Smart healthcare: making medical care more intelligent

The effect of the Smart Health Continuous Feedback For Elderly Exercise (SHe CoFFEE) program on mobility: a randomized controlled pilot study

Objectives

This study determined the effectiveness of a comprehensive home-based online exercise program called "Smart Health Continuous Feedback for elderly exercise (SHe CoFFEE)" on mobility.

Methods

Sixty community-dwelling seniors were randomized in a 1:1 ratio to intervention or control group. Intervention was an 8-week "SHe CoFFEE" program, featuring continuous feedback, self-monitoring, and fall prevention exercises. The primary outcome was mobility, measured at 8 weeks with the Timed Up and Go (TUG) test. Secondary outcomes included TUG at 4 weeks, 10 m walking test, 30-s chair stand test, falls efficacy scale, activity-specific balance confidence, and Euro Quality of Life 5 Dimensions 5 Levels.

Results

At 8 weeks, the intervention group showed improved TUG scores compared with the control group (MD = -1.87, 95% CI, -2.60 to -1.14; ηp 2 = 0.326) in the adjusted intention-to-treat analysis. The per-protocol analysis data showed similar results. All secondary outcomes apart from quality of life improved with intervention to a greater degree than in control.

Conclusion

Smart healthcare and self-managed exercise programs may be viable for community-based health promotion and fall prevention in older adults unfamiliar with online technology.

Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0

Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China's healthcare system

Enhancing efficiency and productivity in countries' healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China's healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region's efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country's attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal-Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system's efficiency, TFPC, and TGR.

Mobile Diagnostic Clinics

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Protective face mask: an effective weapon against SARS-CoV-2 with controlled environmental pollution

Masks are face coverings that give protection from infectious agents, airborne pathogens, bacteria, viruses, surgical fog, dust, and other chemical hazards by acting as a barrier between the wearer and the environment. In the COVID-19 pandemic, this major personal protective equipment's became essential part of our daily life. The aim of this review is to analyze and discuss the different types of masks with their pros and cons, manufacturing procedures, evaluation criteria, and application with some of the sterilization process for reuse and smart mask. The review used a thorough examination of the literature to analyze the preventive effects of surgical, N95, smart mask, and potential environmental damage from those masks. Several studies and evidence were also examined to understand the efficiency of different mask on different environment. N95 respirators are capable of filtering out non-oil-based 95% air-born particles, and surgical masks act as a protective barrier between the wearer and the environment. The application of spoon bond and melt blown techniques in the fabrication process of those masks improves their protective nature and makes them lightweight and comfortable. But the high demand and low supply forced users to reuse and extend their use after sterilizations, even though those masks are recommended to be used once. Universal masking in the SARS-COV-2 pandemic increased the chance of environmental pollution, so the application of smart masks became essential because of their high protection power and self-sterilizing and reusing capabilities.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Security Analysis for Smart Healthcare Systems

The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

Prediction model of deep vein thrombosis risk after lower extremity orthopedic surgery

Purpose

This investigation was conceived to engineer and appraise a pioneering clinical nomogram, crafted to bridge the extant chasm in literature regarding the postoperative risk stratification for deep vein thrombosis (DVT) in the aftermath of lower extremity orthopedic procedures. This novel tool offers a sophisticated and discerning algorithm for risk prediction, heretofore unmet by existing methodologies.

Methods

In this retrospective observational study, clinical records of hospitalized patients who underwent lower extremity orthopedic surgery were collected at the Wuxi TCM Hospital Affiliated to the Nanjing University of Chinese Medicine between Jan 2017 and Oct 2019. The univariate and multivariate analysis with the backward stepwise method was applied to select features for the predictive nomogram. The performance of the nomogram was evaluated with respect to its discriminant capability, calibration ability, and clinical utility.

Result

A total of 5773 in-hospital patients were eligible for the study, with the incidence of deep vein thrombosis being approximately 1 % in this population. Among 31 variables included, 5 of them were identified to be the predictive features in the nomogram, including age, mean corpuscular hemoglobin concentration (MCHC), D-dimer, platelet distribution width (PDW), and thrombin time (TT). The area under the receiver operating characteristic (ROC) curve in the training and validation cohort was 85.9 % (95%CI: 79.96 %-90.04 %) and 85.7 % (95%CI: 78.96 %-90.69 %), respectively. Both the calibration curves and decision curve analysis demonstrated the overall satisfactory performance of the model.

Conclusion

Our groundbreaking nomogram is distinguished by its unparalleled accuracy in discriminative and calibrating functions, complemented by its tangible clinical applicability. This innovative instrument is set to empower clinicians with a robust framework for the accurate forecasting of postoperative DVT, thus facilitating the crafting of bespoke and prompt therapeutic strategies, aligning with the rigorous standards upheld by the most esteemed biomedical journals.

Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications

Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.

Exploring the medical decision-making patterns and influencing factors among the general Chinese public: a binary logistic regression analysis

OBJECTIVE

With the ongoing evolution of the healthcare system and shifts in cultural paradigms, there is a pressing need to delve into the medical decision-making behaviors of general Chinese public and understand their underlying motivations. This research seeks to elucidate the prevailing tendencies in these decision-making processes and to empirically validate the pivotal factors that shape their choices, offering valuable insights for healthcare policymakers and institutions.

METHOD

A comprehensive survey was administered to 2,696 Chinese residents to examine their medical decision-making patterns. These patterns were classified into two primary categories: Unilateral Decision-making (Doctor-dominant, Family-centric, and Patient-driven subtypes) and Collaborative Decision-making (Doctor-led, Doctor-Patient, Patient-Family, and Doctor-Patient-Family subtypes). Binary logistic regression analysis was employed to empirically pinpoint the significant factors influencing these decision-making frameworks.

RESULTS

The study's analysis reveals distinct preferences in medical decision-making among Chinese residents. In the Collaborative Decision-making category, chosen by 70.81% of participants, the subtypes are as follows: Doctor-led (29.90%), Doctor-Patient (13.54%), Patient-Family (2.93%), and Doctor-Patient-Family (24.44%). The Unilateral Decision-making, preferred by 29.19%, includes Doctor-dominant (23.22%), Family-centric (1.74%), and Patient-driven (4.23%) models. The preference for Collaborative Decision-making is associated with higher educational levels, specific marital statuses (particularly married but childless), and choices of rural residents' basic medical insurance or occupational basic medical insurance. In contrast, Unilateral Decision-making correlates with males, individuals with religious beliefs, certain occupational roles (like civil servants), and holders of commercial or publicly funded medical insurance.

CONCLUSION

This study elucidates the complex interplay of socio-cultural and individual determinants shaping medical decision-making in China. The findings reveal a marked inclination towards collaborative models, closely linked to educational level, marital status, and specific insurance types, reflecting an evolving trend towards participatory healthcare. Simultaneously, the persistence of unilateral models, influenced by gender, religious beliefs, and occupational roles, highlights the heterogeneity within Chinese healthcare preferences. These insights are crucial for policymakers and healthcare practitioners, underscoring the need for adaptable, culturally attuned healthcare frameworks that cater to this diversity, thereby enhancing patient engagement and healthcare efficacy.

A comprehensive status update on modification of foley catheter to combat catheter-associated urinary tract infections and microbial biofilms

Present-day healthcare employs several types of invasive devices, including urinary catheters, to improve medical wellness, the clinical outcome of disease, and the quality of patient life. Among urinary catheters, the Foley catheter is most commonly used in patients for bladder drainage and collection of urine. Although such devices are very useful for patients who cannot empty their bladder for various reasons, they also expose patients to catheter-associated urinary tract infections (CAUTIs). Catheter provides an ideal surface for bacterial colonization and biofilm formation, resulting in persistent bacterial infection and severe complications. Hence, rigorous efforts have been made to develop catheters that harbour antimicrobial and anti-fouling properties to resist colonization by bacterial pathogens. In this regard, catheter modification by surface functionalization, impregnation, blending, or coating with antibiotics, bioactive compounds, and nanoformulations have proved to be effective in controlling biofilm formation. This review attempts to illustrate the complications associated with indwelling Foley catheters, primarily focussing on challenges in fighting CAUTI, catheter colonization, and biofilm formation. In this review, we also collate scientific literature on catheter modification using antibiotics, plant bioactive components, bacteriophages, nanoparticles, and studies demonstrating their efficacy through in vitro and in vivo testing.

Searchable Blockchain-Based Healthcare Information Exchange System to Enhance Privacy Preserving and Data Usability

Ensuring the security and usability of electronic health records (EHRs) is important in health information exchange (HIE) systems that handle healthcare records. This study addressed the need to balance privacy preserving and data usability in blockchain-based HIE systems. We propose a searchable blockchain-based HIE system that enhances privacy preserving while improving data usability. The proposed methodology includes users collecting healthcare information (HI) from various Internet of Medical Things (IoMT) devices and compiling this information into EHR blocks for sharing on a blockchain network. This approach allows participants to search and utilize specific health data within the blockchain effectively. The results demonstrate that the proposed system mitigates the issues of traditional HIE systems by providing secure and user-friendly access to EHRs. The proposed searchable blockchain-based HIE system resolves the trade-off dilemma in HIE by achieving a balance between security and the data usability of EHRs.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.

Smart hospital definition: Academic and industrial perspective

BACKGROUNDS

Healthcare is a social and economic challenge in many countries, exacerbated by today's increasing demand. Many studies demonstrate that hospitals that move towards smartness, and some of their processes are smart, can provide more appropriate treatments and deal with problems more flexibly. It is axiomatic that implementing smart hospitals and healthcare tools requires a clear objective. However, the concept of a smart hospital lacks a comprehensive and broadly accepted definition, leading to varied interpretations and misconceptions. Many developments touted as 'smart' merely digitize existing hospital environments without truly embracing the full potential of smart technology. Furthermore, research studies have neglected to consider industrial perspectives, which will soon cause a gap between industry and academics in this concept.

OBJECTIVES

This research aims to explore the attributes of a smart hospital and use them to propose a definition for it, considering both scholarly and industrial viewpoints.

METHOD AND RESULTS

The PRISMA method is employed to select academic and practical papers providing definitions and insights into smart hospitals or healthcare. 17 studies are analyzed, and a total, 83 characteristics are identified to describe the smart hospital. These features are categorized into three primary categories: "technologies", "services", and "goals". The most important features are determined by analyzing the frequencies of these characteristics across all the studies. In the results section, these data are presented in graphical form, highlighting both academic and industrial perspectives separately, as well as a combined analysis. Furthermore, an attempt is made to uncover trends in smart hospitals from 2015 to 2023.

CONCLUSION

A comprehensive definition of the smart hospital, encompassing both academic and industrial perspectives, is proposed using the investigated characteristics. This study also presents research opportunities and discusses the existing gap between academia and industry concerning smart hospitals.

21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework

In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals' functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field's ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study's findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.

Standardizing the Head and Neck Cancer Treatment and Research

Head and neck cancer requires a multidisciplinary approach, with standardized care being essential for consistent, high-quality treatment. Standardization involves evidence-based guidelines and protocols, and collaboration is necessary for research and improving outcomes. However, collaboration can be challenging due to various barriers. Collaboration can improve care by facilitating sharing of knowledge, access to technology, clinical trials, data sharing, funding and education. To improve collaboration, a shared vision, communication channels, guidelines, centralized database, training programs, culture of collaboration and funding should be established.

Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics

Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.

Deep Learner System Based on Focal Color Retinal Fundus Images to Assist in Diagnosis

Retinal diseases are a serious and widespread ophthalmic disease that seriously affects patients' vision and quality of life. With the aging of the population and the change in lifestyle, the incidence rate of retinal diseases has increased year by year. However, traditional diagnostic methods often require experienced doctors to analyze and judge fundus images, which carries the risk of subjectivity and misdiagnosis. This paper will analyze an intelligent medical system based on focal retinal image-aided diagnosis and use a convolutional neural network (CNN) to recognize, classify, and detect hard exudates (HEs) in fundus images (FIs). The research results indicate that under the same other conditions, the accuracy, recall, and precision of the system in diagnosing five types of patients with pathological changes under color retinal FIs range from 86.4% to 98.6%. Under conventional retinopathy FIs, the accuracy, recall, and accuracy of the system in diagnosing five types of patients ranged from 70.1% to 85%. The results show that the application of focus color retinal FIs in the intelligent medical system has high accuracy and reliability for the early detection and diagnosis of diabetic retinopathy and has important clinical applications.

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

Introduction

Medical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.

Methods

In order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.

Results and discussion

We conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data.

Experimental Investigation of Acoustic Features to Optimize Intelligibility in Cochlear Implants

Although cochlear implants work well for people with hearing impairment in quiet conditions, it is well-known that they are not as effective in noisy environments. Noise reduction algorithms based on machine learning allied with appropriate speech features can be used to address this problem. The purpose of this study is to investigate the importance of acoustic features in such algorithms. Acoustic features are extracted from speech and noise mixtures and used in conjunction with the ideal binary mask to train a deep neural network to estimate masks for speech synthesis to produce enhanced speech. The intelligibility of this speech is objectively measured using metrics such as Short-time Objective Intelligibility (STOI), Hit Rate minus False Alarm Rate (HIT-FA) and Normalized Covariance Measure (NCM) for both simulated normal-hearing and hearing-impaired scenarios. A wide range of existing features is experimentally evaluated, including features that have not been traditionally applied in this application. The results demonstrate that frequency domain features perform best. In particular, Gammatone features performed best for normal hearing over a range of signal-to-noise ratios and noise types (STOI = 0.7826). Mel spectrogram features exhibited the best overall performance for hearing impairment (NCM = 0.7314). There is a stronger correlation between STOI and NCM than HIT-FA and NCM, suggesting that the former is a better predictor of intelligibility for hearing-impaired listeners. The results of this study may be useful in the design of adaptive intelligibility enhancement systems for cochlear implants based on both the noise level and the nature of the noise (stationary or non-stationary).

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

Synergizing Intelligence and Building a Smarter Future: Artificial Intelligence Meets Bioengineering

Smart Engineering (SE) describes the methods, processes, and IT tools for the interdisciplinary, system-oriented development of innovative, intelligent, networked products, production plants, and infrastructures [...].

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

Facilitating COVID recognition from X-rays with computer vision models and transfer learning

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.

Chronic disease management program applied to type 2 diabetes patients and prevention of diabetic complications: a retrospective cohort study using nationwide data

BACKGROUND

The outcomes of education and counseling by medical professionals for patients with type 2 diabetes mellitus (T2DM) are unclear. This study examined the effects of the Chronic Disease Management Program (CDMP), a health insurance fee-for-service benefit, on the incidence of diabetic complications in patients newly diagnosed with T2DM using the National Health Insurance data.

METHODS

Patients newly diagnosed with T2DM aged ≥ 20 years from 2010 to 2014 were followed up until 2015. Selection bias was minimized using propensity score matching. A stratified Cox proportional hazards model was used to analyze the association between the CDMP and the risk of incident diabetic complications. Subgroup analysis was performed for patients with high medication adherence, which was indicated by a medication possession ratio (MPR) ≥ 80.

RESULTS

Among the 11,915 patients with T2DM in the cohort, 4,617 were assigned to the CDMP and non-CDMP group each. The CDMP helped reduce the overall and microvascular risks of complications compared to the non-CDMP group; however, the protective effect against macrovascular complications was only observed in those aged ≥ 40 years. Subgroup analysis of the group aged ≥ 40 years with high adherence (an MPR ≥ 80) showed that the CDMP effectively reduced the incidence of micro- and macrovascular complications.

CONCLUSIONS

Effective management of T2DM is crucial in preventing complications in patients with the condition, and includes regular monitoring and adjustment of treatment by qualified physicians. Nevertheless, long-term prospective studies on the effects of CDMP are required to confirm this finding.

Ferroelectret nanogenerators for the development of bioengineering systems

Bioengineering devices and systems will become a practical and versatile technology in society when sustainability issues, primarily pertaining to their efficiency, sustainability, and human-machine interaction, are fully addressed. It has become evident that technological paths should not rely on a single operation mechanism but instead on holistic methodologies that integrate different phenomena and approaches with complementary advantages. As an intriguing invention, the ferroelectret nanogenerator (FENG) has emerged with promising potential in various fields of bioengineering. Utilizing the changes in the engineered macro-scale electric dipoles to create displacement current (and vice versa), FENGs have been demonstrated to be a compelling strategy for bidirectional conversion of energy between the electrical and mechanical domains. Here we provide a comprehensive overview of the latest advancements in integrating FENGs in bioengineering systems, focusing on the applications with the most potential and the underlying current constraints.

Symptom management in adult brain tumours: A literature review

AIM

To review the literature related to symptom management, clinical significance and related theoretical framework systems in adult patients with brain tumours.

BACKGROUND

As understanding of symptoms or symptom clusters and underlying biologic mechanisms has grown, it is apparent that symptom science is moving forward. Although some progress has been made in the symptom science of solid tumours such as breast and lung neoplasms, insufficient attention has been paid to symptom management for patients suffering from brain tumours. Further research is needed to achieve effective symptom management for these patients.

DESIGN

A literature review with a systematic search of symptom management in adult brain tumours.

METHODS

Electronic data bases were searched to obtain relevant published literature on symptom management in adults with brain tumours. This was then analysed and a synthesis of relevant findings is presented.

FINDINGS

Four significant general themes relating to symptom management of brain tumours in adults were identified: (1) The potential theoretical foundation related to symptom management was revealed. (2) Widely accepted validated scales or questionnaires for the assessment of single symptoms or symptom clusters were recommended. (3) Several symptom clusters and the underlying biologic mechanisms have been reported. (4) Specific symptom interventions for adults with brain tumours were collected and classified as evidence-based or insufficient evidence.

CONCLUSION

There are still many challenges in the effective management of symptoms in adults with brain tumours. The guiding role of theoretical frameworks or models related to symptom management should be utilized in future research. Using the concept of symptom clustering for research into symptoms found in patients with brain tumours, exploring common biological mechanisms for specific symptom clusters and making full use of modern big data resources to build a strong evidence base for an effective intervention or management program may inform the management of symptoms among these patients leading to better results.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a literature review.

IMPLICATIONS FOR SYMPTOM MANAGEMENT

The ultimate goal is obviously not only improving the survival rate of patients with brain tumours, but also enhancing their quality of life. Several important findings from our review include the theoretical foundations, validated assessment tools, the assessment of symptom clusters and the underlying biologic mechanism, and the identification of the evidence base for symptom interventions. These are of relevance for managers, researchers and practitioners and may function as a reference to help the effective symptom management for adults with brain tumours.

Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero-one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.

Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring

The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient-physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.

Compression of Bio-Signals Using Block-Based Haar Wavelet Transform and COVIDOA for IoMT Systems

Background: Bio-signals are the essential data that smart healthcare systems require for diagnosing and treating common diseases. However, the amount of these signals that need to be processed and analyzed by healthcare systems is huge. Dealing with such a vast amount of data presents difficulties, such as the need for high storage and transmission capabilities. In addition, retaining the most useful clinical information in the input signal is essential while applying compression. Methods: This paper proposes an algorithm for the efficient compression of bio-signals for IoMT applications. This algorithm extracts the features of the input signal using block-based HWT and then selects the most important features for reconstruction using the novel COVIDOA. Results: We utilized two different public datasets for evaluation: MIT-BIH arrhythmia and EEG Motor Movement/Imagery, for ECG and EEG signals, respectively. The proposed algorithm’s average values for CR, PRD, NCC, and QS are 18.06, 0.2470, 0.9467, and 85.366 for ECG signals and 12.6668, 0.4014, 0.9187, and 32.4809 for EEG signals. Further, the proposed algorithm shows its efficiency over other existing techniques regarding processing time. Conclusions: Experiments show that the proposed method successfully achieved a high CR while maintaining an excellent level of signal reconstruction in addition to its reduced processing time compared with the existing techniques.

Examining behavioural intention of using smart health care technology among females: dynamics of social influence and perceived usefulness

Purpose

The aim of this study is to comprehend the behavioural intention of females' perception toward smart healthcare technology. The study also examines the moderation effect of social influences between perceived smart healthcare technology and perceived usefulness among female users.

Design/methodology/approach

To test the model, this study collected data from female respondents (n = 913) responses. The data were analyzed by structural equation modeling (SEM) using Smart-PLS 3.2. To complement the findings from structural equation modeling, the study also conducted a post-hoc test via experimental research design. The authors also applied a t-test and PROCESS macro analysis to re-confirm the relationship mentioned above.

Findings

The findings revealed that perceived ease of use significantly mediates the relationship between females' perceived smart healthcare technology and intention to use. The findings also show that social influence moderates between smart healthcare technology and the perceived usefulness relationship.

Research limitations/implications

Social influence is one of the major issues while adopting smart healthcare technology because the respondents perceived that they are accustomed to the technologies related to smart health once their surroundings and social environment influence them.

Originality/value

The current study is a pioneer in the context of a developing country and unique in that it makes two contributions: it extends previous research on smart health technology adoption in the healthcare business by considering females, and it gives a broad knowledge of the female healthcare consumers from emerging nations which can be useful for developing technology-driven healthcare services strategies.

Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China

The COVID-19 pandemic has significantly impacted the healthcare industry, especially public health resources and resource allocation. With the change in people's lifestyles and increased demand for medical and health care in the post-pandemic era, the Internet and home healthcare have rapidly developed. As an essential part of Internet healthcare, mobile health (mHealth) applications help to fundamentally address the lack of medical resources and meet people's healthcare needs. In this mixed-method study, we conducted in-depth interviews with 20 users in China (mean age = 26.13, SD = 2.80, all born in China) during the pandemic, based on the unified theory of acceptance and use of technology 2 (UTAUT-2) mode, and identified four dimensions of user needs in mHealth scenarios: convenience, control, trust, and emotionality. Based on the interview results, we adjusted the independent variables, deleted the hedonic motivation and the habit, and added the perceived trust and perceived risk as the variables. Using a structural equation model (SEM), we designed the questionnaire according to the qualitative results and collected data from 371 participants (above 18 years old, 43.9% male) online to examine the interrelationships these variables. The results show that performance expectancy (β = 0.40, p < 0.001), effort expectancy (β = 0.40, p < 0.001), social influence (β = 0.14, p < 0.05), facilitating condition (β = 0.15, p < 0.001), and perceived trust (β = 0.31, p < 0.001) had positive effects on use intention. Perceived risk (β = -0.31, p < 0.001) harmed use intention, and price value (β = 0.10, p > 0.5) had no significant effects on use intention. Finally, we discussed design and development guidelines that can enhance user experience of mHealth applications. This research combines the actual needs and the main factors affecting the use intention of users, solves the problems of low satisfaction of user experience, and provides better strategic suggestions for developing mHealth applications in the future.

Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things

The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.

Insights from COVID-19: Reflecting on the Promotion of Long-Term Health Policies in China

China announced the Healthy China Initiative (2019-2030) in 2019, an action program aimed to support the country's current long-term health policy, Healthy China 2030, which focuses on public health promotion and health awareness. Following the implementation of the policy, China had the COVID-19 pandemic, which had an influence on both the public's degree of health awareness and the adoption of the HCI. This research examines whether the COVID-19 epidemic has increased public understanding and acceptance of China's long-term health policy. In addition, it analyzes whether the Chinese public's awareness of health policy has been impacted by China's usage of smart healthcare in its response to the pandemic. To correspond to these study aims, we used a questionnaire based on the research questions and recent relevant research. The results of the study, based on an examination of 2488 data, demonstrate that the Healthy China Initiative is still poorly understood. More than 70% of respondents were unfamiliar with it. However, the results imply that respondents are becoming more aware of smart healthcare and that public acceptance of official health policies can be aided by the sharing of knowledge about this. As a result, we examine the situation and draw the conclusion that the spread of cutting-edge health-related technology can enhance the communication of health policy and provide participants and policymakers with fresh insights. Finally, this study also can provide lessons for other countries in the early stages of policy dissemination, particularly health policy advocacy and promotion during epidemics.

Model-Driven Engineering Techniques and Tools for Machine Learning-Enabled IoT Applications: A Scoping Review

This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

Interoperability of heterogeneous health information systems: a systematic literature review

BACKGROUND

The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them.

METHODS

In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis.

RESULTS

Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously.

CONCLUSIONS

The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

Investigation and Countermeasures Research of Hospital Information Construction of Tertiary Class-A Public Hospitals in China: Questionnaire Study

BACKGROUND

Medical informatization has initially demonstrated its advantages in improving the medical service industry. Over the past decade, the Chinese government have made a lot of effort to complete infrastructural information construction in the medical and health domain, and smart hospitals will be the next priority according to policies released by Chinese government in recent years.

OBJECTIVE

To provide strategic support for further development of medical information construction in China, this study aimed to investigate the current situation of medical information construction in tertiary class-A public hospitals and analyze the existing problems and countermeasures.

METHODS

This study surveyed 23 tertiary class-A public hospitals in China who voluntarily responded to a self-designed questionnaire distributed in April 2020 to investigate the current medical information construction status. Descriptive statistics were used to summarize the current configurations of hospital information department, hospital information systems, hospital internet service and its application, and the satisfaction of hospital information construction. Interviews were also conducted with the respondents in this study for requirement analysis.

RESULTS

The results show that hospital information construction has become one of the priorities of the hospitals' daily work, and the medical information infrastructural construction and internet service application of the hospitals are good; however, a remarkable gap among the different level of hospitals can be observed. Although most hospitals had built their own IT team to undertake information construction work, the actual utilization rate of big data collected and stored in the hospital information system was not satisfactory.

CONCLUSIONS

Support for the construction of information technology in primary care institutions should be increased to balance the level of development of medical informatization in medical institutions at all levels. The training of complex talents with both IT and medical backgrounds should be emphasized, and specialized disease information standards should be developed to lay a solid data foundation for data utilization and improve the utilization of medical big data.

Developing a Capsule Clinic-A 24-Hour Institution for Improving Primary Health Care Accessibility: Evidence From China

Telehealth is an effective combination of medical service and intelligent technology. It can improve the problem of remote access to medical care. However, an imbalance in the allocation of health resources still occurs. People spend more time and money to access higher-quality services, which results in inequitable access to primary health care (PHC). At the same time, patients' usage of telehealth services is limited by the equipment and their own knowledge, and the PHC service suffers from low usage efficiency and lack of service supply. Therefore, improving PHC accessibility is crucial to narrowing the global health care coverage gap and maintaining health equity. In recent years, China has explored several new approaches to improve PHC accessibility. One such approach is the capsule clinic, an emerging institution that represents an upgraded version of the internet hospital. In coordination with the United Nations, the Yinzhou district of Ningbo city in Zhejiang, China, has been testing this new model since 2020. As of October 2022, the number of applications in Ningbo was 15, and the number of users reached 12,219. Unlike internet hospitals, the entire process-from diagnosis to prescription services-can be completed at the capsule clinic. The 24-hour telehealth service could also solve transportation problems and save time for users. Big data analysis can accurately identify regional populations' PHC service needs and improve efficiency in health resource allocation. The user-friendly, low-cost, and easily accessible telehealth model is of great significance. Installation of capsule clinics would improve PHC accessibility and resolve the uneven distribution of health resources to promote health equity.

Managing challenging behaviours in adults with traumatic brain injury: A scoping review of technology-based interventions

Challenging behaviours are one of the most serious sequelae after a traumatic brain injury (TBI). These chronic behaviours must be managed to reduce the associated burden for caregivers, and people with TBI. Though technology-based interventions have shown potential for managing challenging behaviours, no review has synthesised evidence of technology aided behaviour management in the TBI population. The objective of this scoping review was to explore what technology-based interventions are being used to manage challenging behaviours in people with TBI. Two independent reviewers analysed 3505 studies conducted between 2000 and 2023. Studies were selected from five databases using search strategies developed in collaboration with a university librarian. Sixteen studies were selected. Most studies used biofeedback and mobile applications, primarily targeting emotional dysregulation. These technologies were tested in a variety of settings. Two interventions involved both people with TBI and their family caregivers. This review found that technology-based interventions have the potential to support behavioural management, though research and technology development is at an early stage. Future research is needed to further develop technology-based interventions that target diverse challenging behaviours, and to document their effectiveness and acceptability for use by people with TBI and their families.

Emo-mirror: a proposal to support emotion recognition in children with autism spectrum disorders

Autism spectrum disorder (ASD) is a neurodevelopmental disorder defined as persistent difficulty in maturing the socialization process. Health professionals have used traditional methods in the therapies performed on patients with the aim of improving the expression of emotions by patients. However, they have not been sufficient to detect the different emotions expressed in the face of people according to different sensations. Therefore, different artificial intelligence techniques have been applied to improve the results obtained in these therapies. In this article, we propose the construction of an intelligent mirror to recognize five basic emotions: angry, scared, sad, happy and neutral. This mirror uses convolutional neural networks to analyze the images that are captured by a camera and compare it with the one that the patient should perform, thus supporting the therapies performed by health professionals in children with ASD. The proposal presents the platform and computer architecture, as well as the evaluation by specialists under the technology acceptance model.

A review of advanced technologies available to improve the healthcare performance during COVID-19 pandemic

Information technology (IT) has enabled the initiation of an innovative healthcare system. An innovative healthcare system integrates new technologies such as cloud computing, the internet of things, and artificial intelligence (AI), to transform the healthcare to be more efficient, more convenient and more personalized. This review aims to identify the key technologies that will help to support an innovative healthcare system. A case study approach was used in this research analysis to enable a researcher to closely analyze the data in a particular context. It presents a case study of the coronavirus (COVID-19) as a means of exploring the use of advanced technologies in an innovative healthcare system to help address a worldwide health crisis. An innovative healthcare system can help to promote better patient self-management, reduce costs, relieve staff pressures, help with resource and knowledge management, and improve the patient experience. An innovative healthcare system can reduce the expense and time for research, and increase the overall efficacy of the research. Overall, this research identifies how innovative technologies can improve the performance of the healthcare system. Advanced technologies can assist with pandemic control and can help in the recognition of the virus, clinical treatment, medical protection, intelligent diagnosis, and outbreak analysis. The review provides an analysis of the future prospects of an innovative healthcare system.

Smart home healthcare for chronic disease management: A scoping review

Objective

Chronic disease is the leading cause of morbidity and mortality on a global scale. Recent use of intelligent home environments for remote health monitoring has allowed patients to feel safe at home. By encouraging patient engagement and, if necessary, care delivery, smart home environments may be a useful method for managing chronic diseases at home. The purpose of this study is to synthesize the evidence on the usage of smart healthcare in the home for chronic illness management.

Methods

We conducted a scoping review using the Joanna Briggs Methodology and searched three databases from 2017 to 2022 for original research papers on smart healthcare, smart home technology, home-based technology, home monitoring, and physiological monitoring for chronic illness management. We did a descriptive study on the pertinent data we collected, as well as an analysis of whether the devices met its objectives.

Results

The final analysis included nine papers, the majority of which were randomized controlled trials. All of the studies were carried out in developed countries. The gadgets or smart healthcare in these studies are categorized based on the technology used and the outcomes measured. Respiratory, weight, and ballistocardiograph measurements, as well as changes in questionnaire ratings, hospitalization, activity monitoring, device acceptability, medication adherence, exercise capacity, and body function, were all measured.

Conclusion

Smart healthcare applications boost health by monitoring health and wellness, recording physical activity and rehabilitation, and improving overall quality of life. Not all smart home applications, however, served their intended purpose. As a result, more research into the efficacy of smart healthcare is needed to improve its application for chronic illness treatment.

SHMAD: A Smart Health Care System to Monitor Alzheimer's Disease Patients

BACKGROUND

In the digital era monitoring the patient's health status is more effective and consistent with smart healthcare systems. Smart health care facilitates secure and reliable maintenance of patient data. Sensors, machine learning algorithms, Internet of things, and wireless technology has led to the development of Artificial Intelligence-driven Internet of Things models.

OBJECTIVE

This research study proposes an Artificial Intelligence driven Internet of Things model to monitor Alzheimer's disease patient condition. The proposed Smart health care system to monitor and alert caregivers of Alzheimer's disease patients includes different modules to monitor the health parameters of the patients. This study implements the detection of fall episodes using an artificial intelligence model in Python.

METHODS

The fall detection model is implemented with data acquired from the IMU open dataset. The ensemble machine learning algorithm AdaBoost performs classification of the fall episode and daily life activity using the feature set of each data sample. The common machine learning classification algorithms are compared for their performance on the IMU fall dataset.

RESULTS

AdaBoost ensemble classifier exhibits high performance compared to the other machine learning algorithms. The AdaBoost classifier shows 100% accuracy for the IMU dataset. This high accuracy is achieved as multiple weak learners in the ensemble model classify the data samples in the test data accurately.

CONCLUSIONS

This study proposes a smart healthcare system for monitoring Alzheimer's disease patients. The proposed model can alert the caregiver in case of fall detection via mobile applications installed in smart devices.

Registered nurses' efforts to ensure safety for home-dwelling older patients

BACKGROUND

The international development of health care, an ageing population and rapid technical development mean that more care is being performed in patient homes. This care environment is often unpredictable and involves both formal and informal caregivers, making it potentially unsafe. There is sparse knowledge about how patient safety is protected in home health care in Sweden and how registered nurses work to prevent risks and promote safe care.

AIM

The aim of the study was to explore registered nurses' efforts to reduce perceived risks for home-dwelling older patients and ensure safe home health care.

METHOD

We used a qualitative design with individual interviews with 13 registered nurses working in municipalities in southeast Sweden. The narratives were analysed with inductive content analysis.

FINDINGS

The findings showed that the registered nurses tried to secure a safe care environment and took an active role in care, striving to stay one step ahead of the patient. These three types of efforts are likely interdependent, suggesting they are all needed to reduce perceived risks for home-dwelling older patients and ensure patient safety in home health care.

CONCLUSIONS

It is a challenge for registered nurses to maintain patient safety when performing care in patient homes. Continuity of care is required and must be based not only on self-reliance among registered nurses but also on trusting relationships with patients, next of kin, colleagues and other personnel, as well as on the development of organisational conditions adapted to patient needs.

Virtual Reality in Health Science Education: Professors’ Perceptions

Virtual reality (VR) is a simulated experience in a three-dimensional (3D) computer-simulated world. Recent advances in technology position VR as a multipurpose technology in the healthcare sector and as a critical component in achieving Health 4.0. In this article, descriptive and correlationally quantitative research is carried out on the assessments made by Latin American health sciences university professors on the didactic use of virtual reality technologies. The main objective was to analyze the differences in the perceptions expressed by the public or private tenure of the universities where the professors teach. In addition, gender and age gaps were identified in the assessments obtained from each of the types of universities. The results reveal that Latin American health science professors at private universities have a higher selfconcept of their digital skills for the use of virtual reality in the lectures. This greater selfconcept also leads to a reduction in the gender and age gaps in the participating private universities with respect to the public counterparts. It is advisable to increase both faculty training in the didactic use of virtual reality and funding for its use, mainly in public universities.

Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes

Smart health is considered to be a new phase in the application of information and communication technologies (ICT) in healthcare that can improve its efficiency and sustainability. However, based on our literature review on the concept of smart health, there is a lack of a comprehensive perspective on the concept of smart health and a framework for how to link the drivers and outcomes of smart health. This paper aims to interweave the drivers and outcomes in a multi-dimensional framework under the input-process-output (IPO) logic of the "system view" so as to promote a deeper understanding of the model of smart health. In addition to the collection of studies, we used the modified Delphi method (MDM) to invite 10 experts from different fields, and the views of the panelists were analyzed and integrated through a three-round iterative process to reach a consensus on the elements included in the conceptual framework. The study revealed that smart health contains five drivers (community, technology, policy, service, and management) and eight outcomes (efficient, smart, sustainable, planned, trustworthy, safe, equitable, health-beneficial, and economic). They all represent a unique aspect of smart health. This paper expands the research horizon of smart health, shifting from a single technology to multiple perspectives, such as community and management, to guide the development of policies and plans in order to promote smart health.