Big Data can be combined with new technology to bring about positive conversion in the health care segment. A technology aimed at making Big Data analytics a certainty will act as a key element in transforming the way the health care industry operates today. The study and analysis of Big Data can be used for tracking and managing population health care effectively and efficiently. In ten years, eighty percent of the work people do in medicine will be replaced by technology. And medicine will not look anything like what it does today. Healthcare will change enormously as it becomes a data-driven industry. But the magnitude of the data, the speed at which it’s growing and the threat it could pose to individual privacy mean mastering "big data" is one of biomedicine's most pressing challenges. Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. This also plays a vital role in delivering preventive care. Health care will change a great deal as it becomes a data- driven industry. But the size of the data, the speed at which it’s growing and the threat it could cause to individual privacy mean mastering it is one of biomedicine's most critical challenges. In this research paper we will discuss problems faced by big data, obstacles in using big data in the health industry, how big Data analytics can take health care to a new level by enhancing the overall quality of patient care.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Benefits of Big Data in Health Care A Revolutionijtsrd
Lifespan of a normal human is increasing with the world population and it produces new challenge in health care. big data change the method of data management ,leverage data and analyzing data.with the help of big data we can reduces the costs of treatment, reducing medication and provide better treatment with predictive analytics. Health related data collected from various sources like electronic health record EHR ,medical imaging system, genomic sequencing, pay of records, pharmaceutical research , and medical devices, etc. are refers to as big data in healthcare. Dr. Ritushree Narayan ""Benefits of Big Data in Health Care: A Revolution"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22974.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/22974/benefits-of-big-data-in-health-care-a-revolution/dr-ritushree-narayan
The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.
Big data is generating a lot of hype in every industry including healthcare. As my colleagues and I talk to leaders at health systems, we’ve learned that they’re looking for answers about big data. They’ve heard that it’s something important and that they need to be thinking about it. But they don’t really know what they’re supposed to do with it.
Providers need to move towards real-time analytics that have become critical to demonstrate their quality of care, as reimbursement by government programs can be contingent upon how providers are measured in “Quality of Care”. For example, the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015, also called the Permanent Doc Fix, changes the way Medicare doctors are reimbursed with the implementation of a merit based incentive. The performance-based pressure is huge, which makes it imperative that every provider consider technology solutions. Read more at https://www.solix.com/solutions/data-driven-solutions/healthcare/
Data Governance Talking Points: Simple Lessons From the TrenchesHealth Catalyst
About 7 months ago, one of Health Catalyst's clients asked for a 90-minute cram course on data governance, including time for questions and answers. They were struggling, like so many other healthcare organizations, caught in the swing of extremes from too much to too little, while equilibrium eluded them. With a last-minute rush, Dale Sanders (President of Technology, Health Catalyst) fell back on his time in the Air Force and threw together a talking points paper to facilitate the conversation. At the end of the meeting, the client was effusive with their appreciation, using words like “incredibly insightful,” “brilliant,” and “hugely valuable.” Dale didn’t think it was that good, but their data governance function was “dramatically better,” and they were happy, so something worked.
Since then, Dale has used the same talking points in two other similar meetings, with similar feedback and results. It still doesn’t feel that great or insightful to him, but he's glad to flow with the feedback and share the same style in this webinar in the hope that it’s useful.
After viewing this webinar, Dale hopes that you will have some tactical ideas to assess your organization’s data governance strategy. Are you leveraging the data you have? What could improve?
Healthcare is changing rapidly. It is clear that humans need mechanisms to automate some parts of data processing and help humans in decision making. This talk will concentrate on how to improve the machine understanding of unstructured data.
Data-Driven Healthcare for Manufacturers Amit Mishra
Data-driven healthcare empowers the providers with a common data platform to discover untapped data-driven opportunities. Healthcare data and its impact on the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals, pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out vast amounts of data during their daily operations. This data has tremendous value and can revolutionize patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of patient care. Know more about data-driven healthcare at https://www.solix.com/solutions/data-driven-solutions/healthcare/
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
4 Digital Health Trends Affecting Your Revenue CycleMeduit
The emerging digital trends impacting the healthcare industry are as varied as the new technologies being developed, but there are four trends that are having a more significant impact on the revenue cycle. Find out what they are in this Meduit Innovation Lab guide!
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
www.panorama.com
Panorama Necto uncovers the hidden insights in your data and presents them in beautiful dashboards powered with KPI Alerts, which is managed by a the most secure, centralized & state of the art BI solution.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Data-Driven Healthcare for Manufacturers Amit Mishra
Data-driven healthcare empowers the providers with a common data platform to discover untapped data-driven opportunities. Healthcare data and its impact on the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals, pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out vast amounts of data during their daily operations. This data has tremendous value and can revolutionize patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of patient care. Know more about data-driven healthcare at https://www.solix.com/solutions/data-driven-solutions/healthcare/
This white paper offers a detailed perspective on how big data is impacting the healthcare industry and its underlying implication on the industry as a whole. It outlines the role of big data in healthcare, its benefits, core components and challenges faced by the healthcare sector towards full-fledged adoption & implementation.
As the author of “Big Data in Healthcare Hype and Hope,” Dr. Feldman has interviewed over 180 emerging tech and healthcare companies, always asking, “How can your new approach help patients?” Her research shows that data, as an enabling tool, has the power to give us critical new insights into not only what causes disease, but what comprises normal. Despite this promise, few patients have reaped the benefits of personalized medicine. A panel of leading big data innovators will discuss the evolving health data ecosystem and how big data is being leveraged for research, discovery, clinical trials, genomics, and cancer care. Case studies and real-life examples of what’s working, what’s not working, and how we can help speed up progress to get patients the right care at the right time will be explored and debated.
• Bonnie Feldman, DDS, MBA - Chief Growth Officer, @DrBonnie360
• Colin Hill - CEO, GNS Healthcare
• Jonathan Hirsch - Founder & President, Syapse
• Andrew Kasarskis, PhD - Co-Director, Icahn Institute for Genomics & Multiscale Biology; Associate Professor, Genetics & Genomic Studies, Icaahn School of Medicine at Mt. Sinai
• William King - CEO, Zephyr Health
New York eHealth Collaborative Digital Health Conference
November 18, 2014
4 Digital Health Trends Affecting Your Revenue CycleMeduit
The emerging digital trends impacting the healthcare industry are as varied as the new technologies being developed, but there are four trends that are having a more significant impact on the revenue cycle. Find out what they are in this Meduit Innovation Lab guide!
Presentation on Predictive modeling in Health-care at San Jose, Ca 2015. This presentation talks about healthcare industry in US, provides stats and forecasts. It then discusses a few use cases in health care and goes into detail on a kaggle example.
www.panorama.com
Panorama Necto uncovers the hidden insights in your data and presents them in beautiful dashboards powered with KPI Alerts, which is managed by a the most secure, centralized & state of the art BI solution.
This presentation is about basics of Big data Analytics along with Characteristics,Challenges,Structures,Differences between Traditional and Big data,How Big data is getting benefited in Healthcare Industry,Big data in Real time
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
Presentation covers basics of Big Data & its potential uses in healthcare. Data is growing & moving faster day by day. Getting access to this valuable data & factoring it into clinical & advanced analytics is critical to improve care. So there must be analysis of big data to make effective decisions.
A BIG DATA REVOLUTION IN HEALTH CARE SECTOR: OPPORTUNITIES, CHALLENGES AND TE...ijistjournal
Health care sector grows tremendously in last few decades. The health care sector has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information.
This paper basically studies the impact of implementing the big data solutions on the healthcare sector, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care sector.
Big Data, CEP and IoT : Redefining Holistic Healthcare Information Systems an...Tauseef Naquishbandi
Healthcare industry has been a significant area for innovative application of various technologies over decades. Being an area of social relevance governmental spending on healthcare have always been on the rise over the years. Event Processing (CEP) has been in use for many years for situational awareness and response generation. Computing technologies have played an important role in improvising several aspects of healthcare. Recently emergent technology paradigms of Big Data, Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with pain areas of healthcare domain but also to redefine healthcare offerings. This paper aims to lay the groundwork for a healthcare system which builds upon integration of Big Data, CEP and IoT.
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
Data-driven Healthcare for ManufacturersLindaWatson19
Medical Device Equipment and Hospital Supplies Manufacturers also face increased pressure to comply with strict regulatory procedures to ensure patient safety. Product transparency and efficient end-to-end processes that optimize the manufacturing process and decision making are very important.
Healthcare data and its impact upon the patient care decision process via accurate, real-time, reliable data from disparate sources is creating a digital health revolution. Data-driven healthcare is beginning to have a huge impact addressing the challenges of every provider, through efficient handling of huge volumes of patient care data.
Big Data in Healthcare
Hospital and healthcare providers can use big data to expand the scope of their projects and draw comparisons over larger populations of data. Because big data involves the use of automation and artificial intelligence, data can be processed in larger volumes and higher velocity to uncover valuable insights for Management.
Big data enables management to proactively identify issues with real-time access to the data so that decisions can be base more on hard evidence and facts, rather than emphasizing on guesswork and assumptions about customers, employees, and vendors. Applying analytics to big data creates many opportunities for healthcare businesses to gain greater insight, predict future outcomes and automate non-routine tasks.
Healthcare industries have gone through massive technology driven transformations over the past decade. This is a result of the significant advancement in digitized, disruptive, open sourced and pervasive healthcare information technologies and peripherals in application, that are continuously producing huge volumes of diversified data. In a recent literature review, Agrawal and Prabakaran1 suggested that big data are an integral part of “the next generation of technological developments” that reveal new insights from vast quantities of data being produced from various sectors, including health care. (Shah J Miah, Edwin Camilleria, and H. Quan Vub).
Healthcare requires a lot of analysis and less room for error, with big data and analytics procedure can be game changer. Healthcare busines requires to analyze, store, and continuously update patient’s data and these tasks cannot efficiently be achieved without the help of big data.
According to Pastorino, the use of big data in health care can provision the design of solutions that improve patient care and can generate value and new strategies to overcome dynamic challenges in healthcare organizations. This is attributed to big data in health care providing an opportunity to detect meaningful patterns, which in turn produce actionable knowledge for precision medicine and various healthcare decision-makers. (Shah J Miah, Edwin Camilleria, and H. Quan Vu)
Harmony Alliance stated that opportunities offered by big data “will only materialize when healthcare systems move beyond the mere collection of large amounts of data. Linkage of previously separated data sets and their analysis using appropriate big data analytics offer new ways to accelerate research and to identify the right treatment for individual patients. Access to large data sets that paint a more comprehensive picture of patients allows patient-relevant outcomes to be measured more accurately.”
Big data is becoming crucial in this time of Covid-19, where data need to be collected from different corner of the globe. Data are collected in a big amount and need to be processed in real time so the decision-makers can have enough information to work on. Today’s world is interconnected, and pa ...
Payers are being challenged as the industry shifts from volume-based care to a value-based reimbursement structure that would benefit the patient, the healthcare provider and the payer. New payment models including fee-for-service only and pay-for performance creates impetus for payers to acquire, aggregate, and analyze data.
Please respond to each of the 3 posts with 3 APA sources no older thmaple8qvlisbey
Please respond to each of the 3 posts with 3 APA sources no older than 5 years old. APA format must be exceptional.
Reply 1
Professor,
How can big data impact prescription errors? Be specific and provide examples. Who should be on the team to implement this project and why? Support your work with the literature.
Reply 2
Ruth Niyasimi,
Big Data Risks and Rewards
Big data is defined as the process of collecting, analyzing, and leveraging consumer patient, physical, and clinical data that is too vast or complex to be understood by traditional means of data processing. In healthcare, data is generated from medical records, patient portals, government agencies, research studies, electronic health records, and medical devices. The data generated in healthcare is used to make decisions that will have an impact on patient health outcomes (Raghupathi & Raghupathi, 2014). Healthcare is a critical docket in our society since it is tasked with a duty to prevent, diagnose and treat illnesses and diseases affecting the community. In the past, health information was stored on paper but through advancements in technology, things have significantly changed as patient information is stored on Electronic health records (EHR).
The adoption of big data had significant impacts on customer services and other related issues. According to Raghupathi and Raghupathi (2014), for many years, healthcare has been generating huge volumes of data that was stored in hardcopy. This was a critical step toward improving the quality of healthcare delivery while reducing costs. This huge volume of information is crucial to healthcare because, through digitalization, it has become possible to detect diseases at an early stage and take necessary intervention measures. Secondly, big data enables the ability to enhance continuity, starting when a patient visits a hospital until the last stage of being discharged. For example, the lab tests taken from those patients and other specialized treatments are stored in a way that other departments can access this information in the future preventing duplicate redoing labs and imaging studies (Adibuzzaman et al., 2017). This cuts down costs while improving service delivery.
Although big data has had a tremendous impact on the healthcare systems, it has also created some problems. Firstly, the use of technology such as EHR has resulted in security issues and privacy threats. According to McGonigle and Mastrian (2017), technology has enabled the interoperability of healthcare data. Interoperability means sharing important health data across different organizations while ensuring it is presented understandably to the user. Unauthorized third parties can intersect this information and the Health Insurance Portability and Accountability Act (HIPPA) has shown little concern for patient data breach cases. Another problem is that big data is not static, it requires continuous system updates to ensure that it ...
Big Data Analytics using in Healthcare Management Systemijtsrd
Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s
Data Management - a top Priority for Healthcare PracticesData Dynamics Inc
The healthcare industry has become increasingly data-driven and poised to take a leap into the future, thanks to an increasingly tech-savvy and demanding patient-consumer base. While the Healthcare Data Ecosystem is presently fragmented and often, insufficient, pioneering firms see vast opportunities to be a part of the Healthcare revolution through proper management of their massive amount of Data.
Healthcare has unique data management challenges that other industries do not face, so the solutions that worked in those fields cannot simply be replicated. Challenges in healthcare data management include -
1. Data environment consolidation in acquisitions and mergers
2. Managing the rapid growth of unstructured healthcare data
3. Adhering to the strict healthcare regulations and reforms
On top of this, Healthcare organizations have to ensure that their data management solution must have a dependable & active security protocol to safeguard sensitive information of patients as per HIPAA norms. With the exponential increase in data, risk is only going to amplify.
In case of mergers & acquisitions, a sizable challenge for large healthcare corporates is the Amalgamation and Streamlining Data with the parent company’s processes. This becomes tedious and cost intensive as merging two data environments that are often radically different from each other into a single system, is difficult and tedious.
Healthcare companies need consumer-driven data strategies with patients at the forefront of their planning. How? To know, read on.
Data Dynamics is a leader in intelligent file management solutions that empower enterprises to seamlessly analyze, move, manage and modernize critical data across hybrid, cloud and object-based storage infrastructures for true business transformation.
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Wireless Sensor Networks (WSN) plays a very important role in transmitting the data from source to destination but energy consumption is one of the major challenges in these networks. WSN consists of hundreds to thousands of nodes which consume energy while transmitting the information and with a span of time whole energy get consumed and network life time gets reduced. Clustering and Cluster head (CH) selection are important parameters used to enhance the lifetime of the WSN. Clustering use two methods: rotating CH periodically in every round to distribute the energy consumption among nodes and the node with more residual energy becomes CH.This research paper is focused on the performance of the techniques used to enhance the energy efficiency in Wireless Sensor Networks (WSNs). Low- Energy Adaptive Clustering Hierarchy (LEACH), Fuzzy- Based and Neural Network are some of the important techniques used. MATLAB simulation tool is considered in this paper.
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In this work, a Split Ring Resonator (SRR) unit cell is simulated in a waveguide with electromagnetic field solver High Frequency Structure Simulator (HFSS). Analytical calculations of the inductance and capacitance have been also carried out to obtain the resonant frequencies for SRR dimensions. A comparison between calculated and simulated resonance frequencies)) is done. A good correlation between simulated and measured resonance frequencies is achieved.
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A Survey of Routing Protocols for Structural Health MonitoringIJEEE
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Layout Design Analysis of SR Flip Flop using CMOS TechnologyIJEEE
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Design of Planar Inverted F-Antenna for Multiband Applications IJEEE
Planar Inverted F- Antenna (PIFA) is widely used in handheld devices because of its various advantages like compact size, good bandwidth and moderate radiation patterns. In this paper, a design of Planar Inverted F- Antenna(PIFA) is proposed that resonates at the frequency of 2.5 GHz with a bandwidth of 300MHz. The relative permittivity of the substrate used is 2.2. The antenna is fed by coaxial feed. Also, gain, VSWR and radiation pattern of the antenna are studied.
Layout Design Analysis of CMOS Comparator using 180nm TechnologyIJEEE
Comparator is a very useful and basic arithmetic component of digital system. In the world of technology the demand of portable devices are increasing day by day. This paper presents CMOS design of 1-bit comparator on 180nm technology. The layout of 1-bit comparator has been developed using Automatic and semi-custom techniques. Both the layouts are compared and analyzed in terms of their Power and Area consumption. Automatic layout is generated from its equivalent schematic whereas semi-custom layout is developed manually. The result shows that semi-custom consumes less power as compared to Automatic.
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Application of Big Data in Medical Science brings revolution in managing healthcare of humans
1. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 81
Application of Big Data in Medical Science
brings revolution in managing health
care of humans
Dr. Gagandeep Jagdev , Sukhpreet Singh
1
Dept. of Comp. Science, Punjabi University Guru Kashi College, Damdama Sahib, Bathinda
2
M.Phil (Comp. Sc.), Punjabi University, Patiala(PB)
1
gagans137@yahoo.co.in
Abstract- Big Data can be combined with new technology
to bring about positive conversion in the health care
segment. A technology aimed at making Big Data
analytics a certainty will act as a key element in
transforming the way the health care industry operates
today. The study and analysis of Big Data can be used
for tracking and managing population health care
effectively and efficiently. In ten years, eighty percent of
the work people do in medicine will be replaced by
technology. And medicine will not look anything like
what it does today. Healthcare will change enormously as
it becomes a data-driven industry. But the magnitude of
the data, the speed at which it’s growing and the threat it
could pose to individual privacy mean mastering "big
data" is one of biomedicine's most pressing challenges.
Hiding within those mounds of data is knowledge that
could change the life of a patient, or change the world.
This also plays a vital role in delivering preventive care.
Health care will change a great deal as it becomes a data-
driven industry. But the size of the data, the speed at
which it’s growing and the threat it could cause to
individual privacy mean mastering it is one of
biomedicine's most critical challenges. In this research
paper we will discuss problems faced by big data,
obstacles in using big data in the health industry, how big
Data analytics can take health care to a new level by
enhancing the overall quality of patient care.
Keywords- Big Data, framework, medical science, EMR,
HIS
INTRODUCTION
Big data [1, 2] are rapidly all over the place. Everyone
seems to be collecting, analyzing, and making money
from it. No matter whether we are talking about
analyzing zillions of Google search queries to predict
flu outbreaks, or zillions of phone records to detect
signs of terrorist activity, or zillions of airline stats to
find the best time to buy plane tickets, big data are on
the case. By combining the power of modern
computing with the enormous data of the digital era, it
promises to solve virtually any problem like crime,
public health, the evolution of grammar, etc.
The goal of big data management is to ensure a high
level of data quality and accessibility for business
intelligence and big data analytics applications.
Corporations, government agencies and other
organizations employ big data management strategies to
help them contend with fast-growing pools of data,
typically involving many terabytes or even petabytes of
information saved in a variety of file formats. Effective big
data management helps companies locate valuable
information in large sets of unstructured data and semi-
structured data from a variety of sources, including call
detail records, system logs and social media sites.
Most big data environments go beyond relational
databases and traditional data warehouse platforms to
incorporate technologies that are suited to processing and
storing non-transactional forms of data. The increasing
focus on collecting and analyzing big data is shaping new
platforms that combine the traditional data warehouse with
big data systems in a logical data warehousing architecture.
As part of the process, it must be decided what data must be
kept for compliance reasons, what data can be disposed of
and what data should be kept and analyzed in order to
improve current business processes or provide a business
with a competitive advantage. This process requires
careful data classification so that ultimately, smaller sets of
data can be analyzed quickly and productively.
ISSUES RELATED WITH BIG DATA
CHARACTERISTICS
Data Volume – With the increase in volume, the worth of
different data records will decrease in proportion to age,
type, richness, and quantity among other factors.
Data Velocity – Data is being generated at tremendous
speed with each minute passing. The velocity at which this
data is being generated is beyond the handling power of
traditional systems.
Data Variety - Mismatched data formats, non-aligned data
structures, and inconsistent data semantics represents
significant challenges that can lead to analytic collapse.
Data Value – Often it is witnessed that there is a huge gap in
between the business leaders and the IT professionals. The
main concern of business leaders is to just add value to their
business and to maximize their profit. On the other hand, IT
leaders deal with technicalities of the storage and
processing.
Data Complexity - Data scientists have to link, match,
cleanse and transform data across systems coming from
various sources. It is also necessary to connect and correlate
relationships, hierarchies and multiple data linkages or data
can quickly spiral out of control [3].
Data Veracity - Veracity refers to the messiness or
trustworthiness of the data. With many forms of big data
quality and accuracy are less controllable. [4 , 10].
BIG DATA IN MEDICAL SCIENCE
2. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 82
Big data in health care is different from big data in
marketing and product development because of
regulation, medical ethics, privacy and the diversity of
data sources. Goals differ as well. Understanding these
differences will be important to unlocking its hidden
value. Health data volume is expected to grow
dramatically in the years ahead. Although profit is not
and should not be a primary motivator, it is vitally
important for healthcare organizations to acquire the
available tools, infrastructure, and techniques to
leverage big data effectively or else risk losing
potentially millions of dollars in revenue and profits
Health care is one of the top social and economic issues
in many countries, such as the India, the UK, South
Korea, The United States and even middle-income
countries. In India, health care sector suffers from
underfunding and bad governance. No doubt, India has
made huge improvements since independence; majority
(70%) of the effort has been led by the private sector.
Still India accounts for 21% of the world’s burden of
disease. The term big data [1, 2] refers to the collection
of data sets so large and complex that it becomes
difficult to process using readily available database
management tools. In actual, big data refers to the
situation where more and more portions and objects of
everyday life are available in digital form, like personal
profiles or company profiles, social network and blog
postings, health records etc. through which huge
amount of data gets dynamically produced particularly
on the Internet and on the Web.
Unstructured data forms close to 80% of information in
the healthcare industry and is growing exponentially.
Getting access to this unstructured data such as output
from medical devices, doctor’s notes, lab results,
imaging reports, medical correspondence, clinical data,
and financial data is an invaluable resource for
improving patient care and increasing efficiency.
In the last few years there has been a move toward
evidence-based medicine, which involves making use
of all clinical data available and factoring that into
clinical and advanced analytics. The outcomes of this
movement include improved ability to detect and
diagnose diseases in their early stages, assigning more
effective therapies based on a patient’s genetic makeup,
and adjusting drug doses to minimize side effects and
improve effectiveness.
In recent years the Indian government has increased
spending in the health care industry. The government
plans to increase it even further by 2.5% of the GDP in
the 12th five year plan. As compared to other emerging
economies the amount of public funding that India
invests in health care is very small. India ranks among
the last 5 countries with 6% of GDP expenditure on
health care. Hospital bed density in India has been
stagnating at 0.9 per 1000 population since 2005 and
falls significantly short of WHO laid guidelines of
3.511 per 1000 patients’ population. Moreover, there is
a huge disproportion in utilization of facilities at the
village, district and state levels with state level facilities
remaining the most tensed. India is currently known to
have approximately 600,000 doctors and 1.6 million
nurses. This interprets into one doctor for every 1,800
people. The recommended WHO guidelines suggest
that there should be 1 doctor for every 600 people. This
translates into a resource gap of approximately 1.4 million
doctors and 2.8 million nurses. There is also a clear
disproportion in the man power present in the rural and
urban areas [7, 8].
ROLE PLAYED BY BIG DATA IN MEDICAL
SCIENCE
These are some example use cases that illustrate how big
data is being used in healthcare, helping to increase
efficiency and improve patient care.
Personalized Treatment Planning
Personalized treatment planning is a way to customize
treatment for a patient to continuously monitor the effects of
medication. The dose can be modified or the medication can
be changed based on how the medication is working for that
particular individual.
Assisted Diagnosis
Big data being able to access a broad combination of
knowledge across multiple data sources aids in the accuracy
of diagnosing patient conditions. Assisted diagnosis is
accomplished using expert systems that contain detailed
knowledge of conditions, symptoms, medications and side
effects.
Fraud Detection
Healthcare organizations need to be able to detect fraud
based on analysis of anomalies in billing data, procedural
benchmark data or patient records. For example, they can
analyze patient records and billing to detect anomalies such
as a hospital’s over utilization of services in short time
periods, patients receiving healthcare services from different
hospitals in different locations simultaneously, or identical
prescriptions for the same patient filled in multiple
locations.
Monitor Patient Vital Signs
Healthcare facilities are looking to provide more proactive
care to their patients by constantly monitoring patient vital
signs. The data from these various monitors can be used in
real time and send alerts to nurses or care providers so they
know instantly about changes in a patient’s condition.
Digitization of Data
Till date most of the data in the health care industry are
stored in the form of hard copy, but the current trend is
toward rapid digitization of these large amounts of data.
The medical community has accepted big data as a research
tool and beliefs that the society’s enormous amount of
diverse health information has the potential to help solve
some of medicine’s most troublesome problems. By
discovering relations and understanding patterns within the
data, big data analytics has the potential to improve care,
save lives and lower costs [5, 11, 13].
TECHNOLOGY USED BY BIG DATA
Big Data needs a framework for running applications on
large clusters of commodity hardware which produces huge
data and to process it. One such framework is Hadoop.
Hadoop includes two main components. First one is HDFS
(Hadoop Distributed File System) and the second one is
Map/Reduce technology.The process starts with a user
request to run a MapReduce program and continues until the
results are written back to the HDFS . As MapReduce is an
3. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
83 NITTTR, Chandigarh EDIT-2015
algorithm, it can be written in any programming
language.
Hadoop map reduce works in three stages:
First Stage: mapping: In this stage, a list of elements
is provided to a ‘mapper’ function to get it transferred
into pairs. The mapper function does not modify the
input data, but simply returns a new output list.
Intermediate stages: Shuffling and Sorting: After the
mapping stage, the program exchanges the intermediate
outputs from the mapping stage to different ‘reducers’.
This process is called shuffling.
Final Stage: Reducing: In the final reducing stage, an
instance of a user-provided code is called for each key
in the partition assigned to a reducer. In particular, we
have one output file per executed reduce task.
Fig.1 Working of Map Reduce
IMPACT OF BIG DATA ON HEALTH CARE
SYSTEM
RIGHT LIVING - The right-living pathway focuses on
encouraging patients to make lifestyle choices that help
them remain healthy.
CORRECT CARE - It involves ensuring that patients
get the timely and appropriate treatment available.
ACCURATE PROVIDER - It proposes that patients
should always be treated by high-performing
professionals that are best coordinated to the task and
will achieve the best outcome.
PRECISE VALUE - To fulfill the goals of precise
value, providers will continuously enhance healthcare
value while preserving or improving its quality [6].
RIGHT INNOVATION - It involves the identification
of new therapies and approaches to delivering care,
across all aspects of the system, and improving the
innovation engines themselves.
SECURITY ISSUES IN BIG DATA
A major challenge to healthcare cloud is the security
threats including tampering or leakage of sensitive
patient’s data on the cloud, loss of privacy of patient’s
information, and the unauthorized use of this
information. The main security and privacy
requirements for healthcare clouds are discussed below
Authentication: In a healthcare cloud, both health care
information offered by CSPs (cloud service providers)
and identities of users should be verified at the entry of
every access using user names and passwords assigned
to users by CSPs.
Authorization: It is a crucial security requirement that is
used to control access priorities, permissions and resource
ownerships of the users on the cloud.
Non-repudiation: It implies that one party of a transaction
cannot deny having received a transaction nor can the other
party deny having sent a transaction.
Integrity and Confidentiality: Integrity means preserving the
precision and consistency of data. In the healthcare system,
it refers to the fact that EHRs (electronic health records)
have not been tampered by unauthorized use.
Availability: For any EHR system to serve its purpose, the
information must be available when it is needed. High
availability systems aim to remain available at all times,
preventing service disruptions due to power outages,
hardware failures, and system upgrades.
CONCLUSION
The real issue is not that we are acquiring large amounts of
data. It's what you do with the data that counts. Today, a
significant proportion of the cost and time spent in the drug
development process is attributable to unsuccessful
formulations. By enabling researchers to identify
compounds with a higher likelihood of success, Big Data
can help reduce the cost and the time to market for new
drugs. Also, by integrating learning from medical data in the
early stages of development, researchers will now be able to
customize drugs to suit aggregated patient profiles.
Currently, information privacy concerns are the single
biggest obstacle to Big Data adoption in health care.
Another is the absence of an analytics solution powerful
enough to gather massive volumes of largely unstructured
health data, perform complex analyses quickly, and trigger
meaningful solution, for instance, gather all the data from
ICU monitors, which today goes un-stored, put it on the
Cloud, decipher significant medical patterns that are yet
undiscovered, and trigger a medical action instead of merely
an alarm.
By providing an overview of the current state of big data
applications in the healthcare environment, this research
paper has explored the existing challenges that governments
and healthcare stakeholders are facing. All big data projects
in leading countries and healthcare industries have similar
general common goals, such as the provision of easy and
equal access to public services, better citizens' healthcare
services, and the improvement of medical-related concerns.
However, each government or healthcare stakeholder has its
own priorities, opportunities, and threats, based on its
country's unique environment (e.g., healthcare expenditures
in the United States, the inefficient and wasteful healthcare
system in Japan, regional disparities in the healthcare
resources in India, etc.) which big data projects must
address.
Second, for medical data that cuts across departmental
boundaries, a top-down approach is needed to effectively
manage and integrate big data. Governments and healthcare
stakeholders should establish big data control towers to
integrate accumulated datasets, whether structured or
unstructured, from each silo. In addition to this,
governments and healthcare stakeholders need to establish
4. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 84
an advanced analytics agency, which will be tasked
with developing strategies on how big data could be
best managed through new technology platforms and
analytics as well as how to secure skilled professional
staff to use the new tools and techniques.
Third, real-time analysis of in-motion big data should
be carried out, while protecting privacy and security.
Thus, governments and healthcare stakeholders should
explore new technological playgrounds, such as cloud
computing, advanced analytics, security technologies,
legislation, etc.
Fourth, leading big data governments appear to have
different goals and priorities; therefore, they use
different sets of data management systems,
technologies, and analytics. While such information is
not readily available in the literature, the main concerns
with big data applications among these countries and
companies converge on the following: security, speed,
interoperability, analytics capabilities, and the lack of
competent professionals [11, 12].
Finally, this study is limited in that the practical applications
of big data for investigating healthcare issues have not yet
been fully demonstrated due to the dearth of practice. With
regard to future study, practitioners and researchers should
carefully look at and accumulate information with regard to
the practical applications of big data in order to determine
the best ways of using big data in healthcare issues.
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