Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

Advancement in Biosensor Technologies of 2D MaterialIntegrated with Cellulose-Physical Properties

This review paper provides an in-depth analysis of recent advancements in integrating two-dimensional (2D) materials with cellulose to enhance biosensing technology. The incorporation of 2D materials such as graphene and transition metal dichalcogenides, along with nanocellulose, improves the sensitivity, stability, and flexibility of biosensors. Practical applications of these advanced biosensors are explored in fields like medical diagnostics and environmental monitoring. This innovative approach is driving research opportunities and expanding the possibilities for diverse applications in this rapidly evolving field.

Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. The advent of smart technologies has significantly impacted the management of CVD, offering innovative tools and solutions to improve patient outcomes. Smart technologies have revolutionized and transformed the management of CVD, providing innovative tools to improve patient care, enhance diagnostics, and enable more personalized treatment approaches. These smart tools encompass a wide range of technologies, including wearable devices, mobile applications,3D printing technologies, artificial intelligence (AI), remote monitoring systems, and electronic health records (EHR). They offer numerous advantages, such as real-time monitoring, early detection of abnormalities, remote patient management, and data-driven decision-making. However, they also come with certain limitations and challenges, including data privacy concerns, technical issues, and the need for regulatory frameworks. In this review, despite these challenges, the future of smart technologies in CVD management looks promising, with advancements in AI algorithms, telemedicine platforms, and bio fabrication techniques opening new possibilities for personalized and efficient care. In this article, we also explore the role of smart technologies in CVD management, their advantages and disadvantages, limitations, current applications, and their smart future.

Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care

Objective

In order to improve the refined management of hospitals, promote the scientific development of smart hospitals in medical institutions, and solve the problem of data filling and reporting that is increasing year by year in the country, province, and city.

Methods

A total of 84 high-risk pregnant women admitted to our hospital from January 2020 to October 2021 were selected and screened for high-risk pregnant women. Risk pregnant women were divided into a routine intervention group and a DS medical group, with 42 cases in each group. High-risk pregnant women in the routine intervention group received routine intervention, and the DS medical group applied data to serve smart medical services on the basis of routine intervention. The scores of self-care, anxiety, and depression were compared between the two groups, the coping styles were analyzed, the satisfaction rate and incidence of adverse conditions of the high-risk puerperae were recorded, and the delivery methods of the two groups were compared.

Results

After the intervention, the activities of daily living, follow-up, fetal monitoring, and self-protection behaviors in the DS medical group were higher than those in the routine intervention group, and the difference was statistically significant (P < 0.05). The scores of anxiety and depression in the group were lower, with statistical significance (P < 0.05); after the intervention, the scores of negative coping styles in the DS medical group were lower than those in the conventional intervention group, while the scores for positive coping styles were higher than those in the conventional intervention group; the DS medical group had higher risk. The satisfaction of pregnant women was significantly higher than that of the routine intervention group, and the difference was statistically significant (P < 0.05); the overall incidence of adverse maternal outcomes among high-risk pregnant women in the DS medical group was lower than that of the routine intervention group, and the difference was not statistically significant (P > 0.05). Compared with the routine group, the DS medical group had a higher number of vaginal deliveries and a lower number of cesarean deliveries, and the difference was statistically significant (P < 0.05).

Conclusion

The application of data services in a smart medical high-risk maternity-related data management platform enables the promotion of high-risk pregnant women's self-care behaviors and improves negative emotions, enables them to cooperate in delivery with positive behaviors, and reduces the number of cases of cesarean delivery.

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

BPM Support for Patient-Centred Clinical Pathways in Chronic Diseases

Epidemiological trends over the past decade show a significant worldwide increase in the burden of chronic diseases. At the same time, the human resources of health care are becoming increasingly scarce and expensive. One of the management concepts that can help in solving this problem is business process management (BPM). The results of research conducted in the healthcare sector thus far prove that BPM is an effective tool for optimizing clinical processes, as it allows for the ongoing automatic tracking of key health parameters of an individual patient without the need to involve medical personnel. The aim of this article is to present and evaluate the redesign of diagnostic and therapeutic processes enabling the patient-centric organization of therapy thanks to the use of new telemedicine techniques and elements of hyperautomation. By using an illustrative case study of one of the most common chronic diseases, Chronic Obstructive Pulmonary Disease (COPD), we discuss the use of clinical pathways (CPs) prepared on the basis of the current version of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) as a communication tool between healthcare professionals, the patient and his or her caregivers, as well as the method of identifying and verifying new knowledge generated on an ongoing basis in diagnostic and therapeutic processes. We also show how conducting comprehensive, patient-focused primary health care relieves the health care system, and at the same time, thanks to the use of patient engagement and elements of artificial intelligence (predictive analyses), reduces the significant clinical risk of therapy.