Healthcare Industry Research Articles

Rapid Detection of Microplastics/Nanoplastics Directly Exposed to Blood During Intravenous Injections via Mie Scattering Spectra

Microplastics/nanoplastics (M/NPs) are pervasive in the environment, leading to inevitable human exposure through various pathways and raising significant public and scientific concern. Understanding the sources and levels of M/NPs in human blood is crucial for environmental health studies. This work examined the content, type, shape, and size of M/NPs released directly into the bloodstream from medical devices via saline solution during intravenous (IV) injection. The results of the Mie scattering spectra method show that the M/NPs content from infusion bags was 1.0 ± 0.7μg/L, mainly fibers, polyethylene, and polypropylene, with fragments being predominant. During a IV process, the initial 12mL of saline from infusion tubes contained 8.4 ± 3.6μg/L of M/NPs, primarily polyvinyl chloride and fibers. These results suggest that M/NPs exposure during IV therapy mainly originates from infusion tubing, necessitating high concern for exposure risks. Recommendations include: 1) reducing non-essential IV treatments, 2) discarding the initial 12mL of saline solution flowing through the tubing during essential IV therapy, and 3) expediting the development of legal requirements and detection standards by national authorities and the healthcare industry to mitigate the risk of M/NPs exposure in the bloodstream.

Telemedicine integration with cloud-based IoT: A literature review approach

This paper discusses the possibilities of integrating cloud-based IoT with telemedicine as the ways to advance the healthcare industry performance and change its context for the better. This paper aims at exploring how these technologies can be integrated to enhance the existing solutions to the present problems of health care access, patient interactions and use of analytical data in practices. Possible ways in which IoT devices and cloud technology can enhance patient care and assist clinicians in their process of arriving at diagnoses are problems that the research seeks to uncover. The method used is a qualitative systematic review of prior studies related to telemedicine, IoT, and cloud computing, accompanied by a thematic analysis to reveal emerging patterns and the gaps in the literature examined in the study. The new model ‘‘Telemedicine Integration with Cloud-Based IoT,’’ was categorized thematically by differentiation of real-time severity, data privacy, patient interaction and technological difficulties. This model considers layers for acquiring IoT data, storing and analysis, a healthcare remote interface, a machine learning based analytics, and security, which offer a holistic way to enhance healthcare services. Real-time IoT, cloud-based storage, AI for early diagnosis, secure telemedicine platforms, data protection, and complete os compatibility. Based on this study, the authors posited that the convergence of IoT and cloud is capable of redesigning telemedicine by delivering better quality, efficient, and accessible healthcare solutions. However, they lack socio-economic factors for example, the feasibility cost for implementing it for the health companies and difficulties faced by the patient in the less-developed regions. The main limitation observed in the current research is related to the barriers hindering its implementation and future studies are suggested to address these challenges by exploring improved cost-effective approaches to overcome these and other barriers and analysing the issue of data security and patients’ privacy regulation. Studies should also aim at the long-term implications on the user satisfaction in order to introduce or show the long term positive and negative effects of cloud IoTs in telemedicine.

Evaluating the Impact of Data Protection Compliance on AI Development and Deployment in the U.S. Health sector

This paper evaluates the impact of data protection Compliance on artificial intelligence development and deployment in the U.S. Health sector. As AI technologies develop quickly, worries about data security and privacy have increased, especially in delicate industries like healthcare. HIPAA, or the Health Insurance Portability and Accountability Act, and CCPA, or California Consumer Privacy Act, are two important U.S. laws that are examined in this paper along with their comparison to the European Union's General Data Protection Regulation, or GDPR. The intersection of AI and data protection regulations is also examined. The analysis emphasizes the necessity for privacy-by-design principles and the development of openness and accountability, and it shows how these policies affect AI developers, healthcare providers, and other stakeholders. Through an analysis of the opportunities and obstacles posed by the CCPA and HIPAA, the paper offers useful guidance for managing the regulatory environment and promoting innovation in AI development. The study reports that observing data protection rules is not only required by law but also strategically crucial for fostering public confidence and guaranteeing the moral application of AI in healthcare. The paper finally concludes that maintaining a sustainable growth of AI applications in the U.S. healthcare industry requires striking a balance between technological advancement and stringent data privacy procedures and compliance as AI continues to advance.

Artificial Intelligence for Healthcare and Diagnostics

Abstract: AI models are being applied to several components of healthcare including diagnostics, drug development, personalized healthcare plans and so on. The large datasets which rapidly rise in complexity and volume form part of big data, provide the potential for ML models to make significant progress in diagnostics, medical image interpretation and treatment recommendations. The introduction of AI across the various departments of the healthcare industry has already raised efficiency and dropped prices. Covid 19’s exponential growth rate completely overwhelmed the healthcare sector's capabilities. AI and robotics became paramount in boosting efforts to detect, identify the risk of and monitor the virus, and develop and deliver the vaccine throughout the planet. The systems that did well generally had large datasets, one modality and did well in testing with foreign data. Currently, low efficiency and high costs make research difficult. With big data being produced at astronomical rates, we should theoretically be making constant discoveries and advancements. However, only part of this data is currently being processed and studied. Attempts are being made to resolve this gap by leveraging AI tools

DB-Net and DVR-Net: Optimized New Deep Learning Models for Efficient Cardiovascular Disease Prediction

Cardiovascular Disease (CVD) is one of the main causes of death in recent years. To overcome the challenges faced during diagnosing CVD at an early stage, deep learning has been used. With advancements in technology, the clinical practice in the health care industry is likely to transform significantly. To predict CVD, we constructed two models: Dense Belief Network (DB-Net) and Deep Vanilla Recurrent Network (DVR-Net). Proximity Weighted Random Affine Shadow sampling balancing technique is used for balancing the highly imbalanced Heart Disease Health Indicator dataset. SHapley Additive exPlanations exhibits each feature’s contribution. It is used to visualize features contribution to the output of DB-Net and DVR-Net in CVD prediction. Furthermore, 10-Fold Cross Validation is performed for evaluating the proposed models performance. Cross-dataset evaluation is also conducted on proposed models to see how well our proposed models generalize on unseen data. Various evaluation measures are used for assessment of models. The proposed DB-Net outperforms all the base models by achieving an accuracy of 91%, F1-score of 91%, precision of 93%, recall of 89%, and execution time of 1883 s on 30 epochs with batch size 32. The DVR-Net beats the state-of-art models with an accuracy of 90%, F1-score of 90%, precision of 90%, recall of 90%, and execution time of 2853 s on 30 epochs with batch size 32.

The role and impact of new technologies on healthcare systems

AbstractThe integration of new technologies into healthcare systems is revolutionizing the industry by significantly enhancing the capabilities of healthcare providers, improving patient outcomes, and optimizing administrative operations. This comprehensive examination critically assesses the impact of these technologies on various stakeholders within the healthcare ecosystem, highlighting both the benefits and challenges. Emerging technologies such as artificial intelligence (AI), which refers to the simulation of human intelligence by machines, the Internet of Things (IoT), which involves interconnected devices that collect and exchange health-related data in real time, Blockchain, which is a decentralized and tamper-proof ledger technology used to securely manage patient data, and wearable devices are explored for their potential applications in diagnostics, treatment, and patient monitoring. Despite their transformative potential, these technologies also bring forth a range of limitations and challenges, including ethical concerns regarding patient privacy, legal implications of data security, and technical hurdles related to interoperability and implementation. By addressing these issues, the healthcare industry can better navigate the complexities of technological advancement, ensuring that innovation contributes positively to patient care and operational efficiency.

Barriers and Facilitators to Becoming a Medical Physicist in Bangladesh

The notion of gender equity is debatable, and worldwide health professionals are exploring the argument. However, the contextual situation and experience are different in each country and vary across cultures. There is an extensive amount of literature that presents women’s participation and involvement as increased in science, technology, engineering and mathematics over the last decades (Frize et al., 2022); however, there are variations in data and analyses documented which refer to the gender disparity of medical physicists (MPs) Worldwide (Barabino et al., 2020; Crowe and Kairn, 2016). Medical physics is one of the fields in health care where women are underrepresented, with only 29.8% of the workforce reported identifying as females in this sector (Tsapaki et al., 2018; Tsapaki and Rehani, 2015). Historically, the healthcare profession is male-dominated (Witz, 1990), with women being underrepresented in many specialist fields like academic medicine, surgical specialities and leadership positions (Kramer et al., 2021). The Nursing profession is, however, excluded from this category and considered a woman’s job. Furthermore, women’s contributions to the healthcare industry are not frequently acknowledged (Paradis et al., 2021). Moreover, the career trail for men and women in the healthcare profession is suggestively different (Baig, 2020). Broadly, regarding leadership positions and the range of decision-making, there are observable gender discrepancies in the industry. One of the main reasons for this is social restrictions, which women in specific geographic locations like Bangladesh encounter frequently. The lack of support or a platform for women to practice their profession with autonomy impedes their progress or movement upward in their careers. Women's unhindered involvement, participation and career development in patient care will improve healthcare provision, especially in caring for vulnerable patient groups like the elderly, individuals with significant co-morbidities, and minorities (Covington et al., 2020). In addition, involving more women in healthcare will not only reduce the gender disparity and empower them but also create a sense of confidence in the professions and services provided and, as a result, improve overall well-being. This study will aim to understand the views and experiences of gender-based discrimination among practising medical physicists in Bangladesh.

Assessment of online pharmacy applications in India by employing the mobile application rating scale

IntroductionOver the past few years, the technology powering mobile devices such as smartphones has made significant progress. Furthermore, the healthcare industry is always progressing and actively embracing the latest technological advancements to achieve the highest level of efficiency. With the rising prevalence of smartphones and internet connection, customers are benefiting from reduced prices, convenient home delivery, and effortless accessibility through online pharmacies. Internet-based pharmacies facilitate the internet-based transaction of health-related products, such as drugs, dietary supplements, and various other wellbeing products.Objective of studyThe study assessed digital pharmacy applications in India using the Mobile App Rating Scale (MARS) on Android and iOS devices, aiming to evaluate their quality.MethodsAn investigation examined the digital pharmacy applications in India that were accessible via the Android Market and App Store. The applications were assessed by two researchers using the MARS questionnaire, a tool that evaluates 23 variables categorized into five domains: Engagement, Functionality, Aesthetics, Information, and Subjective Quality. The grading system spanned from one to five for every category.ResultsA Google Play Store and App Store investigation revealed 40 online pharmacy apps in India, with 13 rejected. Seven were non-English language-related apps and seven were not downloaded. Thirteen were chosen and evaluated using the MARS Scale. The MARS demonstrated significant positive associations across its components, namely Engagement, Functionality, Aesthetics, and Information. Specifically, greater levels of user functionality were shown to be indicative of superior app aesthetics and engagement. The mean rating of the 13 apps fell between the range of 3.11 to 4.32 on a 5-point scale.ConclusionThis is the first study to utilize the MARS scale to assess the efficacy of online pharmacy applications in India. This research enhanced the functionality and quality of various online pharmacy applications utilized in India.

Distance and Angle Insensitive Radar-Based Multi-Human Posture Recognition Using Deep Learning

Human posture recognition has a wide range of applicability in the detective and preventive healthcare industry. Recognizing posture through frequency-modulated continuous wave (FMCW) radar poses a significant challenge as the human subject is static. Unlike existing radar-based studies, this study proposes a novel framework to extract the postures of two humans in close proximity using FMCW radar point cloud. With radar extracted range, velocity, and angle information, point clouds in the Cartesian domain are retrieved. Afterwards, unsupervised clustering is implemented to segregate the two humans, and finally a deep learning model named DenseNet is applied to classify the postures of both human subjects. Using four base postures (namely, standing, sitting on chair, sitting on floor, and lying down), ten posture combinations for two human scenarios are classified with an average accuracy of 96%. Additionally, using the centroid information of human clusters, an approach to detect and classify overlapping human participants is also introduced. Experiments with five posture combinations of two overlapping humans yielded an accuracy of above 96%. The proposed framework has the potential to offer a privacy-preserving preventive healthcare sensing platform for an elderly couple living alone.

Semantic Segmentation Based on Geometric Calibration Using AI and AR in Health Care

In the medical field, medical imaging is essential for precise diagnosis, treatment planning, and condition monitoring. The goal of this work is to improve the field of healthcare imaging by investigating the complementary applications of augmented reality (AR) and artificial intelligence (AI) in semantic segmentation. More precise identification and delineation of anatomical structures and abnormalities in medical images is made possible by AI-driven semantic segmentation, opening the door to more precise diagnosis and treatment plans. Modern AI algorithms are employed in the suggested methodology to perform semantic segmentation in a variety of medical imaging modalities, including ultrasound, CT, and MRI scans. The detection of anomalies such as tumours, irregularities in organs, and neurological disorders is made easier by these algorithms. The foundation for integrating augmented reality technologies into the healthcare ecosystem is provided by the segmented medical images. AR enhances the interaction and visualization of segmented medical data in the context of healthcare. Real-time augmented reality overlays help surgeons by improving surgical navigation and precision. Additionally, AR apps help with medical education by offering professionals and students alike an immersive learning environment. AR provides interactive visualizations to help patients understand their medical conditions when they are going for therapy and rehabilitation. The difficulties in integrating these technologies in the healthcare industry are discussed in this paper, with a focus on the significance of data privacy, regulatory compliance, and easy integration with current healthcare systems. The successful deployment of AI technologies necessitates interdisciplinary collaboration among AI developers, healthcare professionals, and AR specialists to ensure compliance with ethical, legal, and clinical standards mandated in the healthcare domain. The proposed Segmentation mask is redefined with geometric calibration and used with U-Net for image segmentation. The segmentation is experimented on Cityscapes and PASCAL VOC 2012 datasets. The experimental results show that proposed semantic segmentation based on geometric calibration yields more accuracy than its counterparts.

Patient safety in a rural sub-Saharan Africa hospital: A 7-year experience at the AIC Kijabe Hospital, Kenya.

The development of a safety culture is challenging, primarily because it often disrupts institutional attitudes, norms and values. In the healthcare industry, most of the data on the results of unsafe care come from High-Income Countries. The Hospital Survey on Patient Safety Culture (HSOPS) is a tool for assessing, building, sustaining and comparing institutional safety cultures within healthcare organizations. We used the HSOPS over a 7-year period, and herein report our experience. The authors report their experience using the HSOPS tool in Kijabe Hospital, an institution with 650 employees, over a 7-year period. The HSOPS tool, with no local modifications, was distributed to all employees during each survey. The institutional HSOPS percent positive dimension scores for 2015, 2017 and 2019 were compared with baseline data from the 2013 survey. The average response rate during the study period was 84.5% (range 65.1% to 93.6%). In general, the mean percentage positive dimension scores of most domains improved in the 2019 survey (p<0.05), including reduced staff turnover and, improved hospital support for patient safety (p<0.0001), amongst other domains. The overall patient safety grade (excellent/very good), was 50% (range 43-64%). Although the dynamics of high staff turnover and hospital leadership change presented challenges in developing and measuring institutional patient safety culture, this study demonstrates that patient safety ideals can be developed and embraced in sub-Saharan Africa. Patient safety champions, a generative institutional leadership that is supportive of patient safety, are important for the development of an institutional safety culture. Creating an institutional just culture creates a patient safety culture.

Orthogonal Nano‐Engineering (ONE): Modulating Nanotopography and Surface Chemistry of Aluminum Oxide for Superior Antibiofouling and Enhanced Chemical Stability

AbstractDecoupling certain material surface properties can be key to attaining critical property‐activity relationships that underpin their antibiofouling performance. Here, orthogonal nano‐engineering (ONE) is employed to decouple the influences of nanotopography and surface chemistry on surface antibiofouling. Nanotopography and surface chemistry are systematically varied with a two‐step process. Controlled nanotopography is obtained by electrochemical anodization of aluminum, which generated anodic aluminum oxide (AAO) surfaces with cylindrical nanopores (diameters: 15, 25, and 100 nm). To modify surface chemistry while preserving nanotopography, an ultrathin (≈5 nm) yet stable zwitterionic coating of poly(divinylbenzene‐4‐vinylpyridyl sulfobetaine) is deposited on these surfaces using initiated chemical vapor deposition (iCVD). Antibiofouling performance is assessed by quantifying 48‐h biomass formed by gram positive and negative bacteria. The ONE surfaces demonstrated enhanced antibiofouling performance, with small‐pore nanotopography and zwitterionic chemistry each lowered biomass accumulation by tested species, with potential additive effects. The most effective chemistry‐topography combination (ZW‐AAO15) enabled an overall reduction of 91% for Escherichia coli, 76% for Staphylococcus epidermidis, 69% for Listeria monocytogenes, and 67% for Staphylococcus aureus, relative to the uncoated nanosmooth control. Additionally, the composite ZW coating exhibited encouraging anticorrosion properties under both static and turbulent cleaning conditions, vital to antibiofouling applications in healthcare and food industries.

Secure Electronic Health Records Against Insider Attacks Using Blockchain

Patient health information is provided by Electronic Health Record (EHR) systems. The security, integrity, and management of electronic health records are critical issues. EHRs facilitate online data storage and lessen human labour by making data maintenance simple. Traditional EHRs are currently vulnerable to a variety of assaults, such as internet and insider transactions. Diverse fraudulent schemes attempt to manipulate data in order to profit from it globally. Usually, this entails manipulating or hacking the data to produce fictitious reports. This is primarily due to the fact that patients are unable to follow their records. Here, a unique framework utilising the blockchain concept in the healthcare industry has been introduced. Implementing Block Chain for electronic health records (EHR) is the main objective of this proposed study. The decentralised nature of Block Chain lessens the possibility of a single point of failure, resulting in a robust system. Together with addressing the extensibility issue that Block Chain shares, the proposed approach makes use of an off-chain data source. The findings show that, as compared to the conventional health record system, computers is far more safe and scam-free. Lastly, the suggested method offers situations, like in EHR, where the given method ought to work well.

Managing digital transformations: the intermediary function of digital readiness in facilitating strategic renewal within the healthcare industry

Managing digital transformations: the intermediary function of digital readiness in facilitating strategic renewal within the healthcare industry

Data-Driven Revenue Cycle Management: Optimizing claims denial resolution and under payment for healthcare providers through SaaS Solution

The healthcare industry is increasingly under financial pressure due to high rates of claims denials, leading to substantial revenue loss and operational inefficiencies. Claims denials result from various factors, including coding errors, incomplete documentation, and regulatory non-compliance, all of which contribute to revenue leakage that often reaches 5–10% annually for healthcare providers. This paper explores a proprietary Software-as-a-Service (SaaS) solution that leverages data analytics, machine learning, and real-time reporting to enhance the claims denial management process. Through predictive and prescriptive analytics, the proposed solution enables healthcare providers to identify denial trends, reduce denial rates, optimize claims resolution, and increase revenue retention. The paper includes system architecture, data security protocols, pilot implementation findings, and a comparative analysis of performance metrics. The paper concludes with a discussion on future developments, including real- time denial prediction, automated appeals generation, and enhanced integration capabilities with existing Electronic Health Record (EHR) systems. Keywords: Revenue Cycle Management, healthcare claims denial, claim under payment, SaaS, data analytics, machine learning, real-time reporting, financial sustainability, healthcare providers

Leveraging Big Data and AI for Optimizing Digital Marketing Strategies: A Data-driven Approach

In this paper, the author discusses how big data and AI can improve the rate of conversion, engagement, and ROI in digital marketing through ’big data’ techniques in improving the effectiveness of the marketing mix. In this study, by counting and comparing campaigns from different industries, the researcher establishes that three strategies facilitated by AI have a positive impact on marketing- predictive analytics, real time personalization and segmentation. Findings further show that some industries such as e-commerce and the financial industry benefit most from these technologies, yet in other areas like the healthcare industry, regulatory issues prevent substantial results. The insights reveal how embedding of AI in the digital marketing strategy give companies a competitive advantage while the aspects of ethics such as data protection cannot be overlooked. Finally, the research study calls attention to the role of AI and big data in developing better, engaging, and relevant digital marketing solutions.

A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.

Modular design and custom development in ERP systems

Abstract. Enterprise Resource Planning (ERP) systems are integral to modern business operations, offering streamlined processes and enhanced efficiency. This paper explores the advantages and disadvantages of modular design in ERP systems and examines custom development methods to address unique industry requirements. The study highlights the benefits of modular design, such as flexibility, scalability, and reduced complexity, while also addressing challenges like integration issues and system fragmentation. Strategies for efficient module integration, including standardized protocols and middleware solutions, are discussed to mitigate these challenges. Custom development methods, including vendor tools, scratch-built modules, and third-party collaborations, are analyzed for their effectiveness in achieving tailored ERP solutions. Case studies from the manufacturing, healthcare, and retail industries illustrate the practical applications and outcomes of modular ERP systems, providing insights into the implementation process and potential obstacles. The findings underscore the importance of careful planning, stakeholder involvement, and comprehensive training to ensure successful ERP system deployment and utilization.

Interorganizational systems withouthierarchy: immunizationinformation systems.

The 2020-2021 COVID-19 pandemic spurred change across multiple healthcare industries. This study explores how managing vaccination data in the United States of America required cooperation among many different organizations necessitated by an emergency response. We studied how individual states interacted with the federal government to address the need for vaccination-related information during the pandemic. In total, 11 interviews were conducted with individuals responsible for collecting vaccination data and reporting it to the US Federal Government. Seven of those individuals were directors of USA jurisdictional Immunization Information Systems (IIS). Archival data were also combined with the interview responses to inform the analysis and development of guidelines. States across the USA had different ways of tracking and storing immunization data that was heavily influenced by state-level and federal legislation. The lack of a universal patient identifier made cross-state patient identification difficult. Federal requirements for reporting dictated much of how the different state-level entities collected, stored and reported data. This study highlights the importance of data interoperability and data sharing by exploring how a loosely coupled set of entities without direct top-down control or a profit motive can govern data effectively. Our analysis provides greater clarity about the interactions between different stakeholders in a complex system. This study presents primary interviews of 11 individuals, each responsible for tracking and reporting immunization information. Analysis of the data expands existing research on IIS on data sharing, system interoperability and dynamic pandemic responses.

Integrating Deep Learning with Generative Design and Topology Optimization for Efficient Additive Manufacturing

Abstract. Additive manufacturing (AM) through generative design and topology optimisation creates complex, lightweight structures with exceptional material efficiency and structural integrity. When coupled with deep learning functionality, generative design and topology optimisation can explore broader design spaces and optimise more efficiently, creating novel AM structures that utilise material more efficiently and have better strength and performance than their counterparts created through conventional AM methods. The study tackles how deep learning models such as convolutional neural networks (CNNs) can be integrated into generative design and topology optimisation and how these integration help optimise material usage, production time and performance. Case studies from the aerospace, automotive, and healthcare industries exemplify how these synergies resulted in more resilient, cost-effective designs that would not have been possible through conventional AM approaches. The study focuses on material usage efficiency, reduction in production time and performance improvement to showcase how deep learning integrations enhance the process from design conceptualisation, through iterations, to final production.