White paper examines the unstructured data management challenges healthcare organizations face and how the Hitachi Data Systems solution employs metadata to address the data storm.
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.
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.
An Adaptive Technique in Electronic Health Record for Clinical Decision Makin...ijtsrd
Cloud computing is a collection of several computer resources that consists of both software and hardware. It is a type of service that is delivered over the internet and can be accessible from anywhere. 1 The data and services can be accessed through the internet. 4 These services are managed by the third party over the internet. They eventually provide access to the servers and resources. Health records consist of patient’s data regarding health. This data is usable by both the hospitals and patients. 6 8 This can be eventually used to track the medical history of patients. Data Visualization is a graphical depiction of the data. It implicates producing images that advertise the link among the data that the users view. Hence, they are used for clinical decision making. In this paper we will be discussing how cloud can be used to maintain health records electronically. Meghana Prakash | Vignesh S "An Adaptive Technique in Electronic Health Record for Clinical Decision Making Based on Data Visualization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30699.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30699/an-adaptive-technique-in-electronic-health-record-for-clinical-decision-making-based-on-data-visualization/meghana-prakash
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
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.
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.
An Adaptive Technique in Electronic Health Record for Clinical Decision Makin...ijtsrd
Cloud computing is a collection of several computer resources that consists of both software and hardware. It is a type of service that is delivered over the internet and can be accessible from anywhere. 1 The data and services can be accessed through the internet. 4 These services are managed by the third party over the internet. They eventually provide access to the servers and resources. Health records consist of patient’s data regarding health. This data is usable by both the hospitals and patients. 6 8 This can be eventually used to track the medical history of patients. Data Visualization is a graphical depiction of the data. It implicates producing images that advertise the link among the data that the users view. Hence, they are used for clinical decision making. In this paper we will be discussing how cloud can be used to maintain health records electronically. Meghana Prakash | Vignesh S "An Adaptive Technique in Electronic Health Record for Clinical Decision Making Based on Data Visualization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30699.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30699/an-adaptive-technique-in-electronic-health-record-for-clinical-decision-making-based-on-data-visualization/meghana-prakash
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
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.
(HLS305) Transforming Cancer Treatment: Integrating Data to Deliver on the Pr...Amazon Web Services
In the past ten years, the cost of sequencing a human genome has fallen from $3 billion dollars to $1,000, unlocking the ability for clinicians to use genomics in routine care. As the volume of genomic data used in the clinic begins to grow, healthcare providers are facing a number of new IT challenges, such as how to integrate this data with clinical data stored in electronic medical records, and how to make both available in real time to inform clinical decisions. In this session, find out how UCSF Medical Center and Syapse met these challenges head-on and solved them using AWS, all while remaining compliant with privacy and security requirements. Learn how Syapse's precision medicine platform uses Amazon VPC, Dedicated Instances, Amazon EC2, and Amazon EBS to build a high performance, scalable, and HIPAA-compliant data platform that enables UCSF to deliver on the promise of precision medicine by dramatically reducing time and increasing the accuracy and utility of genomic profiling in cancer treatment.
Optimizing patient care with Citrix XenApp & XenDestopCitrix
Centrally manage EHR apps in the datacenter, enabling easier app updates, simpler compliance and instant access by clinicians using any device.
Learn more: http://www.citrix.com/health
THE 4 R’S – REASON, REDCAP, REVIEW AND RESEARCH - IN A LARGE HEALTHCARE ORGAN...hiij
This paper outlines the journey of a large Australian academic health service in relation to the acquisition,
installation and roll out of the REDCap platform (RCP) for the betterment of clinical review (clinical audit)
and research data collection. The main aims of the acquisition of the platform were to facilitate data collection and management for audit and research across the organization in a more sustainable way than had previously been possible. We found the platform to be easily installed and maintained. There was rapid uptake of the platform by a range of health service stakeholders across the audit, research and operational domains. We were also able to successfully integrate data from our corporate clinical data environment,
The REASON Discovery Platform R (REASON) into selected REDCap “applications” using the Dynamic Data Pull (DDP) functionality it provides. In summary the acquisition and installation of REDCap at our health service has been hugely successful and has provided a great facility for use by a large number of organizational stakeholders going forwards into the future.
Describes about Technology, health care trend, design converge to enhance patient care and rules for Smart Hospitals. For more information visit: http://www.transformhealth-it.org/
A presentation about the role of informatics standards in facilitating electronic data interchange, and a framework for service-oriented semantic interoperability among data systems.
Slide Presentation for the Week10 Activity of HI 201. Some of the pictures used in the presentation are from http://all-free-download.com/free-photos/.
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
A look at the key trends and challenges in applying Big Data to transform healthcare by supporting research, self care, providers and building ecosystems. Purchase the report here: https://gumroad.com/l/PlXP
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
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.
(HLS305) Transforming Cancer Treatment: Integrating Data to Deliver on the Pr...Amazon Web Services
In the past ten years, the cost of sequencing a human genome has fallen from $3 billion dollars to $1,000, unlocking the ability for clinicians to use genomics in routine care. As the volume of genomic data used in the clinic begins to grow, healthcare providers are facing a number of new IT challenges, such as how to integrate this data with clinical data stored in electronic medical records, and how to make both available in real time to inform clinical decisions. In this session, find out how UCSF Medical Center and Syapse met these challenges head-on and solved them using AWS, all while remaining compliant with privacy and security requirements. Learn how Syapse's precision medicine platform uses Amazon VPC, Dedicated Instances, Amazon EC2, and Amazon EBS to build a high performance, scalable, and HIPAA-compliant data platform that enables UCSF to deliver on the promise of precision medicine by dramatically reducing time and increasing the accuracy and utility of genomic profiling in cancer treatment.
Optimizing patient care with Citrix XenApp & XenDestopCitrix
Centrally manage EHR apps in the datacenter, enabling easier app updates, simpler compliance and instant access by clinicians using any device.
Learn more: http://www.citrix.com/health
THE 4 R’S – REASON, REDCAP, REVIEW AND RESEARCH - IN A LARGE HEALTHCARE ORGAN...hiij
This paper outlines the journey of a large Australian academic health service in relation to the acquisition,
installation and roll out of the REDCap platform (RCP) for the betterment of clinical review (clinical audit)
and research data collection. The main aims of the acquisition of the platform were to facilitate data collection and management for audit and research across the organization in a more sustainable way than had previously been possible. We found the platform to be easily installed and maintained. There was rapid uptake of the platform by a range of health service stakeholders across the audit, research and operational domains. We were also able to successfully integrate data from our corporate clinical data environment,
The REASON Discovery Platform R (REASON) into selected REDCap “applications” using the Dynamic Data Pull (DDP) functionality it provides. In summary the acquisition and installation of REDCap at our health service has been hugely successful and has provided a great facility for use by a large number of organizational stakeholders going forwards into the future.
Describes about Technology, health care trend, design converge to enhance patient care and rules for Smart Hospitals. For more information visit: http://www.transformhealth-it.org/
A presentation about the role of informatics standards in facilitating electronic data interchange, and a framework for service-oriented semantic interoperability among data systems.
Slide Presentation for the Week10 Activity of HI 201. Some of the pictures used in the presentation are from http://all-free-download.com/free-photos/.
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
A look at the key trends and challenges in applying Big Data to transform healthcare by supporting research, self care, providers and building ecosystems. Purchase the report here: https://gumroad.com/l/PlXP
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
Understanding the Need of Data Integration in E Healthcareijtsrd
This paper discusses the current scenario of e healthcare and different dimensions of Big data in healthcare and the importance of data integration in e health care and the challenges associated with data integration and associated uses of data integration with respect to different use cases which might be helpful to physician's decision making because the data driven decision making involves combination of heterogeneous data which includes Electronic Health Record containing different types of data and connected healthcare organization in order to provide value based connected healthcare which would be useful to primary healthcare center located at different location because patients suddenly expect their healthcare experiences to be as exceptional and as transparent as those of retail or banking, and physician's have to scramble to adjust to these new expectations due to lack of data integrity. Mrs. Shashi Rekha. H. | Dr. Chethana Prakash. M | Dr. K. Thippeswamy "Understanding the Need of Data Integration in E- Healthcare" 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/ijtsrd31007.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31007/understanding-the-need-of-data-integration-in-e-healthcare/mrs-shashi-rekha-h
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.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
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.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Theory of Human Caring on APN Role Student PresentationWeb PageMikeEly930
Theory of Human Caring on APN Role Student Presentation
Web Page
Assignment Prompt
Explore the influence of Jean Watson’s Theory of Human Caring on your future role as an APN. The student will explore the concepts and Caritas processes from the Theory of Human Caring and present how these concepts may impact their future APN role.
Directions:
1. The student will create a PowerPoint and include speaker notes that may be added to the speaker note section on each slide.
2. The presentation should be limited to no more than 10 slides. See suggested slides below.
3. If you are unfamiliar with Dr. Watson's theory see this overview.
A suggested outline for the presentation may include the following slides:
Slide 1 - Introduction to yourself and future planned APN role and practice
Slide 2 - Previous experience with Watson’s Theory of Human Caring
Slide 3 - Core Concepts of the Theory Applicable to the APN role
Slide 4 - Core Concepts of the Theory Applicable to the APN role (as needed)
Slide 5 - Five Carative Factors or Caritas Processes You Plan to Use in the APN Role
Slide 6 - Five Carative Factors or Caritas Processes You Plan to Use in the APN Role (as needed)
Slide 7 - What Does the Theory of Human Caring Mean to You
Slide 8 - APN Implications of Theory of Human Caring
Slide 9 - Summary/Main Points
Slide 10 - Reference
Expectations
· Format: PPT Presentation with Speaker Notes
· Length: 10 Slides, maximum
· Plagiarism free.
· Turnitin receipt.
· Please reply to the two-discussion post below.
· APA Format with intext citation
· Each post must have two scholarly references
· 180-to-200-word count minimal
· Make it sound personal
Keyandra W
Discussion 1
Top of Form
Under the healthcare context, big data (BD) signifies immense volumes of data resulting from the adoption of digital tools that gather patients' data and help direct hospital performance. Globally, healthcare systems are increasingly facing incredible challenges due to disability and the aging population, patients' expectations, and increased technology use. The increasing use of BD can help clinicians meet these goals unprecedentedly. The potential of BD in the medical industry relies on the ability to turn high data volumes into actionable knowledge and detect patterns for decision-maker and precision medicine. The use of BD in healthcare contributes towards ensuring patients' safety in several contexts. Evidence bolsters that EHRs can become a vital tool for communication across healthcare teams and a valuable information hub when implemented well (Pastorino et al., 2019). However, the process towards the use of BD requires interdisciplinary collaboration and adapt performance and design of the systems. Additionally, the proliferating use of big data requires the healthcare teams to build technological infrastructure to invest in human capital and cover and house the massive volumes of medical care data to guide people into the novel frontier of health and wellbeing. The ...
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
Outline
Value Based Healthcare System – How it is seen today
Healthcare Challenge & IoT as a Solution
IoT – Big Data Structure
Recent Trends in IoT Big Data Analytics
Challenges & Our Future
In-depth Knowledge of
What causes the most premature death?
Distribution of Disease burden from 1990 - 2020
Challenges in Healthcare
Future Healthcare
IoT Machine Talking to Machine
Prediction of IoT Usage
About PEPGRA HEALTHCARE,
A leading healthcare communication firm with years of excellence serving clients with a dedicated team of Medical, Regulatory and Scientific writers specialized in all therapeutic areas.
Contact us at :
UK: +44-1143520021
US/Canada: +1-972-502-9262
India: +91-8754446690
info@pepgra.com
www.pepgra.com
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.
Application of Big Data in Medical Science brings revolution in managing heal...IJEEE
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.
Hitachi Vantara and our special guest, Dr. Alison Brooks, Research Director at IDC, discuss:
• How video and other IoT data can help your business become smarter, safer and more efficient.
• How to harness IoT data to gain operational intelligence and achieve better business outcomes.
• How Hitachi’s customers are innovating with IoT to excel.
• Which practical applications and best practices will get you started on your own IoT journey to reach your goals and tackle your challenges.
Virtualizing SAP HANA with Hitachi Unified Compute Platform Solutions: Bring...Hitachi Vantara
Virtualizing SAP HANA with Hitachi Unified Compute Platform Solutions: Bringing Flexibility, Agility and Readiness to the Real-Time Enterprise. VMworld 2015
Hitachi Virtual Infrastructure Integrator (Virtual V2I) is a VMware vCenter plugin plus associated software. It provides data management efficiency for large VM environments. Specifically, the latest release addresses virtual machine backup and recovery and cloning services. Customer want to leverage storage based snapshots as it is scalable, more granular backup from hours between backups to minutes resulting in improved RPO. VMworld 2015.
Economist Intelligence Unit: Preparing for Next-Generation CloudHitachi Vantara
Preparing for next-generation cloud: Lessons learned and insights shared is an Economist Intelligence Unit (EIU) research programme, sponsored by Hitachi Data Systems. In this report, the EIU looks at companies’ experiences with cloud adoption and assesses whether the technology has lived up to expectations. Where the cloud has fallen short of expectations, we set out to understand why. In cases of seamless implementation, we gather best practices from firms using the cloud successfully.
HDS Influencer Summit 2014: Innovating with Information to Address Business N...Hitachi Vantara
Top Executives at HDS share how the company is Innovating with Information to address business needs. Learn how the company is transforming now and into the future. #HDSday.”
Information Innovation Index 2014 UK Research ResultsHitachi Vantara
Hitachi Data Systems releases insights from its inaugural ‘Information Innovation Index’, a UK research report, conducted by independent UK technology market research agency, Vanson Bourne, in which 200 IT decision-makers were surveyed during April 2014 to provide insights into how current approaches to IT are thwarting companies’ ambitions to leverage data to drive innovation and business growth.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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Healthcare data's perfect storm
1. W H I T E P A P E R
Healthcare Data’s Perfect Storm
Why Healthcare Organizations Are Drowning in the Data
They Are Creating and Why They Need Even More Data to
Weather This Storm
By William A. Burns
December 2011
2. 2
Table of Contents
Executive Summary 3
About the Challenge 4
Unstructured Data 5
Metadata 6
Associative Metadata 7
Conclusion 8
Appendix A: References 9
Appendix B: About the Author 10
3. 3
Executive Summary
Today's modern healthcare organizations, from hospitals to life sciences companies to healthcare
payers, are struggling to keep up with the very data they are creating. Electronic medical record
(EMR) information, medical images, scanned paper reports, dictated voice files and full motion video
are just a small sampling of the massive amounts of data collected in the process of patient care,
bio-medical research and medical claims administration.
Equally affected are chief information officers who are faced with an onslaught of data, the likes of
which they have never seen before. Unlike the complex but tractable problem of managing struc-
tured databases from the massive to the mundane, the new menace is the management of the
individual files, themselves. Ranging from file servers to Microsoft SharePoint sites to the thousands
of medical applications that populate today's healthcare landscape, this new hurdle is simply called
unstructured data.
This article will help you understand both how and why this problem exists. It looks at why injecting
even more information into this paradigm of unstructured data assets is the only way to stay ahead
of the very data we are creating. Restoring long-term value to this most critical class of data and de-
livering a cost-efficient infrastructure to manage today's healthcare information is a key way to rein in
healthcare costs and maximizing the value of medical information.
4. 4
About the Challenge
There is an unprecedented data surge happening in healthcare organizations around the world. Far
from a "hockey stick" graph that will hit us in 5 to15 years, this is the reality today. Data growth is
happening with such speed and veracity that looking at the numbers alone will leave organizations
wondering how to survive this data explosion, let alone make sense of this valuable information.
Consider the many sources of data. Current medical technology makes it possible to scan a single
organ in 1 second and a complete full-body scan in roughly 60 seconds. The result is nearly 10GB
of raw image data delivered to a hospital's picture archive and communications system (PACS).
Clinical areas in their digital infancy such as pathology, proteomics, and genomics, which are the key
to personalized medicine, can generate over 2TB of data per patient. Add to that the research and
development of advanced medical compounds and devices, which generate terabytes over their
lengthy development, testing and approval process.
And finally, consider the impact of EMRs, which are already mandatory in many European and Asian
countries and will soon be required for every patient in the United States. These sources of data
are just the tip of the iceberg. Consider the thousands of medical applications in use today that
create an individual's files or unstructured data (see Figure 1)1 during the patient care process. That
information gets stored on countless computers, servers and storage systems. It is impossible to
ignore the impact that retiring baby boomers will have on our global healthcare systems. It is widely
accepted that chronic diseases make up the majority of healthcare costs and 80% of an individual's
healthcare is consumed in the last 20 years of his or her life. As retiring baby boomers worldwide
make greater use of healthcare systems, the sheer volume of data will push many institutions past
the breaking point.
Figure 1. Consumption of Disk Capacity by Data Type
1
“Consumption of Disk Storage by Capacity: Forecast, Recovery, Efficiency and Digitization Shaping Customer Requirement for
Storage Systems,” IDC, May 2010, IDC Forthcoming 2011.
5. 5
Unstructured Data
Healthcare, like many other industries, is rushing to unlock the power of broad-based analytics.
Healthcare seeks the abilities to compare millions of medical imaging scans for common relation-
ships and scan laboratory results for population patterns and genetic markers. These and many
other forms of analytical mining are but a few of the keys to unlocking new medical compounds and
delivering care in a far safer and more cost-effective manner.
However, it is the very data, itself, which is preventing us from unlocking these untold analytical
riches. Far from the orderly, aligned and obedient world of databases and structured data lies the
fastest growing type of medical data asset we have today, unstructured data. This includes medical
imaging files, treatment reports, scanned and paper records.
Not only is the growth of unstructured data nearing epidemic proportions, but also, amassing this
very data into the content depots we need to execute our analytical efforts is proving next to impos-
sible (see Figure 2)2.
Unstructured data is big and hard to move. Its very definition fails to provide information about its
content, values or purposes for existence. To streamline the management of unstructured data
and unlock its underlying value, storage vendors and application providers alike must quickly move
to embrace metadata; they must develop new paradigms for creating, managing and utilizing
metadata.
Figure 2. Storage Consumption by Data Type
2
“Storage Consumption by Data Type: Forecast, Recovery, Efficiency and Digitization Shaping Customer Requirement for
Storage Systems,” IDC, May 2010, Forthcoming 2011
6. 6
Metadata
So how do we fix this problem? How do we avoid the data storm and leverage this most valuable
type of information — information regarding our very health and well-being? We do it by adding
even more data to the pile: not just any data but a very dynamic and living type of data called
metadata.
What is metadata? Simply put it is "data about data." Metadata describes other data and, in the
context of our discussion, the massive amounts of unstructured data files being amassed at a blind-
ing rate. It provides information about a certain item's content. For example, a medical image may
include metadata that describes how large the picture is, the bit depth, the image resolution, when
the image was created, and other data about the medical procedure. A text document's metadata
may contain information about how long the document is, who the author is, when the document
was written, and a short summary of the document to include even clinical opinions or findings.
While this may seem rather academic and ethereal, many of us are exposed to metadata on almost
a daily basis through the world's most popular and ubiquitous metadata-driven device, the iPod.
Apple Computer's universally pervasive iPod is nothing more than a fixed-content storage device
managed through metadata. We load our MP3s, MPEG movies, audio books and photos (fixed
content) on our iPods by the thousands and this data never changes. It is static data that remains
the same until we delete it or it is removed in some other fashion. Our metadata is often created for
us, like song title, artist and album. However, it can also be a dynamic and living form of data. For
example, it may report: "This song is one of my favorites, I have listened to it "x" many times, I last
listened to it on "x" date and it is similar to these other songs of the same genre." It is the metadata
that we create about each fixed content object on our iPod that makes it extremely user friendly and
unique in the world of MP3 players.
While the iPod example is fairly basic, this notion of self-assigned metadata on the iPod is far more
advanced than what can be found within corporate and healthcare information systems today.
Commonly accepted types of metadata in use in these areas today include:
■■Basic metadata. This is low-level data, such as block-level information about where data is
stored and how often it is accessed.
■■File-level metadata. This is more complex data from file systems.
■■Content-level metadata. This is metadata that might be found in content management systems,
such as file type. It also may include other more meaningful data derived from its very contents,
such as: "Is this report referring to an MRI?" or "Is the MRI report positive?"
7. 7
Figure 3. Associative Metadata Model
Each metadata type is actionable. For example, basic metadata can be used to automate tiering,
file-system data can be used to speed performance and high-level metadata can be used to take
business actions. The key challenge is how to capture, process, analyze and manage all this meta-
data in an expedient manner. Static metadata (our iPod example) will only take us so far. To reach
the next level, metadata must be dynamic, automatically generated, able to change over time and
associative with the world of applications in which we interact. Much like our traditional models for
computer hardware or systematic interaction with servers, networks and storage devices, metadata
models will need to advance beyond the basics to such models shown in Figure 33.
Associative metadata is but one of the many new and exciting paradigms for helping us deal with
the massive amounts of unstructured data we are seeing in our healthcare environments today.
Associative metadata is a paradigm in which unstructured data assets are indexed based on dis-
parate types of information. This data may range from source, content and creation context, to its
relevance to a user, and allow a user to locate such files without the need to record a filename or
location.
Associative Metadata
Associative metadata allows a user to tailor the criteria used in a file search. The user can dictate
which criteria are and which are not important in the search for a certain file. However, even this
paradigm of associative metadata, applied statically or by the applications that generate this con-
tent, falls far short of where we need to be for true success.
3
‘Angels in our Midst: Associative Metadata in Cloud Storage,’ Tom Coughlin and Mike Alvarado, downloaded 11/3/2011
Coughlin Associates, http://www.tomcoughlin.com/Techpapers/Angels+in+our+Midst,%20102710.pdf
8. 8
Pioneering work is underway right now on metadata robots that apply associative metadata as con-
tent data itself is being formed and continues to enhance a content type metadata stream dynami-
cally and far into the future. Bridging this gap will also allow us to enter the age of "Polymorphic Data
Content" where our root content data exists in many forms throughout our data universe.
Metadata itself is quickly becoming the barrier to enterprise data management and analytics. It has
been said that our recent economic downturn has sped the adoption of cloud computing. It has prom-
ised reduced capital expenditures, pay as you go service models and an on-demand world at your
fingertips. Metadata creation, management and utilization in business applications have the potential
to stop cloud computing in its tracks. Growing, cloud-based content depots and storage pools will
quickly become black holes where we dump our data: never to be seen, used or understood again.
Unlocking metadata, however, holds great promise and paradigm shifts for how we deal with our data.
Rather than shoving the data into a big data repository, concepts like associative metadata allow us
to distribute the metadata and allow parallel processing concepts to operate in tandem. By allowing
the metadata to remain distributed, massive volumes of data can be managed and analyzed in real or
near-real time, thereby providing a step function in metadata exploitation.
Conclusion
Weathering the data storm gets harder every day, and few healthcare organizations are effectively
dealing with it. The challenge of gaining better understanding of how to create, harvest, manage and
exploit metadata is a very near term problem to be addressed by today's information management
professionals. Distributed data storage has been identified as one of the challenges in our paths
towards cloud computing. Without paradigm shifts in metadata management, such as to associative
metadata, our cloud computing initiatives risk quickly becoming "black holes" of lost and low-
relevance data.
As a worldwide leader in data management, Hitachi Data Systems is enabling industries to take their
first steps toward a more productive data future. Hitachi Content Platform and its systematically
defined and embedded metadata stream capabilities have revolutionized application deployment and
management.
The challenges of providing greater quality of care, in a more efficient and cost-effective model are
common themes across all healthcare delivery and research organizations around the globe. Our
ability to generate information about our health and welfare has never been more advanced; it is our
ability to understand this information and harness the power it yields that is trailing today. Metadata is
becoming the key to winning today's healthcare data challenge. Our ability to embrace it is our only
limitation.
9. 9
Appendix A: References
■■"A Novel Taxonomy for Consumer Metadata," T. M. Coughlin and S. L. Linfoot, 2010 ICCE
Conference in January 2010
■■"Angels in our Midst: Associative Metadata in Cloud Storage," Dr. Tom Coughlin and
Dr. Mike Alvarado, September 2010, http://www.tomcoughlin.com/Techpapers/
Angels+in+our+Midst,%20102710.pdf
■■"Distributed Computing Economics," J. Gray, Queue, vol. 6, no. 3, pp. 63-68, 2008.
■■"A Case Study: Polymorphic Query Languages and Their Impact on Data Structures," A.E. Lin-
dite, University of Utah, April 2009.
■■"Efficient Metadata Management for Cloud Computing," Verma, Abhishek; Venkataraman,
Shivaram; 2010-01-19, http://hdl.handle.net/2142/14820
10. 10
Appendix B: About the Author
William A. Burns
Vice President, Global Health & Life Sciences
Hitachi Data Systems
William Burns joined Hitachi in 2008 and leads the Global Health & Life Sciences Team at Hitachi
Data Systems. With extensive experience in the digital healthcare arena, Burns has worked with
point-of-care disease management systems, diagnostic imaging, ambulatory patient monitoring,
clinical research platforms and regulatory compliance. He has proven instrumental in setting the
leadership foundation in the development of technology enhanced clinical and business initiatives for
Hitachi Data Systems clients.
Burns has served on the Healthcare advisory boards of Microsoft, 3M, McKesson Corporation,
The American Red Cross and Gillette Children's Hospitals. He is a lifelong member of Institute
of Electrical and Electronics Engineers and the American Management Association. He is also a
featured speaker at several national and international forums on the topics of healthcare strategy
and digital transformation.