A survey of issues and solutions of health data management systems (original) (raw)
Related papers
Survey paper on Health Care Big Data Analysis
2017
In this world more number facing the problem of blast data and size of data base used in industry has been growing more rates. Many sources are generating data through web media, social media data, sensor data, connected devices (Smart phone) and click stream data. More amount of data analyzing, processing as well as extracting information is big challenging task. Big data is a set of huge amount data such data can't be processed or analyze by using conventional computing technique. Difficulties are included like transfer, storage data, analytics, search, support huge amount of data to database and sharing. Health care needs to be updated with new period of big data this added health care data analysed. Existing database system like Oracle, MySQL, Postgre SQL and RDBMS are used. If you want large dataset size, read/write concurrency or both you will find that the services of an RDBMS come at a vast performance sentence and make division naturally complex. To deal with bulk /larg...
Healthcare is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in human beings. Healthcare is delivered by practitioners in allied health, dentistry, midwifery-obstetrics, medicine, nursing, optometry, pharmacy, psychology and other care providers. In healthcare service to provide better performance to identify some diseases like blood pressure, skin cancer, diabetes etc. with the sensor devices those are connected with home server by using wireless sensor or wired network policies. The size of data sets being collected and analyzed in the industry for healthcare business intelligence is growing rapidly. In healthcare service case we have to concern about massive data services. To store and process huge amount of unstructured data by using traditional database system is so difficult. To alleviate this drawback we can use Hadoop architecture that has functionality to store and process huge amount of unstructured data. Hadoop Distributed file system (HDFS) can store and the Hadoop MapReduce framework process huge amount of unstructured data that enables easy development of scalable parallel applications. This paper’s main motivation is to ensure better data transmission, data reliability and massive data processing with High Availability (HA) based network storage solution. So, in this paper we proposed MapReduce Agent (MRA) to process Big-Data and also proposed iSCSI Protocol adapted Network Attached Storage (NAS) system for healthcare service.
—Over the years with automation more and more systems deployed in multiple industries are generating huge amount of data. In fact IT Industry itself has witnessed phenomenal growth of data in the recent years. The data generated in the last 5 years is much more then the data generated cumulatively by all the industries put together in the past 20 years. In the current work we focus on the ways and means to handle the data generated by PHIS(Personal Healthcare Information System). The big question which we have addressed in this paper is selection of the appropriate tool (Relational MySQL database or NoSQL MongoDB database) to store the patient data, its archival and storage, steps to mine it and concluded the work by depicting the comparative analysis in terms of space and time.
SURVEY OF HEALTH CARE INDUSTRIES BY STUDY THE ARCHITECTURE AND FUNCTIONALITY OF BIG DATA ANALYTICS
The paper advanced here intended to impart cognizance regarding big data analysis and its influence in every expanse such as hospitals, educational organisations, government offices etc. Veracious and scrupulous anatomization of huge volume of data collected from multifarious provenance is essential, as contemporarily real time scrutinization of information & facts using Hadoop and other supportive languages like pig, hiveetc., is playing an efficacious job in decision making for various organizations. The aspiration and insight of the paper is to recount big data use cases for exigency situations in hospitals and health care centres. Big data refers to tremendously & exceedingly huge magnitude of data that has to be analysed and studied computationally or electronically for generating specific and unerring result. The term big data refers to gigantic quantity of data both ordered and unordered strenuous and laborious to compute via orthodox conventional techniques. Apt analysis and examination of bulk of data helps an organisation to make smart decisions. Presently a lot many health care organizations has not clutched the ease and advantage of wangling data analytics. The prudent innuendo and judicious implementation of big data analytics for health care industry is of significant importance. This paper study architecture, analytics, developments and functionalities of big data for its tactical enactment in health care industry. According to the content and results of numerous bid data analytics cases many capabilities were pinpointed. In this paper distinct strategy, functionalities, findings, benefits and capabilities are hatched for potent data analytics. Bulk of data is originating every minute from different departments of a health care industry. Today it is obligatory to digitalize this bulk of data for the sake of cost optimisation, improved quality and service. It is mandatory to examine this massive data to evaluate new situations and to stand on new circumstances. This data analysis helps in exploring and generating new results according to the situation by recognizing different patterns, their relationships using machine learning algorithms. The paper advances information about data generated by different systems, states of data and all the security issues in handling and analysis the data. The massive flood of data & information originated at faster and higher varieties and velocities in health protection organisations adds more complexity to their work load. Poor healthcare information management and incomplete & inadequate assembling of hospital management systems are severely damaging and hampering the efforts. These prevailing circumstancesin industry are needlessly and excessively escalating the costs & expenditure for patients and service providing organizations. There is a genuine entailing of IT/CS aspirants for data digitalization, expanding performance region, excelling patient experience and enhancing service quality, IT artifacts are required in hospitals to data driving traditional system and to evolve the existing traditional governing system to an intelligent decision support system which succour and aids the organisation to examine massive volume, variety and velocity of data. Recent studies and evidences reveal that hardly 44% of health industries are embracing meticulous study & significant analysis to support smart decision making process. Big data analytics circumscribe numerous analytical mechanisms and tricks best fitted for analysing clinics unordered massive data. This huge volume data to be analysed is stored at NoSQL and Apache HBase systems and its management is done via Marklogic, Apache Cassandrae, and MongoDB for data updation, retrieval and integration etc... The utilisation of this management system even provides the ability to transfer data from traditional to new OS. This enactment of this
The Architectural Design of Healthcare Service with Big-Data Storing Mechanism
Abstract: Healthcare is the diagnosis, treatment, and prevention of disease, illness, injury, and other physical and mental impairments in human beings. Healthcare is delivered by practitioners in allied health, dentistry, midwifery-obstetrics, medicine, nursing, optometry, pharmacy, psychology and other care providers. In healthcare service to provide better performance to identify some diseases like blood pressure, skin cancer, diabetes etc. with the sensor devices those are connected with home server by using wireless sensor or wired network policies. The size of data sets being collected and analyzed in the industry for healthcare business intelligence is growing rapidly. In healthcare service case we have to concern about massive data services. To store and process huge amount of unstructured data by using traditional database system is so difficult. To alleviate this drawback we can use Hadoop architecture that has functionality to store and process huge amount of unstructured data. Hadoop Distributed file system (HDFS) can store and the Hadoop MapReduce framework process huge amount of unstructured data that enables easy development of scalable parallel applications. This paper’s main motivation is to ensure better data transmission, data reliability and massive data processing with High Availability (HA) based network storage solution. So, in this paper we proposed MapReduce Agent (MRA) to process Big-Data and also proposed iSCSI Protocol adapted Network Attached Storage (NAS) system for healthcare service. Keywords: HealthCare Service, Big-Data, Hadoop, Sensor Integration and iSCSI.
Intelligent and sustainable approaches for medical big data management
ELSVIER, 2022
Big data, it's a trend in every company whether it is the healthcare or the IT industry. A huge amount of data is generated everywhere which needs to be managed properly. These managed data are important for interpretation and analysis in the future. These will enhance the new computational techniques to work on big "V"s that is volume, velocity, veracity, variety, and value. In healthcare too, lots of data are generated which is stored in electronic form. The evolution of data on daily basis requires proper handling and management. As big data to knowledge is the latest demand (Margolis et al., 2014). The term big data is introduced in 1997 (Cox & Ellsworth, 1997) by John R. Mashey. And is a big challenge in biomedical research. All the big databases like international cancer genome consortium, some disease databases like international rare disease consortium, and the international human epigenome consortium, are the ones that are part of data resources. These analytics require capabilities for the representation and modeling of the health data, optimization of different algorithms, and computational power. In the healthcare industry, five distinctive capabilities of data are required: identification of patterns in data that provides care, analysis of unstructured data, providing decision support, better prediction, and traceability (Wang, Kung, & Byrd, 2018). Data generation and collection are faster than data preprocessing and analysis. To cover this gap there is a need for technological progress in various kinds of data acquisition. All this information generated and fed to computers is of utmost importance whether it is molecular information, phenotypic information of an individual patient, or others. These biomedical data need protection, storage capacity, etc. Hence cloud computing techniques come into existence. The cloud computing (CC) standard has engrossed a lot of attraction from both industry and scholars. It proposes diverse services including asset pooling, multi-tenancy, and flexibility (Chkirbene et al., 2020). While the cloud computing standard raises economic efficiency, security is one of the important concerns in adopting the cloud computing model (Chkirbene, Erbad, & Hamila, 2019). Cloud computing is a new operating technology that advanced the information technology (IT) industry, it is an expansion of equivalent computing, distributed computing, and grid computing over the same or different networks, (Sniezynski, Nawrocki, & Wilk, 2019). Cloud computing technology is the combination and development of virtualization, utility computing, and all the
Business Challenges of Big Data Application in Health Organization
2018
The growing trend of using information technology (IT) in the present era has been associated with generating a huge amount of data. Throughout the history, the healthcare industry has generated a large amount of data on patient care. The current trend of is in the direction towards digitalization of these large amounts of data. Digital data and information in healthcare organizations are growing extensively. These data are gathered from a variety of sources and create new challenges, which lead to a lot of changes in health sciences. In the near future, the high availability of digital data makes it difficult to handle them, and big data will overcome the traditional scales and dimensions. Today, improving the performance of the healthcare industry depends on having more information and more organized knowledge. Big data allow us to do a lot of works that could not have been done in the past. The progress of IT and solutions for management of big data can lead to more effective out...
Implementation of Big Data in Health Information Systems: Sample Approaches in Saudi Hospital
International Journal of Computer Applications
Big data concept provides opportunity to exchange patient's medical information to the different healthcare providers. Health Information System (HIS) has created the ability to electronically store, maintain and move data across the world in a matter of seconds and has the potential to provide healthcare with tremendous increasing productivity and quality of services. Big data analytics is a growth area with the potential to provide useful insight in health information system. Big Data can unify all patient related data to get more option to view patient records to analyze and predict early disease detection. Big data supports and improve clinical practices, new drug development and health care financing process. Implementation of Health Information system (HIS) continues to expand infrastructure in Medical field due to enormous number of patient comes across to store medical data. In this paper we focus the Big data concept to increase and store patients details in Saudi public hospitals with maximum utilization. Most of the Saudi public and private hospitals Health information system locally connected and maintained by own hospital admin. There is no system implemented to share the patient health record, treatment details and medical prescription data to other hospital. The main problem in the Saudi hospital, Health information is not centralized due to unstructured, semi structured data maintain by the Saudi hospital. Proper Health information system is able to offer correct and complete personal health and medical summary through the Big data methods. This paper introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. Big Data methods and challenges in medical applications and health information system are also discussed in this study. This study provides a base model to increase the use of big data in health information system and can assist to understand the breadth of big data applications.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 2022
The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the ri...
Big Data Analytics and Challenges in Healthcare Datasets: Analysis & Management
'Big data' is a large measure of facts that can do something amazing. Big records in healthcare discuss the digital health records sets, which are so gigantic and complex that they are extraordinarily awkward to manipulate with the obsolete programming/equipment or even thru the traditional data management apparatuses and methods. In the healthcare business, exceptional hotspots for massive data include emergency health facility records, scientific information of patients, and aftereffects of scientific examinations, and gadgets that are a piece of IoT. That is the reason to supply appropriate options for improving open health, and healthcare suppliers are required to be equipped with becoming the foundation to produce and inspect astronomical data correctly. These facts should be of essential importance for finding out understandings that would be useful for enhancing consideration conveyance, reducing squanders, and in the implied time diminishing expenses. It can change the way healthcare suppliers utilize refined advances to accomplish information from their scientific records and different information distribution facilities and settle on knowledgeable choices.