Medical Sector Research Articles

A Review of Basic Analytics, Medical Data Sources, and Advanced Data Analytics in the Medical Sector

This article provides a thorough examination of healthcare data analytics and identifies various unresolved questions that necessitate further investigation. The implementation of healthcare analytics has the potential to generate two supplementary advantages for healthcare providers, specifically heightened patient contentment and ameliorated health consequences. The field of data analytics has been propelled forward by the facilitation of healthcare data acquisition through technological and software advancements. The lack of a predetermined framework in the data, along with the constraints imposed by privacy considerations in data gathering and dissemination methods, have presented significant obstacles to the progression of the field. The expeditious handling and comprehension of data requires prompt decision-making in the presence of substantial information quantities. In specific situations, the retrieval and analysis of data may necessitate the utilisation of more advanced techniques owing to the intricacy of the data. The emergence of data collection technologies that facilitate analytics has presented new obstacles, despite their effectiveness in accumulating vast quantities of data. The healthcare industry employs a wide array of methodologies, which can be attributed to the inherent distinctions in the fundamental data types.

A Combined Compromise Solution (CoCoSo) of MCDM Problems for Selection of Medical Best Bearing Ring

Bearings offers a variety of products designed to meet the rigorous requirements of the medical sector for patient safety, such as diagnostic and laboratory equipment, surgical and dental instruments, ventilators, heart pumps, etc. In the field of medical instruments, the bearing rings compromise from superior quality metals to perform surgical operations safely. The selection of the best bearing ring materials for medical applications is a critical task that involves considering various factors to ensure optimal performance, reliability, and safety. The study explores challenges and criteria in selecting the ideal bearing ring for medical devices and equipment. The criteria discussed encompass material compatibility, biocompatibility, lubrication requirements, precision, cleanliness, durability, noise and vibration control, size and dimension, load capacity, temperature and environmental conditions, regulatory compliance, manufacturing considerations, compatibility with adjacent components, maintenance, and serviceability. The challenges mainly include biocompatibility validation, material selection, performance requirements, regulatory compliance, size limitations, cost considerations, supply chain reliability, long-term stability, maintenance, expertise, and collaboration. To address the current challenges of medical bearing the study needs to understand the medical requirements, and engagement with trusted suppliers and regulatory bodies. The study presents a multi-criteria decision-making methodology (MCDM) problem with various criteria and alternatives with uncertainty conditions. The decision maker’s perspectives evaluate the importance of criteria and alternatives. The neutrosophic set is used to deal with uncertainty conditions. The study illustrates a solution to select the best bearing ring to ensure the success and safety of medical applications. The MCDM provides solutions to choose the best bearing ring. A Combined Compromised Solution (CoCoSo) method is used as an MCDM method with neutrosophic theory to select the best alternatives. A numerical case study is presented to show the applicability and availability of the proposed methodology.

Unlocking the potential of big data through open innovation on strategic foresight: An empirical analysis

The aim of this study is to examine the impact of Big Data on strategic foresight in the presence of open innovation as a (mediating variable) in the companies of the therapeutic industry and the medical supplies sector in Amman the capital city of Jordan. Out of the (121) companies making up the total of such companies, the study focused on (18) industrial companies that had more than (100) employees in total, however only (11) of those have consented to take part in the study. The study population consisted of (271) employees occupying different jobs (general manager, deputy general manager, unit manager, department manager). Due to the limited size of the population, the entire number of employees were included as participants using the comprehensive survey technique, and the number of returned and validated questionnaires for analysis were (259), representing (95.5%) of the total. In order to determine the study problem, pilot structured interviews were used in a sample of the mentioned companies. The questionnaire was employed as the key tool to measure the study variables through data collection, and the descriptive and inferential statistics methods were used to analyze the collected data, through calculations of the arithmetic mean, standard deviation, t-test and half-segmentation, exploratory and confirmatory factor analysis and the structured equation model using SMART PLS3 for hypothesis testing. The study culminated in several results, the most important of which was evidence that open innovation played a partial mediating role in the relationship between big data and strategic foresight in the companies of the therapeutic industries and medical supplies sector. Accordingly, a set of recommendations were put forward, the most important of which is to increase investment in big data in the companies due to its importance in foreseeing the future. As well as applying open innovation practices and strengthening strategic foresight practices due to its important role in avoiding extensive losses and seizing new opportunities. Highlighting the need to pay attention to open innovation practices that generate ideas and help to have strategic foresight.

A digital twin-driven ultra-precision machining system

The demand for ultra-precision machining has expanded significantly across industries such as aerospace, automotive, electronics, and medical sectors. These industries require parts manufactured to micrometre tolerances in a timely and cost-effective manner. To address these demands, efforts have been focused on developing digital twin technology for ultra-precision machining, aimed at improving production accuracy and efficiency. One of the primary challenges in ultra-precision machining is time-consuming setup due to manual workpiece handling. Additionally, machining speeds are limited to mitigate dynamic errors, further prolonging production times. This paper proposes a digital twin system designed to automate workpiece handling and correct dynamic errors in real time to tackle these challenges. The proposed digital twin comprises two systems: one for controlling a collaborative robot arm (COBOT) to automate workpiece handling with corrective action, eliminating the need for manual loading and unloading; and another for controlling a hybrid mill to mitigate dynamic errors through real-time machine learning-based prediction of elastic deformation allowing for higher machining speeds. In this paper, the current progress is discussed, and a methodology for validating this digital twin system is proposed. The proposed validation process will involve machining microfluidic devices using the digital twin system, compared to conventional machining methods to assess the effectiveness.

Surface roughness improvement of Ti-6Al-4V alloy overhang structures via process optimization for laser-powder bed fusion

In recent years, additive manufacturing of Ti-6Al-4V alloy components via Laser-Powder Bed Fusion has seen increasing application in the aerospace, military, and medical sectors. However, the high surface roughness of up- and down-skin for overhang structures remains a primary impediment to expanded implementation. This study aims to investigate the effects of the overhang angle and process parameters on the up- and down-skin surface roughness of the overhang structure. In addition, the underlying mechanisms leading to the different roughness of the up- and down-skin of the overhanging structure are comparatively analyzed. Subsequently, a response surface model was generated using the biharmonic spline interpolation (V4) method to optimize the process parameters, aiming to simultaneously improve the roughness of both up- and down-skin. Results demonstrate that the optimized parameters reduced the roughness of up- and down-skin by approximately 50 %.

Application of three-dimensional printing technology to the customized design of spinal implants

Abstract In recent years, the field of 3D printing technology has experienced rapid advancements, notably expanding its application within the medical sector. This study focuses on the custom design of 3D-printed spinal implants, specifically examining porous interbody fusion products. It integrates considerations of mechanical strength and bone ingrowth to establish a finite element model of porous interbody fusion, subsequently conducting topology optimization to design three distinct types of spinal interbody fusion implants. Analytical investigations were carried out on the stress and displacement responses of these three implant types under compressive loading. Furthermore, a detailed stress analysis was conducted on implants varying in porosity, length, and screw angle of the bone graft to assess the performance characteristics of the porous interbody fusion devices. Results indicated that the Type C implant exhibited superior performance, demonstrating a stress reduction to 89.21 MPa and a displacement change of 0.006 mm, optimally at a 60% porosity level. Adjustments in the lengths and screw clamp angles of the splint ensured that the maximal stress experienced by each vertebra remained below the yield limits of both cortical and cancellous bone, thus preventing vertebral damage. This paper presents a comparative analysis of three types of porous interbody fusion devices, providing substantial data support and a theoretical framework that can inform the future development of fusion products.

Hydrogel microrobots for biomedical applications.

Recent years have witnessed a surge in the application of microrobots within the medical sector, with hydrogel microrobots standing out due to their distinctive advantages. These microrobots, characterized by their exceptional biocompatibility, adjustable physico-mechanical attributes, and acute sensitivity to biological environments, have emerged as pivotal tools in advancing medical applications such as targeted drug delivery, wound healing enhancement, bio-imaging, and precise surgical interventions. The capability of hydrogel microrobots to navigate and perform tasks within complex biological systems significantly enhances the precision, efficiency, and safety of therapeutic procedures. Firstly, this paper delves into the material classification and properties of hydrogel microrobots and compares the advantages of different hydrogel materials. Furthermore, it offers a comprehensive review of the principal categories and recent innovations in the synthesis, actuation mechanisms, and biomedical application of hydrogel-based microrobots. Finally, the manuscript identifies prevailing obstacles and future directions in hydrogel microrobot research, aiming to furnish insights that could propel advancements in this field.

The relationship between intellectual capital development and strategic competency development:An applied study on the medical sector in Bahrain

The relationship between intellectual capital development and strategic competency development:An applied study on the medical sector in Bahrain

The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector

The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector

DESIGN FOR ADDITIVE MANUFACTURING IN THE MEDICAL SECTOR. TOOLS AND STUDY CASE

This work presents a review of the three main axes that constitute the knowledge base when designing products for additive manufacturing in the medical industry: technology, materials and design tools. Through four different study cases of diverse medical products that match the existing categories in the established classification of standards, different design approaches and strategies are presented and suggested as feasible solutions that generate medical products. These cases studies intend to cover some categories where additive manufacturing can be used in the medical sector: a post-surgery protection cast developed using reverse engineering technologies and free-form modelling tools customized not only regarding anatomic shapes but also in terms of aesthetics, a surgical simulator that improves the surgeon’s education process in a non-expensive manner, which could also be shared and spread combining soft and hard parts, a hip surgery retractor designed together with experts that increase the performance of the commercial solutions and a bone replacement implant with an innovative approach that combines engineering polymers with metallic surface coatings. All these case studies present the diversity of possible applications when working with the medical industry, as well as the required skills and knowledge needed to start with this task. Beside this, it has been stated the convenience of engineers to work hand by hand with medical professionals, combining skills and visions to innovate and improve the patient’s journey.

Rapid production of the anaesthetic mepivacaine through continuous, portable technology.

Local anaesthetics such as mepivacaine are key molecules in the medical sector, so ensuring their supply chain is crucial for every health care system. Rapid production of mepivacaine from readily available commercial reagents and (non-dry) solvents under safe conditions using portable, continuous apparatus could make an impactful difference in underdeveloped countries. In this work, we report a continuous platform for synthesising mepivacaine, one of the most widely used anaesthetics for minor surgeries. With a focus on sustainability, reaction efficiency and seamless implementation, this platform afforded the drug in 44% isolated yield following a concomitant distillation-crystallisation on a gram scale after N-functionalisation and amide coupling, with full recovery of the solvents and excess reagents. The use of flow chemistry as an enabling tool allowed the use of "forbidden" chemistry which is typically challenging for preparative and large scale reactions in batch mode. Overall, this continuous platform presents a promising and sustainable approach that has the potential to meet the demands of the healthcare industry.

Performance evaluation of machine learning algorithms for Parkinson’s disease identification model

Though Medical sector is referred to as one of the most advance sector in the Country, there are some implications which shows disorientation of medical sector and early prediction of various ailments. There are some diseases which does not have any preventive measures and are incurable till date. Parkinson’s disease is one of those disease that is incurable till date and its clinical diagnosis requires early detection of symptoms which may vary according to age parameter. Parkinson’s disease is one of the incurable diseases which affects the Central Nervous System (CNS) of Human brain which leads to various symptoms like barely noticeable tremor on limbs, slowness in movement, improper balance and posture of body, Loss of automated movement and sometimes change in vocal frequency. With theadvent of Machine Learning (ML) and Artificial Intelligence (AI) technique are used for diagnosis of brain diseaseswhich is helping the doctor for early detection of the diseases.Considering the performance evaluation parameters, we have given equal importance to every performance measures used. The dataset for the Parkinson’s disease detection is taken from Oxford UCI Machine repository. Analysis and Comparative study show that all the algorithms can detect the person is affected by the Parkinson’s disease or not. Out of them random forest algorithm yielded highest performance measures.

Health 4.0 in the medical sector: a narrative review.

Health 4.0 in the medical sector: a narrative review.

Innovations in medical tourism

Medical tourism in the modern world is becoming a new, actively developing market segment. This market segment is expanding due to the active introduction of highly professional medical services. Instruments such as innovation certificates (vouchers) for small and medium-sized high-tech companies to pay for medical R&D services. This is an investment by the government to support innovation in medicine and population wellness. The results of the global ranking for 2022 made it possible to highlight the key advantages and disadvantages of the innovation system of the Russian Federation. It is noted that the main purpose of innovation implementation in medical tourism is to improve its efficiency, the question of measuring the impact of innovation on the quality of medical services provided to tourists inevitably arises. In this regard, we have reviewed some of the innovations that can be used in medicine such as: artificial intelligence, DNA sequencing for deciphering the genetic code, CRISPR-Cas9 system for genetic "scissors", virtual and augmented reality technology for treating neurological and psychosomatic disorders, VR technology, patient-specific devices (PSD) for customized implants, Cast21 for prosthetic limbs, bioprinting, smart insulin port, Kardiaelectrocardiograph. They can be used in the development of proposals for medical tours. With the application of innovations in medicine and the introduction of an international certification system in the medical sector, the attractiveness of medical tourism will increase significantly and attract new clients.

Eigen-Chain : A unique retinopathy detecting medical image retrieval blockchain

Securing medical images is an essential requirement in healthcare industry. Managing medical image for storing patient sensitive information has remained a challenging task for researchers across the globe. Different technologies are struggling to provide a solution to the problem. Blockchain can possibly solve the security aspect problem of medical images. However, images sizes possess a difficulty in safely storing them on blockchain due to associated costs. It also creates scalability problem in blockchain. The proposed Eigen-Chain is a novel framework for creating a secure Hard Exudates detecting mechanism and storing images on blockchain using Principal Component Analysis (PCA) eigen vectors. These eigen vector can significantly reduce the images size which reduces the energy required for storage making it a sustainable model. Eigen-chain can retrieve image from blockchain efficiently. The experimental result on benchmark databases proved the superiority of proposed approach. Moreover, the Eigen-chain has been made capable of detecting exudates in the stored images making it useful in medical sector.

IRIS - The Italian research infrastructure on Applied Superconductivity for Particle Accelerators and Societal Applications

The Italian Minister for University and Research has recently funded a large program for an Innovative Research Infrastructure on applied Superconductivity (IRIS) in Italy. Based on the LASA laboratory in Milan, it is a partnership, in the form of a strongly coordinated work, of existing laboratories of various institutes: INFN (leader, participating with 4 labs: Frascati, Genoa, Milan, Salerno); CNR (SPIN institute in Genoa, Naples and Salerno); five Universities: Genoa, Milan, Naples, Salento and Salerno. IRIS will be an upgrade of existing infrastructures, with new state-of-the-art instruments, reinforcing the capability of Italy in the domain of superconductivity aimed to accelerators. IRIS foresees a strong coordination of the activity of the participating laboratories until 2035, at least, thus enhancing the participation of Italian laboratories to future projects requiring advanced superconducting technology, like FCC or the Muon-Collider, and also for developing societal applications of technologies, pursued for high-energy accelerators, especially for the energy domain and the medical sector. In this paper, we present the two novel demonstrators, part of the initial IRIS program: 1) a green superconducting line, 130 m long and designed for 40 kA current capability at 25 kV; 2) a 1 m long HTS dipole magnet with some characteristics similar to LHC dipoles: 10 T, 50 mm × 80 mm bore, but operating at 20 K rather than 1.9 K.

Drivers and barriers of patients' acceptance of video consultation in cancer care.

Due to digitization in the medical sector, many healthcare interactions are switched to online services. This study assessed the acceptance of video consultations (VCs) in cancer care, and determined drivers and barriers of acceptance. A cross-sectional online-based survey study was conducted in Germany from February 2022 to February 2023. Recruitment took place at oncology outpatient clinics, general practitioners, oncology practices and via cancer-related social media channels. Inclusion criteria were a cancer diagnosis, cancer treatment and internet access. Sociodemographic, medical data, eHealth-related data were acquired via an online assessment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used to determine the acceptance of VC and its predictors. Of N = 350 cancer patients, 56.0% (n = 196) reported high acceptance of VC, 28.0% (n = 98) stated moderate acceptance and 16.0% (n = 56) indicated low acceptance. Factors influencing acceptance were younger age (β = -.28, p < .001), female gender (β = .35, p = .005), stage of disease (β = .11, p = .032), high digital confidence (β = .14, p = .010), low internet anxiety (β = -.21, p = .001), high digital overload (β = -.12, p = .022), high eHealth literacy (β = .14, p = .028), personal trust (β = -.25, p < .001), internet use (β = .17, p = .002), and the UTAUT predictors: performance expectancy (β = .24, p < .001), effort expectancy (β = .26, p < .001), and social influence (β = .34, p < .001). Patients' acceptance of VC in cancer care is high. Drivers and barriers to acceptance identified should be considered for personalized applications. Considering the growing demand for cancer care establishing digital healthcare solutions is justified.

Pathology of Intellectual Capital in the Healthcare and Medical Education System During the COVID-19 Pandemic at North Khorasan University of Medical Sciences

Objective: The objective of this study was to analyze and diagnose intellectual capital challenges within the healthcare and medical education system of North Khorasan University of Medical Sciences during the COVID-19 pandemic. Methodology: This research was conducted using a mixed-methods approach, including both qualitative and quantitative analyses. The qualitative section involved interviews with 17 experts in the healthcare and medical education system, selected through purposive sampling. For the quantitative part, 278 presidents, deputies, and experts from the healthcare and medical education sectors were surveyed using stratified random sampling. Data collection involved field methods (interviews, questionnaires) and library research. MAXQDA software was used for qualitative data analysis, while SPSS22 and PLS3 were used for quantitative testing of components' validity and reliability. Findings: The study identified three main themes related to intellectual capital issues: structural weaknesses, human resource challenges, and communication problems. These factors were found to have a significant impact on the current and desired state of the healthcare and medical education systems during the pandemic. The analysis showed that the influence of these issues on intellectual capital retention exceeded the threshold of 1.96, indicating their critical role in affecting organizational efficiency and effectiveness during the COVID-19 crisis. Conclusion: The findings highlight that intellectual capital, particularly in terms of human resources, structural processes, and communication, plays a crucial role in the successful management of healthcare and educational systems during a crisis. Addressing these challenges through improved infrastructure, human resource development, and enhanced communication strategies can significantly improve organizational performance during future crises.

Enhancing Radiation Shielding with Gadolinium(III) Oxide in Cerium(III) Fluoride‐Doped Silica Borate Glass

This study investigates the radiation shielding properties of glass samples within the xGd2O3‐5SiO2‐40Na2O‐(54.5‐x) B2O3:0.5CeF3 composition, where x varies from 0 to 10 mol.%, which is coded as GSNBCX (X = 1, 2, 3, and 4). The assessment is done through comprehensive Monte Carlo simulation and Phy‐X/PSD software analyses. The primary objective of this study is to comprehensively evaluate the radiation shielding properties of glass compositions with varying Gd2O3 concentrations. This evaluation encompasses both the attenuation of gamma radiation within the broad energy range of 0.015 MeV to 15 MeV and the assessment of fast neutron removal cross sections, with a specific focus on simulations spanning energy levels from 0.5 to 10 MeV. By examining these parameters, we aim to elucidate the impact of Gd2O3 concentration on the material’s overall effectiveness in radiation shielding applications. The results reveal a significant variation in the mass attenuation coefficient (μm) across the investigated glass samples. For instance, μm values range from 3.244 to 0.019 cm2·g−1 for GSNBC1, 20.471 to 0.025 cm2·g−1 for GSNBC2, 27.245 to 0.027 cm2·g−1 for GSNBC3, and 32.223 to 0.029 cm2·g−1 for GSNBC4. Notably, GSNBC4, characterized by a substantial Gd2O3 concentration (10 mol.%), exhibits the highest values of both μm and linear attenuation coefficient (μ). Furthermore, the investigation delves into the fast neutron removal cross section (FNRCS), which displays values of 0.97, 0.95, 0.094, and 0.93 cm−1, respectively. GSNBC1, marked by its elevated B2O3 content (54.5 mol.%), showcases the highest FNRCS. These findings underscore the efficacy of Gd2O3‐doped materials in shielding against gamma rays, holding promise for various applications in radiation protection, particularly in the medical and nuclear sectors. This study contributes valuable insights into developing effective radiation‐blocking materials for diverse industrial and scientific contexts.

Building Cross-sectoral Collaborations to Address Perinatal Health Inequities: Insights From the Dutch Healthy Pregnancy 4 All-3 Program.

Addressing perinatal health inequities is the joint responsibility of professionals working for local governments, the medical, social, and public health sector. Cross-sectoral collaboration between these professionals is challenging. For such collaborations to succeed, a transition, ie, a fundamental shift in the dominant structure, culture, and practices at the systems level, is necessary. We investigated facilitators and barriers for cross-sectoral collaborations, when addressing perinatal health inequities in the Netherlands. Additionally, we studied how cross-sectoral collaborations can be facilitated by action research. We used interview and questionnaire data of the Healthy Pregnancy 4 All-3 (HP4All-3) program, which resulted from action research in six Dutch municipalities. All interviews were coded using open codes related to facilitators and barriers for cross-sectoral collaboration and categorized into three subgroups: structural, cultural, or practical. The answers to the questionnaire were analyzed and summarized quantitatively. We conducted 53 interviews with a total of 81 professionals. The most important ingredients for cross-sectoral collaborations mentioned by the interviewees were: (1) structural: having a solid network with a clear overview of professionals working in the different sectors, (2) cultural: having a joint vision/goal, and (3) practical: short lines of communication and timely sharing of information. A total of 85 professionals filled in (parts of) the questionnaire. Two-thirds to over 80 percent replied that the HP4All-3 program had an added value in building cross-sectoral collaborations. Our research shows that cross-sectoral collaborations in the context of perinatal health are hampered by structural, cultural, and practical barriers. Analyzing facilitators and barriers at these three levels helps to identify bottlenecks in cross-sectoral collaboration. Action researchers can be of great advantage in facilitating collaboration, as they can offer an open setting for reflection and instigate a sense of urgency for building collaborations.