Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Examining attitudes to uptake and sustainment of technology among care aides in the delivery of care: a scoping review protocol

INTRODUCTION

Care aides are health workers who deliver hands-on care to patients across the healthcare continuum. The use of technology in healthcare delivery is increasing, and evidence regarding how care aides' attitudes may either facilitate or hinder the adoption of healthcare technologies is lacking.The aim of the proposed scoping review is to examine available evidence regarding care aides' attitudes towards the adoption of innovation and factors that may influence the sustainable use of technology in healthcare delivery. Published studies, grey literature and review articles that identify a method for the review, conference abstracts and website publications regarding the attitude, uptake and sustainable use of technology in care delivery by care aides will be included. For abstracts that have resulted in publications, the full publications will be included. The search for evidence commenced in June 2023 and will end in March 2024.

METHODS AND ANALYSIS

The Joanna Briggs Institute (JBI) method will be used to conduct the review. The CINAHL, Cochrane Library, EMBASE, MEDLINE, ProQuest, PubMed, SCOPUS, PROSPERO, Web of Science and JBI Evidence Synthesis databases will be searched using keywords for publications within the last 20 years to examine trends in health technology and attitudes of care aides towards innovation over the last two decades. A search of grey literature and websites will be conducted. The reference list of the retrieved articles will be used to identify additional literature. The search results will be exported into a literature management tool for screening and analysis. Article screening will be performed by two authors and if a third is needed to resolve any differences. Data analysis will be guided by two theoretical frameworks.

ETHICS AND DISSEMINATION

No ethics approval is required. The findings will be disseminated in a peer-reviewed journal and presented in conferences.

REGISTRATION DETAILS

https://doi.org/10.17605/OSF.IO/CZQUP.

Unlocking insights: Navigating COVID-19 challenges and Emulating future pandemic Resilience strategies with strengthening natural immunity

The original COVID-19 vaccines, developed against SARS-CoV-2, initially mitigated hospitalizations. Bivalent vaccine boosters were used widely during 2022-23, but the outbreaks persisted. Despite this, hospitalizations, mortality, and outbreaks involving dominant mutants like Alpha and Delta increased during winters when the population's vitamin D levels were at their lowest. Notably, 75 % of human immune cell/system functions, including post-vaccination adaptive immunity, rely on adequate circulatory vitamin D levels. Consequently, hypovitaminosis compromises innate and adaptive immune responses, heightening susceptibility to infections and complications. COVID-19 vaccines primarily target SARS-CoV-2 Spike proteins, thus offering only a limited protection through antibodies. mRNA vaccines, such as those for COVID-19, fail to generate secretory/mucosal immunity-like IgG responses, rendering them ineffective in halting viral spread. Additionally, mutations in the SARS-CoV-2 binding domain reduce immune recognition by vaccine-derived antibodies, leading to immune evasion by mutant viruses like Omicron variants. Meanwhile, the repeated administration of bivalent boosters intended to enhance efficacy resulted in the immunoparesis of recipients. As a result, relying solely on vaccines for outbreak prevention, it became less effective. Dominant variants exhibit increased affinity to angiotensin-converting enzyme receptor-2, enhancing infectivity but reducing virulence. Meanwhile, spike protein-related viral mutations do not impact the potency of widely available, repurposed early therapies, like vitamin D and ivermectin. With the re-emergence of COVID-19 and impending coronaviral pandemics, regulators and health organizations should proactively consider approval and strategic use of cost-effective adjunct therapies mentioned above to counter the loss of vaccine efficacy against emerging variants and novel coronaviruses and eliminate vaccine- and anti-viral agents-related serious adverse effects. Timely implementation of these strategies could reduce morbidity, mortality, and healthcare costs and provide a rational approach to address future epidemics and pandemics. This perspective critically reviews relevant literature, providing insights, justifications, and viewpoints into how the scientific community and health authorities can leverage this knowledge cost-effectively.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Revolutionizing Gastrointestinal Disorder Management: Cutting-Edge Advances and Future Prospects

In recent years, remarkable strides have been made in the management of gastrointestinal disorders, transforming the landscape of patient care and outcomes. This article explores the latest breakthroughs in the field, encompassing innovative diagnostic techniques, personalized treatment approaches, and novel therapeutic interventions. Additionally, this article emphasizes the use of precision medicine tailored to individual genetic and microbiome profiles, and the application of artificial intelligence in disease prediction and monitoring. This review highlights the dynamic progress in managing conditions such as inflammatory bowel disease, gastroesophageal reflux disease, irritable bowel syndrome, and gastrointestinal cancers. By delving into these advancements, we offer a glimpse into the promising future of gastroenterology, where multidisciplinary collaborations and cutting-edge technologies converge to provide more effective, patient-centric solutions for individuals grappling with gastrointestinal disorders.

A Systematic Review and Bayesian Network Meta-Analysis Comparing In-Person, Remote, and Blended Interventions in Physical Activity, Diet, Education, and Behavioral Modification on Gestational Weight Gain among Overweight or Obese Pregnant Individuals

BACKGROUND

Despite the well-documented adverse outcomes associated with obesity during pregnancy, this condition remains a promising modifiable risk factor.

OBJECTIVES

The aim of this study was to ascertain the most effective treatment modalities for gestational weight gain (GWG) in pregnant women classified as overweight or obese.

METHODS

A systematic search was conducted across 4 electronic databases: Embase, EBSCOhost, PubMed, and Web of Science. To assess the quality of evidence, the Confidence In Network Meta-Analysis (CINeMA) approach, grounded in the Grading of Recommendations Assessment, Development, and Evaluation framework, was employed. A Bayesian network meta-analysis was conducted to synthesize the comparative effectiveness of treatment modalities based on GWG outcomes.

RESULTS

The analysis incorporated 60 randomized controlled trials, encompassing 16,615 participants. Modes of intervention administration were classified as remote (R: eHealth [e] and mHealth [m]), in-person (I), and a combination of both (I+R). The interventions comprised 5 categories: education (E), physical activity (PA), dietary (D), behavior modification (B), and combinations thereof. The quality of the evidence, as evaluated by CINeMA, ranged from very low to high. Compared to the control group, the I-D intervention (mean difference [MD]: -1.27; 95% confidence interval [CI]: -2.23, -0.32), I-PADB (MD: -0.60, 95% CI: -1.19, -0.00), and I-B (MD: -0.34, 95% CI: -0.57, -0.10) interventions showed significant efficacy in reducing GWG.

CONCLUSIONS

Preliminary findings suggest that the I-D intervention is the most efficacious in managing GWG among pregnant women who are overweight or obese, followed by I-PADB and I-B+R-B(m) treatments. These conclusions are drawn from evidence of limited quality and directness, including insufficient data on PA components used in the interventions. Owing to the absence of robust, direct evidence delineating significant differences among various GWG management strategies, it is tentatively proposed that the I-D intervention is likely the most effective approach. This study was registered with PROSPERO as CRD42023473627.

Health disparity among older adults in urban China: The role of local fiscal conditions

This study explores the disparities in older adults' self-rated health within the urban landscape of China. Drawing on the 1% national population survey of China in 2015, it highlights how variations in city development contribute to geographical health disparities among older residents. In the era of the decentralized fiscal system, a crucial mechanism identified is the role of cities' local fiscal revenue in connecting their socioeconomic development and the health status among older adults. Despite efforts by cities in lower socioeconomic positions to increase fiscal expenditure and address deficits through central transfer payments, they prove inadequate in effectively mitigating population health disparities. The prioritization of economic growth and neglect of public service provision responsibilities are fundamental causes within this fiscal framework. The findings underscore the urgent need for increased central transfer payments in public services to address the growing disparities in older adults' health.

Putting the pieces together: towards an integrative framework for healthcare performance

PURPOSE

In the past few decades, performance measuring systems have become important managerial tools for healthcare organizations. Healthcare performance metrics are a useful tool in understanding how healthcare organizations achieve their goals while satisfying the needs of their patients and conforming to national and international standards. Various efforts have been made to assess healthcare performance. Most of these measures are focused on a single perspective or developed by a single source to meet management and strategic objectives on time.

DESIGN/METHODOLOGY/APPROACH

We develop a review of the literature to shed light on the measures used to assess performance in the healthcare sector at various points in time, as well as to establish a thorough understanding of healthcare performance measurement.

FINDINGS

Developing real-time digital traceability of metrics and an integrative perspective that increases the actionability of information acquired is an attractive potential made possible by the introduction of new technologies and the digitization of data.

ORIGINALITY/VALUE

We conclude that a proper measurement system should be one to combine patient, physician, non-medical staff and system perspective, which will further facilitate the assessment of healthcare performance and the comparative function.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

Aguhyper: a hyperledger-based electronic health record management framework

The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off-chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper's decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identified in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper's implementation configurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes significantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the field.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Transformative Frontiers: A Comprehensive Review of Emerging Technologies in Modern Healthcare

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape. Virtual reality and AR revolutionize medical training, IoMT extends healthcare boundaries, RPM facilitates proactive care, and fintech integration enhances financial processes. Cloud migration ensures scalable and efficient data management, while ML harnesses algorithms for diagnostic precision and personalized treatment.

Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review

This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.

Digital Health in Diabetes and Cardiovascular Disease

BACKGROUND

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD).

PURPOSE OF THE REVIEW

In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease.

CONCLUSION

We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Digitalization in orthopaedics: a narrative review

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

A public value-based model to understand patients' adoption of eHealth: Theoretical underpinnings and empirical application

Objective

The main objective of this study is to develop an eHealth adoption model based on patients' perceptions of public value dimensions and empirically apply the model to understand the adoption of a governmental health app by Jordanian patients. The study attempts to contribute to overcoming the narrow focus of contemporary theories such as UTAUT and, ultimately, to designing more effective implementation strategies in order to address the current delays in global eHealth adoption.

Methods

We conducted a quantitative survey of 430 Jordanian patients, utilizing structural equation modeling (SEM) to process the empirical data. Specifically, we applied an SEM two-step approach that involved (a) evaluating the model-data fit through a review of the measurement model(s) and common method bias; and (b) analyzing the structural model.

Results

Our findings confirm that the proposed patients' value scale is valid and reliable. Its five dimensions (hedonistic motivation, utilitarian motivation, social value, ethical public value, and public trust value) significantly correlate with patients' public value, and the latter directly affects their use of eHealth apps, with habits mediating this relationship. Among the dimensions, hedonistic motivations tend to be prioritized over utilitarian ones. Ethical and trust values also play an essential role, particularly in how health technology handles patients' data and upholds their dignity and self-esteem.

Conclusions

This study highlights the holistic nature of eHealth adoption by patients and the crucial role of public values in their use behavior. It provides useful insights for policymakers and developers.

Advancements in Understanding and Classifying Chronic Orofacial Pain: Key Insights from Biopsychosocial Models and International Classifications (ICHD-3, ICD-11, ICOP)

In exploring chronic orofacial pain (COFP), this review highlights its global impact on life quality and critiques current diagnostic systems, including the ICD-11, ICOP, and ICHD-3, for their limitations in addressing COFP's complexity. Firstly, this study outlines the global burden of chronic pain and the importance of distinguishing between different pain types for effective treatment. It then delves into the specific challenges of diagnosing COFP, emphasizing the need for a more nuanced approach that incorporates the biopsychosocial model. This review critically examines existing classification systems, highlighting their limitations in fully capturing COFP's multifaceted nature. It advocates for the integration of these systems with the DSM-5's Somatic Symptom Disorder code, proposing a unified, multidisciplinary diagnostic approach. This recommendation aims to improve chronic pain coding standardization and acknowledge the complex interplay of biological, psychological, and social factors in COFP. In conclusion, here, we highlight the need for a comprehensive, universally applicable classification system for COFP. Such a system would enable accurate diagnosis, streamline treatment strategies, and enhance communication among healthcare professionals. This advancement holds potential for significant contributions to research and patient care in this challenging field, offering a broader perspective for scientists across disciplines.

Factors Influencing the Aged in the Use of Mobile Healthcare Applications: An Empirical Study in China

Mobile healthcare applications are of significant potential value in the development of the aged-care industry due to their great convenience, high efficiency, and low cost. Since the cognition and utilization rates of mobile healthcare applications for the elderly are still low, this study explored the factors that affect the elderly's adoption of mobile healthcare applications. This study conducted a questionnaire survey on the elderly in China and received 365 valuable responses. This study combined the technology acceptance model, protection motivation theory, and perceived risk theory to build a research model of factors affecting the use of mobile healthcare applications by the elderly. The data were analyzed using a structural equation model. The results were as follows: according to the empirical research, (1) perceived usefulness and perceived ease of use positively affect the use attitude of the elderly; perceived usefulness and user attitude positively affect the behavior intention of the elderly; perceived ease of use positively affects perceived usefulness; (2) perceived severity has a significant positive correlation with use attitude; perceived susceptibility and attitude to use have no significant impact; (3) perceived risk is negatively correlated with the use attitude and behavioral intention. The above-mentioned factors should be taken into consideration during the development of mobile healthcare applications for the aged to upgrade the overall service quality of mobile healthcare applications, thus enhancing the operational level of mobile healthcare applications and the health literacy of the aged.

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.