A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

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.

Investigation into Application of AI and Telemedicine in Rural Communities: A Systematic Literature Review

Recent advances in artificial intelligence (AI) and telemedicine are transforming healthcare delivery, particularly in rural and underserved communities.

BACKGROUND/OBJECTIVES

The purpose of this systematic review is to explore the use of AI-driven diagnostic tools and telemedicine platforms to identify underlying themes (constructs) in the literature across multiple research studies.

METHOD

The research team conducted an extensive review of studies and articles using multiple research databases that aimed to identify consistent themes and patterns across the literature.

RESULTS

Five underlying constructs were identified with regard to the utilization of AI and telemedicine on patient diagnosis in rural communities: (1) Challenges/benefits of AI and telemedicine in rural communities, (2) Integration of telemedicine and AI in diagnosis and patient monitoring, (3) Future considerations of AI and telemedicine in rural communities, (4) Application of AI for accurate and early diagnosis of diseases through various digital tools, and (5) Insights into the future directions and potential innovations in AI and telemedicine specifically geared towards enhancing healthcare delivery in rural communities.

CONCLUSIONS

While AI technologies offer enhanced diagnostic capabilities by processing vast datasets of medical records, imaging, and patient histories, leading to earlier and more accurate diagnoses, telemedicine acts as a bridge between patients in remote areas and specialized healthcare providers, offering timely access to consultations, follow-up care, and chronic disease management. Therefore, the integration of AI with telemedicine allows for real-time decision support, improving clinical outcomes by providing data-driven insights during virtual consultations. However, challenges remain, including ensuring equitable access to these technologies, addressing digital literacy gaps, and managing the ethical implications of AI-driven decisions. Despite these hurdles, AI and telemedicine hold significant promise in reducing healthcare disparities and advancing the quality of care in rural settings, potentially leading to improved long-term health outcomes for underserved populations.

Detecting anomalies in smart wearables for hypertension: a deep learning mechanism

Introduction

The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).

Methods

This paper introduces a novel neural network architecture, ResNet-LSTM, to predict BP from physiological signals such as electrocardiogram (ECG) and photoplethysmogram (PPG). The combination of ResNet's feature extraction capabilities and LSTM's sequential data processing offers improved prediction accuracy. Comprehensive error analysis was conducted, and the model was validated using Leave-One-Out (LOO) cross-validation and an additional dataset.

Results

The ResNet-LSTM model showed superior performance, particularly with PPG data, achieving a mean absolute error (MAE) of 6.2 mmHg and a root mean square error (RMSE) of 8.9 mmHg for BP prediction. Despite the higher computational cost (~4,375 FLOPs), the improved accuracy and generalization across datasets demonstrate the model's robustness and suitability for continuous BP monitoring.

Discussion

The results confirm the potential of integrating ResNet-LSTM into SHM for accurate and non-invasive BP prediction. This approach also highlights the need for accurate anomaly detection in continuous monitoring systems, especially for wearable devices. Future work will focus on enhancing cloud-based infrastructures for real-time analysis and refining anomaly detection models to improve patient outcomes.

Relationship between work readiness, transition shock, and job competency among new nurses in oncology hospitals - A longitudinal study based on a latent growth model

BACKGROUND

Nursing job competency is critical for talent development both globally and in China, relating to work readiness and transition shock. Previous studies, which have typically relied on average measurements at fixed time points, have not provided comprehensive longitudinal insights.

AIM

This study aimed to investigate the developmental trajectories of transition shock in new nurses at oncology specialty hospitals. Furthermore, we sought to explore the longitudinal mediating role of transition shock in the relationship between work readiness and the development of nursing job competency.

DESIGN

Longitudinal follow-up study.

METHODS

We conducted three follow-up surveys over 8 months using the Work Readiness Scale, the Transition Shock Scale, and the Nursing Job Competency Scale to assess 252 novice nurses at two high-volume oncology centers. The surveys were conducted at 0, 4, and 8 months, with demographic information collected during the first survey. Data were analyzed using R 4.1.2 and Mplus 8.0.

RESULTS

(1) Over the 8-month period, transition shock exhibited a linear decrease. Notably, nurses with a higher initial transition shock experienced a slower rate of decline. (2) There were positive correlations between work readiness and nursing job competency at all three measurement points. Conversely, transition shock was negatively correlated with both work readiness and nursing job competency. (3) Transition shock functioned as a longitudinal mediator in the relationship between work readiness and nursing job competency.

CONCLUSION

This study clarified the longitudinal mediating role of transition shock in the relationship between work readiness and job competency in oncology settings. Targeted interventions are necessary to mitigate excessive transition shock, thereby improving the nursing job competency of new nurses in oncology hospitals.

REGISTRATION

23/313-4055.

Digital approaches in post-COVID healthcare: a systematic review of technological innovations in disease management

Post-COVID conditions (PCC) emerged during the pandemic, prompting a rise in the use of Digital Health Technologies (DHTs) to manage lockdowns and hospital overcrowding. Real-time tracking and information analyses were crucial to strengthening the global research response. This study aims to map the use of modern digital approaches in estimating the prevalence, predicting, diagnosing, treating, monitoring, and prognosis of PCC. This review was conducted by searching PubMed and Scopus databases for keywords and synonyms related to DHTs, Smart Healthcare Systems, and PCC based on the World Health Organization definition. Articles published from 1 January 2020 to 21 May 2024 were screened for eligibility based on predefined inclusion criteria, and the PRISMA framework was used to report the findings from the retained studies. Our search identified 377 studies, but we retained 23 studies that used DHTs, artificial intelligence (AI), and infodemiology to diagnose, estimate prevalence, predict, treat, and monitor PCC. Notably, a few interventions used infodemics to identify the clinical presentations of the disease, while most utilized Electronic Health Records and AI tools to estimate diagnosis and prevalence. However, we found that AI tools were scarcely used for monitoring symptoms, and studies involving SHS were non-existent in low- and middle-income countries (LMICs). These findings show several DHTs used in healthcare, but there is an urgent need for further research in SHS for complex health conditions, particularly in LMICs. Enhancing DHTs and integrating AI and infodemiology provide promising avenues for managing epidemics and related complications, such as PCC.

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.