can Exergame Be of Support in the Management of Atrial Fibrillation?

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia, currently affecting 2-3% of the world's population. Traditional exercise and physical activity interventions have been successfully implemented in the management of AF, with the aim of improving patients' quality of life and their exercise capacity, as well as reducing their mortality rate. Currently, new technology-mediated approaches to exercise, defined as exergame, have been shown to be successful in the delivery of exercise home-based interventions in patients with cardiovascular diseases. However, data on the effects of exergame on AF are not yet available. In this paper, we summarise the current literature on the role of traditional exercise in AF and how it affects the pathophysiology of this condition. We also review the current literature on exergame and its employment in cardiac rehabilitation and suggest its potential role in the management of AF patients. A review of the evidence suggests that traditional exercise (of light-to-moderate intensity) is beneficial in patients with AF. Additionally, exergame seems to be a promising approach for delivering exercise interventions in patients with cardiovascular diseases. Exergame may be a promising tool to improve the quality of life and exercise capacity in patients with AF, with the additional advantage of being remotely delivered, and the potential to increase patients' engagement. Proper guidelines are required to prescribe exergame interventions, considering the principles of traditional exercise prescription and applying them to this new e-health approach. Further studies are needed to validate the use of exergame in patients with AF.

Sleep well, worry less: A co-design study for the development of the SMILE app

Objective

With the coronavirus disease 2019 pandemic exacerbating mental health concerns, the prevalence rates of anxiety and sleep problems have increased alarmingly among youth. Although 90% of patients with anxiety experience sleep problems, current interventions for anxiety often do not target sleep problems in youth. Given this lack, we designed the SMILE app, an intervention that addresses both anxiety and sleep problems simultaneously.

Methods

As users' perspectives are essential to ensure app engagement and uptake, the features, designs, and functions of the SMILE app were evaluated using a participatory app design approach. Participants (N = 17) were youth aged 15 to 25 who reported co-morbid anxiety and sleep issues above clinical thresholds. After completing an online screening survey assessing demographics, anxiety, and sleep problems, participants shared app feedback through group-based, semi-structured co-design sessions. Qualitative analyses were conducted to identify common themes from participants' feedback.

Results

While participants expressed enthusiasm for the SMILE app's features, particularly the Visualization, Journaling, and Psychoeducation features, and their variety, they criticized the design aspects of the app, such as the font and text amount. Most participants stated they would use the SMILE app or recommend it to a friend.

Conclusion

By actively involving the target population in the design process, the SMILE app has the potential to notably improve the mental well-being of youth, though further research and development are required to realize this potential fully.

The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions

The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem.

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

Continuous Critical Respiratory Parameter Measurements Using a Single Low-Cost Relative Humidity Sensor: Evaluation Study

BACKGROUND

Accurate and portable respiratory parameter measurements are critical for properly managing chronic obstructive pulmonary diseases (COPDs) such as asthma or sleep apnea, as well as controlling ventilation for patients in intensive care units, during surgical procedures, or when using a positive airway pressure device for sleep apnea.

OBJECTIVE

The purpose of this research is to develop a new nonprescription portable measurement device that utilizes relative humidity sensors (RHS) to accurately measure key respiratory parameters at a cost that is approximately 10 times less than the industry standard.

METHODS

We present the development, implementation, and assessment of a wearable respiratory measurement device using the commercial Bosch BME280 RHS. In the initial stage, the RHS was connected to the pneumotach (PNT) gold standard device via its external connector to gather breathing metrics. Data collection was facilitated using the Arduino platform with a Bluetooth Low Energy connection, and all measurements were taken in real time without any additional data processing. The device's efficacy was tested with 7 participants (5 men and 2 women), all in good health. In the subsequent phase, we specifically focused on comparing breathing cycle and respiratory rate measurements and determining the tidal volume by calculating the region between inhalation and exhalation peaks. Each participant's data were recorded over a span of 15 minutes. After the experiment, detailed statistical analysis was conducted using ANOVA and Bland-Altman to examine the accuracy and efficiency of our wearable device compared with the traditional methods.

RESULTS

The perfused air measured with the respiratory monitor enables clinicians to evaluate the absolute value of the tidal volume during ventilation of a patient. In contrast, directly connecting our RHS device to the surgical mask facilitates continuous lung volume monitoring. The results of the 1-way ANOVA showed high P values of .68 for respiratory volume and .89 for respiratory rate, which indicate that the group averages with the PNT standard are equivalent to those with our RHS platform, within the error margins of a typical instrument. Furthermore, analysis utilizing the Bland-Altman statistical method revealed a small bias of 0.03 with limits of agreement (LoAs) of -0.25 and 0.33. The RR bias was 0.018, and the LoAs were -1.89 and 1.89.

CONCLUSIONS

Based on the encouraging results, we conclude that our proposed design can be a viable, low-cost wearable medical device for pulmonary parametric measurement to prevent and predict the progression of pulmonary diseases. We believe that this will encourage the research community to investigate the application of RHS for monitoring the pulmonary health of individuals.

Brave New Healthcare: A Narrative Review of Digital Healthcare in American Medicine

The digital revolution has had a profound effect on American and global healthcare, which was accelerated by the pandemic and telehealth applications. Digital health also includes popular and more esoteric forms of wearable monitoring systems and interscatter and other wireless technologies that facilitate their telemetry. The rise in artificial intelligence (AI) and machine learning (ML) may serve to improve interpretation from imaging technologies to electrocardiography or electroencephalographic tracings, and new ML techniques may allow these systems to scan data to discern and contextualize patterns that may have evaded human physicians. The necessity of virtual care during the pandemic has morphed into new treatment paradigms, which have gained patient acceptance but still raise issues with respect to privacy laws and credentialing. Augmented and virtual reality tools can facilitate surgical planning and "hands-on" clinical training activities. Patients are working with new frontiers in digital health in the form of "Dr. Google" and patient support websites to learn or share medical information. Patient-facing digital health information is both a blessing and curse, in that it can be a boon to health-literate patients who seek to be more active in their own care. On the other hand, digital health information can lead to false conclusions, catastrophizing, misunderstandings, and "cyberchondria." The role of blockchain, familiar from cryptocurrency, may play a role in future healthcare information and would serve as a disruptive, decentralizing, and potentially beneficial change. These important changes are both exciting and perplexing as clinicians and their patients learn to navigate this new system and how we address the questions it raises, such as medical privacy in a digital age. The goal of this review is to explore the vast range of digital health and how it may impact the healthcare system.

Reviewing the Health Care Impacts of Attacks on Critical Infrastructure

INTRODUCTION

Health care provision depends on reliable critical infrastructure (CI) to power equipment and to provide water for medication and sanitation. Attacks on CI limiting such functions can have a profound and prolonged influence on delivery of care.

METHODS

A retrospective analysis of the Global Terrorism Database (GTD) was performed of all attacks occurring from 1970-2020. Data were filtered using the internal database search function for all events where the primary target was "Utilities," "Food or Water Supply," and "Telecommunications." For the purposes of this study, the subtype "Food Supply" was excluded. Events were collated based on year, country, region, and numbers killed and wounded.

RESULTS

The GTD listed 7,813 attacks on CI, with 6,280 of those attacks targeting utilities, leading to 1,917 persons directly killed and 1,377 persons wounded. In total, there were 1,265 attacks targeting telecommunications causing 205 direct deaths and 510 wounded. Lastly, 268 attacks targeted the water supply with 318 directly killed and 261 wounded. Regionally, South America had the most attacks with 2,236, followed by Central America and the Caribbean with 1,390. Based on infrastructure type, the most attacks on utilities occurred in El Salvador (1,061), and the most attacks on telecommunications were in India (140). Peru (46) had the most attacks on its water supply.

CONCLUSION

The regions with the highest number of total attacks targeting CI have historically been in South America, with more attacks against power and utilities than other infrastructure. Although the numbers of persons directly killed and wounded in these attacks were lower than with other target types, the true impacts on lack of health care delivery are not accounted for in these numbers. By understanding the pattern and scope of these attacks, Counter-Terrorism Medicine (CTM) initiatives can be created to target-harden health care-related infrastructure.

Identifying and Addressing Barriers and Facilitators for the Implementation of Internet of Things in Distributed Care: Protocol for a Case Study

BACKGROUND

 The internet of things (IoT) is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. This study aims to identify the facilitators and barriers associated with implementing IoT solutions in health care within a Scandinavian context. It addresses the pressing need to adapt health care systems to the demographic changes occurring in Scandinavia. The vision of "Vision eHealth 2025," a long-term strategic direction for digitalization in Sweden, serves as the background for this project. The implementation of IoT solutions is a crucial aspect of achieving the vision's goal of making Sweden a global leader in using digitalization and eHealth opportunities by 2025. IoT is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. Previous research has shown that there is a gap in our understanding of social and organizational challenges related to IoT and that the implementation and introduction of new technology in health care is often problematic.

OBJECTIVE

 In this study, we will identify facilitating and hindering factors for the implementation of IoT solutions in social and health care.

METHODS

 We will use an explorative design with a case study approach. The data collection will comprise questionnaires and qualitative interviews. Also, a literature review will be conducted at the start of the project. Thus, quantitative and qualitative data will be collected concurrently and integrated into a convergent mixed methods approach.

RESULTS

 As of June 2023, data for the review and 22 interviews with the stakeholders have been performed. The co-design with stakeholders will be performed in the fall of 2023.

CONCLUSIONS

 This study represents a unique and innovative opportunity to gain new knowledge relevant and useful for future implementation of new technology at health care organizations so they can continue to offer high-quality, person-centered care. The outcomes of this research will contribute to a better understanding of the conditions necessary to implement and fully use the potential of IoT solutions. By developing cocreated implementation strategies, the study seeks to bridge the gap between theory and practice. Ultimately, this project aims to facilitate the adoption of IoT solutions in health care for promoting improved patient care and using technology to meet the evolving needs of health care.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/44562.

Drivers and Barriers to Implementing the Internet of Things in the Health Care Supply Chain: Mixed Methods Multicase Study

BACKGROUND

Over the past 2 years, the COVID-19 pandemic has placed enormous pressure on the health care industry. There has been an increase in demand and, at the same time, a shortage of supplies. This has shown that supply chain management in the health care industry cannot be taken for granted. Furthermore, the health care industry is also facing other major challenges, such as the current labor market shortage. In the literature, the Internet of Things (IoT) is highlighted as an effective tool to build a more resilient and efficient supply chain that can manage these challenges. Although using IoT in supply chain management has been extensively examined in other types of supply chains, its use in the health care supply chain has largely been overlooked. Given that the health care supply chain, compared to others, is more complex and is under growing pressure, a more in-depth understanding of the opportunities brought by IoT is necessary.

OBJECTIVE

This study aims to address this research gap by identifying and ranking the drivers of and barriers to implementing IoT in the health care supply chain.

METHODS

We conducted a 2-stage study. In the first, exploratory stage, a total of 12 semistructured interviews were conducted to identify drivers and barriers. In the second, confirmatory stage, a total of 26 health care supply chain professionals were asked in a survey to rank the drivers and barriers.

RESULTS

The results show that there are multiple financial, operational, strategy-related, and supply chain-related drivers for implementing IoT. Similarly, there are various financial, strategy-related, supply chain-related, technology-related, and user-related barriers. The findings also show that supply chain-related drivers (eg, increased transparency, traceability, and collaboration with suppliers) are the strongest drivers, while financial barriers (eg, high implementation costs and difficulties in building a business case) are the biggest barriers to overcome.

CONCLUSIONS

The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides guidance for practitioners and health care provider leaders intending to implement IoT in the health care supply chain.

Adapting Clinical Guidelines for the Digital Age: Summary of a Holistic and Multidisciplinary Approach

The Centers for Disease Control and Prevention's Adapting Clinical Guidelines for the Digital Age initiative aims to redesign and improve guideline development, implementation, and standardization. Historically, aspects of guideline development and implementation have been siloed. This leads to long lag times for guidelines to reach patient care, unnecessary redundancy, and potential for misinterpretation, leading to inconsistencies in how the recommendations are applied. A multidisciplinary, multiorganizational holistic approach brought together experts in guideline development, informatics, communication, implementation, and evaluation to understand and identify problems in guideline development and implementation, define an ideal state with no constraints, and then design a future state that advances the process close to the ideal state. The Adapting Clinical Guidelines for the Digital Age workgroups each worked on one focus area and included experts from the other areas to help analyze the current state and develop holistic solutions for the future state. Each workgroup produced interrelated standards, processes, and tools that can be used across the continuum of guideline development and implementation.