In this new video, Carl Johnson, senior physician at Optum Analytics, talks about how data analytics can help physicians reach the triple aim: improve the patient experience, improve the health of populations, and lower costs.


Take a look at the clip and let us know what questions you have about big data analytics in healthcare. What’s your vision?


Dr.  Charles Macias is the Chief Clinical Systems Integration Office for Texas Children’s Hospital in Houston and a leading proponent of population health analytics. In his practice as an emergency room physician, Macias has seen first-hand the impact of population health and the potential it has to streamline workflows and improve outcomes. We recently sat down with him to discuss his views on population health analytics and where it is headed in the future.


Intel: What is your definition of population health analytics?


Macias: Population health analytics really refers to how an organization, or government, is addressing the healthcare issues of a population at large. While many people think of population health as an entire region, state or country, there’s variable definitions for how we could parse out one’s segment of a population. In my particular setting, for example, we service the pediatric population up to age 21. Our definition of population health is really about what’s happening to children.


Intel: In another blog you told the story of a young asthma patient. How does that experience years ago compare to today in terms of analytics?


Macias: From a population health perspective, in 2004, when that story took place, population health really wasn’t about population health; it was about treating single patients. That was a paper-based world. We had to depend on published research to understand something about the populations, and when you depend only on the published evidence, you’re assuming that somewhere out in this periphery of research you’re going to be able to translate it down to a population that looks like your own. So, if that direct connection doesn’t exist, if your population is very different, you’re at odds with what you’re really going to know about how to treat your population. Today, the story is very different. Today, we have electronic medical records. Today, we have an electronic data warehouse. We can store data and information about our populations. What used to take me six months to find out now can take about 24 hours thanks to updates in our enterprise data warehouse. I have the answer at my fingertips.


Intel: Today in your practice, how do analytics impact your workflow?


Macias: Analytics today has a completely different impact than it did on clinicians five years ago, 10 years ago, and certainly 20 years ago. Number one, it’s given us the understanding that the 800,000 medical articles that are out there that are essentially non-digestible bits of information. They can systematically be filtered into some kind of clinical standards that can be placed into the analytics and matched against the analytics to say this population parallels what this evidence is telling us and, therefore, this clinical standard should really interdigitate with that work and we should understand how that population fits in with that clinical standard. So, now we have the ability to use best practice alerts, health maintenance reminders, and create long term plans of care embedded directly within the medical record.


Intel: What’s your vision for the future of analytics?


Macias: My vision for analytics is in the world of decision support. It’s really about making clinicians’ workflow much smarter and quicker, and much easier. We already know that when we start a day, we have so many patients to see. In my setting I know I’m going to be overwhelmed with a number of patients in the emergency department. If there are ways to translate the work that’s ongoing, the workflow within the EMR to the kind of decision support that’s going to make prediction rules and strategies much easier, that’s going identify the patients at risk for bad outcomes and link them to the right strategies that will help obviate a need for much more escalated care in the future. That’s a win/win. As we begin to place resources against the value that’s given, I see a lot better alignment with where our healthcare infrastructure supports those strategies.


Intel: How do you work with Health Catalyst to get the information you need?


Macias: The role that Health Catalyst has had in our data governance has been critical to evolving to where we are as an organization. We have learned from how we look at populations of care and how we look at our approaches to merging the science of care with operational care process teams. Predictive analytics comes from how we house data in our enterprise data warehouse. It really goes beyond the EMR’s capability of doing bedside analytics; it’s about the bigger picture of integrating all of those critical domains to effectively improve outcomes. It would not have been possible without our partnership with Health Catalyst.


In the above video, Dave Antsey, global head of life sciences at Cray, Inc., talks about scientific innovation and how it relates to computer infrastructure. He says the coming of big data will have an impact on an organization’s processes and workflow.


Watch the clip and let me know what questions you have. What’s your view of big data in healthcare?

Genome sequencing has moved from bench research into clinical care—and it’s producing medical miracles. Now, it’s time to make genome sequencing an everyday part of our clinical workflows. That day is closer than you might think.


Those were the messages James Lowey shared at HIMSS 2015 in Chicago. As VP of technology at TGen—the nonprofit Translational Genomics Research Institute—James is at the forefront of efforts to bring next-generation genomic sequencing into the clinical mainstream and use it to transform the way we diagnose, treat, and prevent illness.


At the HIMSS session, James described the broad range of areas of clinical interest for genomic data. He also discussed the compute infrastructure necessary to provide cost-effective performance and scalability for large-scale production processing of genomic data.


Recently, our healthcare team interviewed James to learn more about his TGen’s strategy. In this case study, James tells us where he thinks we’re heading—and how fast we’re getting there. He also highlights social and policy issues that must be addressed, and points out the need for ongoing research to establish evidence-based clinical protocols.




I hope you’ll read the TGen case study and join the conversation. Is your organization incorporating genomic analysis into clinical workflows? If so, can you share any advice or best practices? If not, how close are you? What are your next steps? What’s holding you back? Let me know in the comments section. Or become a member of the Intel Health and Life Sciences Community.


Learn more about Intel® Health & Life Sciences.


Download the session handout from HIMSS 2015, Using Genomic Data to Make a Difference in Clinical Care.


Stay in touch: @IntelHealth, @hankinjoan

In my last blog, Healthcare Breaches from Loss or Theft of Mobile Devices or Media, I looked at breaches resulting from loss or theft of mobile devices containing sensitive patient data. In this blog I build on this with another very common type of breach that results from healthcare employee accidents or workarounds. In this case a workaround is defined as a well-intended action the employee takes to get their job done but that is out of compliance with the privacy and security policy of the healthcare organization and adds risk.


The Ponemon 2015 Cost of a Data Breach: United States study reveals that 19 percent of all breaches across industries, including healthcare, are caused by human error. A further 32 percent are caused by system glitches that include both IT and business process failures, in which human error can be a key contributing factor. The total average cost of a single data breach event is $6.53 million, or $398 per patient record (the highest across all industries).


In a previous blog Is Your Healthcare Security Friendly? I discussed how if usability in healthcare solutions is lacking, or security is cumbersome, it can drive the use of workarounds. The use of workarounds is further exacerbated with so many BYOD options and apps now available, giving well intentioned healthcare workers amazing new tools to improve the quality and lower the cost of care, but these tools often were not designed for healthcare and add significant additional risk and in a worst case lead to breaches.


An example of this type of breach is shown in the info graphic below where the first failure is ineffective security awareness training for healthcare workers on how to avoid accidents and workarounds. The second failure is usability is lacking in a solution used by healthcare workers, or security is too cumbersome for example too many logins, or the healthcare IT department is perceived by healthcare workers to be too slow or overly restrictive in enabling new technologies. A 2014 HIMSS Analytics Study Curbing Healthcare Workarounds: Driving Efficient Co-Worker Collaboration reveals that 32 percent of workers use workarounds every day, and 25 percent use workarounds sometimes.


Keeping in mind that any one of these could result in a breach this is a staggering finding and highlights how common workarounds are and how significant the associated privacy and security risks are. The third failure leading to breach in this example involves the healthcare worker using a BYOD device such as a smartphone with an app that has a cloud backend, in order to collaborate with a healthcare co-worker. An example of this could be a healthcare worker taking a photo of a patient and attempting to use a file transfer app to share it with a colleague. In step four any data the healthcare worker puts into the app, or data collected by the app itself such as location history, is sent to the app backend or “side cloud” where in step 5 it is accessed by unauthorized individuals leading to a breach.


David_Security sept.png


Security is complex, and there are many safeguards required to effectively mitigate this type of breach. Maturity models have achieved wide adoption and success in healthcare, for example the HIMSS EMRAM (EMR Adoption Model) has been used by 5300+ provider organizations worldwide. Maturity models are a great way to simplify complexity and enable rapid assessment of where you are and what you need to do to improve.


In the infographic above, beneath the sequence of events leading to this type of breach, is a breach focused maturity model that can be used to rapidly assess your security posture and determine next steps to further reduce residual risk. There are three levels in this maturity model, Baseline includes orange capabilities, Enhanced adds yellow capabilities, and Advanced adds green capabilities. Only safeguards relevant to mitigating this type of breach are colored in this maturity model. Other grayed out blocks, while important in mitigating risk of other types of breaches, do not play a significant role in mitigating risk of breaches from insider accidents or workarounds. There are many risks in healthcare privacy and security. This model is focused on breaches. A holistic approach is required for effective security, including administrative, physical and technical safeguards. This maturity model is focused mostly on technical safeguards. Below I briefly review each of the safeguards relevant to this type of breach.


A baseline level of technical safeguards for basic mitigation of healthcare breaches from insider risks requires:


  • User Awareness Training: educates healthcare workers on how to be privacy and security savvy in delivering healthcare, and the risk of accidents and workarounds, and viable safer alternatives
  • Device Control: prevents the unauthorized use of removable media, for example USB sticks that workers may attempt to use to move sensitive patient data unsecured
  • Mobile Device Management: keeps mobile devices secure, including BYOD devices used by healthcare workers, addressing risks including patient data loss or unauthorized access
  • Anti-Malware: detects and remediates malware infections of healthcare worker devices, including malware employees may accidentally encounter on infected websites or apps
  • DLP Discovery: discovers where sensitive patient data is at rest and how it moves over the network, a key first step in an ongoing inventory of sensitive data you need to protect. This can be used to detect unsecured sensitive data and uncover accidents or workarounds leading to it, enabling correction before a breach
  • Vulnerability Management and Patching: involves proactively identifying vulnerabilities and patching them to close security holes before they can lead to a breach. This is particularly important with healthcare worker devices used to access the Internet and at risk of being exposed to malware and attacks
  • Email Gateway:  enables you to catch unsecured patient data attached to emails and also defends against malware attached to emails, and phishing attacks
  • Web Gateway: can detect malware from healthcare workers web browsing the Internet, and defend against attempted drive-by-downloads that may otherwise lead to data loss and breach


An enhanced level of technical safeguards for further improved mitigation of risk of this type of healthcare breach requires addition of:


  • Secure Remote Administration: enables healthcare IT to efficiently, securely and remotely administer endpoint devices so they are up to date with the latest patches and safeguards to defend against breaches from accidents and workarounds
  • Endpoint DLP: Data Loss Prevention enforced on endpoint devices to monitor and address day-to-day end-user risky actions that can lead to accidents, or be used in workarounds
  • Policy Based File Encryption: can automatically encrypt files containing sensitive healthcare data based on policy and protect the confidentiality of those files even if put at risk in an accident or workaround
  • Network DLP Monitor / Capture: enables healthcare security to gather information about data usage patterns, enabling proactive risk identification, and better decisions on how to mitigate


An advanced level of security for further mitigation of risk of this type of breach adds:


  • Network DLP Prevention: ensures that sensitive healthcare data only leaves the healthcare network when appropriate, and helps defend against loss of sensitive healthcare information from accidents or workarounds
  • Digital Forensics: enables you to determine in the event of an accident or workaround whether a breach actually occurred, and if so the nature of the breach, and exact scope of data compromised


Healthcare security budgets are limited. Building security is an ongoing process. The maturity model approach discusses here can be used in a multi-year incremental approach to improve breach security while keeping within limited annual budgets and resource constraints.


What questions on healthcare security do you have?

There’s a lot of talk about Big Data in healthcare right now but for me the value of Big Data is not in the size of the data at all, the real value is in the analytics and what that can deliver to the patient. Healthcare reform is underpinned by a shift to value-based care where identifying best care, best treatment and best prognosis are all driven by business intelligence and business analytics.


I want to share my thoughts on this in a little more detail from a presentation I gave at the NHS England Health and Care Innovation Expo in Manchester, where Intel and Oracle highlighted some of the great work happening around identifying healthcare needs using predictive analytics.


Opportunities for Data Use in Healthcare are Rich

Everywhere I look in healthcare there seems to be an abundance of data, for example, it’s estimated that the average hospital generates 665TB of data annually. But it’s not just the volume of data that presents challenges, the variety of data means that the opportunities for its use are rich but often tempered by some 80 percent of that data being unstructured. Think X-rays, CT and 3D MRI scans as just one area where technology has vastly improved the quality of delivery of these services - but with a consequential exponential growth in resulting data.


Does more data really bring better care though? I’d argue that it’s the analysis of data that holds the key to solving some of the big challenges faced by providers across the world rather than how much data can be captured or accessed. With that in mind Intel and Oracle are working to help providers integrate, store and analyse data in better ways to deliver improved patient outcomes, including:


  • Enabling early intervention and prevention
  • Providing care designed for the individual
  • Enhancing access to the care for the underserved


Our approach to developing solutions in this area encompasses several areas on the Big Data stack. There’s the core technology which covers the CPU’s, SSD, Flash, Fabrics, Networking and Security. And then there’s the investment in the Big Data platform which talks to the proliferation of Hadoop by making it easier to deploy. Finally, but no less important, are the analytics tools and utilities which help broaden analysis and accelerate application development.


Oracle and Project O-sarean Empowers Citizens

I’d like to highlight a couple of great examples where data sharing is helping to deliver active patient management. Oracle has played a part in the successful Project O-sarean in the Basque Country where the regional public healthcare system covers some 2.1m inhabitants with 80 percent of patient interactions related to chronic diseases. It has been predicted that by 2020 healthcare expenditure would need to double if systems and processes did not change. The results of this new multi-channel health service, powered by voluminous amounts of data, are impressive and include:


  • Empowered citizens with access to Personal Health records
  • Active patient monitoring for those with chronic diseases
  • Health and drug advisory service providing evidence-based advice


The clinician benefits too as 11 acute hospitals, 4 chronic hospitals, 4 mental health hospitals, 1,850 GPs and 820 pharmacies are connected using Oracle solutions to collaborate through the sharing and analysis of patient data. This is a fantastic example of interoperability in healthcare. (Download a PDF from Oracle for more information on the Project O-sarean).


Intel helps Partners Deliver Predictive Analytics Innovations

Here at Intel we’ve been working with MimoCare to improve support for independent living with the Intel® Intelligent Gateway™. Through the use of sensors MimoCare technology will help the elderly remain safe living independently in their homes for longer. The use of analytics to identify normal patterns of behavior and predict events means that trigger alerts can be set at the family, friends and carers while the consolidation of aggregated data can help wider clinical research too. Read more on the great work of MimoCare and Intel’s role in the Internet of Things in Healthcare here.


I think you’ll find a recent blog by my colleague, Malcolm Linington, interesting too – he takes a look at how GPC are innovating to help guide wound care specialists to deliver the most effective treatment plan possible, develop standardized assessment practices, enhances clinical-based decision-making and ultimately provides cost-savings by streamlining wound care procedures.


I’m excited to share these stories with you as I feel we are only at the start of what is going to be a fantastic journey of using predictive analytics in healthcare. It would be great to hear about some of your examples so please do tweet us via @intelhealth or register and post a comment below.


Find Claire Medd RGN BSc (Hons) on LinkedIn.


Read More:


Today I gave a presentation to the NHS England Health and Care Innovation Expo alongside Dr. Jonathan Sheldon, Global VP Healthcare at Oracle where we discussed the role of precision medicine. I wanted to be able to share some of our thoughts from the session with a wider audience here in our Healthcare and Life Sciences community.


More specifically we talked through trends impacting healthcare and population health, what’s driving innovation to enable the convergence of precision medicine and population health and how we at Intel are working with Oracle on a shared vision.


Delivering Precision Medicine to Tackle Chronic Conditions

I’d like to underline all of what we discuss in precision medicine by reinforcing what I’ve said in a previous blog, that as somebody who spends a portion of my time each week working in a GP surgery, it’s essential that I am able to utilise some of the fantastic research outcomes to help deliver better healthcare to my patients. And for me, that means focusing in on the chronic conditions, such as diabetes, which are a drain on current healthcare resources.


The link between obesity and diabetes is well-known but it’s only when we see that 1/3rd of the global population are obese and every 30 seconds a leg is lost to diabetes somewhere in the world can we start to grasp the scale of the problem. The data we have available around diabetes in the UK highlights the scale succinctly:


  • 1 in 7 hospital beds are taken up by diabetics
  • 3.9m Britons have diabetes (majority Type 2, linked to obesity)
  • 2.5m thought to have diabetes but not yet diagnosed


To combat the rise of diabetes there is some £14bn spent by the NHS each year treating the condition, including £869m spend by family doctors. What role can precision medicine play in creating a new standard of clinical care to help meet the challenges presented by chronic conditions such as diabetes?


Changing Care to Reduce Costs and Improve Outcomes

I see three changing narratives around care, all driven by technology. First, ‘Care Networking’ will see a move from individuals working in silos to a team-based approach across both organisations and IT systems. Second, ‘Care Anywhere’ means a move to more mobile, home-based and community care away from the hospital setting. And third, ‘Care Customization’ brings a shift from population-based to person-based treatment. Combine those three elements and I believe we have a real chance at tackling those chronic conditions and consequently reducing healthcare costs and improving healthcare outcomes.


How do we achieve better care at lower costs though from a technology point of view? This is where Intel and Oracle,with industry and customers, are working together to make this possible by overcoming the challenges of storing and analysing scattered structured and unstructured data, moving irreproducible manual analysis processes to reproducible analysis and unlocking performance bottlenecks through scalable, secure enterprise-grade, mission-critical infrastructure.

Convergence of Precision Medicine and Population Health

Currently we have two separate themes of Precision Medicine and Population Health around healthcare delivery. On the one hand Population Health is concerned with operational issues, cutting costs and resource allocation around chronic diseases, while Precision Medicine still very much operates in silos and is research-oriented with isolated decision-making. Both Intel and Oracle are focused on bringing together Precision Medicine and Population Health to provide a more integrated view of all healthcare related data, simplify patient stratification across care settings and deliver faster and deeper visibility into operational financial drivers.


Shared Vision of All-in-One Day Genome Analysis by 2020

We have a shared vision to deliver All-in-One Day primary genome analysis for individuals by 2020 which can potentially help clinicians deliver a targeted treatment plan. Today, we’re not quite at the point where I can utilize the shared learning and applied knowledge of precision medicine to help me coordinate care and engage my patients, but I do know that our technology is helping to speed up the convergence between healthcare and life sciences to help reduce costs and deliver better care.


Keep up-to-date with our healthcare and life sciences work by leaving your details here.

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.

We’re experiencing ever-increasing volumes of data within health and life sciences. If we were to sequence just once the ~14M new cancer patients (T/N) worldwide[1], it would require more than 5.6 Exabytes (and the reality is we need to be able to sequence them multiple times during the course of treatment using a variety of omics and analytics approaches). The technical challenges of big data are many, from how do we manage and store such large volumes of data to being able to analyse hugely complex datasets. However, we must meet these challenges head-on as the rewards are very real.


I’m pleased to tell you about a significant project that Intel is supporting to help overcome these types of challenges which will assist in the drive to comprehensively analyse cancer genomes. Our HPC solutions are already facilitating organisations around the world to deliver better healthcare and individuals to overcome diseases such as cancer. And our relationship with the Pan-Cancer Analysis of Whole Genomes (PCAWG) project is helping scientists to access and share analysis of more than 2,600 whole human genomes (5200 matched Tumor/Normal pairs).


Scientific discovery can no longer operate in isolation – there is an imperative to collaborate internationally working across petabytes of data and statistically significant patient cohorts. The PCAWG project is turning to the cloud to enhance access for all which will bring significant advances in healthcare through collaborative research.


By working directly with industry experts to accelerate cancer research and treatment, Intel is at the forefront of the emerging field of precision medicine. Advanced biomarkers, predictive analytics and patient stratification, therapeutic treatments tailored to an individual’s molecular profile, these hallmarks of precision medicine are undergoing rapid translation from research into clinical practice. Intel HPC Big Data/Analytics technologies support high-throughput genomics research while delivering low-latency clinical results. Clinicians together with patients formulate individualized treatment plans, informed with the latest scientific understanding.


For example, Intel HPC technology will accelerate the work of bioinformaticists and biologists at the German Cancer Research Centre (DKFZ) and the European Molecular Biology Laboratory (EMBL), allowing these organisations to share complex datasets more efficiently. Intel, Fujitsu, and SAP are helping to build the infrastructure and provide expertise to turn this complex challenge into reality.


The PCAWG project is in its second phase which began with the uploading of genomic data to seven academic computer centres, creating what is in essence a super-cloud of genomic information. Currently, this ‘academic community cloud’ is analysing data to identify genetic variants, including cancer-specific mutations. And I’m really excited to see where the next phase takes us as our technology will help over 700 ICGC scientists worldwide to remotely access this huge dataset, performing secondary analysis to gain insight into their own specific cancer research projects.


This is truly ground-breaking work made possible by a combination of great scientists utilising the latest high-performance big data technologies to deliver life-changing work. At Intel it gives us great satisfaction to know that we are playing a part in furthering knowledge in both the wider genomics field, but also specifically in better understanding cancer which will lead to more effective treatments for everyone.





Giselle Sholler is the Chair of the Neuroblastoma and Medulloblastoma Translational Research Consortium (NMTRC) and the Director of the Hayworth Innovative Therapeutic Clinic at Helen DeVos Children’s Hospital. The NMTRC is a group of 15 pediatric hospitals across the U.S, plus the American University in Beirut, Lebanon, and Hospital La Timone in Marseilles, France. We sat down recently with Dr. Sholler to talk about to role of precision medicine in her work and how it impacts patients.

Intel: What are the challenges of pediatric oncology and how do you tackle those challenges?


Sholler: As a pediatric oncologist, one of the most challenging times is when we’re faced with a child who is not responding to standard therapy and we want to figure out how we can treat this patient. How can we bring hope to that family? A project that we are working on in collaboration with TGen, Dell and Intel has brought that hope to these families.


Intel: What is the program?


Sholler: When a child has an incurable pediatric cancer, we a take a needle biopsy and send it to TGen where the DNA and RNA sequencing occurs. When ready, that information comes back to the Consortium. Through a significant amount of analysis of the genomic information, we’re able to look at what drugs might target specific mutations or pathways. On a virtual tumor board, we have 15 hospitals across the U.S. and now two international hospitals in Lebanon and France that come together and discuss the patient’s case with the bioinformatics team from TGen. Everyone is trying to understand that patient and with the help of pharmacists create individualized treatment plans for that patient so that patient can have a therapy available to them that might result in a response for their tumor.


Intel: Why is precision medicine important?


Sholler: Precision medicine is about using the genomic information data form a patient’s tumor to identify which drugs not only will work, but which ones may not work on that patient’s specific cancer. With precision medicine, we can identify the right treatment for a patient. We’re not saying chemotherapy is bad, but for many of our patients chemotherapy is attacking every rapidly dividing cell and leaves our children with a lot of long term side effects. My hope for the future is that as we can target patients more specifically with the correct medications, we can alleviate some of the side effects that we’re seeing in our patients. Half our children with neuroblastoma have hearing loss and need hearing aids for the rest of their lives. They have heart conditions, kidney conditions, liver conditions that we’d like to see if we can avoid in the future.


Intel: How does the collaboration work to speed the process?


Sholler: The collaboration with Dell and Intel has been critical to making this entire project possible. The grant from Dell to fund this entire program over the last four years has been unparalleled in pediatric cancer. The computer power has also been vital to the success. Three years ago we were doing only RNA expression profile and it took two months; now, we’re doing RNA sequencing and DNA exomes completely and it takes less than two weeks to get the answers for our patients. The data transfer and networking used to entail shipping hard drives a few years ago. Now, we can send a tumor sample from Lebanon to TGen, complete the sequence in a few days and have a report for the tumor board a few days after that. It’s just been amazing to see the speed and accuracy improve for profiling.


Intel: Anything else?


Sholler: Another very critical piece that Dell has helped provide is the physician portal. Physicians are able to work together across the country, and across the world, and have access to patient records. The database now has grown and grown. When we do see patients, we can also pull up previous patients with similar sequencing or similar profiles, or treated with similar drugs, and see what was used in treatment. And how did they do? What was the outcome? We’re learning more and more with every patient and it doesn’t matter where we live anymore. Everything’s virtual online. It’s just been incredible.

Health and Human Services Breaches Affecting 500 or More Individuals website shows that there were 97 breaches of this type involving 500 or more patients in 2014, and 46 breaches of this type so far in 2015. These breaches often occur when there are a sequence of failures. An example is show in the graphic below where the first failure is a lack of effective healthcare worker security awareness training.


A mobile device they are using either lacks encryption, or the employee has the password on or near the device, for example on a sticky note on the laptop screen, which shockingly is not uncommon. This is followed by the employee leaving the mobile device vulnerable, whether on the backseat of a car, on a desk unsecured, in a coffee shop, or other location vulnerable to loss or theft of the device. This leads to loss or theft of the mobile device containing sensitive data in the form of electronic health records, and ultimately can lead to breach.

infographic DH.png


The HIPAA Breach Notification Rule requires notification of HHS, patients, and media for HIPAA Covered Entities and Business Associates operating in the US. The vast majority of US states now also enforce state level security breach notification laws which also cover sensitive healthcare information. If the number of records compromised is 500 or more this can lead to a new entry in the HHS “Wall of Shame”. The Ponemon 2015 Cost of a Data Breach Study reports that the average per patient cost of a data breach was $398, the highest across all industries. Based on the number of patient records compromised this can easily result in a total average healthcare organization business impact of $6.5 million, and an abnormal churn rate of 6 percent. Clearly this staggering cost means it is imperative for all healthcare organizations and business associates to take a proactive approach to securing themselves.


This has propelled breaches to a top concern across all healthcare organizations, often even a higher priority than regulatory compliance, which is seen as a minimal requirement but not sufficient to adequately reduce risk of breaches.


The above infographic presents a healthcare breaches maturity model. As such, it is focused on healthcare, and breach risks. A holistic approach is required for effective risk mitigation, including administrative, physical and technical safeguards. This maturity model is focused on technical safeguards for healthcare breaches. Gray blocks are applicable for other types of healthcare breaches, but not so much for breaches resulting from loss or theft of mobile device or media. We will discuss these other types of breaches more in future blogs. Here we focus more on the colored capability blocks of the security model, representing safeguards that help mitigate risk of breach from loss or theft of mobile devices or media.


A baseline level of technical safeguards for basic mitigation of healthcare breaches from loss or theft of mobile devices requires:


  • Endpoint Device Encryption to protect the confidentiality of sensitive data
  • Mobile Device Management, to provide a secure managed container for healthcare apps and sensitive data
  • At least single factor “what you know” / username and password access control which his usually provided at both the OS and enterprise application levels


An enhanced level of technical safeguards for further improved mitigation of risk of this type of healthcare breach requires addition of:

  • Anti-Theft enables the ability to remotely locate, lock or wipe your device in the event of loss or theft
  • Client SSD (Solid State Drive) with Encryption automatically encrypts all files stored on the client device to protect their confidentiality
  • MFA (Multi-Factor Authentication) with Timeout strengthens the authentication or login with the device, and automatically times out and locks the device after some period of inactivity
  • Secure Remote Administration enables system administrators to remotely access the device to diagnose and remediate issues and can be used to keep the device secure and healthy for effective security
  • Policy Based File Encryption can automatically encrypt files on a mobile device based on their type and contents, as well as the policy of the healthcare organization, in order to protect confidentiality
  • Server DB (Database) Backup Encryption encrypts files on the server, including databases and backups. Although loss or theft of servers and backups is more rare than loss / theft of a mobile device, when it does occur it can be much more impactful to the business due to more data and patient records stored on the server


An advanced level of security for further mitigation of risk of this type of breach adds:

  • MFA with Walk-Away Lock which further reduces the possibility of a hijacked session by detecting when the authenticated user has left the device and automatically locking the device
  • Server SSD with Encryption automatically encrypts files stored on the server to protect their confidentiality in the event of loss or theft of the server
  • Digital Forensics enables the healthcare organization to rapidly determine if a lost or stolen device was accessed and if so what specific sensitive data was accessed. This can be important in determining if a breach actually occurred, and if so the specific patients involved. The business impact of the breach is proportional to the number of patient records compromised so this can be an important strategy to avoid or minimize business impact from a breach.


The reality is most healthcare organizations don’t lack ideas for what security they could add. However, budget and resources are always finite. Security is also complex. The maturity model above presents a way to address the top concern of breaches from loss or theft of mobile devices or media in three increments. Using this method an organization may choose to implement the baseline level of security in year one, add enhanced security in year two, and complete the security by adding advanced security in year three.


What questions do you have?

One of the topics I hear frequently from the health IT community is about barriers to innovation. From my perspective, closed loop automation is a huge issue that we face and will have to deal with. We clearly allow closed loop automation in other parts of our lives, yet somehow we have this reverence and reluctance to do it in healthcare. Why?


Everyone I have ever run across in the healthcare industry—from my previous role as a doctor to the role in technology—is dedicated to goodness, kindness, and supporting their patients. Yet the process is so complicated we inadvertently, systematically hurt people over and over again. The only way to cure this is to automate the automatable.


And just what is automatable? It’s a moving target, but here’s a start:


  • Respirator settings: We've talked about very simple things like automating respirator settings. Why should I as a doctor, since I have an output in mind, monitor the physiology of a patient in a stable manner? Algorithms, through experience, could do this a whole lot better than a junior doctor. I want to use the power of the most senior doctor built into the algorithm and teach the respirator to be as smart as possible and then actually learn with individual physiologic feedback and how it responds to that patient to maintain a parameter.


  • IV pumps: As with respirators, we could do the same with IV pumps. The IV pumps would have Ethernet or wireless connections that can talk to the electronic medical records that can talk to lab data. Why not have the pump start to deliver a drug like heparin? In this scenario, a nurse can't make a mistake and a doctor can't inadvertently write the wrong order. By the 80/20 rule, we'll default to the average most of the time, anyway. Let machines help us where they can.


The benefits of closed loop automation are many, but freeing doctors and nurses from mundane tasks that are repeatable would be a game changer. That’s one of the biggest alterations we can make towards improving the delivery of care worldwide.


Maybe it's a big transition, but we need to trust the machines. They can do a really good job at certain things. I'm not asking the machines to think for us; but where things follow well developed patterns allowing that process to occur makes sense. Naturally, there will be resistance from those who see automation as a threat to job security. It has happened in other industries where automation replaces human activity. That’s to be expected.


But at the end of the day, a robot can paint a car better than a human can. A robot can be better at welding. There are things that closed loop automation can do better in healthcare and we need to give it a try.


What do you think? How would closed loop automation be viewed in your facility?


In the above clip, Bill Muth, a solution architect at CDW, explains how strategies for CIOs need to compliment an organization’s mission and usually focus on one of three areas: cost, differentiation, and focus. He says mobility is vital to a good health IT strategy.


Watch the video and let me know what questions you have. How did you develop your mobile health IT strategy?

In a time of rapid change, innovation is crucial for any enterprise. But I haven’t seen many organizations approach innovation as thoughtfully and systematically as Front Porch. This California-based nonprofit supports a family of companies offering assisted living, skilled nursing, retirement, and other communities across four states.


Front Porch has a Center for Innovation and Wellbeing as well as a commitment to disruptive, caused-based innovation called Humanly Possible℠. “We want everyone at every part of our organization to focus on what’s possible and what’s next—to look at how we can do what we do better, to bring new value to people we serve,” says Kari Olson, chief innovation and technology officer for Front Porch and president of its innovation center.


Olson and other Front Porch leaders were quick to see value in flexible 2 in 1 devices based on Intel® technologies and Windows.


“Two-thirds of our workforce are out and about, not sitting at a desk,” Olson says. “If we can give them portable devices that let them do their computing in a secure, reliable way, when and where they need to, we can have a big impact—both on their productivity and on our ability to meet the needs of the people we serve. If we can do that and stay consistent with our enterprise applications and tools—that’s huge.”

front porch.jpg

Front Porch staff saved time and increased patient engagement by using their 2 in 1 devices in members’ residential rooms, care centers, activity rooms, team meetings, and other settings.

But could 2 in 1 devices help deliver transformative value? And how would Front Porch’s people-focused helping professionals—who often have an “I’ll use it if I have to” attitude toward technology—feel about the new devices?


Intel just completed a case study that answers these questions. In it, Front Porch leaders describe surprises they encountered as employees ranging from nurses to activities coordinators began using 2 in 1s. Front Porch shares best practices for mobile technology adoption, and highlights the benefits they’re seeing for patient engagement, organizational efficiency, quality of care, and more.


I found their results fascinating. They’re relevant not just for healthcare, but for any organization that wants to empower a mobile workforce.


Read the case study and let me know your thoughts. Where might enterprise-capable 2 in 1s add value in your organization? Post a comment, or join and participate in the Intel Health and Life Sciences Community.


Learn more about Intel® Health & Life Sciences.


Read more about Front Porch and the Front Porch Center for Innovation and Wellness.


Stay in touch: @IntelHealth, @hankinjoan

Each year millions of people all over the world, including more than 1 million patients in the United States, learn that they have a cancer diagnosis. Instead of going through painful chemotherapy that can kill healthy cells along with cancerous cells, what would happen if those patients were able to be treated as individuals based on their specific genome sequencing, and a precision treatment plan could be tailored specifically for their disease? And what if it could happen within 24 hours?


Today, I announced at the Intel Developer Forum that we are setting our sights on making this scenario a reality through an ambitious, open Platform-as-a-Service solution called the Collaborative Cancer Cloud.


The Collaborative Cancer Cloud is a precision medicine analytics platform that allows institutions to securely share patient genomic, imaging and clinical data for potentially lifesaving discoveries. It will enable large amounts of data from sites all around the world to be analyzed in a distributed way, while preserving the privacy and security of that patient data at each site.


The end goal is to empower researchers and doctors to help patients receive a diagnosis based on their genome and potentially arm clinicians with the data needed for a targeted treatment plan. By 2020, we envision this happening in 24 hours -- All in One Day. The focus is to help cancer centers worldwide—and eventually centers for other diseases—securely share their private clinical and research data with one another to generate larger datasets to benefit research and inform the specific treatment of their individual patients.


The Rise of Precision Medicine                        

Precision medicine – taking into account individual differences in people’s genes, environments, and lifestyles – is one of the biggest of the big data problems and is on the cusp of a remarkable transformation in medicine. We view genomics as the first wave of precision medicine, and we’re working with our partners to drive adoption of genomic sequencers, genomic appliances, and cloud-based genomic analytics. With the Collaborative Cancer Cloud, we are combining next generation Intel technologies and bio-science advancements to enable solutions that make it easier, faster, and more affordable for developers, researchers, and clinicians to understand any disease that has a genetic component, starting with Cancer.


Initially, Intel and the Knight Cancer Institute at Oregon Health & Science University (OHSU) will launch the Collaborative Cancer Cloud. We expect two new institutions will be on board by 2016, addressing the critical need for larger patient pools and practitioner awareness. And from there, we can open up this federated, secure Collaborative Cancer Cloud network to dozens of others institutions—or let them create their own--to accelerate the science and the precision treatment options for clinicians to share with their patients. They can also apply it to advance personalized research in other diseases that are known to have a genetic component, including Alzheimer’s, diabetes, autism, and more.


In the same timeframe, we also intend to deliver open source code contributions to ensure the broadest developer base possible is working on delivering interoperable solutions. Open sourcing this code will drive both interoperability across different clouds, and allow analytics across a broader set of data – resulting in better insights for personalized care.


A Complementary Effort

You may be asking, “Haven’t we seen efforts like this before?” There have been numerous multi-institution partnerships formed to utilize big data analytics to look for insights about cancer and its treatment. Our focus on the federation/distribution of private datasets is complementary to the exciting work that’s happening to make public data sets more accessible to research. In CCC, each partner will maintain control of its patients’ data, while the shareable cancer treatment knowledgebase grows in availability and in impact. We want to help harness the power of that data — in a way that benefits clinicians, researchers and patients with a better knowledgebase and preserves security and privacy. By securely sharing clinical and research data amongst many institutions while maintaining patient privacy, the entire research community can benefit from insights revealed in large data cohorts.


In the end, precision medicine will only be as precise as available data allows. To better understand complex diseases like cancer, the medical and technology industries must collaborate to make the growing wealth of insights resulting from secure analysis of public and private genetic datasets accessible for the patient’s benefit. And if we do, we can turn an agonizing and uncertain process for the patient into a personalized process that occurs all in one day.


We encourage you to view the links below to learn more about our work with OHSU:

OHSU’s Exacloud

Collaborative Analytics for Personalized Cancer Care


Learn more about precision medicine and genomic code research at these resources:

Filter Blog

By date:
By tag:
Get Ahead of Innovation
Continue to stay connected to the technologies, trends, and ideas that are shaping the future of the workplace with the Intel IT Center