Healthcare Scenario Research Articles

On logistic regression versus support vectors machine using vaccination dataset

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.

Qualitative investigation of military surgical resuscitation teams: what are the drivers of success of a rapid response team?

ObjectivesThis qualitative study explores the characteristics of a specialised military medical rapid response team (MRRT), the surgical resuscitation team (SRT). Despite mixed evidence of efficacy, civilian MRRTs are widely employed,...

Automatic edge app placement for personalized heart attack predictions

Edge computing brings data processing, analytics and storage closer to the source, but the major limitation of edge devices is that they have limited processing power and storage. Some have argued that cloud can be a solution to overcome the edge computing limitations. However, performing all tasks on the cloud introduces latency issues. Therefore, we need a solution that can use edge and cloud computing intelligently and interchangeable such that the merits of both can be utilized based on the specific scenario. Such a model has not been discussed in the literature so far which poses a very important limitation. We proposed a novel architecture that intelligently switches data processing between the cloud and edge just-in-time based on specific conditions, i.e., if a healthcare scenario demands low latency data are processed at the edge, otherwise data are automatically processed on the cloud. We illustrate this by creating an Automatic edge application to monitor high risk cardiovascular disease patients who are at the risk of heart attacks after a post-operative surgery. Low latency is very important while monitoring such patients and the application is built to automatically detect all such cases and performs heart attack predictions on the edge while other patients data is processing on the cloud. The experimental results have demonstrated that our application can automatically detect high risk cardiovascular disease patients and place their workloads on the edge which is a new and unique invention in the area of automated edge computing. We have also demonstrated that the data retrieval from the edge is 55% faster than the cloud thereby ensuring low latency with edge.

Optimization and characterization of a 3D-printed wearable strain sensor for respiration and heartbeat measurements

Recently, the healthcare scenario recognized the advantages of embedding fiber Bragg gratings (FBGs) into 3D-printed structures for biomedical purposes. Material extrusion is considered a next-generation method to develop sensors based on FBG with exciting characteristics (e.g., low cost, lightweight, and fast prototyping with customized designs). The aim of this study is to combine the FBG and material extrusion benefits for proposing a novel strain sensor for respiration and heartbeat measurements from the detection of chest wall deformations. A finite-element analysis guided the sensor design to maximize the strain localization in the sensor area. The bonding at the fiber - encapsulation material interface was discussed by analyzing the spectral response and microstructural imaging. The sensor response was investigated by applying mechanical and thermal inputs. Finally, the skin-mountable system was used in a pilot test for estimating respiratory and heart rates, showing promising results in human physiological monitoring.

The unexpected social cost of inguinal hernioplasty procedures derived from the COVID-19 pandemic: Surgical trend analysis based on an Italian hospital series.

The majority of inguinal hernias are usually paucisymptomatic, so are restored electively. The main purpose of this study is to assess the trends in hernia repair surgery before and during the pandemic period, analyzing an Italian hospital series of 390 patients, in an attempt to quantify the negative impact regarding social costs derived from the Covid-19 outbreak. Moreover, we want to focus on the concept of apparently minor pathology as hernioplasty which could represent a life-threatening condition for patients. The study population consisted of all patients operated for inguinal hernia in a General Surgery Unit from 2019 to 2021, divided into a pre-pandemic and a pandemic period. The Covid-19 pandemic increased urgent operations in a complicated setting. A statistically significant difference was found regarding the trend of hospitalization length as well as a strong positive correlation between the severity of hernia and the hospitalization length. During the pandemic, it has been registered a mishandling of inguinal hernias to the detriment of both the healthcare system and patients, due to multifactorial issues and, in particular, to the restrictions imposed by the regional government that erroneously declassed hernia pathology as a minor problem for public health. We do believe that patients, after diagnosis of inguinal hernia, should learn the Taxis maneuver for its feasibility and ease of execution, in order to reduce access to emergencies in many cases and likewise to better pain and discomfort perceived, even in the event of unexpected worldwide healthcare scenario.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Porous Conductive Textiles for Wearable Electronics.

Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.

Towards a Regulatory Framework for Electronic Medical Record Data Visualization.

Physicians struggle to retrieve data from electronic medical records. We evaluated a digital tool that enhances physician efficiency in retrieving and analyzing patient information for treatment decision-making. Our use case is the care of diabetic patients. Evaluation results showed that healthcare providers who used the i4C (Insights for Care) dashboard experienced greater time efficiency than those who used traditional EMR information retrieval methods. A comprehensive evaluation of the i4C Dashboard confirms its effectiveness in facilitating diabetic care data management, as well as its potential application to a wide range of healthcare scenarios. In order to further maximize its effectiveness on clinical efficiency and patient care, future research should focus on improving its usability and scalability.

A network-based simulation framework for robustness assessment of accessibility in healthcare systems with the consideration of cascade failures

The accessibility of healthcare system is vulnerable to various types of hazards, where the failure of one system component may lead to a diffusion of the pressure and result in cascading failures. This study proposes a network-based simulation framework for robustness assessment of access to healthcare through integrating cascading failure mechanism. Weighted complex networks are constructed to model the accessible patient transfer under both general and elderly healthcare scenarios. The cascade failure mechanism is incorporated into the constructed networks, and several attack strategies (including random, initial degree (ID), initial betweenness (IB), recalculated degree (RD), and recalculated betweenness (RB) attack) are adopted to simulate the process of system robustness assessment. Results indicate that the proposed framework enables to discover the vulnerable nodes in the constructed healthcare accessibility networks, where the robustness metric combining network efficiency and relative size of the largest component acts as a benchmark; all the intentional attack strategies outperform the random attack strategy, which indicates the effectiveness of the detection of vulnerable healthcare facilities by the developed model; and the metrics of node degree and betweenness centrality make progress on identifying the vulnerable healthcare facility nodes, which should be taken heed of to optimize the management and operation of healthcare systems.

A Survey of Health Care Chatbot for Patient Support

This literature survey delves into the evolving realm of healthcare chatbots, analyzing a variety of research papers on AI chatbot systems. The papers cover diverse objectives, methodologies, and applications within healthcare, including microservice architectures for chronic patient support, disease prediction, herbal remedies, and mental health support. Common threads across the papers include the adoption of natural language processing, machine learning, and AI markup languages. Ethical considerations, such as data privacy and consent, are identified as crucial aspects. Some papers focus on specific medical domains, while others propose comprehensive frameworks integrating IoT and health knowledge graphs. The survey highlights challenges like unified semantic approaches for patient data and underscores the ongoing need for research to address gaps, enhance chatbot intelligence, and ensure ethical deployment in real-world healthcare scenarios

Multi-Level Pyramidal Microstructure-Based Pressure Sensors with High Sensitivity and Wide Linear Range for Healthcare Monitoring.

Flexible pressure sensors have garnered significant attention in the field of wearable healthcare due to their scalability and shape variability. However, a crucial challenge in their practical application for various healthcare scenarios is striking a balance between the sensitivity and sensing range. This limitation arises from the reduced compressibility of the microstructures on the surface of pressure-sensitive materials under high pressure, resulting in progressive saturation of the sensor's response and leading to a restricted and nonlinear pressure sensing range. In this study, we present a novel approach utilizing multi-level pyramidal microstructures in flexible pressure sensors to achieve both high sensitivity (8775 kPa-1) and linear response (R2 = 0.997) over a wide pressure range (up to 1000 kPa). The effectiveness of the proposed design stems from the compensatory behavior of the lower pyramidal microstructures, which counteracts the declining sensitivity associated with the gradual hardening of the higher pyramidal microstructures. Furthermore, the sensor demonstrates a fast response time of 11.6 ms and a fast relaxation time of 3.8 ms and can reliably detect pressures as low as 30.2 Pa. Our findings highlight the applicability of this flexible pressure sensor in diverse human body health detection tasks, ranging from weak pulses to finger flexion and plantar pressure distribution. Notably, the proposed sensor design eliminates the need for replacing flexible pressure sensors with varying ranges, thereby enhancing their practical utility.

Barriers and Implications of 5G Technology Adoption for Hospitals in Western China: Integrated Interpretive Structural Modeling and Decision-Making Trial and Evaluation Laboratory Analysis.

5G technology is gaining traction in Chinese hospitals for its potential to enhance patient care and internal management. However, various barriers hinder its implementation in clinical settings, and studies on their relevance and importance are scarce. This study aimed to identify critical barriers hampering the effective implementation of 5G in hospitals in Western China, to identify interaction relationships and priorities of the above-identified barriers, and to assess the intensity of the relationships and cause-and-effect relations between the adoption barriers. This paper uses the Delphi expert consultation method to determine key barriers to 5G adoption in Western China hospitals, the interpretive structural modeling to uncover interaction relationships and priorities, and the decision-making trial and evaluation laboratory method to reveal cause-and-effect relationships and their intensity levels. In total, 14 barriers were determined by literature review and the Delphi method. Among these, "lack of policies on ethics, rights, and responsibilities in core health care scenarios" emerged as the fundamental influencing factor in the entire system, as it was the only factor at the bottom level of the interpretive structural model. Overall, 8 barriers were classified as the "cause group," and 6 as the "effect group" by the decision-making trial and evaluation laboratory method. "High expense" and "organizational barriers within hospitals" were determined as the most significant driving barrier (the highest R-C value of 1.361) and the most critical barrier (the highest R+C value of 4.317), respectively. Promoting the integration of 5G in hospitals in Western China faces multiple complex and interrelated barriers. The study provides valuable quantitative evidence and a comprehensive approach for regulatory authorities, hospitals, and telecom operators, helping them develop strategic pathways for promoting widespread 5G adoption in health care. It is suggested that the stakeholders cooperate to explore and solve the problems in the 5G medical care era, aiming to achieve the coverage of 5G medical care across the country. To our best knowledge, this study is the first academic exploration systematically analyzing factors resisting 5G integration in Chinese hospitals, and it may give subsequent researchers a solid foundation for further studying the application and development of 5G in health care.

Decision aids for promoting shared decision-making: A review of systematic reviews.

In the context of shared decision-making (SDM), experts have advocated the use of validated decision aids (DAs) as valuable tools for facilitating SDM in various healthcare scenarios. This comprehensive review attempts to analyze a vast corpus of DA research by performing thorough searches across four prominent databases (PubMed, CINAHL, Embase, and Web of Science). Independent reviewers selected relevant reviews, extracted data, and assessed review quality using the AMSTAR II tool. A total of 34 systematic reviews were identified and evaluated in this review, encompassing a wide range of outcomes associated with using DAs. These outcomes include patient knowledge, patient involvement in SDM, decision conflict, decision regret, satisfaction, and adherence. In addition, DAs positively affect healthcare provider outcomes by increasing satisfaction, reducing decision conflicts, and lengthening clinical consultations. This review highlights the need for additional research in specific contexts such as long-term care, mental health, and reproductive health to better understand the benefits and challenges of implementing DAs in these settings. Such research can contribute to the improvement of SDM practices and patient-centered care.

Continual Learning with Deep Neural Networks in Physiological Signal Data: A Survey.

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning-known for its adaptive learning capabilities over time-offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.

Towards the implementation of law n. 219/2017 on informed consent and advance directives for patients with psychiatric disorders and dementia. Physicians’ knowledge, attitudes and practices in four northern Italian health care facilities

BackgroundOn December 2017 the Italian Parliament approved law n. 219/2017 “Provisions for informed consent and advance directives” regarding challenging legal and bioethical issues related to healthcare decisions and end-of life choices. The law promotes the person’s autonomy as a right and provides for the centrality of the individual in every scenario of health care by mean of three tools: informed consent, shared care planning and advance directives. Few years after the approval of the law, we conducted a survey among physicians working in four health care facilities specific for the care of people suffering from psychiatric disorders, cognitive disorders and dementia located in the North of Italy aiming to investigate their perceived knowledge and training need, attitudes regarding law n. 219/2017 provisions, and practices of implementation of the law.MethodsA semi-structured questionnaire was developed on an online platform. The invitation to participate in the survey was sent by email to the potential participants. Information was collected by means of the online platform (Google Forms) which allows to export data in a spreadsheet (Windows Excel) to perform basic statistical analysis (frequency distributions, bar chart representation).ResultsTwenty-five out of sixty physicians participated in the survey. None of the respondents value their knowledge of the law as very good, 10 good, 13 neither poor nor good, 1 poor and 1 very poor. All the respondents want to learn more about the law (21 yes and 4 absolutely yes). The majority of respondents agrees with the content of the law as a whole (3 absolutely agree, 13 agree), and on each provision. The question on the clarity of the concept of capacity in the law received mixed answers and this impacted on the physicians’ opinion regarding the legitimacy in principle for our groups of patients to realize shared care planning and write advance directives. Thirteen physicians neither introduced the theme of shared care planning nor arranged for shared care planning and the main reason for this was that no patient was in a clinical situation to require it. When shared care planning is realized, a variability in terms of type and number of meetings, mode of tracking and communication is registered.ConclusionsOur survey results indicate a need for more clarity regarding the interpretation and implementation of the law in the patient groups under study. There are in particular two related areas that deserve further discussion: (1) the question of whether these patient groups are in principle legitimized by the law to realize shared care planning or write advance directives; (2) the notion of capacity required by the law and how this notion can be declined in real-life situations.

Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma?

Technology has greatly influenced and radically changed human life, from communication to creativity and from productivity to entertainment. The authors, starting from considerations concerning the implementation of new technologies with a strong impact on people's everyday lives, take up Collingridge's dilemma and relate it to the application of AI in healthcare. Collingridge's dilemma is an ethical and epistemological problem concerning the relationship between technology and society which involves two approaches. The proactive approach and socio-technological experimentation taken into account in the dilemma are discussed, the former taking health technology assessment (HTA) processes as a reference and the latter the AI studies conducted so far. As a possible prevention of the critical issues raised, the use of the medico-legal method is proposed, which classically lies between the prevention of possible adverse events and the reconstruction of how these occurred.The authors believe that this methodology, adopted as a European guideline in the medico-legal field for the assessment of medical liability, can be adapted to AI applied to the healthcare scenario and used for the assessment of liability issues. The topic deserves further investigation and will certainly be taken into consideration as a possible key to future scenarios.

A Rapid Response System for Elderly Safety Monitoring Using Progressive Hierarchical Action Recognition.

The global trend of population aging presents an urgent challenge in ensuring the safety and well-being of elderly individuals, especially those living alone due to various circumstances. A promising approach to this challenge involves leveraging Human Action Recognition (HAR) by integrating data from multiple sensors. However, the field of HAR has struggled to strike a balance between accuracy and response time. While technological advancements have improved recognition accuracy, complex algorithms often come at the expense of response time. To address this issue, we introduce an innovative asynchronous detection method called Rapid Response Elderly Safety Monitoring (RESAM), which relies on progressive hierarchical action recognition and multi-sensor data fusion. Through initial analysis of inertial sensor data using Kernel Principal Component Analysis (KPCA) and multi-class classifiers, we efficiently reduce processing time and lower the false-negative rate (FNR). The inertial sensor identification serves as a pre-filter, enabling the identification of filtered abnormal signals. Decision-level data fusion is then executed, incorporating skeleton image analysis based on ResNet and the inertial sensor data from the initial step. This integration enables the accurate differentiation between normal and abnormal behaviors. The RESAM method achieves an impressive 97.4% accuracy on the UTD-MHAD database with a minimal delay of 1.22 seconds. On our internally collected database, the RESAM system attains an accuracy of 99%, ranking among the most accurate state-of-the-art methods available. These results underscore the practicality and effectiveness of our approach in meeting the critical demand for swift and precise responses in healthcare scenarios.

Cross-Contextual Examination of Older Adults' Privacy Concerns, Behaviors, and Vulnerabilities

A growing body of research has examined the privacy concerns and behaviors of older adults, often within specific contexts. It remains unclear to what extent older adults' privacy concerns and behaviors vary across contexts and whether old age is the primary factor influencing privacy vulnerabilities. To address this gap, we conducted semi-structured interviews with 43 older adults (aged 65 to 89) in the United States. Our interviews were grounded in five scenarios: account and device sharing, healthcare, online advertising, social networking, and cybercrime. Our cross-contextual analysis showed that cybercrime was a recurring and pressing concern across scenarios; privacy concerns and protective behaviors were rarely mentioned in the healthcare scenario. Across all scenarios, participants' threat models and strategies revolved around data collection rather than other stages in which privacy harms may occur; they employed various active strategies to safeguard their privacy while trusting service providers to protect their information. Our findings underscore the need to revisit the discussion around privacy vulnerability and aging. Vulnerability levels among our participants varied widely and were often influenced by factors beyond age, such as tech savviness and income. We discuss opportunities for privacy interventions, technologies, and education that promote positive aging and recognize diversity among older adults.

Electroless silver plating on fabrics for antimicrobial coating: comparison between cotton and polyester.

In the past few years, due to the Covid-19 pandemic, the interest towards textiles with antimicrobial functionalities faced a significant boost. This study proposes a rapid and convenient method, in terms of reactants and equipment, for fabricating antimicrobial coatings on textiles. Through the electroless silver plating reaction, silver coatings were successfully applied on cotton and polyester, rapidly and at room temperature. Functionalized samples were characterized by morphological (optical and scanning electron microscopies) and chemical tests (X-ray photoelectron spectroscopy, XPS) to investigate the nature of the silver coating. Although distinct nanoparticles did not form, XPS analysis detected the presence of silver, which resulted in an increased surface roughness and hydrophobicity of both cotton and polyester textiles. Ag-coated samples exhibited approximately 80% biocompatibility with murine L929 fibroblasts or human HaCaT cells, and strong antibacterial properties against Escherichia coli in direct contact tests. In antiviral experiments with SARS-CoV-2 virus, treated cotton showed a 100% viral reduction in 30 min, while polyester achieved 100% reduction in 1 h. With a human norovirus surrogate, the Feline Calicivirus, both treated textiles have a faster antiviral response, with more than 60% viral reduction after 5 min, while achieving a 100% reduction in 1 h. In conclusion, this study presents a fast, efficient, and low-cost solution for producing antimicrobial textiles with broad applications in medical and healthcare scenarios.

EHR Innovations: Shedding Light on Anemia in the Healthcare Paradigm

This study introduces a novel approach to Electronic Health Record (EHR) analysis, extending the use of phenotyping with machine learning (ML) models to enhance the recognition and treatment of anemia. It first examines the healthcare scenario in India and suggests potential improvements through data-driven personalized care. Using the MIMIC-III dataset, the research involves extensive data preprocessing and analysis to uncover key insights into anemia’s prevalence, gender distribution, comorbidities, and Intensive Care Unit (ICU) stays. Partitioning clustering algorithms like K-Means, K-medoids, Fuzzy C-means, and hierarchical clustering algorithms such as Agglomerative Clustering, DIANA, and HDBSCAN are used to identify groups of patients with similar medical profiles. The distance metrics employed are Levenshtein and Euclidean distances combined with TF-IDF Vectorization. The effectiveness of these algorithms is evaluated based on Length of Stay (LoS) estimation, a critical parameter in EHR studies. To predict a new patient’s LoS, the patient is at first classified into an existing cluster, which shows the highest support to the patient’s clinical activities. A decision tree regressor is then trained using data from the selected cluster to predict the new patient’s LoS, significantly improving predictive accuracy and reliability. Notably, the HDBSCAN algorithm, applied to the Tf-Idf Vectorizer object, achieves a 50.82% reduction in Root Mean Squared Error (RMSE) compared to baseline model. The novelty of this study lies in proposing an efficient approach for EHR analysis, specifically for predicting ICU patients’ LoS, and identifying the most effective clustering algorithm to improve healthcare delivery for anemic patients in healthcare scenario of India.