Posts Tagged ‘IOT’

Unwrapping Machine Learning

Ashvin Naik

Cloud Infrastructure Marketing at Dell EMC

In a recent IDC spending guide titled Worldwide cognitive systems and artificial intelligence spending guide,   some fantastic numbers were thrown out in terms of opportunity and growth 50+ % CAGR, Verticals pouring in billions of dollars on cognitive systems. One of the key components of cognitive systems is Machine Learning.

According to wikipedia Machine Learning is a subfield of computer science that gives the computers the ability to learn without being explicitly programmed. Just these two pieces of information were enough to get me interested in the field.

After hours of daily  searching, digging through inane babble and noise across the internet, the understanding of how machines can learn evaded me for weeks, until I hit a jackpot. A source, that should not be named pointed me to a “secure by obscurity” share that had the exact and valuable insights on machine learning. It was so simple, elegant and completely made sense to me.

Machine Learning was not all noise, it worked on a very simple principle. Imagine, there is a pattern in this world that can be used to forecast or predict a behavior of any entity. There is no mathematical notation available to describe the pattern, but if you have the data that can be used to plot the pattern, you can use Machine Learning to model it.  Now, this may sound like a whole lot of mumbo jumbo but allow me to break it down in simple terms.

Machine learning can be used to understand patterns so you can forecast or predict anything provided

  • You are certain there is a pattern
  • You do not have a mathematical model to describe the pattern
  • You have the data to try to figure out the pattern.

Viola, this makes so much sense already. If you have data, know there is a pattern but don’t know what that is, you can use machine learning to find it out. The applications for this are endless from natural language processing, speech to text to predictive analytics. The most important is forecasting- something we do not give enough credit these days. The Most critical component of Machine Learning is Data – you should have the data. If you do not have data, you cannot find the pattern.

As a cloud storage professional, this is a huge insight. You should have data. Pristine, raw data coming from the systems that generate it- sort of like a tip from the horses mouth. I know exactly where my products fit in. We are able to ingest, store, protect and expose the data for any purposes in the native format complete with the metadata all through one system.

We have customers in the automobile industry leveraging our multi-protocol cloud storage across 2300 locations in Europe capturing data from cars on the roads. They are using proprietary Machine Learning systems to look for patterns in how their customers- the car owners use their products in the real world to predict the parameters of designing better, reliable and efficient cars. We have customers in the life-sciences business saving lives by looking at the patterns of efficacy and effective therapies for terminal diseases. Our customers in retail are using Machine Learning to detect fraud and protect their customers. This goes on and on and on.

I personally do not know the details of how they make it happen, but this is the world of the third platform. There are so many possibilities and opportunities ahead if only we have the data. Talk to us and we can help you capture, store and secure your data so you can transform humanity for the better.

 

Learn more about how Dell EMC Elastic Cloud Storage can fit into your Machine Learning Infrastructure

 

 

Digital Health Strategies – An introduction to Elastic Cloud Storage (ECS)

Nathan Bott

Healthcare Solutions Architect at EMC

This past April, my father reached two important milestones – he turned 70 and retired from a 40-plus year career in food science.  He is now planning to head back to Spain to complete the Camino de Santiago – or the Way of St. James – a journey he started in 2014.  Unfortunately he had to stop 150 miles into the 500 mile trek because of severe back and hip pain due to the emergence of degenerative disc disease.  After working with his physician to manage this new condition, he started to prepare for the upcoming trip by walking between 5 and 10 miles three times a week.  Along with this training came other ailments that would be expected with anybody his age:  pulled muscles, strained knees, and “light-headedness.”  This last ailment can be attributed to another condition he happens to have – Type 2 Diabetes.  And so it goes, as he gets older and tries to maintain a high level of activity, he will suffer more ailments, and spend more time and money (via Medicare benefits) managing these chronic conditions.

And he will not be alone.  My father was born in 1946 and is thus a first year baby-boomer, the first wave of new Medicare beneficiaries in which about 10,000 enroll every day.  The Congressional Budget Office expects over 80 million Americans will be Medicare eligible by 2035, an almost 50% increase in enrollment from 2015.  The cost per beneficiary is expected to increase even more as each patient will have multiple chronic conditions to manage; per the National Council on Aging:

  • About 68% of Medicare beneficiaries have two or more chronic diseases and 36% have four or more.
  • More than two-thirds of all health care costs are for treating chronic diseases.

The US government and the healthcare industry are well aware of the current “silver tsunami” and planning has been underway.

For the past 7 years, since the passage of the Hi-Tech provision in the American Recovery and Reinvestment Act (ARRA) in 2009 and the Medicare Shared Saving Program (MSSP) in 2011 the ground work has been laid to implement various programs and incentives to distribute the efforts to manage the cost of delivering healthcare to an ever expanding beneficiary population.  The prolific adoption of electronic health records technology by healthcare providers and the reorganization of reimbursements to these providers – from a fee-for-service to an outcomes based model – have combined to become a catalyst for a digital revolution in healthcare.

Government led healthcare reform programs like Accountable Care Organizations (ACO), the Patient Centered Medical Home, and the Precision Medicine initiative are predicated with having a digital technology platform that can use the demographic, financial, clinical and genetic data acquired from the vast population of patients to develop evidence-based plans of care that are specifically tailored based on the genetic disposition and the disease(s) of a given patient.

Medicine doctor hand working with modern medical iconsRegardless of the industry, product or service, a disruptive technology that drives innovation through digitization requires a re-assessment of the infrastructure that supports it; the healthcare industry is no different.  As healthcare providers have implemented electronic medical records systems, deployed enterprise imaging solutions, piloted next generation sequencing programs, and developed clinical informatics capabilities, new infrastructure requirements and operating modes have emerged.  Furthermore, in response to the evolving markets and reimbursement models explained above, many healthcare entities – providers, payers, and pharmaceuticals alike – have consolidated through mergers and acquisitions which also necessitate re-evaluating infrastructure architectures in order to rationalize operational capabilities, drive utilization efficiency and decrease both operational and capital costs.

Working directly with healthcare customers, collaborating with healthcare software vendors, and partnering with IT service providers, EMC has been on the front line to provide architectural guidance and infrastructure solutions to support this digital revolution and its emerging infrastructure requirements. A key infrastructure solution to support the digitization revolution in healthcare is a highly durable, geo-distributed, performant storage platform that will work with legacy monolithic systems using file system interfaces as well as cloud-native distributed applications using standard storage APIs like AWS S3 or OpenStack Swift.

ECSEMC’s Elastic Cloud Storage system (ECS) is a modern object storage platform that does just that…and more.  Just as important, the ECS object platform can be used for a myriad of use cases specifically for the healthcare industry to support:

  • Innovative technology platforms which enable coordinated and accessible medical services such as outlined by the Patient-Centered Medical Home program
  • Collaboration and data sharing as needed for programs such as the Accountable Care Organization initiative
  • An increase in IT operational agility using a storage platform that can be provisioned with cloud-based API’s
  • A decrease in costs through storage utilization efficiency at scale using modern data protection and replication methods

In my follow-up blog entries here, I will provide more details on the functional capabilities of ECS as well as map these capabilities to specific use cases that are driving the digital revolution to take on the challenges of delivering collaborative and personalized healthcare services to an aging population with multiple complex chronic conditions while driving down IT operational costs as well as the overall cost of the healthcare system.

Examples of the use cases I mentioned above include various new technology trends like the emerging Internet of Things (IoT) solutions that support remote patient monitoring, telehealth, and behavior modification tools to help manage chronic diseases; data lake functionality with the Hadoop ecosystem for population and precision health based analytics programs; and cloud-native development efforts to launch distributed mobile applications that can capture and access data from any location.

I look forward to exploring these use cases and examining how ECS’s unique capabilities will help our healthcare customers move towards meeting their technical, operational, and “digitized-mission” goals.

Delivering the Best Patient Care Possible

Barry Morris

Sales DVP at EMC Federal Division

Latest posts by Barry Morris (see all)

Military Health – From Volume to Value

The digital world is driving us to act as both patients and consumers of healthcare information. For example – wearable devices, such as Fitbits, provide users with personalized data and the insight required to make more informed lifestyle decisions.

IoT_HealthcareAt the same time, healthcare providers are shifting to value-based care – from “pay per pill” to “pay for performance” and “pay for outcomes” while working to meet meaningful use goals. Comprehensive patient and population health data, and data collection opportunities enabled by the Internet of Things (IoT) can provide opportunities for healthcare providers – including military – to attain new insights and deliver the best care possible.

In a recent article in Health IT Analytics, EMC’s Roberta Katz writes, “In the current accountable care environment, where electronic health record documentation is being prioritized, this new realm of patient generated data can build on a caregiver’s clinical expertise and augment hospital protocols…With the use of IoT tools and sensors, we can review our own data in real-time, from the number of steps we are taking, cardio output, sleep cycles, blood pressure, and even mood, to become an ‘empowered’ patient.”

Modern tablet showing medical diagnosisWorking toward a transformative goal to achieve, among others, the results stated by Katz show that the Military Health System (MHS) http://www.health.mil/ is undergoing a disruptive application migration from the current system (AHLTA) to the new Cerner-based  MHS GENESIS – targeted for launch at the end of this year. Their goal: the ability to share health records electronically and document the complete continuum of care between MHS locations, private providers, and possibly Veteran Affairs.

Centralized data collection and analysis as part of an extended Electronic Health Record (EHR) system can provide a picture otherwise impossible to obtain – bringing together disparate information that alone does not raise an alert, but pulled together, can signal a need for intervention.

EMC is proud to participate in the Defense Health Information Technology Symposium (DHITS) 2016 on August 2-4 – and will focus on supporting the MHS Transformation. Please visit us at booth 401 to learn more about how Big Data analytics and data lakes are transforming military health.

For additional information, check out:

What’s Next for Hadoop? Examining its Evolution, and its Potential

John Mallory

CTO of Analytics at EMC Emerging Technologies Division

In my last blog post, I talked about one of the most popular buzzwords in the IT space today – the Internet of Things – and offered some perspective in terms of what’s real and what’s hype, as well as which use cases make the most sense for IoT in the short-term.

Today I’d like to address the evolution of Apache’s Hadoop, and factors to consider that will drive Hadoop adoption to a wider audience beyond early use-cases.

First, consider that data informs nearly every decision an organization makes today. Customers across virtually every industry expect to interact with businesses wherever they go, in real-time, across a myriad pf devices and applications. This results in piles and mounds of information that need to be culled, sorted and organized to find actionable data to drive businesses forward.

This evolution mirrors much of what’s taking place in the Apache-Hadoop ecosystem as it continues to mature and find its place among a broader business audience.

The Origins & Evolution of Hadoop

HadoopLet’s look at the origins of Hadoop as a start. Hadoop originally started out as a framework for big batch processing, which is exactly what early adopters like Yahoo! needed – an algorithm that could crawl all of the content on the Internet to help build big search engines and then take the outputs and monetize them with targeted advertising. That type of a use case is entirely predicated on batch processing on a very large scale.

The next phase centered on how Hadoop would reach a broader customer base. The challenge there was to make Hadoop easier to use by a wider audience. Sure, it’s possible to do very rich processing with Hadoop, but it also has to be programmed very specifically, which can make it difficult to use by enterprise users for business intelligence or reporting. This drove the trend around SQL on Hadoop, which was the big thing about two years ago with companies like Cloudera, IBM, Pivotal and others entering the space. (more…)

Examining the Internet of Things: What’s hype? What’s real?

John Mallory

CTO of Analytics at EMC Emerging Technologies Division

The Internet of Things is one of the biggest buzzwords in technology today, and indeed, it does have the potential to be a truly transformational force in the way that we live and work today.Internet of Things

However, if you peel back the “potential” and excitable future-speak surrounding IoT, and look at the actual reality of where it is today, the story is much, much different.  Yes, Internet-enabled “things” ranging from phones to watches to cars are getting smarter by being able to access, share and interpret data in new ways. But in our enthusiasm to embrace a Jetsons-like future powered by IoT, we’re losing sight of the infrastructure required (both at the literal hardware and organizational/institutional levels) to actually elevate this technology beyond buzzword status.

Consider, for example, the hype cycle over “big data” about three years ago when it became the industry’s hot topic without much, well, data to back it up. Hadoop is another example – it too had early adopters, but even now is only being rolled out into Fortune 1000/5000 companies. Organizations are still struggling with how to monetize it.

(more…)

Telemedicine Part 1: TeleRadiology as the growth medium of Precision Medicine

Sanjay Joshi

CTO, Healthcare & Life-Sciences at EMC
Sanjay Joshi is the Isilon CTO of Healthcare and Life Sciences at the EMC Emerging Technologies Division. Based in Seattle, Sanjay's 28+ year career has spanned the entire gamut of life-sciences and healthcare from clinical and biotechnology research to healthcare informatics to medical devices. His current focus is a systems view of Healthcare, Genomics and Proteomics for infrastructures and informatics. Recent experience has included information and instrument systems in Electronic Medical Records; Proteomics and Flow Cytometry; FDA and HIPAA validations; Lab Information Management Systems (LIMS); Translational Genomics research and Imaging. Sanjay holds a patent in multi-dimensional flow cytometry analytics. He began his career developing and building X-Ray machines. Sanjay was the recipient of a National Institutes of Health (NIH) Small Business Innovation Research (SBIR) grant and has been a consultant or co-Principal-Investigator on several NIH grants. He is actively involved in non-profit biotech networking and educational organizations in the Seattle area and beyond. Sanjay holds a Master of Biomedical Engineering from the University of New South Wales, Sydney and a Bachelor of Instrumentation Technology from Bangalore University. He has completed several medical school and PhD level courses.

Real “health care” happens when telemedicine is closely joined to a connected-care delivery model that has prevention and continuity-of-care at its core. This model has been defined well, but only sparsely adopted. As John Hockenberry, host of the morning show “The Takeaway” on National Public Radio, eloquently puts it: “health is not episodic.” We need a continuous care system.

Telemedicine makes it possible for you to see a specialist like me without driving hundreds of miles

Image source: Chest. 2013,143 (2):295-295. doi:10.1378/chest.143.2.295

How do we get the “right care to the right patient at the right time”? Schleidgen et al define Precision Medicine also known as Personalized Medicine (1) as seeking “to improve stratification and timing of health care by utilizing biological information and biomarkers on the level of molecular disease pathways, genetics, proteomics as well as metabolomics.” Precision Medicine (2) is an orthogonal, multimodal view of the patient from her/his cells to pathways to organs to health and disease. There are several devices and transducers that would catalyze telemedicine: Radiology, Pathology, and Wearables. I will focus on Radiology for this part of my three-part series, since all of these modalities use multi-spectral imaging.

Where first?
The world is still mostly rural. According to World Bank statistics, 19% of the USA is rural, but the worldwide average is about 30% which is a spectrum from 0% rural (Hong Kong) to 74% rural (Afghanistan). With the recent consolidations (since 2010 in the US) of hospitals into larger organizations (3), it is this 30% to 70% of the world with sparse network connectivity that needs telemedicine sooner than the well-off “worried well” folks who live in dense urban areas with close access to healthcare. China has the world’s largest number of hospitals at around 60,000 followed by India at around 15,000. The US tally is approximately 5,700 hospitals. The counter-argument to the rural needs in the US is the risk of reduction of physician numbers (4), the growing numbers of the urban poor and the elderly. Then there is the plight of poor health amongst the world’s millions of refugees who are usually stuck in no-mans-lands, fleeing conflicts that never seem to wane. All these use-cases are valid, but need prioritization.

Connected Health and the “Saver App”
Many a fortune has been made by devising and selling “killer apps” on mobile platforms. In healthcare what we need is a “saver app.” Using the pyscho-social keys to the success of these “sticky” technologies, Dr. Joseph C. Kvedar succinctly builds the case for connected health in his recent book “The Internet of Healthy Things” with three strategies and three tactics:

Strategies: (1) Make It about Life; (2) Make It Personal; and (3) Reinforce Social Connections.

Tactics: (1) Employ Messaging; (2) Use Unpredictable Rewards; and (3) Use the Sentinel Effect.

Dr. Kvedar calls this “digital therapies.”

The Vendor Neutral Archive (VNA) and Virtual Radiology
The Western Roentgen Society, a predecessor of the Radiological Society of North America (RSNA), was founded in 1915 in St. Louis, Missouri (soon after the invention of the X-Ray tube in Bavaria in 1895). An interactive timeline of Radiology events can be seen here. Innovations in Radiology have always accelerated the innovations in healthcare.

The Radiology value chain is in its images and clinical reporting, as summarized in the diagram below (5):

Radiology value chain

To scale this value-chain for telemedicine, we need much larger adoption of VNA, which is an “Enterprise Class” data management system. A VNA consolidates multiple Imaging Departments into:

  • a master directory,
  • associated storage and
  • lifecycle management of data

The difference between PACS (Picture Archiving and Communications System) (6) and VNA is the Image Display and the Image Manager layers respectively.

The Image Display layer is a PACS Vendor or a Cloud based “image program”. All Admit, Discharge and Transfer (ADT) information must reside with the image. This means DICOM standards and HL7 X.12N interoperability (using service protocols like FHIR) are critical. The Image Manager for VNA is the “storage layer of images”, either local or cloud based. For telemedicine to be successful, VNA must “scale-out” exponentially and in a distributed manner within a privacy and security context.

VNA’s largest players (alphabetically) are: Agfa, CareStream, FujiFilm (TeraMedica), IBM (Merge), Perceptive Software (Acuo), Philips and Siemens. The merger of NightHawk Radiology with vRad which was then acquired by MedNax and IBM’s acquisition of Merge Healthcare (in Aug 2015) are important landmarks in this trend.

One of the most interesting journal articles in 2015 was on “Imaging Genomics” (or Radiomics) of glioblastoma, a brain cancer. By bidirectionally linking imaging features to the underlying molecular features, the authors (7) have created a new field of non-invasive genomic biomarkers.

Imagine this “virtual connected hive” of patients on one side and physicians, radiologists and pathologists on the other, constantly monitoring and improving the care of a population in health and disease at the individual and personal level. Telemedicine needs to be the anchor architecture for Precision Medicine. Without Telemedicine (and VNA), there is no Precision Medicine.

Postscript: Telepresence in mythology
Let me end this tale of distance and care with a little echo from my namesake, Sanjaya, who is mentioned in the first chapter of the first verse of the Bhagvad Gita (literally translated as the “Song of the Lord”) – an existential dialog between the warrior Arjuna and his charioteer, Krishna. The Gita, as it is commonly known, is set within the longest Big Data poem with over 100,000 verses (and 1.8 million words), the Mahabharata, estimated to be first written around 400 BCE.

Dhritarashtra, the blind king, starts this great book-within-book by enquiring: “O Sanjaya, what did my sons and the sons of Pandu decide about battle after assembling at the holy land of righteousness Kurukshetra?”

Sanjaya starts the Gita by peering into the great yonder. He is bestowed with the divine gift of seeing events afar (divya-drishti); he is the king’s tele-vision – and Dhritarashtra’s advisor and charioteer (just like Krishna in the Gita). The other great religions and mythologies also mention telepresence in their seminal books.

My tagline for the “trickle down” in technology innovation flow is “from Defense to Life Sciences to Pornography to Finance to Commerce to Healthcare.” One interpretation of the Mahabharata is that it did not have any gods – all miracles were added later. Perhaps we have now reached the pivot point for telepresence which has happened in war to “trickle down” into population scale healthcare without divine intervention or miracles!

References:

  1. Schleidgen et al, “What is personalized medicine: sharpening a vague term based on a systematic literature review”, BMC Medical Ethics, Dec 2013, 14:55
  2. “Toward Precision Medicine”, Natl. Acad. Press, June 2012
  3. McCue MJ, et al, “Hospital Acquisitions Before Healthcare Reform”, Journal of Healthcare Management, 2015 May-Jun; 60(3):186-203.
  4. Petterson SM, et al, “Estimating the residency expansion required to avoid projected primary care physician shortages by 2035”, Annals of Family Medicine 2015 Mar; 13(2):107-14. doi: 10.1370/afm.1760
  5. Enzmann DR, “Radiology’s Value Chain”, Radiology: Volume 263: Number 1, April 2012, pp 243-252
  6. Huang HK, “PACS and Imaging Informatics: Basic Principles and Applications”, Wiley-Blackwell; 2 edition (January 12, 2010)
  7. Moton S, et al, “Imaging genomics of glioblastoma: biology, biomarkers, and breakthroughs”, Topics in Magnetic Resonance Imaging. 2015

 

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