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Intel Health & Life Sciences

18 Posts authored by: CHRISTOPHER GOUGH

The 2nd webinar in Frost & Sullivan’s series on Big Data in Healthcare took place recently and featured a fantastic insight from Vijay Venkatesan of Sutter Health and Shawn Dolley of Cloudera on the subject of Predictive Analytics in a Big Data world. The webinar is now available on-demand via the Frost & Sullivan website.


This on-demand webinar features some great learnings from Sutter Health including:

  • Objective and role for Data and Enterprise Management
  • Dealing with Volume and Variety of Data
  • Best Approach to Transformation
  • Critical Success Factors
  • Closing Q&A


Listen to this webinar now and register for the 3rd and final webinar which features Dr. David Delaney of SAP sharing insights into how SAP’s HANA in-memory database along with Intel tailored hardware are being leveraged by the American Society of Clinical Oncology, and Dr. Kevin Fitzpatrick, CEO of CancerLinQ who will discuss the fantastic work around the aggregation and analysis of a huge web of real-world cancer care data.


  • Register Now: Big Data in Clinical Medicine: Bringing the Benefits of Genome-Aware Medicine to Cancer Patients
  • Watch Webinar 2: Predictive Healthcare Analytics in a Big Data World: Use Cases from the Field
  • Watch Webinar 1: Future of Healthcare is Today: Leveraging Big Data

Read Part I of this blog series on wearables in healthcare

Read Part II of this blog series on wearables in healthcare

Read Part III of this blog series on wearables in healthcare


This blog series is about how wearables have become more than a passing trend and are truly changing the way people and organizations think about managing health. I hear from many companies and customers who want to understand how the wearables market is impacting patient care as well as some of the changes taking place with providers, insurers, and employers. In this series, I’m sharing some of their questions and my responses. This blog’s question is:

Are there applications for tracking employee movement or flow apart from wellness applications?


Yes, this is a fairly widespread and growing practice in healthcare – monitoring the location and movement of employees, patients, and equipment. So you know where the patient is, where the care team is, where the closest infusion pump is. The “wearable” in this case would be the badge with integrated active or passive RFID. This is typically referred to as RTLS or real-time location system.

I haven’t come across many healthcare organizations that monitor their clinicians; patient and medical device monitoring is more widespread. There are some privacy concerns with employees being monitored, but there are benefits such as easily locating the care team or adjusting workflow throughout the hospital. I suspect a growing number of organizations will deem examples like these to have high enough ROI to allow for the minor privacy intrusion.


What do you think? Have you seen a trend in monitoring medical staff?

Big Data is a hot topic across the entire healthcare ecosystem so I was happy to join a panel of experts in the first of a series of Frost and Sullivan webinars that will help healthcare organizations to better understand how they can make use of the data they have. This first in this webinar series is now available for you to view on demand via the Frost and Sullivan website. The 2nd webinar continues the Big Data theme with a focus on Predictive Analytics in healthcare, please do register now for this must-see session on November 5th.

I was joined by Tod Davis, Manager of BI and Data Warehousing at Children’s Healthcare of Atlanta and Amandeep Khurana, Principal Solutions Architect at Cloudera. I started the webinar by providing an overview of the current state of the healthcare industry in relation to data use, where many disparate data types are held in silos that limit the value considerably. At Intel we’re helping healthcare organizations move towards integrated computing and use of unified data to help deliver better patient outcomes and reduced costs.


An inspirational presentation from Tod Davis highlighted the outstanding work he and colleagues at Children’s Healthcare of Atlanta have been undertaking, including the benefits they are reaping from focusing on making use of data. I’d highly recommend listening to Tod for his take on:


  • Lessons learned in 3 years with Hadoop
  • Use cases including retinopathy of prematurity and using vital signs to monitor pediatric stress
  • An overview of ecosystem tools and high-level data pipeline solution


Amandeep Khurana from Cloudera shared his thoughts on how Cloudera is helping to drive the big data ecosystem. Amandeep’s presentation sparked a number of great questions at the end of the webinar from viewers and I think you’ll find the answers of particular interest. Watch webinar 1 now and share with your own professional network.


Webinar 2 focuses on Predictive Analytics in Healthcare and highlights use cases from the field, including what is sure to be an informative session with Vijay Venkateson of Sutter Health on November 5th 2015. Join us by registering today for webinar 2 to receive the latest updates and reminders for this series.


- Register for Webinar 2 now

- Watch Webinar 1 on demand

- Intel’s role in Big Data in Healthcare

Read Part I of this blog series on wearables in healthcare

Read Part II of this blog series on wearables in healthcare


As I mentioned in the first part of this blog series, wearables have become more than a passing trend and are truly changing the way people and organizations think about managing health. I hear from many companies and customers who want to understand how the wearables market is impacting patient care as well as some of the changes taking place with providers, insurers, and employers. In this blog series, I'll share some of their questions and my responses. This blog’s question is:

What are the primary challenges that companies face in collecting, analyzing, and sharing data generated by wearables?


Data integration and technology interoperability pose challenges. Data in healthcare is still very siloed. In most cases, the provider owns and maintains the electronic health record, the payer the claims data. Lab and prescription data are in their own systems. It’s difficult to access this data where it resides and pull it into a unified repository. Some of the leading electronic healthcare record vendors have built adaptors to pull in some fitness and wellness data. However, a lot of the wearable manufacturers compound the problem by being very insular and not offering an easy API for transferring the data. And there are no standards in place for wearables data. So it can be challenging to integrate patient generated data into traditional healthcare applications.

However, with healthcare today, one can argue there are bigger fish to fry than wearables when it comes to interoperability.

Another big issue is privacy: how will the data be used? When you start tying wearable data to corporate wellness programs and health plans, there is natural concern by employees and members wondering if the data can be used against them. The successful programs are often opt-in, and some include financial incentives or lower premiums if certain performance milestones are reached. Those are the “carrots” that will get people to participate. I have not heard of an example where employees are required to participate in wearing devices, but I imagine that would be less successful.


What questions about data do you have?

Read Part I of this blog series on wearables in healthcare

As I mentioned in the first part of this blog series, wearables have become more than a passing trend and are truly changing the way people and organizations think about managing health. I hear from many companies and customers who want to understand how the wearables market is impacting patient care as well as some of the changes taking place with providers, insurers, and employers. In the next several blogs, I'll share some of their questions and my responses. Today's question is:


What are some of the ways that wearables are impacting providers, payers, and employers as well as patients?


For providers, one example is a pilot that the Mayo Clinic did with Fitbit to track patients recovering from cardiac surgery. They were able to predict which of those patients would be discharged sooner than others based on their activity in the hospital. You can easily see how this use case could be extended outside of the hospital, where you might be able to use wearables to more accurately predict which patients are at the highest risk for hospital readmission. This of course is a key quality metric that hospitals are incentivized to reduce.


On the payer side, organizations are using wearable devices to influence the behavior of their members, encourage a healthier lifestyle, and delay the onset of conditions like obesity and diabetes. Cigna has a program for their own employees where they identify individuals who may be at risk for diabetes. They created a wearables program that encouraged increased activity in those individuals’ daily lives, and it’s making a difference.


Gartner finds that over 2,000 corporate wellness programs have integrated wearables to track employees’ physical activity and incentivize them, sometimes financially, to have a healthier lifestyle. BP rolled out a program with 14,000 employees. Those who were able to achieve 1 million steps (equivalent to roughly 500 miles for an average-size person) over the course of a one year period received a health plan premium reduction the following year.


Now, has anybody been able to aggregate enough wearable data for some serious predictive analytics, or is that down the road? I think that’s down the road; certainly before it becomes mainstream. This will entail significant data integration and big data analytics. We’re looking to pull in multi-structured data from multiple distributed entities and repositories – data from electronic health records, health insurance claims, in some cases socioeconomic data, and all the new sensor data from wearables. If we can pull the continuous stream of patient-generated data into a repository, and overlay more traditional payer and provider data, I suspect the accuracy of predictive models will be significantly improved. We’ll be much better able to identify high-risk patients that will benefit most from additional outreach by a provider organization.


What questions do you have?


In my next blog, I’ll look at the primary challenges companies are facing in collecting, analyzing, and sharing data generated by wearables.

With significant growth projections, wearables have become more than a passing trend and are truly changing the way people and organizations think about managing health. I hear from many companies and customers who want to understand how the wearables market is impacting patient care as well as some of the changes taking place with providers, insurers, and employers. In the next several blogs, I'll share some of their questions and my responses. The first question is:


Please give an overview of the wearable technology industry as it relates to healthcare, and what is the projected growth?


In healthcare there are two main vectors of activity, starting with the health and fitness-oriented consumer devices such as the Fitbit, Fuel Band, and some of the emerging smart watches. These devices measure steps, sleep activity or general activity to try to encourage a healthy lifestyle.


The second vector includes devices found in clinical settings. For example, a company called Sotera Wireless has a vital signs monitoring device that is worn by the patient in the hospital. The key value proposition is that you have continuous monitoring rather than having a nurse come in periodically to take the patient’s vitals. Plus, the patient is not tethered to the wall or the bed so they can move and walk around more freely.


Another example that was not necessarily designed for clinicians is Google Glass, which has received a fair amount of press. It has been approved for use in some hospitals, and there are a number of use cases emerging. One is around streamlining clinician workflow support, so the clinician doesn’t have to interact with other screens in other areas of the patient room or the operating room.


In this first wave of wearable device adoption in the healthcare industry, we see a lot of repurposing of devices that were originally designed for other uses. Over time, we’ll see more sophisticated devices targeted at the industry. Some wearable devices will likely be regulated, for example, those for real-time glucose monitoring. But the payoff will be that purpose-built devices can better meet the complex needs of the healthcare industry, whether it’s, for example, remote monitoring of patients or encouraging health plan members to adopt a healthier lifestyle.


According to IDC, vendors will ship over 45 million wearable units in 2015; an increase of over 133 percent from 2014 worldwide shipments. They predict 45 percent annual growth for shipment volumes over the next several years, meaning roughly 126 million devices in 2019. If you look more broadly at the Internet of Things (IoT), in which wearables are a category, IDC is predicting a 36 billion dollar market for healthcare by 2018, so there are very aggressive growth projections.


In healthcare, the key driver of growth is moving from periodic monitoring, traditionally associated with the occasional visit to a doctor, to daily or even continuous monitoring of the patient’s specific conditions and general wellness. Wearables won’t replace the doctor visit, but they can establish baselines to measure against, and the streaming patient-generated data they sense and collect will improve the accuracy of predictive models to give insight into how a patient is really doing in near-real time. We are seeing just the tip of the iceberg today as companies target the healthcare vertical and build more sensing capabilities into devices.

What questions about wearables do you have? What do you think?

In my next blog, I'll look at some of the ways that wearables are impacting providers, payers and employers as well as patients.

I'm often reminded that within the health IT sector we overlook some of the more simple opportunities to provide a better healthcare experience for both clinical staff and patients. A great example of this was the news that the NHS is investigating the feasibility of providing free Wi-Fi across its estate which it estimates will 'help reduce the administrative burden currently estimated to take up to 70 percent of a junior doctor's day'. I'll cover the often-talked about benefits to clinicians in a later blog but here I want to focus on how access to free Wi-Fi could impact the patient in a myriad of positive ways.


Today many of us see access to the internet via Wi-Fi just like any other utility. It's not something we think of too deeply but we expect it to be there, all day, every day. But access to Wi-Fi in an NHS hospital can either come at a price or is not available at all. The vision put forward by Tim Kelsey, NHS England’s National Director for Patients and Information, could truly revolutionise the continuum of care experience and fundamentally change the relationship between patient/family and hospital. I've highlighted five of the main benefits below:


1. Enhances Education

Clinicians will say that a better informed patient is more likely to buy in to their treatment plan. Traditionally an inpatient will be delivered updates on their condition verbally by a doctor 'doing the rounds' once or twice per day at the bedside. With the availability of free Wi-Fi in hospitals and the much-anticipated electronic patient access to all NHS funded services by 2020, I anticipate a patient being able to simply log-in to see real-time updates about their condition at any time of the day via their electronic health record. And Wi-Fi may offer opportunities to provide access to online educational material approved by the NHS too.  I would add a cautionary note here though around the differing levels of interpretation of medical data by clinicians and patients.


2. Connecting Families

A prolonged stay in hospital affects not just the patient but the wider family too. Free Wi-Fi changes what can sometimes be a lonely and isolated period for the patient by bringing the family 'to the bedside' outside of traditional visiting hours through technologies such as Skype or email. And those conversations may well include patient progress updates thus reducing the strain on nurses who, at times, provide updates over the telephone. Additionally, family will be able to spend more time visiting patients while still being able to work remotely using free Wi-Fi.


3. Future Wearables

As the Internet of Things in healthcare becomes more commonplace we're likely to see increasing examples of how wearable technology can be used to not only monitor patients in the home but in a clinical setting too. Tim Kelsey used the example of patients with diabetes, 1/5th of whom will have experienced an avoidable hypoglycaemic episode while in hospital. Using sensor technology connected to Wi-Fi will help minimise these incidents and ensure patients do not experience additional (and avoidable) complications during their stay in hospital. Again, the upside to the healthcare provider is a reduction in the cost of providing care.


4. Happier Patients

Talk to patients (young or old) that have spent an extended time in hospital and they will more often than not tell you that at times they felt a drop in morale due to having their regular routine significantly disrupted. By offering free Wi-Fi patients can use their own mobile devices to pull back and continue to enjoy some of those everyday activities that go a long way to making all of us happy. That might include watching a favourite TV programme, reading a daily newspaper or simply playing an online game. Being connected brings a sense of normality to what is undoubtedly a period of worry and concern, resulting in happier patients.


5. Reducing Readmissions

When we look at the team of people providing care for patients it’s easy to forget just how important family and friends are, albeit in a less formal way than clinicians. When it comes to reducing readmission my mind is drawn to the patient setting immediately after discharge from hospital where it’s likely that family and close friends will be primary carers when the patient returns home. I’m seeing a scenario whereby the patient and caregiver in a hospital connect to family members, using Skype via Wi-Fi for example, to talk through recovery and medication to help ease and increase the effectiveness of that transition from hospital to home. I believe this could have a significant impact on readmission rates in a very positive way.


Meeting Security Needs

Wi-Fi networks in a hospital setting will, of course, bring concerns around security, especially when we talk of accessing sensitive healthcare data. This should not stop progress though as there are innovative security safeguards created by Intel Security Group that can mitigate the risks associated with data transiting across both public and private cloud-based networks. And I envisage healthcare workers and patients will access separate Wi-Fi networks which offer enhanced levels of security to clinicians.


Vision to Reality

Currently there are more than 100 NHS hospitals providing Wi-Fi to patients, in some cases free and in others on a paid-for basis. What really needs to happen though to turn this vision of free Wi-Fi for all into a reality? There are obvious financial implications but I think there are great arguments for investment too, especially when you look at the clinical benefits and potential cost-savings. A robust and clear strategy for implementation and ongoing support will be vital to delivery and may well form part of the NHS feasibility study. I look forward to seeing the report and, hopefully, roll-out of free Wi-Fi across the NHS to provide an improved patient experience.


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Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.

Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts


If you haven’t noticed lately, we’re seeing an increase in demand for analytics driven by health reform. However, for many organizations, the culture needs to change in order to fully embrace analytics as part of the standard practice of care. Many will agree that there is too much information for clinicians to rely only on training and experience as they treat patients rather than leverage insights from analytics for clinical decision support. Providers who embrace analytics will be best positioned to improve patient care from the perspective of decreased cost, improved efficiency and enhanced patient experience.

In my role at Intel, I’m often asked where “big data” capabilities can apply to healthcare. One of the areas that always top my list are the clinical records. Roughly 70 percent of the electronic health record (EHR) includes clinically relevant information that is unstructured or in free form notes, meaning potentially critical pieces of information are not easily accessible to providers. There are many tools that can use sophisticated natural language processing techniques to pull out the clinically relevant information however, the culture has to be ready to accept those kinds of solutions and use them effectively.


Overcoming challenges

Personalized medicine analytics is a combination of data bits coming from multiple data sources and each comes with its own unique set of challenges. There is the payer side, the clinical side, the biology, life sciences and genomics side and finally, the patient side and the work that we’ve been doing is in all of these areas. We look at big data and health and life sciences as the aggregation of all of these different data sources and address the challenge of how this content will be generated, moved, stored, curated and analyzed. 

The goal is to take advantage of the sophisticated analytics and sophisticated technology capabilities and merge those with the changes to workflow on the healthcare side and the life sciences side and pull those two areas together to deliver care specific to an individual.  This is very different from treating a large cohort of all diabetes patients or all breast cancer patients in exactly the same way. 

Personalized medicine is really two different perspectives. First, is on the genomics side, where you include as an attribute to the patient care pathway the genome of that patient, comparing it against a reference genome to determine what is different about the patient as an individual or how their tumor genome differs from their normal DNA. Second, there’s the population health aspect to personalization; really understanding all of the data that is available in patient records whether it be structured or unstructured data and then developing care plans specific to that individual.  For example, micro segmenting a population taking into account comorbidities and socio-economic factors with the help of advanced analytic tools.


Safety opportunities

There was a recent article in the Journal of Patient Safety[1] that stated that there may be more than 400,000 premature deaths per year that are preventable in a hospital setting. Furthermore, 10-20 times more than that statistic cause serious harm but don’t result in death.  For example, big data and analytics are being used to help identify and diagnose sepsis earlier so that it can be treated more effectively and be less costly for the payer and provider.

A great example of using wearables to better understand disease progression is the work that Intel is conducting in partnership with the Michael J. Fox Foundation for Parkinson’s research (see video above). Individuals wearing specialized devices will be tracked around the clock; observations will be recorded 300 times a second and all information will be stored in the cloud. What this means for researchers is that they will go from evaluating a few data points per month to observing 1 gigabyte of data every day.


By analyzing the existing data that is available, adding wearables, improving the velocity in analyzing data, there are a lot of opportunities to improve patient safety using some of these tools. 

What questions about clinical analytics do you have? How are you using data in your practice or organization?

[1] James, John T. PhD. “A New, Evidence-based Estimate of Patient Harms Associated with Hospital Care.” Journal of Patient Safety (2013): http://journals.lww.com/journalpatientsafety/Fulltext/2013/09000/A_New,_Evidence_based_Estimate_of_Patient_Harms.2.aspx


The goal of personalized medicine is to shift from a population-based treatment approach (i.e. all people with the same type of cancer are treated in the same way) to an approach where the care pathway with the best possible prognosis is selected based on attributes specific to a patient, including their genomic profile.


After a patient’s genome is sequenced, it is reconstructed from the read information, compared against a reference genome, and the variants are mapped; this determines what’s different about the patient as an individual or how their tumor genome differs from their normal DNA.  This process is often called downstream analytics (because it is downstream from the sequencing process).


Although the cost of sequencing has come down dramatically over the years (faster than Moore’s law in fact), the cost of delivering personalized medicine in a clinical setting “to the masses” is still quite high. While not all barriers are technical in nature, Intel is working closely with the industry to remove some of the key technical barriers in an effort to accelerate this vision:


  • Software Optimization/Performance: While the industry is doing genomics analytics on x86 architecture, much of the software has not been optimized to take advantage of parallelization and instruction enhancements inherent with this platform
  • Storing Large Data Repositories: As you might imagine, genomic data is large, and with each new generation of sequencers, the amount of data captured increases significantly.  Intel is working with the industry to apply the Lustre (highly redundant/highly scalable) file system in this domain
  • Moving Vast Repositories of Data: Although (relatively) new technologies like Hadoop help the situation by “moving compute to the data”, sometimes you can’t get around the need to move a large amount of data from point A to point B. As it turns out, FTP isn’t the most optimal way to move data when you are talking Terabytes


I’ll leave you with this final thought: Genomics is not just for research organizations. It is accelerating quickly into the provider environment. Cancer research and treatment is leading the way in this area, and in a more generalized setting, there are more than 3,000 genomic tests already approved for clinical use. Today, this represents a great opportunity for healthcare providers to differentiate themselves from their competition… but in the not too distant future, providers who don’t have this capability will be left behind.


Have you started integrating genomics into your organization? Feel free to share your observations and experiences below.


Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.

Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts

How frequently does your organization lose track of medical devices in your hospital?  How many precious seconds and minutes are lost tracking down the closest infusion pump, ventilator or wheelchair?  For many healthcare organizations, these are everyday (re: multiple times per day) occurrences. An even greater concern is losing track of a patient, which happens more frequently than the average citizen might expect.


Real-time location system (RTLS) solutions are used in healthcare to help address these challenges. Placing an RFID tag on a medical device or providing patients with wristbands that have these tags embedded can make it much easier for employees to track down devices, equipment and patients, but we have barely scratched the surface of the positive impact location, or premises-aware capabilities can have on healthcare organizations and clinical workflows.


mHealth is taking the world by storm, and hospitals are no exception. Many health systems are empowering their clinicians with tablets to enable more efficient access to health information and better collaboration with the patient at the point of care. What if the patient chart itself was premises-aware?  When the clinician walked up to their patient, the chart could be auto-populated with the patient’s health record. A clinician on rounds could examine the chart to ensure their next patient is in their room as expected.  As team-based care proliferates, clinicians can track down other team members more easily. Devices or storage drives that are improperly removed from the hospital can be automatically disabled, preventing an expensive security breach.


Some of these use cases are enabled (or improved) through the integration of premises-aware capabilities into computing devices such as tablets, laptops, PC’s and even servers and storage drives. Intel is working with the Intermountain Healthcare Transformation Lab to investigate the utility of these use cases, with some key findings described in this whitepaper and we are building these capabilities into client devices to bring solutions to the healthcare industry with the help of our strategic partners.


Getting a Headstart on Location-Based Services in the Enterprise


Quickly Find the Resources You Need With a Real-Time Location System


Have any of you had successes or challenges integrating location aware solutions into your organization?  Please share your thoughts below.


Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.

Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts

It wasn’t too long ago when the coolest new gadgets were provided by your IT department. Today, the latest and greatest devices are coming from the consumer world, with IT departments being asked to support a growing number of employee owned smartphones, tablets, 2 in 1 units and everything in-between. 


Employees are now accustomed to the capabilities they have in their consumer lives and expect a comparable experience on the job. This transformation is not only on the “device side.” The cloud, which has fueled this device revolution is characterized by apps that are available 24x7x365 on any device, in any location. This always-on, always connected model has significantly enhanced the ability for people to collaborate with apps that enable file sharing, text/voice/video communication, note taking and the like.


So what happens when this consumer world and the enterprise world collide in a regulated industry like healthcare? The answer is that end-users will use these devices and apps, with or without the blessing of their IT department. This brings significant risks to healthcare organizations that are subject to stringent security and privacy regulations, breach reporting requirements, and audits. 


An example of this that I have heard on multiple occasions is a clinician taking a picture on their smartphone, and texting it to a colleague to get an opinion. I like this example because it simultaneously demonstrates the power of cloud, collaboration, and communication capabilities that have emerged in (relatively) recent years but also raises some obvious concerns regarding the security and privacy of PHI.


HIMSS Analytics collaborated with Intel earlier this year on research in this area. Forty six percent of the clinical end-users surveyed thought these kind of end-user workarounds were happening regularly in their organizations. The top reasons for these workarounds centered on security controls being too cumbersome and IT departments being too slow to enable new technologies. Co-worker collaboration was cited as (easily) the top activity leading to these workarounds.


ChrisG Graphic.jpg


So what does this research tell us? I think there are several key takeaways that IT departments should consider to limit these kind of risks:


  • Disallowing BYOD has limited effectiveness: Unless a healthcare organization is going to collect end-user owned devices at the door and return them at the end of the day, end-users will engage in this kind of activity on their personal devices. IT needs to think about how it can empower clinicians safely.
  • Need to offer employees compliant alternatives: There are solutions for messaging, video conferencing and file sharing that follow healthcare regulations such as HIPAA (vendors will sign BAA’s, etc.). IT needs to offer employees a comparable experience to what they are used to as consumers.
  • Co-worker collaboration is a good place to start: While there are hundreds of thousands of consumer apps, the research cited above highlights co-worker collaboration as the area that is leading to the highest number of end-user workarounds.
  • Clinical end-user experience is critical: Often times, complex/cumbersome security controls will drive activity that is out of compliance with security policy. Engaging clinicians and seeking security controls that integrate seamlessly to their workflow is essential.


Have any of you had success mitigating these risks in your organization?


Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.

Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts


A growing number of healthcare organizations view data and analytics as instrumental to achieving their objectives for improved quality and reduced cost. Glenn D. Steele Jr., MD, CEO of Geisinger, recently outlined how his organization is using analytics to advance their population health initiatives.


While healthcare is currently behind other industries when it comes to use of business intelligence and analytics, this is changing. The fundamental transformation driving this change is the (worldwide) migration from volume-based care to value-based care. Organizations with the capacity to optimize care based on the latest medical literature, their patients’ specific condition(s), and, ultimately, their genomic profile, will survive. Those that are unable to update their culture, rely only on personal experience, medical training, and (often times) a trial and error approach, will be left behind.


The above video excerpt from the Intel Health & Life Sciences Innovation Summit panel, Care Customization: Applying Big Data to Clinical Analytics & Life Sciences, lets you hear how leaders from provider, payer, life sciences and analytics organizations describe key use cases they have implemented, infrastructure trends, and practical steps to get started.


While payers are typically farther along in their use of analytics than providers (particularly in the area of claims analytics to optimize claims processing and reduce false claims), providers are using analytics in the following (representative) areas:


  • Reduce unplanned readmissions
  • Reduce hospital acquired infections
  • Identify cost inefficiencies
  • Measure quality / outcome improvements (across a health system if applicable)


One of the key barriers to the use of analytics we often see in healthcare is the organizational culture. This can be challenging as culture is something that doesn’t change overnight. So what can we do about it? I will leave you with two pieces of simple advice:


  1. Identify a clinical champion: Culture change won’t happen based on a top-down approach or through programs driven exclusively by the IT department. There must be a partnership between IT and the clinical side of the house to identify needs and create value for the organization.
  2. Start with real use cases: Before you build anything, identify a small set of use cases that will deliver value and demonstrate early success for your organization. Build on that success to scale.


Are you deploying big data or analytics solutions in your organizations?


Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.

Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts

In one of my previous blogs, I talked about the top three benefits of an open, standards-based server architecture for Epic EHR. This time around, I’d like to highlight Hackensack University Medical Center (HackensackUMC), as one of the hospitals leading the way with this approach.



HackensackUMC is a top-rated, 775-bed, non-profit teaching and research hospital based in New Jersey. Like many other healthcare organizations that use Epic EHR, it previously had Epic deployed on two computing platforms: virtualized x86 servers for the end-user computing environment, and RISC-based platforms for the backend environment (Caché database tier).


Challenge: Against the backdrop of fast growing data volumes, increasing performance requirements and competition for patients from other healthcare organizations, HackensackUMC wanted to reduce its costs while at the same time enabling scalability to accommodate future growth. Its previous environment ran counter to these objectives in terms of hardware/software cost, support cost and maintenance cost. Not only did it require separate groups of administrators with expertise to support the two distinct computing platforms, but it also needed to maintain separate processes for disaster recovery and business continuity.


Solution & Benefits: HackensackUMC decided to standardize its Epic deployment on a virtualized x86 server infrastructure for end-user and backend environments as well as its storage subsystem. It measured a 50 percent reduction in TCO with the new environment and 40-50 percent reduction in operating costs (related to hardware, software and OS support). 


In addition, HackensackUMC achieved a 70 percent reduction in the datacenter footprint of its Epic deployment. Virtualization of the backend environment enabled the organization to move workloads around more easily and improved application up-time with software features such as DRS (Distributed Resource Scheduler) and HA (High Availability).


Finally, to ensure the environment was secure, HackensackUMC relied not only on administrative and technical safeguards, but also sophisticated technical safeguards such as advanced DLP (Data Loss Prevention), in order to mitigate the risk of unauthorized access to sensitive information such as protected health information.


To learn about this project in more detail, visit here.


Have you deployed Epic EHR on a standard, x86 architecture or are you considering this approach? Please feel free to share your observations and experiences below. You can follow me on Twitter @CGoughPDX.



Chris Gough is a lead solutions architect in the Intel Health & Life Sciences Group and a frequent blog contributor.


Find him on LinkedIn

Keep up with him on Twitter (@CGoughPDX)

Check out his previous posts

As the healthcare industry transitions from fee-for-service to fee-for-value, and to team-based care models that require a high degree of care coordination (such as PCMH), a more holistic, 360 degree view of the patient is needed. Over time, this patient view will be built not only from traditional data types such as claims data and healthcare data (e.g. from the EHR), but also non-traditional data types such as patient or member sentiment data from social networks. So what new approaches are needed to respond to this changing data landscape?


Organizations need to be able to apply analytics to Big Data; data from varied repositories that exist structured, semi-structured and unstructured form.  Solutions that enable this need to be high performance, horizontally scalable, and balanced across compute, network and storage domains (e.g. to mitigate impact of I/O bottlenecks). High-performance analytics software, with capabilities such as natural language processing, machine learning, and rich visualization also enable these Big Data solutions. 


Innovative Payers and Providers are pursing these solutions to improve the user experience for their patients and members, better market produces and improve outreach to encourage healthy lifestyles. Take a look at this paper to learn what Blue Cross Blue Shield of North Carolina and Carolinas HealthCare System are doing in these areas.


The paper also describes 5 steps for getting started with Big Data:


1. Work with business units to articulate opportunities

2. Get up to speed on technology

3. Develop use cases

4. Identify gaps between current and future state capabilities

5. Develop a test environment


Payment reform and care models that foster a patient-centric approach have the potential to transform healthcare.  Analytics solutions that break down traditional data silos to develop a complete view of the patient, enable effective outreach programs, and promote collaboration across the continuum of care will be the technical foundation of this transformation.


Are any of you deploying Big Data or advanced analytics solutions in your organizations? Please feel free to share your observations and experiences below.  You can follow me on Twitter @CGoughPDX.

In 2012, Epic added Red Hat Enterprise Linux (RHEL) running on x86 servers to its list of supported platforms for its mission critical Electronic Health Record (EHR) database (previously, Epic only supported database software on UNIX servers).


To learn about this solution and key benefits firsthand, I encourage you to register for the upcoming webinar, How TriRivers Health Partners Optimized and Virtualized Its Electronic Records Infrastructure. In this space, however, I will provide an overview of the solution and describe the top three benefits over alternative, RISC-based, database platform architectures.


Epic’s solution for Linux on x86 is virtualized, and subsequently provides the benefits that HIT organizations have come to expect from virtualized infrastructure. Intel and VMware have collaborated closely over the years to ensure that software runs in virtual machines with near-native performance. Barriers that may have prevented some mission critical workloads from being virtualized in the past are reduced or eliminated with each passing generation of Xeon and vSphere (for example, VMware recently announced that the host-level configuration maximum for RAM has doubled from 2TB to 4TB with the introduction of vSphere 5.5).


With this background, here are the top three benefits of an open, standards-based, server architecture for Epic EHR:


Supportability: From 1996 to 2016 (estimated by IDC), the installed base of x86 servers will have increased from 56 percent of the overall server market to 98 percent. Accordingly, the number of administrators qualified to support and maintain these systems has also increased, making it easier to find qualified staff. Furthermore, the “end-user computing” side of the Epic EHR has already moved to x86.  Standardizing on one server architecture can simplify the support model by reducing training and headcount requirements)


Reliability: Not only is RHEL running on x86 a proven platform for hosting mission critical applications, but the Epic solution further improves reliability by virtualizing the infrastructure with VMware vSphere. The solution includes a virtualized cluster of x86 servers running the database (and associated capabilities that enable reporting, disaster recovery, etc). Should one of the hosts fail, advanced vSphere capabilities such as Distributed Resource Scheduling (DRS) and High Availability (HA) will automatically move affected VM’s to another host in the cluster


Flexibility: When there is an ecosystem of vendors/OEMs developing compatible (x86) systems, the end-user (HIT organization) benefits. Competition provides choice and leads to improved quality and reduced cost through economies of scale.


Do any of you have experience yet deploying the Epic database tier on Linux/x86? Please feel free to share your observations and experiences below. You can follow me on Twitter @CGoughPDX.

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