The Impact of Big Data on the Healthcare Information Systems

This article explores the possible impact of big data on healthcare information systems. Possible research issues include: 1). What applications in healthcare information systems are impacted most? 2). What algorithm/programs will be used for big data? 3). What privacy, security, and ethical issues are there for big data? In the biology area, big data becomes the newest technology for genomics. Other possible areas include pharmacovigilance, patient care, and medical supply chain management

The Impact of the Big Data on the Healthcare Information Systems

This article explores the possible implications of the big data on Informatics of health. Possible research questions are: 1). What are the applications in health care information systems are the most affected? 2). What algorithm/program shall be used for big data? 3). What the privacy, security, and ethical issues are there for big data? In the field of biology, big data becomes the latest technology for genomics. Other possible areas: Parma co monitoring, the care of patients and the management of the chain of medical supplies

Security Infrastructure Technology for Integrated Utilization of Big Data

This open access book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especially focuses on cryptographic primitives and privacy-preserving techniques, which are essential prerequisites. The book also covers elliptic curve cryptosystems, which offer compact public key cryptosystems; and LWE-based cryptosystems, which are a type of post-quantum cryptosystem. Since big data distribution platforms require appropriate data handling, the book also describes a privacy-preserving data integration protocol and privacy-preserving classification protocol for secure computation. Furthermore, it introduces an anonymization technique and privacy risk evaluation technique. This book also describes the latest related findings in both the living safety and medical fields. In the living safety field, to prevent injuries occurring in everyday life, it is necessary to analyze injury data, find problems, and implement suitable measures. But most cases don’t include enough information for injury prevention because the necessary data is spread across multiple organizations, and data integration is difficult from a security standpoint. This book introduces a system for solving this problem by applying a method for integrating distributed data securely and introduces applications concerning childhood injury at home and school injury. In the medical field, privacy protection and patient consent management are crucial for all research. The book describes a medical test bed for the secure collection and analysis of electronic medical records distributed among various medical institutions. The system promotes big-data analysis of medical data with a cloud infrastructure and includes various security measures developed in our project to avoid privacy violations

Biomedical Research Data Management Open Online Education: Challenges & Lessons Learned

The Best Practices for Biomedical Big Data project is a two year collaboration between Harvard Medical School and University of Massachusetts Medical School, funded by the NIH Big Data to Knowledge (BD2K) Initiative for Resource Development. The Best Practices for Biomedical Research Data Management Massive Open Online Course (MOOC) provides training to librarians, biomedical researchers, undergraduate and graduate biomedical students, and other interested individuals on recommended practices facilitating the discoverability, access, integrity, reuse value, privacy, security, and long term preservation of biomedical research data. This poster highlights lessons learned from the first year of this project. Built upon the New England Collaborative Data Management Curriculum, the development team sought to use existing curricular materials to create a fully online course. The course is designed with an open course platform, WordPress Learning Management System (WPLMS), in order to facilitate broad access. Each of the MOOC’s nine modules is dedicated to a specific component of data management best practices and includes video lectures, presentation slides, research teaching cases, readings, activities, and interactive quizzes. The project team overcame multiple challenges related to creating an open online course: curriculum, audience and software. Working towards overcoming these, the Best Practices for Biomedical Research Data Management MOOC development team has moved slowly and deliberately, created additional content, and added content experts to provide guidance. These lessons learned will assist course development beyond this project, adding to best practices for creating massive open online courseware. Lessons learned include: teaching method influences the curriculum and content should not be developed in isolation from the teaching method; content is dependent on audience and supplementary content can be used to bridge audience gaps; and implementing new or unfamiliar technologies is challenging so allow more time in the timeline for project team to work with open source platform

A Machine Learning SDN-Enabled Big Data Model for IoMT System

[EN] In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.Haseeb, K.; Ahmad, I.; Iqbal Awan, I.; Lloret, J.; Bosch Roig, I. (2021). A Machine Learning SDN-Enabled Big Data Model for IoMT System. Electronics. 10(18):1-13. https://doi.org/10.3390/electronics10182228S113101

The Demand for the Healthcare Services: the Opportunities of Big Data in Predicting Patient Flow

Nowadays, healthcare field is tightly connected with information technologies, in particular, big data technologies. The simulation of the patients flow in the health care system can significantly enhance the effectiveness of its work. The main purpose of the model improvement is to take into account the features of patient care to enable end users to obtain forecast values at the output. Thus, these values can subsequently become the starting point for decision-making by the management. In this paper, the analysis of the demand for medical services in European hospitals was carried out to determine the required amount of resources within the use of the modern analytical methods. The study contains a description of big data technologies in healthcare; executes the beneficial side of its implementation. The research showed that the science community made a great effort in the development of the indus- try, however, privacy and security issues, standards establishments still require enormous attention and new efforts to be made in the study area. The study is also focused on the predicting the resources, which are needed for a medical institution

Protection and efficient management of big health data in cloud environment

University of Technology Sydney. Faculty of Engineering and Information Technology.Healthcare data has become a great concern in the academic world and in industry. The deployment of electronic health records (EHRs) and healthcare-related services on cloud platforms will reduce the cost and complexity of handling and integrating medical records while improving efficiency and accuracy. To make effective use of advanced features such as high availability, reliability, and scalability of Cloud services, EHRs have to be stored in the clouds. By exposing EHRs in an outsourced environment, however, a number of serious issues related to data security and privacy, distribution and processing such as the loss of the controllability, different data formats and sizes, the leakage of sensitive information in processing, sensitive-delay requirements has been naturally raised. Many attempts have been made to address the above concerns, but most of the attempts tackled only some aspects of the problem. Encryption mechanisms can resolve the data security and privacy requirements but introduce intensive computing overheads as well as complexity in key distribution. Data is not guaranteed being protected when it is moved from one cloud to another because clouds may not use equivalent protection schemes. Sensitive data is being processed at only private clouds without sufficient resources. Consequently, Cloud computing has not been widely adopted by healthcare providers and users. Protecting and managing health data efficiently in many aspects is still an open question for current research. In this dissertation, we investigate data security and efficient management of big health data in cloud environments. Regarding data security, we establish an active data protection framework to protect data; we investigate a new approach for data mobility; we propose trusted evaluation for cloud resources in processing sensitive data. For efficient management, we investigate novel schemes and models in both Cloud computing and Fog computing for data distribution and data processing to handle the rapid growth of data, higher security on demand, and delay requirements. The novelty of this work lies in the novel data mobility management model for data protection, the efficient distribution scheme for a large-scale of EHRs, and the trust-based scheme in security and processing. The contributions of this thesis can be summarized according to data security and efficient data management. On data security, we propose a data mobility management model to protect data when it is stored and moved in clouds. We suggest a trust-based scheduling scheme for big data processing with MapReduce to fulfil both privacy and performance issues in a cloud environment. • The data mobility management introduces a new location data structure into an active data framework, a Location Registration Database (LRD), protocols for establishing a clone supervisor and a Mobility Service (MS) to handle security and privacy requirements effectively. The model proposes a novel security approach for data mobility and leads to the introduction of a new Data Mobility as a Service (DMaaS) in the Cloud. • The Trust-based scheduling scheme investigates a novel composite trust metric and a real-time trust evaluation for cloud resources to provide the highest trust execution on sensitive data. The proposed scheme introduces a new approach for big data processing to meet with high security requirements. On the efficient data management, we propose a novel Hash-Based File Clustering (HBFC) scheme and data replication management model to distribute, store and retrieve EHRs efficiently. We propose a data protection model and a task scheduling scheme which is Region-based for Fog and Cloud to address security and local performance issues. • The HBFC scheme innovatively utilizes hash functions to cluster files in defined clusters such that data can be stored and retrieved quickly while maintaining the workload balance efficiently. The scheme introduces a new clustering mechanism in managing a large-scale of EHRs to deliver healthcare services effectively in the cloud environment. • The trust-based scheduling model uses the proposed trust metric for task scheduling with MapReduce. It not only provides maximum trust execution but also increases resource utilization significantly. The model suggests a new trust-oriented scheduling mechanism between tasks and resources with MapReduce. • We introduce a novel concept “Region” in Fog computing to handle the data security and local performance issues effectively. The proposed model provides a novel Fog-based Region approach to handle security and local performance requirements. We implement and evaluate our proposed models and schemes intensively based on both real infrastructures and simulators. The outcomes demonstrate the feasibility and the efficiency of our research in this thesis. By proposing innovative concepts, metrics, algorithms, models, and services, the significant contributions of this thesis enable both healthcare providers and users to adopt cloud services widely, and allow significant improvements in providing better healthcare services

A Review of Big Data Trends and Challenges in Healthcare

The healthcare sector produces an enormous amount of complicated data from several sources, such as health monitoring systems, medical devices, and electronic health records. Big data analytics may improve healthcare by enabling more effective decision-making, improving patient outcomes, and reducing costs. To improve the operational efficiency of healthcare organizations, scientific studies must search for the standardization and integration of data analysis equipment and methods. This systematic literature review aims to provide current insights on the topic by analyzing a total of 60 relevant articles published between 2017 and 2023. The review explores the challenges and opportunities in using big data in healthcare, including data security, privacy, data quality, interoperability, and ethical considerations. The article also explores big data analytics' potential uses in healthcare, such as personalized treatment, disease prediction and prevention, and population health management. It provides significant insights for healthcare providers, researchers, and practitioners to make evidence-based decisions, as well as underlines the need for more research in this area to fully realize the promise of big data in healthcare