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Disrupting Healthcare Silos: Addressing Data Volume, Velocity and Variety With a Cloud-Native Healthcare Data Ingestion Service
Abstract
Healthcare enterprises are starting to adopt cloud computing due to its numerous advantages over traditional infrastructures. This has become a necessity because of the increased volume, velocity and variety of healthcare data, and the need to facilitate data correlation and large-scale analysis. Cloud computing infrastructures have the power to offer continuous acquisition of data from multiple heterogeneous sources, efficient data integration, and big data analysis. At the same time, security, availability, and disaster recovery are critical factors aiding towards the adoption of cloud computing. However, the migration of healthcare workloads to cloud is not straightforward due to the vagueness in healthcare data standards, heterogeneity and sensitive nature of healthcare data, and many regulations that govern its usage. This paper highlights the need for providing healthcare data acquisition using cloud infrastructures and presents the challenges, requirements, use-cases, and best practices for building a state-of-the-art healthcare data ingestion service on cloud.
... Healthcare big data comprise complex interactions among various features though it has high flexibility to changes in surroundings (Ranchal et al., 2020). Because healthcare data are frequently partitioned over several medical systems which vary across several healthcare companies and are particularly concerned about the sharing and availability of information over the range of medical treatment (Ranchal et al., 2020). ...
... Healthcare big data comprise complex interactions among various features though it has high flexibility to changes in surroundings (Ranchal et al., 2020). Because healthcare data are frequently partitioned over several medical systems which vary across several healthcare companies and are particularly concerned about the sharing and availability of information over the range of medical treatment (Ranchal et al., 2020). Soft computing systems which draw attention over interpretations and predictions about the factors or other certain conditions thus have been accentuated. ...
... (Continued ) Ranchal et al. (2020) studied the necessity of providing healthcare data acquisition by employing cloud structures and offered the limitations, needs, use cases and optimal solutions for implementing an existing healthcare data ingestion service on cloud computing. Kim and Chung (2020) proposed a multi-modal autoencoder to evaluate missing data in the context of healthcare big data. ...
Understanding huge amounts of data with a wide variety of data kinds is referred to as big data analytics. “Human, Machine and Material” development strategy will result in an enormous amount of data. The management department may enhance its potential to process big data by assessing and analysing current network big data issues. As a consequence, it considerably plays a role in minimising resource costs and consumption in every sector. Every sector can effortlessly transition into the following information and digitalisation phase of development. Big data will aid in tackling challenges and enhancing knowledge across various sectors. Although, the efficiency of big data analytics is still questioned by some challenges. The challenges that arise in big data analytics are storage, data quality, lack of data science professionals, data accumulation and data validation. Therefore, this discusses the term “Big data analytics” by configuring its applications, tools, Machine Learning (ML) models and challenges in existing approaches. A comprehensive analysis of over 58 research papers, covering various aspects of big data analytics across multiple domains including healthcare, education, agriculture, multimedia and travel is presented in this study. The main objective of this survey is to contribute to advancing knowledge, facilitating informed decision-making and guiding future research efforts in the dynamic and rapidly evolving landscape of big data analytics. Through meticulous paper selection, a diverse representation of the latest advancements in big data analytics techniques was curated. Each domain underwent a thorough review, elucidating methodologies, tools, datasets and performance measures. Further, the general steps involved in big data analytics techniques are outlined by providing a foundational understanding. Key areas of analysis include chronological review, algorithms utilised, tools and datasets employed and performance evaluation measures. By addressing these aspects, the study offers valuable insights into the evolution, methodologies and performance of big data analytics techniques across diverse domains. Additionally, it identifies research gaps and challenges, paving the way for future research to address critical issues such as data interoperability, privacy concerns and scalability. This study serves as a comprehensive resource for researchers, practitioners and policymakers, contributing to advancing knowledge and facilitating informed decision-making in the rapidly evolving landscape of big data analytics.
... CPSs have been successfully employed in various areas, including the process industry [10], transportation [12], and manufacturing [13]. While their use in healthcare systems is still in its early stages [14], they have already been recognized as a concrete paradigm for realizing the next generation of healthcare systems [7], [15], [16], [17], [18], [19]. ...
... Finally, at the cloud level, recent research has focused on architectural aspects as well as promoting interoperability. A relevant effort in this regard is presented in [19], where a cloud-native healthcare data ingestion service is proposed to facilitate the transfer of data into the cloud. The authors provide a set of requirements for building this service and present a proof of concept. ...
In the pursuit of a personalized healthcare experience, data-driven decision-making has become increasingly relevant. In this context, there is a growing need for technological systems specifically tailored to efficiently manage vast amounts of healthcare data. To address this need, in this work, we contribute by presenting a cyber-physical solution for monitoring and analyzing physiological data obtained from wearable devices. The proposed system is designed following a service-oriented architecture, which promotes modularity and enables efficient data access and analysis. The system is capable of ingesting and consolidating wearable data produced by a variety of commercial devices, scaling to accommodate a large number of data producers, and accepting queries from a multitude of consumers through various mechanisms. Performance tests conducted in various scenarios using real data demonstrate the system’s effectiveness in maintaining real-time data access.
... New tools like Docker provide a better way to pack software, and Linux container (LXC) provide better isolation. Microservice architecture and container patterns provide technologies like service mesh and enhance the system security, testing, and logging, and also improve observability, etc. Cloud-native architecture facilitates ease of data sharing by encouraging interoperability and improving data accessibility across organization boundaries [13]. It's on-demand availability of processing and storage capacity, intelligent data processing and analytics services, and secure and compliant infrastructure help in the rapid development of new applications, cross-domain system integration, and coupling with existing healthcare systems. ...
... It's on-demand availability of processing and storage capacity, intelligent data processing and analytics services, and secure and compliant infrastructure help in the rapid development of new applications, cross-domain system integration, and coupling with existing healthcare systems. Hence, it can enable a better access to healthcare data for all stakeholders like consumers, payers, clinicians, and providers [13]. ...
Over the past few decades, the world has faced the huge demographic change in the aging population, which makes significant challenges in healthcare systems. The increasing older adult population along with the current health workforce shortage creates a struggling situation for current facilities and personnel to meet the demand. To tackle this situation, cloud computing is a fast-growing area in digital healthcare and it allows to settle up a modern distributed system environment, capable of scaling to tens of thousands of self healing multitenant nodes for healthcare applications. In addition, cloud native architecture is recently getting focused as an ideal structure for multi-node based healthcare monitoring system due to its high scalability, low latency, and rapid and stable maintainability. In this study, we proposed a cloud native-based rapid, robust, and productive digital healthcare platform which allows to manage and care for a large number of patient groups. To validate our platform, we simulated our Cloud Nativebased Healthcare Monitoring Platform (CN-HMP) with real-time setup and evaluated the performance in terms of request response time, data packets delivery, and end-to-end latency. We found it showing less than 0.1 ms response time in at least 92.5% of total requests up to 3K requests, and no data packet loss along with more than 28% of total data packets with no latency and only ≈ 0.6% of those with maximum latency (3 ms) in 24-hour observation. Clinical Relevance- This study and relevant experiment demonstrate the suitability of the CN-HMP to support providers and nurses for elderly patients healthcare with regular monitoring in older adult facilities.
... Additionally, imputation methods like K-Nearest Neighbors (KNN) are frequently employed to address missing data, ensuring that gaps in information do not negatively impact the model's ability to make accurate predictions [67]. These techniques are especially important in real-world healthcare applications, where data are often incomplete or inconsistent due to various factors, including patient non-compliance, device malfunctions, and data entry errors [68]. The successful implementation of such methods ensures that predictive models can operate effectively even in the presence of imperfect data, which is crucial in clinical settings where timely and accurate predictions are necessary for patient care [69]. ...
Heart disease continues to rank among the leading causes of mortality globally, and due to their extremely small database, traditional risk prediction algorithms frequently fall short of providing a truly personalized evaluation. This study outlines a machine-learning-based model that uses a wide range of health indicators, including age, blood pressure, cholesterol, heart rate, and lifestyle risk factors, to predict the likelihood of a heart attack. In order to overcome the shortcomings of current procedures and provide timely insight for the prevention and early treatment of cardiac illnesses, the model aims to include real-time health data from IoT-enabled healthcare equipment. Recursive Feature Elimination (RFE), one of the sophisticated pre-processing strategies used in this model, trains the model independently of noisy or missing input. Excellent results in a variety of metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, indicated how well the model predicted the likelihood of a heart attack. The current work demonstrates how a machine-learning paradigm may be used as a scalable and dynamic approach to customized health solutions, helping to improve heart disease prediction and prevention.
... Traditional methods used for data analysis and resource management seem inadequate when it comes to the high volumes and complexities of datasets and would instead create delays rather than speed up the decision-making process, limit resource applications and lead to unfavourable outcomes in patients [67]. Likewise, traditional existing systems do not seem to appreciate or accommodate mainstream scalability that is necessary to manage increasingly huge amounts of healthcare data [68]. Cloud computing has emerged as a revolutionary panacea that offers scalable resources and supports real-time collaborative access to data [69]. ...
The amount of sensitive patient data has increased exponentially as a result of the healthcare industry's digital revolution, making scalable and secure storage solutions necessary. Although cloud computing provides an effective platform for handling this data, it also poses serious privacy, data integrity, and secure transmission issues. In order to guarantee the security and integrity of medical data saved in the cloud, this study suggests a safe architectural framework that combines preprocessing, AES-128 encryption, and HTTPS-based data transfer. Data gathering and cleansing are the first steps in the design, which is then followed by strong encryption and a safe transmission to the cloud. The suggested architecture successfully scales for bigger datasets and retains good encryption performance with low latency, according to experimental results. The results validate the framework's capacity to handle data security issues while adhering to HIPAA and other healthcare regulations. This method offers a dependable and effective way to protect cloud-based medical data.
... From our literature review, metadata is one of the solutions for data silos (Ranchal et al., 2020). Hospitals need metadata to avoid dealing with multiple data definitions from different units (Karkošková, 2022). ...
... All health care actors acquire real-world data, defined as any health care-related information captured from the patient [4]. The volume, velocity, and variety of acquired data, however, raise challenges for data processing systems [5]. Data engineers work in interdisciplinary environments to ensure that users receive data in the right format, at the right time and place to generate real-world evidence [4]. ...
Health data integration platforms are vital to drive collaborative, interdisciplinary medical research projects. Developing such a platform requires input from different stakeholders. Managing these stakeholders and steering platform development is challenging, and misaligning the platform to the partners’ strategies might lead to a low acceptance of the final platform. We present the medEmotion project, a collaborative effort among 7 partners from health care, academia, and industry to develop a health data integration platform for the region of Limburg in Belgium. We focus on the development process and stakeholder engagement, aiming to give practical advice for similar future efforts based on our reflections on medEmotion. We introduce Personas to paraphrase different roles that stakeholders take and Demonstrators that summarize personas’ requirements with respect to the platform. Both the personas and the demonstrators serve 2 purposes. First, they are used to define technical requirements for the medEmotion platform. Second, they represent a communication vehicle that simplifies discussions among all stakeholders. Based on the personas and demonstrators, we present the medEmotion platform based on components from the Microsoft Azure cloud. The demonstrators are based on real-world use cases and showcase the utility of the platform. We reflect on the development process of medEmotion and distill takeaway messages that will be helpful for future projects. Investing in community building, stakeholder engagement, and education is vital to building an ecosystem for a health data integration platform. Instead of academic-led projects, the health care providers themselves ideally drive collaboration among health care providers. The providers are best positioned to address hospital-specific requirements, while academics take a neutral mediator role. This also includes the ideation phase, where it is vital to ensure the involvement of all stakeholders. Finally, balancing innovation with implementation is key to developing an innovative yet sustainable health data integration platform.
... Strategies such as data standardization, normalization, interoperability frameworks, semantic mapping, advanced data integration platforms, and robust data governance practices are deployed to tackle these challenges. Addressing data heterogeneity is pivotal for CDRs to unleash their full potential in enhancing patient care and advancing healthcare research [105,106]. ...
A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond.
... ions within an organization store their data in isolated systems, making it difficult to access and share information across the organization. This can hinder the integration of accounting models with supply chain management, as it requires seamless data flow and communication between these systems (Okoye, et. al., 2024, Ortega-Calvo, et. al., 2023, Ranchal, et. al., 2020. ...
This concept paper proposes the integration of accounting models with supply chain management in the aerospace industry as a strategic approach to enhancing efficiency and reducing costs in the United States. The aerospace sector faces numerous challenges, including complex supply chains, stringent regulatory requirements, and cost pressures. By combining accounting principles with supply chain management strategies, this approach aims to optimize operations, improve financial transparency, and foster collaboration among stakeholders. Key objectives of this initiative include streamlining financial processes, enhancing decision-making capabilities, and mitigating risks associated with supply chain disruptions. By leveraging accounting models such as activity-based costing, lean accounting, and performance measurement systems, organizations can gain insights into cost structures, identify inefficiencies, and allocate resources effectively across the supply chain. Moreover, integrating accounting with supply chain management enables real-time monitoring of financial performance metrics, enabling timely interventions and adjustments to achieve strategic goals. This approach also facilitates better coordination between finance and operations teams, leading to improved communication, alignment of objectives, and ultimately, enhanced organizational performance. In the context of the aerospace industry, where precision, reliability, and cost-efficiency are paramount, this integrated approach offers significant benefits. It enables companies to optimize inventory management, minimize waste, and identify opportunities for cost savings throughout the supply chain. Additionally, by fostering a culture of continuous improvement and data-driven decision-making, organizations can adapt more effectively to market dynamics and gain a competitive edge. This concept paper outlines the theoretical framework and practical implications of integrating accounting models with supply chain management in the aerospace industry. Through case studies, best practices, and implementation strategies, it provides a roadmap for organizations to embrace this strategic approach and realize its full potential in enhancing efficiency and reducing costs in the U.S. aerospace sector.
... Omics refers to the comprehensive characterization and quantification of molecules such as genes and proteins clustered depending on their structural or functional similarities, and include genomics, proteomics, metabolomics, etc. Multi-omics data integration combines information from different layers of omics data to obtain understandings regarding how different biological systems interact at a molecular level [103,104], which is a crucial step to recognize inherent pathogenic pathways in different disease phenotypes. It is pertinent in neurodegenerative diseases (NDs) such as AD and PD, which involve a multifactorial etiology with heterogeneous clinical pictures and mixed pathologies [105]. EHRs are composed of patients' demographics along with clinical measurements, interventions, clinical laboratory tests, and medical data. ...
Precision medicine-based approaches differentiate themselves by taking into consideration subpopulation variability (e.g. genetic variations, age, gender, race, addictions). To date, traditional models, such as Trial-and-Error Dosing, Empirical Treatment Guidelines, Statistical and Actuarial Models, Pathophysiological Models, and Clinical Judgment and Experience, have been generalized in healthcare fields. However, more comprehensive and innovative technologies are required besides these conventional modelings, which are subject to various limitations such as low efficiency and incapability of processing complex biological systems. Here we review diverse machine learning (ML) algorithms integrated with big data and omics and its applications in various aspects of precision medicine. ML is the branch of artificial intelligence (AI), which has been rapidly developed and highlighted as a promising method to decrease diagnostic errors and aid clinicians with decision-making in recent decades. We focused on applications of ML models such as support vector machine (SVM), K Nearest Neighbor (KNN) random forest (RF), convolutional neural networks (CNNs) and deep learning in drug toxicity prediction, cardiovascular diseases, neurodegenerative diseases, and cancer therapies within precision medicine and specific benefits and challenges of each. This review provides insights on the wider utilization in clinical environments by recognizing current advantages that are expected to expand the scope of AI-driven methods and issues that need to be addressed for further studies.
... By choosing the suitable big data technology, an organisation will be able to handle the elevation in volume, velocity, and variety of data in a better way [8]. Big data analytics involves the usage of various techniques and tools to manage and extract insights from large datasets. ...
... Ensuring security and privacy stands as a foremost priority in the realm of centralized medical data repositories. Such healthcare repositories [60], [61] amass vast quantities of delicate patient data [62], encompassing personal health records, medical chronicles, and diagnostic evaluations. While the centralization paradigm proffers advantages like expedited data retrieval and cohesive healthcare workflows, it concurrently ushers in pronounced vulnerabilities [63]. ...
The advent of the fifth-generation mobile communication technology (5G) era has catalyzed significant advancements in medical diagnosis delivery, primarily driven by the surge in medical data from wearable Internet of Medical Things (IoMT) devices. Nonetheless, the IoMT paradigm grapples with challenges related to data security, privacy, constrained computational capabilities at the edge, and an inadequate architecture for handling traditionally error-prone data. In this context, our research offers: (1) an exhaustive review of large-scale medical data propelled by IoMT, (2) an exploration of the prevailing cloud-edge Artificial Intelligence (AI) framework tailored for IoMT, and (3) an insight into the application of Edge Federated Learning (EFL) in bolstering medical big data analytics to yield secure and superior diagnostic outcomes. We place a particular emphasis on the proliferation of IoMT wearable devices that incessantly stream medical data, either from patients or healthcare institutions, to centralized repositories. Furthermore, we introduce a federated cloud-edge AI blueprint designed to position computational resources proximate to the edge network, facilitating real-time diagnostic feedback to patients. We conclude by delineating prospective research trajectories in enhancing IoMT through AI integration.
... To this end, robust data governance frameworks are being developed, with clear guidelines on data access, consent management, and anonymization, striking a balance between promoting data sharing and safeguarding patient privacy. 72,73 ...
Artificial Intelligence (AI) has transformed how we live and how we think, and it will change how we practice medicine. With multimodal big data, we can develop large medical models that enables what used to unimaginable, such as early cancer detection several years in advance and effective control of virus outbreaks without imposing social burdens. The future is promising, and we are witnessing the advancement. That said, there are challenges that cannot be overlooked. For example, data generated is often isolated and difficult to integrate from both perspectives of data ownership and fusion algorithms. Additionally, existing AI models are often treated as black boxes, resulting in vague interpretation of the results. Patients also exhibit a lack of trust to AI applications, and there are insufficient regulations to protect patients�� privacy and rights. However, with the advancement of AI technologies, such as more sophisticated multimodal algorithms and federated learning, we may overcome the barriers posed by data silos. Deeper understanding of human brain and network structures can also help to unravel the mysteries of neural networks and construct more transparent yet more powerful AI models. It has become something of a trend that an increasing number of clinicians and patients will implement AI in their life and medical practice, which in turn can generate more data and improve the performance of models and networks. Last but not the least, it is crucial to monitor the practice of AI in medicine and ensure its equity, security, and responsibility.
... Cloud platforms frequently provide data governance and compliance management tools and frameworks. These tools enable organisations to define data retention policies, consent management mechanisms, and data access audit trails to maintain regulatory compliance, such as GDPR or CCPA [30], as cloud-based solutions continue to transform intelligent transportation applications, data governance and compliance become of utmost importance [10]. Data governance incorporates the procedures, policies, and guidelines that prescribe how data is managed, accessed, and protected. ...
The interaction between transportation networks and intelligent transportation systems has been revolutionized by cloud computing. However, the reliance on cloud-based solutions raises security and privacy concerns. This article examines the challenges of safeguarding data and privacy in intelligent transportation applications and emphasizes the potential of cloud-based solutions to resolve these issues. Organizations can protect sensitive data and user privacy by employing encryption, access controls, threat detection mechanisms, and privacy protection measures. Adopting these cloud-based solutions will encourage the extensive adoption of intelligent transportation applications while infusing users and stakeholders with confidence.
... Instead of performing computations such as traditional solutions, the CC layer in the proposed architecture receives the results from the EC layer and then analyzes and stores the data to serve big data computing tasks such as statistics and disease diagnosis in the medical domain [42][43][44][45]. ...
History has demonstrated that healthcare and medical systems play a crucial role in enforcing the development of science and technology. Humans have been seeing an explosive growth of e-health applications, where cloud computing has dominated e-healthcare systems and all domains in the past decades. However, one primary barrier to cloud-based e-health systems is their high response time. In emergencies, data aggregation and handling and decision-making regarding treatment need to be performed in seconds and directly affect the life of patients. These issues relate to resource constraints, such as medical equipment, ambulances, medical staff, medical facilities, and other conditions. To address these problems, computing architectures have been introduced, such as edge, fog, and cloud computing methods. In this work, we propose an all-in-one computing framework based on combining different computing solutions in real-time smart healthcare domains. Then, considering the efficiency of the proposed solution by the queue network model tool, we give recommendations for the optimal network infrastructure deployment depending on the scale-patients of hospitals. We believe that the results of this work will play an important role in the network infrastructure optimization of medical facilities while ensuring the provision of real-time healthcare services.
... Researchers found that using mobile phones apps may help in data silos, 81 while others focused on cloud computing. 82 Similarly, the application of FAIR principles (Findable, Accessible, Interoperable, and Reusable) in health research data is a hot topic of the day. There have been several research groups working this aspects. ...
Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.
... To accomplish fast recovery of error with high scalability, Yang et al. 23 presented the Euclidean distance-based recursive approximation (EDRA) approach which predicts time series accurately. This approach neglects the use of a large dataset thereby providing whole data operation under a small subset. ...
In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.
... The infrastructures of cloud computing can continuously acquire data from various heterogeneous sources and integrate data efficiently. Simultaneously, affordability, disaster recovery, and security are all significant factors in cloud computing acceptance [1]. Data which is unpreserved have required to be sent to blockchain networks and is stored on the user's computer or in cloud services. ...
The healthcare sector is continuously evolving, and it is anticipated that the next healthcare model would be information-focused. Cloud computing technology can help the sector handle heterogeneous sources of data and the effective convergence of data. Due to security and privacy considerations, it is challenging to transfer a medical record from multiple hospital databases. Consequently, the article proposed a blockchain-based patient information management system by using the Blockchain-based Privacy-Preserving and Robust Healthcare data (BPPRH). This transfers the medical data to the patient through the cloud more securely. The cloud is modelled with the hypervisor and VMs. During the data transmission or storage in the cloud, there are several attacks like ransomware, malicious insiders, Man-in-the-middle attack, malware injection attacks, and Denial-of-Service attacks (DoS) occur in the network. Modified Fuzzy Particle Swarm Optimization (MFPSO) is presented to detect these threats. After detecting the attacks, Edge-Cloud-based Collaborative Systems (ECCS) prevent the data from attackers with the use of Regularized Maximum Likelihood Estimation, Inverse Network, and Shadow Model Reconstruction models. The results showed that the system provides better security with high accuracy of 99.32% respectively. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems’ robustness and to avoid recorded security limitations in commonly used systems for smart healthcare respectively.
... Data isolation is a major problem for healthcare organizations, as it prevents them from taking advantage of the latest IT technologies, such as cloud computing's data processing and analytics capabilities, which can help optimize treatment while lowering costs. Modern IT systems areto an increasing extent taking over cloud computing and transferring their workloads to the cloud to gain economic advantages, of scale, energy consumption, scalability, and elasticity [1]. The Popularity of long-term in-home smart healthcare Monitoring Systems has resulted from the aging population and the prevalence of chronic diseases [2]. ...
... In CNA, existing studies have primarily concentrated on the CNA principles [15,22], CNA architecture [21], CNA methods [23,24], CNA properties [20], etc. Meanwhile, scholars look at ways to apply cloud-native to modern-day problems, such as healthcare scenarios (e.g., healthcare data acquisition [25], eHealth services orchestration platform [26]), and scientific fields (e.g., cloud-native data repositories [27], autonomous driving [28], e-commerce platform [29], et al.) Cloud-native technologies have become the key components of enterprises' digital transformation and migration to the cloud, driven by the advantages of efficiency, security, cost, and developer needs. ...
Cloud-native is an innovative technology and methodology that is necessary to realize the digital transformation of enterprises. Promoting the wide adoption of cloud-native in cloud providers and enterprises has gained popularity in recent years. According to the technological and commercial characteristics of cloud-native, this paper analyzes the game relationship between cloud providers and enterprises on the selection of cloud-native, and combines evolutionary game theory to establish a model. In addition, empirical analysis indicates the impact of parameter changes on the dynamic evolution process. The results show that (1) enterprises are more vulnerable to the impact of direct benefit to adopt cloud-native, and cloud providers are especially affected by the cost of providing cloud-native; (2) enterprises are more likely to be impacted by the invisible benefit than cloud providers, but the impact has a marginal decreasing effect; (3) the low price is one of the reasons to attract enterprises; (4) enterprises are more concerned about the potential loss caused by the supply and demand mismatch. The results of the discussion provide a reference for all stakeholders to promote the implementation of cloud-native and the digital transformation of enterprises.
... In addition, many samples save extra time when large amounts of data are being processed in places such as hospitals and companies. Ranchal et al. 29 studied the need to access health data using cloud infrastructure and the challenges, requirements, plans, and best practices for creating innovative cloud data health services. Cloud computing infrastructure enables consistent data recovery, efficient data integration and large-scale data analysis from multiple sources. ...
With the rapid development of health information systems in recent years covering big data trends, clinical information is being adapted to understand important health trends and provide timely prophylactic treatment. However, the current plan fails to maintain a highly organized analysis and processing of health data. In addition, it collects issues that play an important role in determining quality. In this article, we propose a hybrid optimization based learning technique for multi‐disease analytics (HOL‐MD) from healthcare big data using optimal pre‐processing, clustering, and classifier. First, we introduce a capuchin search based optimization algorithm for preprocessing which removes the unwanted artifacts to enhance the detection accuracy. Second, we introduce a modified Harris Hawks optimization based clustering (MHHOC) technique used to select optimal features among multiple features which discovers the subgroups and reduce the dimensionality issues. We evaluated the performance of proposed HOL‐MD technique using standard US healthcare organization dataset, SUSY and HIGGS datasets. Finally, we proved the effectiveness of proposed RTL‐DNN techniques in terms of 11.5%, 9.3%, 2.75%, and 3.45% higher than the existing state‐of‐art techniques in terms of accuracy, precision, recall, and F‐measure respectively.
... During the last decade, the expansion of M2M communications has introduced some novel forms of interactions and data generation mechanisms with great potential for healthcare applications. In these interactions, large volumes of data are continuously being generated from various heterogeneous sources and transmitted for remote medical diagnostics, thus raising the challenges in data acquisition and analysis [49,50]. The scalability and interoperability of the IoT healthcare network infrastructure should be supported by providing Quality of Service (QoS) guaranties. ...
The recent global COVID-19 pandemic has revealed that the current healthcare system in modern society can hardly cope with the increased number of patients. Part of the load can be alleviated by incorporating smart healthcare infrastructure in the current system to enable patient’s remote monitoring and personalized treatment. Technological advances in communications and sensing devices have enabled the development of new, portable, and more power-efficient biomedical sensors, as well as innovative healthcare applications. Nevertheless, such applications require reliable, resilient, and secure networks. This paper aims to identify the communication requirements for mass deployment of such smart healthcare sensors by providing the overview of underlying Internet of Things (IoT) technologies. Moreover, it highlights the importance of information theory in understanding the limits and barriers in this emerging field. With this motivation, the paper indicates how data compression and entropy used in security algorithms may pave the way towards mass deployment of such IoT healthcare devices. Future medical practices and paradigms are also discussed.
We explores the design of scalable architectures for real-time big data stream processing in the cloud. Real-time data analytics faces unique challenges due to the high-velocity and high-volume nature of incoming streams. The core objectives here are to ensure low-latency processing, fault tolerance, and elasticity for handling fluctuating workloads. We propose an approach that employs dynamic allocation of computing resources to balance throughput and processing latency, while respecting cost constraints in multi-tenant cloud environments. This approach builds on fundamental queueing and probabilistic models to capture uncertainties and bursty traffic patterns and integrates advanced load-balancing and parallelization techniques to accommodate large-scale clusters. Furthermore, we emphasize the interplay between horizontal and vertical scaling decisions to optimize resource utilization. We develop a mathematical framework that addresses how data ingestion rates, system reliability, and fault recovery requirements inform the necessary parallelism level and distributed storage layout. Experimental findings suggest that a carefully tuned architecture can reduce end-to-end latency while maintaining operational cost within practical limits. We then examine limitations, including the potential for bottlenecks within specific nodes or network links, along with a discussion of how dynamic workload profiles can strain resource allocation strategies. We conclude by articulating future directions for refining the proposed approach to meet ever-evolving requirements.
Technology has changed rapidly and has changed the way we store, manage and read data. Data management is one of the most attractive aspects of cloud computing and is an alternative to the problem faced by database platforms, which can be scaled, modularized, and inexpensive. In this research paper, we will learn the main reasons why databases to cloud migration has taken place and the pros and cons of it. In the past few years, cloud computing has changed the way organizations hold, store and use data. The era of databases is fast changing and the core of data management in companies are being dispensed with. There are many good reasons why cloud databases are the preferred choice for today's businesses.
Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.
This review paper explores the complexities of financial management in multi-cloud environments, emphasizing the need for streamlined budgeting and forecasting processes. It begins by addressing the challenges inherent in managing finances across multiple cloud platforms, such as integration difficulties, data silos, and cost management issues. The paper then provides an overview of dynamic financial models, detailing their components and benefits in overcoming these challenges. It highlights the role of automation, real-time data utilization, and predictive analytics in enhancing financial accuracy and efficiency. Furthermore, the paper discusses the importance of collaborative platforms in fostering stakeholder communication and coordination. Finally, strategic planning, best practices for multi-cloud financial management, and emerging trends and prospects are outlined. The review underscores the significance of aligning financial models with organizational goals and leveraging advanced technologies to achieve optimal financial outcomes in multi-cloud settings. Keywords: Multi-Cloud Financial Management, Budgeting and Forecasting, Dynamic Financial Models, Automation in Finance, Predictive Analytics.
Cybersecurity risk analysis identifies, assesses, and prioritizes potential cyber threats, vulnerabilities, risks, and their impact on information confidentiality, integrity, and availability. The main goal of cybersecurity risk analysis is to develop strategies for managing and mitigating those risks effectively. A healthcare organization can better protect patient data, ensure the integrity of medical services, and contribute to overall patient safety by adopting a proactive and comprehensive approach to cybersecurity risk analysis, assessment, and mitigation strategies. This chapter presents an organizational approach for analyzing cybersecurity risks, assessment, and mitigation strategies in the healthcare industry.
Detection and prevention of cyberattacks in healthcare are crucial to protect sensitive patient data and ensure the integrity and availability of healthcare systems and services. The healthcare industry is a prime target for cyberattacks. There are some reasons why the healthcare industry is the prime target (Bhosale, 2021).
This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports.
This chapter highlights the different techniques employed to measure different types of healthcare corruption within and across healthcare sectors. Different techniques—surveys, audits, Fraud Loss Measurement etc. are used to measure different types of corruption—informal payments, dual practice, absenteeism, ghost employees etc. The usefulness and limitations of these measurements are consider in this chapter. In addition, I consider the impact of the volume, velocity, variety and veracity of healthcare data and how this can either help reduce corruption or is a conduit of it within and across the healthcare sectors.
This study advances healthcare process optimization, focusing on surgery roadmaps in the healthcare system, a scenario exemplifying optimization challenges due to limited information. This chapter emphasizes enhancing efficiency and resource utilization by employing lean manufacturing, operational research, simulation, data envelopment analysis, and non-dominance analysis. This study discovers 32 correlated patient pathways, showing shared activities and simultaneous multi-route impact from optimization strategies. Instead of merely analyzing pathway performance, the study applies data envelopment analysis to various investment scenarios, providing insights for healthcare decision-makers on improving patient care quality and efficiency. The juxtaposition of data envelopment analysis with a non-dominance algorithm in this mixed-method approach offers a robust framework for addressing healthcare operational challenges, particularly under information scarcity, and promotes continuous process improvement.
Aims and Background
For video understanding and analysis, human activity recognition (HAR) has emerged as a challenging field to investigate and implement. Patients can be monitored in real-time by a group of healthy individuals, and abnormal behaviors can be used to identify them later. Patients who do not engage in customary physical activities are more likely to suffer from stress, cardiovascular disease, diabetes, and musculoskeletal disorders. Thus, it is critical to collect, evaluate, and analyze data to determine their activities.
Objectives and Methodology
Deep learning-based convolutional neural networks (CNNs) can be used to solve the problem of patient activities in the healthcare system by identifying the most efficient healthcare providers. Healthcare relies heavily on the integration of medical devices into cyberphysical systems (CPS). Hospitals are progressively employing these technologies to maintain a high standard of patient care. The CNN-CPS technique can be used by a healthcare organization to examine a patient's medical history, symptoms, and tests to provide personalized care. A network of medical devices must be integrated into healthcare. Hospitals are increasingly using these technologies to ensure that patients get the best possible care at all times. Healthcare automation can dramatically improve quality and consistency by reducing human errors and fatigue. The multiobjective optimization is achieved considering various factors like the time required to find emergency case identification, new disease prediction, and accuracy of data protection.
Results
Consequently, patients are assured of receiving a consistent, attentive service at every visit. Data and orders can be stored and entered more easily via automation, market research suggests. The outcome of this article is obtained based on a comparison of various approaches in health monitoring systems, such as collection of patient data is 82.3%, new disease prediction is 80.14%, emergency case identification is 78.25%, data protection is 81.35%, immune to impersonation attack reduction is 78.36% and overall accuracy of data protection and transmission performance is 86.24% is achieved.
Conclusion
Compared with existing methods DM-CC and HE-WSN for health monitoring and patient’s treatment process, the proposed method CNN-CPS is better in maintaining the data and proper information passed to the medical care is 92.56%.
Result
Consequently, patients are assured of receiving consistent, attentive service at every visit. Data and orders can be stored and entered more easily via automation, market research suggests. The outcome of this article is obtained based on a comparison of various approaches in health monitoring systems as Collection of patient’s data is 82.3%, new disease prediction is 80.14%, emergency case identification is 78.25%, Data protection is 81.35%, immune to impersonation attack reduction is 78.36% and overall accuracy of data protection and transmission performance is 86.24% is achieved.
Conclusion
Compared with existing methods DM-CC and HE-WSN for health monitoring and patient’s treatment process, the proposed method CNN-CPS is better to maintaining the data’s ad proper information passed to the medical care is 92.56%.
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
Cyber risks and data breaches have expanded considerably across a wide range of business areas. Due to digitalization and the Internet of Things (IoT), the medical community generates a massive amount of heterogeneous clinical records on a daily basis. This unprocessed heterogynous medical data contains a wealth of clinical information that is vital for the medical field's advancement. However, patient privacy concerns arise when storing and analysing this medical big data on a private cloud. In this present study, the Optimized Ciphertext Attribute-Based Encryption (ECT-ABE) algorithm is introduced to enhance the safety of healthcare cloud storage. Additionally, the suggested system design incorporates a separate proxy server to isolate communication between clients and the cloud server, hence limiting direct attacks on cloud servers and lowering computational pressure on cloud servers. In comparison to earlier methods, the proposed cryptosystem is faster to execute and produces a lighter ciphertext. Additionally, both re-encryption and pre-decryption require only a single arithmetic operation.
Administrative and medical processes of the healthcare organizations are rapidly changing because of the use of artificial intelligence (AI) systems. This change demonstrates the critical impact of AI at multiple activities, particularly in medical processes related to early detection and diagnosis. Previous studies suggest that AI can raise the quality of services in the healthcare industry. AI-based technologies have reported to improve human life quality, making life easier, safer and more productive. This study presents a systematic review of academic articles on the application of AI in the healthcare sector. The review initially considered 1,988 academic articles from major scholarly databases. After a careful review, the list was filtered down to 180 articles for full analysis to present a classification framework based on four dimensions: AI-enabled healthcare benefits, challenges, methodologies, and functionalities. It was identified that AI continues to significantly outperform humans in terms of accuracy, efficiency and timely execution of medical and related administrative processes. Benefits for patients’ map directly to the relevant AI functionalities in the categories of diagnosis, treatment, consultation and health monitoring for self-management of chronic conditions. Implications for future research directions are identified in the areas of value-added healthcare services for medical decision-making, security and privacy for patient data, health monitoring features, and creative IT service delivery models using AI.
The development of intelligent healthcare systems (IHS) has raised the added value of digital medical data. With an efficient exploitation of digital medical data for diagnosis assistance, federated learning (FL) is promising in future digital health care. However, in multiple task performances, federated nodes deployed at the edge of IHS are constrained by computing and storage resources, as well as increased privacy breach risks. On account of these challenges, this paper proposes a more elaborated cloud–edge collaboration (CEC) framework of IHS combining FL and blockchain. Thus, a bi-level optimization scheduling IHS model is proposed, considering the large-scale access requirement of distributed generation (DG), energy storage (ES) and controllable load (CL) access to the IHS. Simulation results confirm an effective reduction of execution delay and power consumption, and a better interest coordination among multi-stakeholders.
Atrial fibrillation is a dangerous heart arrhythmia event, potentially leading to strokes and thrombosis, diagnosable by means of an electrocardiographic (ECG) exam where the patient’s heart activity is monitored continuously for several hours. The recent advances in technology have led to the development of several telemedicine applications where patients monitoring is performed in a real-time, remote fashion. Furthermore, artificial intelligence has risen as a powerful instrument for the reliable detection of heart rhythm abnormalities, and the realization of healthcare networks would allow the prompt achievement of this task. One of the challenges in remote monitoring concerns the development of signal processing algorithms tailored to data traffic resources of an healthcare network and fitting for the involved nodes hardware/software capabilities. In this direction, we present a novel MUlti-lead Sub-beat ECG (MUSE) based technique for atrial fibrillation detection using machine learning. MUSE relies on a flexible and customizable framework, allowing the exploitation of edge computing principles to conveniently distribute the signal processing effort among different network nodes and optimize the data traffic flow as well. The proposed algorithm for atrial fibrillation detection is based on a robust principal component analysis performed on the sub-beats identified on the ECG signal coming from one or more leads. Then, the investigated signal and a subject-dependent physiological heartbeat pattern are matched to extract several metrics that drive the final ECG classification. Tests performed on public datasets and on a real Holter record demonstrate the high reliability provided by MUSE and, differently from other schemes proposed in the literature, a low sensitivity to the ECG signal quality. Moreover, the restrained computational effort required for signal processing makes MUSE perfectly tailored to the implementation in a remote healthcare network.
Healthcare cyber physical systems (HCPS) always pursuing high availability allow software providers to adopt multiple kinds of development languages to reuse third-party program codes, while leading to the wide propagation of hidden software vulnerabilities. However, it is impossible to accurately trace execution paths and locate the key elements during the software execution process, which makes semantic features of vulnerabilities in the binary code can not bed extracted. This is the key support in automated vulnerability detection practices. To address these problems, a novel fast vulnerability detection mechanism based on recurrent semantic learning is proposed, which does not require high-level permissions to access the compiling process and traverse all execution paths. Firstly, a programframe is constructed to integrate software run-time logic and executing environment, detecting vulnerabilities from multi-programming language binary codes. Secondly, to achieve the powerful software execution context-awareness ability, a cascaded-LSTM recurrent neural network is designated to extract semantic features from binary files with vulnerabilities. Besides, we establish an experimental toolkit named an intelligent vulnerability detector (IntVD) to demonstrate the effectiveness of the proposed methods. Extensive and practical experiments validate that the vulnerability recognition accuracy on the HCPS software including VLC and LibTIFF can reach more than 95%.
Among the most often discussed topics in the healthcare industry are medical terminology standardization and the establishment of a centralized electronic medical record (EMR). There is a wide range of information in patient master records and treatment statistics records, as well as a dynamic data structure. Over time, the number of records has increased at a rapid rate, resulting in a large volume of data that necessitated the implementation of a structured data base management system. Even while data science and analytics have the potential to alleviate the problem of data management, the lack of confidence in cloud-based data storage may become a big issue in the future. For improved storage management, these records may be preserved, and they may be replicated over different clouds or maintained in a distributed way for increased security and reliability. Medical data generated by wearable medical devices or portable devices used by home care patients, on the other hand, may be connected to several cloud servers in order to improve accessibility and security. For data accessibility and security, the proposed system will build a generic medical record management system that will be deployed across various clouds. It will also design a Body Area Network (BAN) architecture that will be connected to a distributed cloud
The increasing volume of data being produced, curated, and made available by research infrastructures in the environmental science domain require services that are able to optimize the delivery and staging of data for researchers and other users of scientific data. Specialized data services for managing data life cycle, for creating and delivering data products, and for customized data processing and analysis all play a crucial role in how these research infrastructures serve their communities, and many of these activities are time‐critical—needing to be carried out frequently within specific time windows. We describe our experiences identifying the time‐critical requirements of environmental scientists making use of computational research support environments. We present a microservice‐based infrastructure optimization suite, the Dynamic Real‐Time Infrastructure Planner, used for constructing virtual infrastructures for research applications on demand. We provide a case study whereby our suite is used to optimize runtime service quality for a data subscription service provided by the Euro‐Argo using EGI Federated Cloud and EUDAT's B2SAFE services, and to consider how such a case study relates to other application scenarios.
Background:
Although digital health tools are increasingly recognized as effective in improving clinical outcomes such as asthma control and medication adherence, few studies have assessed patient experiences and perception of value.
Objective:
The aim of this study was to evaluate patient satisfaction, perception of usability and value, and desire to continue after 12 months of using a digital health intervention to support asthma management.
Methods:
Participants were enrolled in a randomized controlled study evaluating the impact of a digital health platform for asthma management. Participants used electronic inhaler sensors to track medication use and accessed their information in a digital health platform. Electronic surveys were administered to intervention arm participants aged 12 years and older after 12 months of use. The survey assessed asthma control, patient satisfaction with the sensor device, and perception of the usability and value of the digital health platform through closed-ended and open-ended questions. Logistic regression models were used to assess the impact of participants' characteristics on survey completion, satisfaction, and perception of value.
Results:
Of the 207 intervention arm participants aged 12 years and older, 89 submitted survey responses (42.9% response rate). Of these 89 participants, 70 reported being very satisfied (79%, 70/89) or somewhat satisfied (20%, 18/89) with the inhaler sensor device. Moreover, 93% (83/89) expressed satisfaction with the reports, and 90% (80/89) found the information from the reports useful for learning about their asthma. In addition, 72% (64/89) of the participants reported that they were interested in continuing to use the sensor and platform beyond the study. There were no significant differences in satisfaction with the device or the platform across participants' characteristics, including device type, age, sex, insurance type, asthma control, or syncing history; however, participants with smartphones and longer participation were more likely to take the survey.
Conclusions:
Electronic sensors and a digital health platform were well received by participants who reported satisfaction and perceived value. These results were consistent across multiple participants' characteristics. These findings can add to a limited literature to keep improving digital health interventions and ensure the meaningful and enduring impact on patient outcomes.
Over the past few decades, the amount of scientific articles and technical literature has increased exponentially in size. Consequently, there is a great need for systems that can ingest these documents at scale and make the contained knowledge discoverable. Unfortunately , both the format of these documents (e.g. the PDF format or bitmap images) as well as the presentation of the data (e.g. complex tables) make the extraction of qualitative and quantitive data extremely challenging. In this paper, we present a modular, cloud-based platform to ingest documents at scale. This platform, called the Corpus Conversion Service (CCS), implements a pipeline which allows users to parse and annotate documents (i.e. collect ground-truth), train machine-learning classification algorithms and ultimately convert any type of PDF or bitmap-documents to a struc-tured content representation format. We will show that each of the modules is scalable due to an asynchronous microservice architecture and can therefore handle massive amounts of documents. Furthermore, we will show that our capability to gather ground-truth is accelerated by machine-learning algorithms by at least one order of magnitude. This allows us to both gather large amounts of ground-truth in very little time and obtain very good preci-sion/recall metrics in the range of 99% with regard to content conversion to structured output. The CCS platform is currently deployed on IBM internal infrastructure and serving more than 250 active users for knowledge-engineering project engagements.
Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results.
To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts.
By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.
With the widespread use of electronic health record (EHR), building a secure EHR sharing environment has attracted a lot of attention in both healthcare industry and academic community. Cloud computing paradigm is one of the popular healthIT infrastructure for facilitating EHR sharing and EHR integration. In this paper we discuss important concepts related to EHR sharing and integration in healthcare clouds and analyze the arising security and privacy issues in access and management of EHRs. We describe an EHR security reference model for managing security issues in healthcare clouds, which highlights three important core components in securing an EHR cloud. We illustrate the development of the EHR security reference model through a use-case scenario and describe the corresponding security countermeasures and state of art security techniques that can be applied as basic security guards.
Time-critical applications, such as early warning systems or live event broadcasting, present particular challenges. They have hard limits on Quality of Service constraints that must be maintained, despite network fluctuations and varying peaks of load. Consequently, such applications must adapt elastically on-demand, and so must be capable of reconfiguring themselves, along with the underlying cloud infrastructure, to satisfy their constraints. Software engineering tools and methodologies currently do not support such a paradigm. In this paper, we describe a framework that has been designed to meet these objectives, as part of the EU SWITCH project. SWITCH offers a flexible co-programming architecture that provides an abstraction layer and an underlying infrastructure environment, which can help to both specify and support the life cycle of time-critical cloud native applications. We describe the architecture, design and implementation of the SWITCH components and describe how such tools are applied to three time-critical real-world use cases.
An unprecedented volume of data is being generated in healthcare and life sciences, ranging across medical records, claims, lab data, genomics data, medical images, emerging exogenous data, and knowledge. Much of this data is moving to the cloud. In this paper, we describe examples of how the data from systems of record, exogenous data sources and knowledge sources can be combined at cloud scale and speed to create industry-transforming insights to improve health outcomes. We then describe a cloud architecture and building blocks that enable these solutions, and the compliance aspects that are critical to healthcare solutions. Finally, we outline a realization of this architecture and outline further research topics in this domain.
Protecting the security and privacy of data is a paramount concern of enterprises in medical, educational, financial, and other highly regulated industries. While some industries have moved rapidly to take advantage of the cost savings, innovations in data analysis, and many benefits provided by cloud platforms, regulated enterprises with sensitive data have proceeded with caution. In this paper, we explore a fully public cloud-based architecture that is able to handle both service requirements and security requirements. In such a public cloud environment, the traditional notion of static perimeter-based reactive security can leave internal system components vulnerable to accidental data disclosures or malicious attacks originating from within the perimeter. Therefore, ensuring security and compliance of such a solution requires innovation and new approaches in several directions, including proactive log monitoring and analysis of virtually all parts of the cloud-based solution, full end-to-end data encryption from the client through Internet transmission to data storage and analytics in the solution, and robust firewall and network-intrusion detection systems. We discuss many of these techniques as applied to a specific real-world application known as the Watson Genomic Analytics Prototype.
There's a large push toward offering solutions and services in the cloud due to its numerous advantages. However, there are no clear guidelines for designing and deploying cloud solutions that can seamlessly operate to handle Web-scale traffic. The authors review industry best practices and identify principles for operating Web-scale cloud solutions by deriving design patterns that enable each principle in cloud solutions. In addition, using a seemingly straightforward cloud service as an example, they explain the application of the identified patterns.
The dissemination of Electronic Health Records (EHRs) can be highly beneficial for a range of medical studies, spanning from clinical trials to epidemic control studies, but it must be performed in a way that preserves patients' privacy. This is not straightforward, because the disseminated data need to be protected against several privacy threats, while remaining useful for subsequent analysis tasks. In this work, we present a survey of algorithms that have been proposed for publishing structured patient data, in a privacy-preserving way. We review more than 45 algorithms, derive insights on their operation, and highlight their advantages and disadvantages. We also provide a discussion of some promising directions for future research in this area.
Electronic health records (EHR) and electronic billing systems have been proposed as mechanisms to help curb the rising costs of health care in the United States. Given this scenario, our research efforts have targeted the idea of using open-source cloud computing technologies as the mechanism to build an affordable, secure, and scalable platform that supports billing as well as EHR operations. We call this platform MedBook, and in this paper we present the architecture and implementation status of this system. MedBook provides patients, health care providers, and health care payers a platform for exchange of information about EHR, billing activities, and benefits inquiries. MedBook is a Software-as-a-Service (SaaS) application built on top of open source technologies and running on an Infrastructure-as-a-Service platform. The client applications are mobile apps run from iPhone and iPad devices.
The integration of medical data coming from multiple sources is important in clinical research. Amongst others, it enables the discovery of appropriate subjects in patient-oriented research and the identification of innovative results in epidemiological studies. At the same time, the integration of medical data faces significant ethical and legal challenges that impose access constraints. Some of these issues can be addressed by making available aggregated instead of raw record-level data. In many cases however, there is still a need for controlling access even to the resulting aggregated data, e.g., due to data provider's policies. In this paper we present the Linked Medical Data Access Control (LiMDAC) framework that capitalizes on Linked Data technologies to enable controlling access to medical data across distributed sources with diverse access constraints. The LiMDAC framework consists of three Linked Data models, namely the LiMDAC metadata model, the LiMDAC user profile model, and the LiMDAC access policy model. It also includes an architecture that exploits these models. Based on the framework, a proof-of-concept platform is developed and its performance and functionality are evaluated by employing two usage scenarios.
We present a cloud-based approach for the design of interoperable electronic health record (EHR) systems. Cloud computing environments provide several benefits to all the stakeholders in the healthcare ecosystem (patients, providers, payers, etc.). Lack of data interoperability standards and solutions has been a major obstacle in the exchange of healthcare data between different stakeholders. We propose an EHR system - cloud health information systems technology architecture (CHISTAR) that achieves semantic interoperability through the use of a generic design methodology which uses a reference model that defines a general purpose set of data structures and an archetype model that defines the clinical data attributes. CHISTAR application components are designed using the cloud component model approach that comprises of loosely coupled components that communicate asynchronously. In this paper, we describe the high-level design of CHISTAR and the approaches for semantic interoperability, data integration, and security.
Cloud computing is an emerging technology that is expected to support Internet scale critical applications which could be essential to the healthcare sector. Its scalability, resilience, adaptability, connectivity, cost reduction, and high performance features have high potential to lift the efficiency and quality of healthcare. However, it is also important to understand specific risks related to security and privacy that this technology brings. This paper focuses on a home healthcare system based on cloud computing. It introduces several use cases and draws an architecture based on the cloud. A comprehensive methodology is used to integrate security and privacy engineering process into the software development lifecycle. In particular, security and privacy challenges are identified in the proposed cloud-based home healthcare system. Moreover, a functional infrastructure plan is provided to demonstrate the integration between the proposed application architecture with the cloud infrastructure. Finally, the paper discusses several mitigation techniques putting the focus on patient-centric control and policy enforcement via cryptographic technologies, and consequently on digital rights management and attribute based encryption technologies.
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