Care Technology Research Articles

Writing research-based theatre on aged care: the ethnodrama, After Aleppo

ABSTRACT This paper documents the visioning, crafting, and writing of an ethnodrama – a type of embodied writing that takes the form of a research-based play script – about one unusual experience within an aged care organization: what might happen when technology, specifically virtual reality, is implemented into this unique organizational setting? As well as reflecting on the artistic and scientific value of research-based theatre, the script of one of the monologue plays, After Aleppo, is shared. Play scripts are a uniquely creative form of embodied writing, helping people see, imagine, and better understand the experiences of others, with ethnodrama the translation and adaption of research into a written script. As well as generating empathy, this research-based theatre provides a novel form of knowledge translation to engage and educate the public and those working in aged care organizations, a diverse workforce dealing with issues of care relationships, ageing, technology, and death.

Awareness and intention-to-use of digital health applications, artificial intelligence and blockchain technology in breast cancer care.

Emerging digital technologies promise to improve breast cancer care, however lack of awareness among clinicians often prevents timely adoption. This study aims to investigate current awareness and intention-to-use of three technologies among breast cancer healthcare professionals (HCP): (1) digital health applications (DHA), (2) artificial intelligence (AI), and (3) blockchain technology (BC). A 22-item questionnaire was designed and administered before and after a 30 min educational presentation highlighting technology implementation examples. Technology awareness and intention-to-use were measured using 7-point Likert scales. Correlations between demographics, technology awareness, intention-to-use, and eHealth literacy (GR-eHEALS scale) were analyzed. 45 HCP completed the questionnaire, of whom 26 (57.8%) were female. Age ranged from 24 to 67 {mean age (SD): 44.93 ± 12.62}. Awareness was highest for DHA (68.9%) followed by AI (66.7%) and BC (24.4%). The presentation led to a non-significant increase of intention-to-use AI {5.37 (±1.81) to 5.83 (±1.64)}. HCPs´ intention-to-use BC after the presentation increased significantly {4.30 (±2.04) to 5.90 (±1.67), p < 0.01}. Mean accumulated score for GR-eHEALS averaged 33.04 (± 6.61). HCPs´ intended use of AI significantly correlated with eHealth literacy (ρ = 0.383; p < 0.01), intention-to-use BC (ρ = 0.591; p < 0.01) and participants´ age (ρ = -0.438; p < 0.01). This study demonstrates the effect that even a short practical presentation can have on HCPs´ intention-to-use emerging digital technologies. Training potential professional users should be addressed alongside the development of new information technologies and is crucial to increase HCPs´ corresponding awareness and intended use.

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward.

This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

Rational and design of the Light After Armageddon project focusing on the impact of emerging technologies for cardiovascular care

Rational and design of the Light After Armageddon project focusing on the impact of emerging technologies for cardiovascular care

Podocyte involvement in the pathogenesis of preterm-related long-term chronic kidney disease.

With the continuous advancement of neonatal intensive care technology, the survival rate of preterm infants is gradually increasing. However, this improvement in survival is accompanied by long-term prognostic implications in various systems. In the field of renal diseases, current epidemiological data indicate that preterm birth is a significant risk factor for the development of long-term chronic kidney disease (CKD). This not only imposes an economic burden on patients families but also severely impacts their quality of life. Understanding the underlying mechanisms involved in this process could offer potential strategies for early prevention and management of CKD. Although the nephron number hypothesis is currently widely accepted as a mechanism, there has been limited exploration regarding podocytes - one of the most important structures within nephrons - in relation to long-term CKD associated with preterm birth. Therefore, this review aims to summarize current knowledge on how prematurity influences CKD development overall, while specifically focusing on our current understanding of podocytes in relation to prematurity.

Evaluating Chat Generative Pre-trained Transformer Responses to Common Pediatric In-toeing Questions.

Chat generative pre-trained transformer (ChatGPT) has garnered attention in health care for its potential to reshape patient interactions. As patients increasingly rely on artificial intelligence platforms, concerns about information accuracy arise. In-toeing, a common lower extremity variation, often leads to pediatric orthopaedic referrals despite observation being the primary treatment. Our study aims to assess ChatGPT's responses to pediatric in-toeing questions, contributing to discussions on health care innovation and technology in patient education. We compiled a list of 34 common in-toeing questions from the "Frequently Asked Questions" sections of 9 health care-affiliated websites, identifying 25 as the most encountered. On January 17, 2024, we queried ChatGPT 3.5 in separate sessions and recorded the responses. These 25 questions were posed again on January 21, 2024, to assess its reproducibility. Two pediatric orthopaedic surgeons evaluated responses using a scale of "excellent (no clarification)" to "unsatisfactory (substantial clarification)." Average ratings were used when evaluators' grades were within one level of each other. In discordant cases, the senior author provided a decisive rating. We found 46% of ChatGPT responses were "excellent" and 44% "satisfactory (minimal clarification)." In addition, 8% of cases were "satisfactory (moderate clarification)" and 2% were "unsatisfactory." Questions had appropriate readability, with an average Flesch-Kincaid Grade Level of 4.9 (±2.1). However, ChatGPT's responses were at a collegiate level, averaging 12.7 (±1.4). No significant differences in ratings were observed between question topics. Furthermore, ChatGPT exhibited moderate consistency after repeated queries, evidenced by a Spearman rho coefficient of 0.55 ( P = 0.005). The chatbot appropriately described in-toeing as normal or spontaneously resolving in 62% of responses and consistently recommended evaluation by a health care provider in 100%. The chatbot presented a serviceable, though not perfect, representation of the diagnosis and management of pediatric in-toeing while demonstrating a moderate level of reproducibility in its responses. ChatGPT's utility could be enhanced by improving readability and consistency and incorporating evidence-based guidelines. Level IV-diagnostic.

Recent Development in Solar-Powered Grass Trimmer and Lawn Care Technology

Recent Development in Solar-Powered Grass Trimmer and Lawn Care Technology

Teknologi Edukasi Perawatan Bayi Prematur

This study aims to identify the most effective technology for preterm infant care, so as to improve knowledge, maternal skills and growth of preterm infants. The research method used was systematic review using five databases, namely Proquest, ScienceDirect, Emeraldinsight, PubMed, ClinicalKey. The results showed that 10 research articles could improve the knowledge, skills, and growth of preterm infants, such as: Booklet, tele-rehabilitation-based educational technology EI (Early Intervention), Family Integrated Care and Mobile Technology (m-FiCare m-Health, Telenursing education of premature, web application. Conclusion The most effective educational technology and has the advantage of accessing education is the web. Keywords: Baby Care, Premature, Technology

Editor's Choice – A Core Outcome Set for Intact Abdominal Aortic Aneurysm Repair

Technology and advances in clinical care have changed the management of abdominal aortic aneurysms (AAAs) but the clinical effectiveness of continuing advances needs to be assessed. To facilitate rapid synthesis of new evidence and improve stakeholder representation, including patients, the concept of core outcome sets (COS) has been developed. COS, reflecting the needs of all stakeholders, have been established across several surgical specialties. This study aimed to develop an international core outcome set for intact AAA repair. Following COMET methodology, potential outcomes were identified from a systematic review of published outcomes and focus groups involving patients, carers, and nurses. A 38 question Delphi consensus survey in lay language was developed (with translation to local languages); this included 35 themes identified from the findings of the systematic review and three themes from the focus groups. All three of the themes identified by the focus groups (cognitive, physical, and social functioning) can be evaluated from quality of life instruments, with overall quality of life being identified from the systematic review. The survey was completed by patients, carers or family members, vascular nurses, vascular surgeons, trainees, interventional radiologists, anaesthetists, and industry partners from six European countries. After two rounds of the survey, the top outcomes were discussed at a face to face multistakeholder consensus meeting. The 38 item questionnaire was amended after piloting among all stakeholder groups. After the first round of the Delphi survey (98 respondents) 15 questions were eliminated, and 11 further questions were eliminated after round 2 (90 respondents). This left two outcome questions for discussion at the consensus meeting, where the top six outcomes were unanimously endorsed: death at 30 days (or in hospital if longer), secondary AAA rupture, overall quality of life and retention of cognitive functioning after recovery, five year survival, and continued sac growth. Six core outcomes are recommended for use as a minimum framework in all future studies and registries of intact open and endovascular AAA repair. Further work to select instruments for quality of life and to define instruments for cognitive functioning is needed.

Exploring the Role of Complexity in Health Care Technology Bottom-Up Innovations: Multiple-Case Study Using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability Complexity Assessment Tool.

New digital technology presents new challenges to health care on multiple levels. There are calls for further research that considers the complex factors related to digital innovations in complex health care settings to bridge the gap when moving from linear, logistic research to embracing and testing the concept of complexity. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to help study complexity in digital innovations. This study aims to investigate the role of complexity in the development and deployment of innovations by retrospectively assessing challenges to 4 digital health care innovations initiated from the bottom up. A multicase retrospective, deductive, and explorative analysis using the NASSS complexity assessment tool LONG was conducted. In total, 4 bottom-up innovations developed in Region Västra Götaland in Sweden were explored and compared to identify unique and shared complexity-related challenges. The analysis resulted in joint insights and individual learning. Overall, the complexity was mostly found outside the actual innovation; more specifically, it related to the organization's readiness to integrate new innovations, how to manage and maintain innovations, and how to finance them. The NASSS framework sheds light on various perspectives that can either facilitate or hinder the adoption, scale-up, and spread of technological innovations. In the domain of condition or diagnosis, a well-informed understanding of the complexity related to the condition or illness (diabetes, cancer, bipolar disorders, and schizophrenia disorders) is of great importance for the innovation. The value proposition needs to be clearly described early to enable an understanding of costs and outcomes. The questions in the NASSS complexity assessment tool LONG were sometimes difficult to comprehend, not only from a language perspective but also due to a lack of understanding of the surrounding organization's system and its setting. Even when bottom-up innovations arise within the same support organization, the complexity can vary based on the developmental phase and the unique characteristics of each project. Identifying, defining, and understanding complexity may not solve the issues but substantially improves the prospects for successful deployment. Successful innovation within complex organizations necessitates an adaptive leadership and structures to surmount cultural resistance and organizational impediments. A rigid, linear, and stepwise approach risks disregarding interconnected variables and dependencies, leading to suboptimal outcomes. Success lies in embracing the complexity with its uncertainty, nurturing creativity, and adopting a nonlinear methodology that accommodates the iterative nature of innovation processes within complex organizations.

Blockchain-Enabled Security Solutions for Medical Device Integrity and Provenance in Cloud Environments

The current period of medicine using digital technology for patient care presents a new level of integration of monitoring devices with the cloud computing environment that enables the collection, storage and access to data in ways that were never possible earlier. As the obvious part of this development, it is worth noting that the objective of such innovation is mostly on the integrity of data, provenance and security. Data integrity from as well as security of the Internet connected healthcare devices should be assured in the first place to keep patient safety and protect data privacy along with improve data-based decision-making. The centralized system and crowded nature of the current equipment are susceptible to single point of failure, data breach and potential manipulations of data, which raise questions and create doubts with regards data management processes pertaining to medical device systems. This work is addressed to the analysis of a novel security system based on blockchain that guarantees the implementation of a high performance with the solution of two medical device integrity and provenance safety issues in the cloud ecosystem. Fundamentally differentiating from the centralized systems that exist today, blockchain technology that is based on distributed database architectures, immutable logs, and consensus mechanisms provides for a new way to bring reliability and traceability to the entire medical device data chain. The suggested procedure is based on properties of blockchain technology. Such a solution can help to provide a clear and secure audit trail for medical devices. Storing, securing and accessing the device data can be carried out credibly, maintaining these data’s integrity and provenance. Ultimately, the solution, rely on the implementation of smart contracts, cryptocurrency processes, and the confidentiality and privacy of data, can be the answer which make up the practice of secure data sharing, data accessing and complying with regulations. The journal creates a modular system combining Medical devices, a cloud platform, and Blockchain solution. The architecture is intended to display the blockchain network's essential components, data validation and access control, and secure data storage mechanisms. Furthermore, the recommended solution implies state-of-the-art security tools, such as data encryption, access control, and abidance by regulatory systems, including HIPAA and GDPR. Implementation of an actual scenario of the proof-of-concept and performance evaluation are done to show the efficiency and performance of the blockchain-based solution provided. The results suggest that the proposed solution can establish the data reliability level, record all the various versions of modifications, and strengthen the security and transparency of medical device data processing in cloud computing. Through the exploration of the applications of blockchain for medical data management that this study proposes, we are laying the foundations of a future healthcare environment, which is expected to be more secure and trustworthy, where the sensor data of medical devices can be reliably controlled and accessed without jeopardizing the patient's safety or data privacy. To a great extent, the suggested solution can contribute to building trust in the digital tools utilized in health care, leading to more well-informed clinical decisions and ultimately improving the patients' results.

Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Health Care Professionals.

As the health care industry increasingly embraces large language models (LLMs), understanding the consequence of this integration becomes crucial for maximizing benefits while mitigating potential pitfalls. This paper explores the evolving relationship among clinician trust in LLMs, the transition of data sources from predominantly human-generated to artificial intelligence (AI)-generated content, and the subsequent impact on the performance of LLMs and clinician competence. One of the primary concerns identified in this paper is the LLMs' self-referential learning loops, where AI-generated content feeds into the learning algorithms, threatening the diversity of the data pool, potentially entrenching biases, and reducing the efficacy of LLMs. While theoretical at this stage, this feedback loop poses a significant challenge as the integration of LLMs in health care deepens, emphasizing the need for proactive dialogue and strategic measures to ensure the safe and effective use of LLM technology. Another key takeaway from our investigation is the role of user expertise and the necessity for a discerning approach to trusting and validating LLM outputs. The paper highlights how expert users, particularly clinicians, can leverage LLMs to enhance productivity by off-loading routine tasks while maintaining a critical oversight to identify and correct potential inaccuracies in AI-generated content. This balance of trust and skepticism is vital for ensuring that LLMs augment rather than undermine the quality of patient care. We also discuss the risks associated with the deskilling of health care professionals. Frequent reliance on LLMs for critical tasks could result in a decline in health care providers' diagnostic and thinking skills, particularly affecting the training and development of future professionals. The legal and ethical considerations surrounding the deployment of LLMs in health care are also examined. We discuss the medicolegal challenges, including liability in cases of erroneous diagnoses or treatment advice generated by LLMs. The paper references recent legislative efforts, such as The Algorithmic Accountability Act of 2023, as crucial steps toward establishing a framework for the ethical and responsible use of AI-based technologies in health care. In conclusion, this paper advocates for a strategic approach to integrating LLMs into health care. By emphasizing the importance of maintaining clinician expertise, fostering critical engagement with LLM outputs, and navigating the legal and ethical landscape, we can ensure that LLMs serve as valuable tools in enhancing patient care and supporting health care professionals. This approach addresses the immediate challenges posed by integrating LLMs and sets a foundation for their maintainable and responsible use in the future.

Practical Exploration of Talent Cultivation for Smart Healthy Aging Serviceand Management Professionals

With the accelerated aging of China's population, the development of the profession of intelligent healthy aging service and management has received increasing attention. This paper discusses the background of this specialty, the list of competencies of professionals and the practical strategies of talent cultivation, aiming to improve the quality of professional talent cultivation. By analyzing the cultivation strategies of key competencies such as geriatric health assessment, smart elderly care technology application, elderly care service planning and management, psychological support as well as innovation and learning ability, the study emphasizes the importance of interdisciplinary curriculum integration, internship and practical training enhancement, industry-university-research cooperation and case study teaching.

Ensemble Deep Learning Models for Heart Failure Prediction from Real Healthcare Records

Cardiovascular diseases (CVDs) remain the leading cause of death globally, necessitating advancements in non-invasive and efficient monitoring techniques. "Retinabeat" proposes a novel approach to CVD monitoring by leveraging the diagnostic potential of retinal vascular patterns. Utilizing state-of-the-art retinal imaging combined with the VGG19 convolutional neural network, this system aims to detect and analyze subtle changes in the retinal vasculature that correlate with cardiovascular health. Preliminary studies suggest that these retinal indicators can serve as reliable markers of cardiovascular anomalies, providing a non-invasive, cost-effective, and readily accessible method for early detection and ongoing management of heart health. This paper details the development and validation of the Retinabeat system, evaluates its performance against traditional methods, and discusses the implications of integrating advanced image processing technologies in routine cardiovascular care. The outcomes highlight the system’s potential to enhance predictive diagnostics and personalize health monitoring, paving the way for broader applications in telemedicine and preventive healthcare. Keywords— Cardiovascular diseases, VGG19, telemedicine, Retinal images

Harnessing the power of artificial intelligence: A new door for quick surgery in Pakistan.

Artificial intelligence (AI) has the potential to transform surgery in Pakistan, improving results, reducing complications, and increasing patient safety. Deep learning, a branch of machine learning, can assist surgeons in making wise judgments. Pubmed and Research Gate discuss the promise of AI in surgery. Incorporating AI in healthcare systems can expedite diagnosis and management while avoiding injudicious resource allocation. AI-based techniques in cardiology, nephrology, and neurology enable high-accuracy detection of cardiovascular disease risks, kidney disease treatment, and epileptic episode identification. Neurological devices like bispectral index monitor (BIS) and Near-infrared spectroscopy (NIRS) utilize advanced technology for reliable monitoring and objective diagnosis of neurological issues (Ahmed, 2022). AI uses electronic data and neural network methods to evaluate operating room logistics, time management, and anesthesiologist activities using electronic data and neural network methods (Khan F. H., 2019). AI algorithms are effectively detect minute differences for accurate diagnosis, with studies showing high specificity, sensitivity, and inter-operator repeatability. AI can be used to train and assess neurosurgical residents and early mid-career surgeons, improving diagnosis and 3D simulation labs (Shlobin, 2022). Surgeons play a crucial role in adopting AI-based technologies for surgical care by partnering with data scientists to capture novel clinical data and generate meaningful interpretations. They should demand transparency and interpretability in algorithms to hold AI accountable for its predictions and recommendations.AI advancements in plastic surgery practice, research, and education offer opportunities for improvement. Combining AI-enabled decision-making tools with predictive analytics and human intuition, surgeons can make real-time decisions based on 3D planning, anatomical localization, and navigation (Rasteau, 2022). While current AI tools cannot perform complex surgical procedures, advancements may enable them to perform more complex tasks in the future. Pakistan is a significant market for AI-based solutions, utilizing technology in various industries to address challenges and boost demand. The country has enhanced its self-security systems, including AI-powered missiles, cyber security, and effective cameras (Khan, 2022). AI allows tasks to be completed more precisely and efficiently, with machine learning and deep learning being advanced subtypes. In developing countries like Pakistan, AI tools are needed for patient-centred diagnosis and treatment assistance, especially in emergency surgery such as cardiac illnesses or life threatening bleeding caused road traffic accident. Hence insuring patient receive care timely. An appropriate budget should be allocated for AI technologies in the health sector.

Enhancing the usability of older DCD donors through strategic approaches in liver transplantation in the United States.

The use of older donors after circulatory death (DCD) for liver transplantation (LT) has increased over the past decade. This study examined whether outcomes of LT using older DCD (≥50y) have improved with advancements in surgical/perioperative care and normothermic machine perfusion (NMP) technology. A total of 7602 DCD LT cases from the United Network for Organ Sharing database (2003-2022) were reviewed. The impact of older DCD donors on graft survival was assessed using the Kaplan-Meier and HR analyses. In all, 1447 LT cases (19.0%) involved older DCD donors. Although there was a decrease in their use from 2003 to 2014, a resurgence was noted after 2015 and reached 21.9% of all LTs in the last 4 years (2019-2022). Initially, 90-day and 1-year graft survivals for older DCDs were worse than younger DCDs, but this difference decreased over time and there was no statistical difference after 2015. Similarly, HRs for graft loss in older DCD have recently become insignificant. In older DCD LT, NMP usage has increased recently, especially in cases with extended donor-recipient distances, while the median time from asystole to aortic cross-clamp has decreased. Multivariable Cox regression analyses revealed that in the early phase, asystole to cross-clamp time had the highest HR for graft loss in older DCD LT without NMP, while in the later phases, the cold ischemic time (>5.5h) was a significant predictor. LT outcomes using older DCD donors have become comparable to those from young DCD donors, with recent HRs for graft loss becoming insignificant. The strategic approach in the recent period could mitigate risks, including managing cold ischemic time (≤5.5h), reducing asystole to cross-clamp time, and adopting NMP for longer distances. Optimal use of older DCD donors may alleviate the donor shortage.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Blockchain-Enabled Security Solutions for Medical Device Integrity and Provenance in Cloud Environments

The current period of medicine using digital technology for patient care presents a new level of integration of monitoring devices with the cloud computing environment that enables the collection, storage and access to data in ways that were never possible earlier. As the obvious part of this development, it is worth noting that the objective of such innovation is mostly on the integrity of data, provenance and security. Data integrity from as well as security of the Internet connected healthcare devices should be assured in the first place to keep patient safety and protect data privacy along with improve data-based decision-making. The centralized system and crowded nature of the current equipment are susceptible to single point of failure, data breach and potential manipulations of data, which raise questions and create doubts with regards data management processes pertaining to medical device systems. This work is addressed to the analysis of a novel security system based on blockchain that guarantees the implementation of a high performance with the solution of two medical device integrity and provenance safety issues in the cloud ecosystem. Fundamentally differentiating from the centralized systems that exist today, blockchain technology that is based on distributed database architectures, immutable logs, and consensus mechanisms provides for a new way to bring reliability and traceability to the entire medical device data chain. The suggested procedure is based on properties of blockchain technology. Such a solution can help to provide a clear and secure audit trail for medical devices. Storing, securing and accessing the device data can be carried out credibly, maintaining these data’s integrity and provenance. Ultimately, the solution, rely on the implementation of smart contracts, cryptocurrency processes, and the confidentiality and privacy of data, can be the answer which make up the practice of secure data sharing, data accessing and complying with regulations. The journal creates a modular system combining Medical devices, a cloud platform, and Blockchain solution. The architecture is intended to display the blockchain network's essential components, data validation and access control, and secure data storage mechanisms. Furthermore, the recommended solution implies state-of- the-art security tools, such as data encryption, access control, and abidance by regulatory systems, including HIPAA and GDPR. Implementation of an actual scenario of the proof-of-concept and performance evaluation are done to show the efficiency and performance of the blockchain-based solution provided. The results suggest that the proposed solution can establish the data reliability level, record all the various versions of modifications, and strengthen the security and transparency of medical device data processing in cloud computing. Through the exploration of the applications of blockchain for medical data management that this study proposes, we are laying the foundations of a future healthcare environment, which is expected to be more secure and trustworthy, where the sensor data of medical devices can be reliably controlled and accessed without jeopardizing the patient's safety or data privacy. To a great extent, the suggested solution can contribute to building trust in the digital tools utilized in health care, leading to more well-informed clinical decisions and ultimately improving the patients' results.

Application of big data and artificial intelligence inmental health prediction and intervention

In this article, we delve into applying Convolutional Neural Networks (CNNs) and big data in predicting and intervening in mental health issues, emphasizing the potential for early detection and personalized treatment. By analyzing patterns in social media data, CNNs can identify indicators of mental health conditions, offering insights for tailored interventions. The discussion highlights the importance of addressing privacy, data security, and algorithmic bias to ensure ethical implementation. Future directions include enhancing predictive accuracy, expanding AI applications in therapy, fostering interdisciplinary collaborations, developing ethical frameworks, and engaging the public. Embracing these technologies in mental health care promises significant advancements but necessitates careful consideration of ethical imperatives to maximize benefits while safeguarding patient welfare.

Mapping Dementia Care Technology: Tailored Digital Solutions across Stages

Over the years, there has been an increase in the development of assistive technologies. To date, the stages of dementia that these technologies can be best implemented has been underexplored. In previous work, researchers developed an infographic providing guidance on when best to introduce assistive technologies for people with dementia, and although this infographic is relevant, it was based solely on the experience of the authors during various national and international projects. However, various studies have been conducted on the use and implementation of technology for people with dementia, providing information on when care technology should be implemented. Therefore, the aim of this paper was to conduct a literature review, and based on the results, to design an infographic showing recommendations on when to implement an assistive technology. The infographic was evaluated during interviews with experts on the implementation of care technology for people with dementia. Our results propose earlier implementation for several technologies, as well as the importance of considering contextual factors such as personal characteristics, background and technical experience when implementing technology. The designed infographic can facilitate the digital transition for healthcare institutions and personnel by offering information into the implementation process of care technology.