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Connected Social Media

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Connected Social Media episodes

  • Audio White Paper: Data Center Strategy Leading Intel’s Business Transformation

    To better meet Intel’s business requirements while providing internal customers with optimal data center infrastructure capabilities and innovative business services, Intel IT has overhauled its data center strategy. Its data center transformation strategy is to run Intel data center services like a factory, affecting change in a disciplined manner and applying breakthrough technologies, solutions, and processes.

    In this white paper, Intel uses three key metrics to measure data center transformation success: meet growing customer demand (service-level agreements and quality of service) within constrained spending targets (cost-competitiveness) while optimally increasing the utilization of infrastructure assets (operational efficiency).

    See the entire white paper here:

    intel.com/content/www/us/en/it-management/intel-it-best-practices/data-center-strategy-paper.html

    0 min
  • Cell and Gene Therapy: Tapping into the Potential of Curative Treatment

    For patients diagnosed with rare diseases like hemophilia and muscular dystrophy, treatment has historically been difficult and the prognoses terminal. But the advent of new cell and gene therapies are providing hope, as they fight the root cause of disease and provide patients with life-altering, and in some cases, life-saving treatments.

    Listen to this episode of the Business Group on Health podcast to hear from Sarah Emond, ICER’s President-Elect, about the complexities of cell and gene therapies including how they differ from traditional treatments, challenges like access and manufacturing, and promising payment mechanisms to finance these high-cost therapies.

    Guest: Sarah Emond, President-Elect, Institute for Clinical and Economic Review (ICER)

    37 min
  • Audio White Paper: Wallarm and GDPR

    In 2016, the European Commission approved the most significant change in the EU Data Protection Directive since its inception in 1995. The new General Data Protection Regulation law, also known as GDPR, became enforceable in May of 2018. It is designed to strengthen security, give control of personal data to data owners in Europe, and to make sure that data regulations are consistent across all European countries.

    As a security company, Wallarm recognizes the importance of data protection laws. In this audio version of the white paper, Wallarm demonstrates that its solution follows the guidelines and requirements of GDPR and that many of Wallarm’s features can help customers achieve GDPR compliance in their web applications.

    The PDF version of this paper can be found here:

    wallarm.com/resources/#whitepapers

    0 min
  • Winding the Clock for Better Health: The Power of Circadian Rhythms

    We often think of circadian rhythms only in the context of sleep, but research shows that they relate to – and influence – so much more when it comes to our health and well-being. In this episode of the Business Group on Health podcast, we speak with Professor Russell Foster about what circadian rhythms are, the factors that influence these rhythms, and how the timing of activities like eating, exercising, and even taking medication can impact health outcomes for the better… or worse.

    Listen to this wide-ranging conversation to hear about how individuals and employers can apply research on circadian rhythms to improve health and quality of life.

    Guest: Russell Foster, Professor of Circadian Neuroscience, University of Oxford and author of Life Time: The New Science of the Body Clock, and How It Can Revolutionize Your Sleep and Health

    Thank you to today’s episode sponsor Day Two.

    54 min
  • Alex Karargyris’s Path to Becoming a Pioneer of Medical AI

    Alex Karargyris is helping a nonprofit called ML Commons develop open-source technologies that bring global standards to medical AI/ML initiatives.

    Find more enterprise cloud news, features stories and profiles at The Forecast.

    Transcript:

    Alex Karargyris:
    Healthcare technology, what it used to be like 50 years ago, has changed dramatically. You go right now to some operating room or even a radiology department, and there’s so many sensors and gadgets everywhere. It’s natural for machines to be able to process this information and funnel it to something that is meaningful to the human and let the human make the final decision. Right?

    Jason Lopez:

    That’s Alex Karargyris, co-chair for the Medical Working Group, which is a part of a large nonprofit organization called ML Commons. He’s also the co-author of the paper, “Federated Benchmarking of Medical Artificial Intelligence With Med Perf.” AI models have become an integral part of our lives, whether it’s your smartphone, recognizing your voice or a healthcare facility using a model to detect cancer and X-rays. That’s AI at work. This is the Tech Barometer Podcast, I’m Jason Lopez. AI models perform specific tasks. It’s like an expert who’s had extensive training and learned from vast amounts of information to become great at their assigned job.

    Alex Karargyris:

    So all these models are developed by different groups. It could be academics, it could be vendors like companies that develop AI systems. It could be internal researchers at the hospitals. It could be a kid on the blog that writes some code in Python and writes a model using cargo, open source datasets. So all these models, they do some specific task and they’re trained on data that you find either internally or you acquire, you paid for them or in the public domain. So once you build these models, then you have to deploy them. You have to take them to the hospital to do a specific task like organizing cancer or an x-rays.

    Jason Lopez:

    AI models start off as a blank slate. They have to be fed data and to improve, have to continually be fed. Deep learning models have an insatiable appetite for data, and the more they consume, the better they perform.

    Alex Karargyris:

    Similar to a doctor that is in 1 million, let’s say patients, right? And once you train this model, you need to test it. You need to validate it, and you need to validate it in a diverse selection of data out there in the real world. These were, the testing comes, it’s called benchmarking.

    Jason Lopez:

    This piece of the story is really important. Alex and his colleagues are focusing on getting AI models to perform as accurately as possible, which means reducing the bias that an AI model might produce. So let’s go back to the blank slate.

    Alex Karargyris:

    So you have a model that knows nothing. You have a machine. If you give ’em an X-ray, it doesn’t tell you if it’s cancer or not cancer. So you have to train it. So you need to keep all this data. You have privacy concerns, so you need be careful with that when it comes to healthcare,

    Jason Lopez:

    And that carefulness means a hypersensitivity to security concerns. Centralizing the data of several or several hundred healthcare facilities in order to train AI models will not work. That’s where federated learning comes in, deploying an AI model to the hospital where it learns on site, the place where the data securely resides.

    Alex Karargyris:

    But when you take that model, and let’s say you train until, let’s say in Indiana, and you see population data from that particular area, and you take that model and you deploy, let’s say in California, the population are different, right? So your model, which was trained in Indiana population data might be biased to that particular population. So when you move to California and then maybe in the Bay Area, which a large Asian descent population, it might fail. So this is where before you deploy it in the clinical workflow, you need to validate it. You need to make sure that you test it thoroughly in a diverse population.

    Jason Lopez:

    The med perf idea is to test AI models on real world data from diverse locations, ensuring the models meet high quality standards and maintain security and privacy.

    Alex Karargyris:

    We wanted to contribute to this effort to build an open platform that allows you to facilitate this testing. How can you test models that were already trained in a limited dataset or whatever dataset? How can you test them out in real world data from multiple diverse locations so that you know can feel confident that these particular models or any model are meeting high quality requirements? This is where we are.

    Jason Lopez:

    Alex Karargyris is from Greece in the late eighties and nineties. His father brought in some of the first computers to his hometown and started a school of computer science.

    Alex Karargyris:

    We used to get this expensive monochrome, green color screen computers at home, and also he had plenty of books like CBA and Assembly and me and my brother. We would spend a lot of time hitting some programming back in the days, print asterisk and stuff if then statements, and eventually we moved on to have a little bit more serious machines in the early nineties with the Intel pendulum. And of course, like any other kids, we got into the commuter games, video games, games, so a lot of video gaming, but eventually decided to go to electrical engineering and computer engineering in Greece. And during the last two years of the school, you have to choose which direction you want to go to. I was always intrigued with medical. My brother was also in the medical school back then. At some point I was considering switching to biology, so quite earlier.

    Jason Lopez:

    Alex’s mother was a biologist, and that’s one factor in his future at the time. And as a student being pulled in two directions, it was later that he discovered he could sort of do both.

    Alex Karargyris:

    Right now, we are living in a world that computers are everywhere, and AI right now is one of the major forces around us, and it’s becoming mainstream. And I’m glad I’m working in a field which is intersection of improving healthcare, but also the scientific approaches like artificial intelligence.

    Jason Lopez:

    One side note Alex gave us when we chatted with him about his computer science background was his introduction to remote computing through learning about programs like the search for Extraterrestrial intelligence or seti. That was an early example of what we now think of as cloud storage and resources.

    Alex Karargyris:

    When it comes to healthcare, I would say the tensions are stronger there. Sometimes, yes, there’s a tendency to go to move to the cloud, but there’s also this tendency of for protecting privacy to keep it locally. But I think healthcare systems and organizations understand that it’s hard to maintain local resources, and that’s why many of these laws that were written back in the nineties HIP and all this, that forced privacy, they cannot take into consideration right now to allow basically the migration to the cloud.

    Jason Lopez:

    AI’s increasing presence with the rise of cloud services is overall a good thing for healthcare. Despite the extra effort needed to satisfy security and privacy requirements, anyone with knowledge to develop AI systems from anywhere in the world can contribute.

    Alex Karargyris:

    Healthcare goes, skyrockets in the United States, I think it’s like, I don’t know how many trillion right now. There’s a shortage of experts to support these tasks, and it’s natural that people are looking into AI to help fill in the gaps and also to reduce the overload that maybe clinicians have into this field.

    Jason Lopez:

    As AI’s capabilities expand, especially with innovations like ChatGPT, it’s critical to understand its role as an assistant, not a substitute. Over time, as with any new technology, apprehensions will settle and AI will seamlessly integrate into clinicians’ daily tasks.

    Alex Karargyris:

    We are witnessing how AI can be transformative to many industries lately. And similarly, I think AI has impacted multiple facets of healthcare. I mean, from drug discovery, diagnosing therapeutics, it can contribute to reduction in healthcare costs as well as the improvement of healthcare delivery quality. More and more people need to be taken care of as well as the limited amount of experts are being around to support this delivery. There’s a gap that needs to be filled, and I think that’s the opportunity for that AI can offer.

    Jason Lopez:

    Alex Karargyris is a member of the nonprofit organization, ML Commons, where he’s co-chair for the medical working group. He’s also the co-author of the paper, federated Benchmarking of Medical Artificial Intelligence with Med Perf. There are two other stories in our series on Med Perf, one on the Med perf paper, and another on Alex’s colleague Debo Dutta, another pioneer in the intersection of AI in healthcare. This is the Tech Barometer podcast produced by the Forecast. You can find more reporting on technology at theforecastbynutanix.com.

    11 min
  • Can We Really “End Ridiculous Drug Prices?” Mark Cuban Thinks So.

    The pharmaceutical supply chain has long been ripe for disruption and who better to lead the way than Mark Cuban, a “shark” who has never shied away from a challenge. Mark launched the Mark Cuban Cost Plus Drug Company in 2022 with the audacious goal to bring trust back to health care with wholly transparent pricing available to all stakeholders of the pharmaceutical supply chain.

    In this episode of our podcast, we sit down with Mark to discuss his vision to “end ridiculous drug prices” and improve every aspect of the pharmacy experience. And while still a young company, the Mark Cuban Cost Plus Drug Company has already evolved to address more of the pressing health care challenges facing employers and consumers alike. Tune in to hear what he plans to tackle next to continue to improve health and well-being for all.

    Guest: Mark Cuban, Co-founder, Mark Cuban Cost Plus Drug Company

    33 min
  • Organic Valley Cooperative Modernizes IT

    In this video, members from the IT team at Organic Valley explain the cost, time and performance benefits of modernizing their data operations with Nutanix.

    Find more enterprise cloud news, features stories and profiles at The Forecast.

    Transcript:

    Joshua Krzych VP of IT (00:00):

    I am Joshua Krzych. I’m Vice President of IT operations at Organic Valley, so we have about 2000 small family farms at ebbs and flows from year to year. When I’m making a big decision, such as when we purchase Nutanix, one of the first decisions I have in my head is, what would a farmer think if they saw that this is what I’m spending their dollars on? Because in the end, we’re trying to make them the most profitable small family farmers out there in the world. That’s what we want to do to make sure they’re sustainable and can continue supporting their families and their livestock.

    Nick Korte, Dir of Tech Ops (00:30):

    My name is Nick Korte and I’m the Director of Technology Operations. When we went to Nutanix, it was a decision around, we went from a traditional three-tier infrastructure, and it worked really well for us for a long time, but we realized that this was a lot less management, a lot less backend nights of doing firmware updates, so simplifying it for our folks and giving them more opportunities to kind of stretch into what could be with Nutanix, whether it’s files or some sort of database as a service operation. It just opens up the door for us.

    Joshua Krzych VP of IT (01:13):

    Our current ERP solution is there, our warehouse management systems, our supply chain analysis.

    Nick Korte, Dir of Tech Ops (01:22):

    I look at it from the people perspective of we were spending our network teams and our storage teams and our compute teams we’re all working weekends and just doing updates. There was a fear of breaking something. You have a much more complex infrastructure. When we went to Nutanix, it was like, Hey, we have an opportunity to simplify and we have an opportunity to take that time back for ourselves. If I remember right,

    Joshua Krzych VP of IT (01:48):

    There was some DBA database maintenance activities that happen nightly that usually took several hours to complete before and after we moved over to Nutanix. It took minutes, maybe not five minutes, but it was way under an hour. Very impressive performance boost there. Moving to the Nutanix platform has really allowed our techs to focus more in engineers, to focus more broadly without having to deeply specialize in an area and then create themselves a backup as well with someone else on their team so they can take a vacation.

    Nick Korte, Dir of Tech Ops (02:24):

    Really, support was the main thing that we were looking at was, and that’s what really showed Nutanix rose to the top because of the support they work with the hardware, they’re really close with their vendors, so it just was a much better experience, and to me that’s an easy investment for our employees. Again, getting back to how they live their lives. I don’t want them sitting on the phones every night or coming in on a weekend because they can’t figure it out. I want ’em to be at home with their families and enjoying life. I think the main goal will be to get onto the platform a hundred percent, get rid of some of that technical debt that we’ve had and utilized in the past to try and save a couple bucks, and now that we’ve seen the value and being on Nutanix, I think that that’s a great opportunity for us to pivot towards that.

    4 min
  • IT Decisions Differed During Tough Economy

    In this video interview, Steve McDowell, principal analyst at NAND Research, talks about how the 2023 economy is impacting IT decision makers.

    Find more enterprise cloud news, features stories and profiles at The Forecast.

    Related stories:

    5 Hybrid Multicloud Cost Optimization Strategies

    Transcript:

    Ken Kaplan: It is a very strange economy. You know, there’s all these different indicators and mis-indicators, but in general, because you’ve been following this for a while, there’s a bit of, I don’t know if you want to call it a pendulum or these things come around where IT folks have to make it through these times that are tough, and then there are times when it’s go and grow. Can you just describe where we are today? What’s your take on the economy in these cycles?

    Steve McDowell: Yeah, so the macro economy is impacting IT decisions, right? I don’t think there’s a problem with the industry delivering solutions that IT wants to consume.

    The challenge is there’s so much uncertainty, whether it’s, you know, what’s happening in Europe or, or, or the continuing saga with China. the, and, and, you know, the interest rates. CIOs are deferring decisions.

    Well, CFOs are asking CIOs to defer decisions, and that trickles down. And what that really means practically is the IT guys have to do more with less oftentimes.

    And it comes back to, you know, we talk about sustainability and getting the most bang for my buck outta my resources. You know, if I’m not allowed to buy new resources, I really have to do that, sustainability aside. So anything that helps an IT practitioner, you know, better utilize his servers, his storage, his networking, his cloud usage. Man, that’s goodness.

    And that’s all because of the economy because, you know, we’re deferring purchasing decisions. And you look at the earnings of HPE of Dell, of all the tier one enterprise OEMs, they’re all talking about this. They’re all saying at some point, second half, maybe, we’ll, we’ll start to see some recovery.

    But there’s pent-up demand, but sales cycles are much elongated. And, and, you know, we’re not delivering servers as fast as IT guys want them or that we can do that, and it’s because of the economy.

    3 min
  • Testing healthcare AI and ML apps with the MedPerf Platform

    In this special Tech Barometer podcast series, Alex Karagyris and Debojyoti “Debo” Dutta explain how the MedPerfs platform paves the way for artificial intelligence and machine learning applications in healthcare.

    Find more enterprise cloud news, features stories and profiles at The Forecast.

    Related stories:

    Moving Healthcare IT Faster Into the Future
    The Amalgamation of AI and Hybrid Cloud
    Healthcare Technologies Paving the Way From Treatment to Prevention
    How Digital Twin Technology Can Propel Personalized Healthcare

    Transcript:

    Alex Karargyris: MedPerf is a platform that evaluates medical AI models on diverse real-world medical data.

    Jason Lopez: In the public conversation about artificial intelligence, one thing just about everyone agrees on is that healthcare will be a big beneficiary of AI. This is the Tech Barometer podcast. I’m Jason Lopez. In this story, we’re going to explore how AI can impact healthcare, how testing in the real world can improve trust and how it can quantify medical AI performance on a platform called MedPerf.

    Alex Karargyris: MedPerf can securely distribute the AI models to healthcare organizations and test those models on their local data. No data leaves the premises of the healthcare organization. It states within the private network and only high-level aggregated results are shared among the participating organizations.

    Jason Lopez: Alex Karargyris is co-chair for the medical working group within the nonprofit ML Commons, and he’s one of Med Perfs co-founders and developers.

    Alex Karargyris: The platform has been designed with patient privacy protection at its very core.

    Jason Lopez: During our conversation, Alex circled back to the issue of security and privacy a number of times to emphasize how sensitive the healthcare industry is to the risks of deploying new technologies, especially in the light of HIPAA security rules.

    Alex Karargyris: We aim for patient privacy first and foremost, and one more thing because we are developing source code, met Pref is open source so that everyone can contribute and reuse freely, right?

    Jason Lopez: The high-level description of Med Perf you’ve heard so far was outlined in a 2023 paper Carris authored, entitled, announcing Med Perf Open Benchmarking platform for Medical AI. It lays down what the organization is, what it does, and how to get involved.

    Debo Dutta: The reactions after the paper came out were different from different walks of life.

    Jason Lopez: Debo Dutta is vice President of Engineering at Nutanix, and he was integral to starting the group ML Commons.

    Debo Dutta: When I talked to technologists, they say, wow, this is a cool system. The ML folks were saying, oh, that’s a very good application for federated learning. Awesome. Some of my colleagues in computer science, they were quite amazed at the amount of effort it took to get so many institutions together and do clinical trials. I think people were just amazed by the sheer amount of effort and passion that this whole group took to get to this point. Then there are doctors.

    Jason Lopez: Debo says, in the area of cancer, some oncologists who’ve been a bit skeptical about AI say the paper changes things.

    Debo Dutta: I have had oncologists come and tell me this, that this is awesome. Oncologists who were skeptical about ai, one of my collaborators on this paper, told me, now that this is done, we can move on to doing even more amazing things that have a bigger clinical impact.

    Jason Lopez: The story of the Med Perf platform essentially started in 2020 with the medical AI benchmark project that was done through ML Perf, an organization that benchmarks computing platforms for machine learning workloads, initially focused on brain tumor detection and M R I scans. People from organizations such as Nutanix, Intel, I B M, Google, and the Dana-Farber Cancer Institute wondered if they could replicate the success of it benchmarking in healthcare, they aim to build a platform that could test machine learning models on real-world medical data while fostering trust in AI for healthcare. Over time, the group’s membership grew to include people from over 20 organizations across five continents with full-time engineers and volunteers working together to develop the platform and establish best practices within the medical AI community.

    Alex Karargyris: We were discussing with other people how the same benchmark philosophy could be applied to medical AI and how to expand the capabilities of the whole community. Some questions we’re asking are, how can we validate medical AI efficiently, and mutually, and make sure this privacy is guaranteed? So we set out to test this hypothesis three years ago, and we found out there were more and more people that did the same questions. The group grew from seven people to right now we have people from 20 organizations across five different continents, and we have full-time engineers in the group and also a lot of volunteers contributing to the development of the platform as well as best practices framework around the platform.

    Jason Lopez: The Med Perf framework is a system designed to coordinate the evaluation of medical AI models. It consists of two main components, the Med Perf server and the Med Perf client, a lightweight software installed in hospitals and healthcare organizations. The framework allows the initiation of model evaluations on the med perf server, download the model weights, and push them to the client nodes.

    Alex Karargyris: And then the clients can execute this particular model against the data by executing the tests.

    Jason Lopez: This is the basic workflow that enables medical research using AI at hospitals, universities, and labs around the world. The Med Perf platform makes it as seamless as possible, but arguably the highest priority is security and privacy.

    Alex Karargyris: So we did observe during the development and prototyping of MET PERF with our partners that this is the number one. Number two, major concerns, security and privacy in this context with a regulatory framework that has been developed around the world will force even stricter and deeper security considerations. I think that IT teams will have to look for professional solutions that offer tight and holistic security that cover AI as well, because it comes back to resource management. How do you handle heavy workloads?

    Jason Lopez: This is one of the challenges. Med Perf is figuring out how to run things in the cloud because many healthcare organizations are not necessarily heavily IT-oriented.

    Alex Karargyris: They don’t have the infrastructure to run this, but they want to join this AI ecosystem, right? So this is the way to go to be able to execute in a very trusted, secure way on the cloud. All these workloads from training all the way to validation that Met PERF does.

    Jason Lopez: Alex says there’s a broad diversity of workloads in healthcare, and there’s not necessarily a right computing platform, but there is a common thread from an IT perspective,

    Alex Karargyris: Better application management on the available resources. These AI systems could be very resource-hungry, GPU to be used accelerators to run these things. I think this is the value that many of the cloud providers can bring because they support this, right,

    Jason Lopez: And always a high priority in the health IT conversation, security and privacy

    Alex Karargyris: Major major importance like Nutanix, there is secure trust execution as well is important to be able to close the space. You can run these workloads. Depo has supported the group from its inception, and he has been a provider of wisdom, I would say, a strong believer in AI for healthcare, and she has been supported in open efforts like ours. Without email, it would be impossible to do this.

    Debo Dutta: I was one of the founding members of the ML Commons organization. It was not called ML Commons before. It was just called ml perf.org. It was just a small organization of mostly AI infrastructure vendors and a bunch of academics who wanted to redefine how machine learning performance would be evaluated because we saw the tide of ML coming very soon, and we said, okay, we need to evaluate performance in a meaningful way, in a repeatable way, and we want to be a nonprofit so that people trust us. And then over time, ML perf evolved into ML Commons. The goal of this organization, it’s an amazing organization by the way, is to basically improve the state of the art and accelerate the AI transformation via open artifacts like performance benchmarks, data sets, and best practices.

    Alex Karargyris: What we try to achieve with this, with MedPerf, if you’re an AI researcher and you run our study, MedPerf can help you collaborate with many institutions and evaluate your model on a much wider, and I would say diverse patient population than you could do possibly right now. Similar to what Mel Commons has been doing with its other benchmarks in other fields such as computing power platforms, we are creating benchmarks that are neutral an reproducible and can help improve the effectiveness of medical AI.

    Debo Dutta: I’m elated because this has been a long journey to get the leadership on board and then getting hold of all the top-tier oncologists and medical researchers, AI researchers, and companies who do AI to converge on the one cause. That’s let us actually build clinically impactful ML platform that respects data privacy and patient privacy. Frankly, to evaluate the next-gen AI models that could change the world of therapeutics, cancer therapeutics in particular. So I’m super excited.

    Jason Lopez: Debo Dutta is Vice President of Engineering at Nutanix and a founding member of ML Commons. Alex Karargyris, who led the effort with his collaborators to write the MedPerf paper, is the co-chair of the medical working group in ML Commons. You can find the paper “Federated Benchmarking of Medical Artificial Intelligence with MedPerf” at mlcommons.org. This is the Tech Barometer podcast, produced by the Forecast. I’m Jason Lopez. We’ll be doing some deeper dives into the MedPerf platform – a profile of Alex and one of Debo in future podcasts. You’ll be able to find those at the forecastbynutanix.com.

    12 min
  • Managing Migraines in the Workplace Shouldn’t be a Headache

    Migraines are a leading cause of disability and presenteeism in workplaces worldwide, a fact that’s underrecognized. Symptoms of the disease can be hard to spot, claims data on migraine are elusive and stigma lingers. Moreover, patients often have limited access to care – in the U.S. there are 39 million migraine sufferers but only 560 headache specialists.

    On this episode of the Business Group on Health podcast, we speak to one of those 560 headache specialists, Dr. Olivia Begasse De Dhaem. We discuss how migraines peak during the most productive years of life, and the impact migraines have on the workplace. She shares the results of a study she co-authored about a successful headache education program, which she helped implement at a large employer in Japan, and offers insights for other large employers for impact

    Guest: Olivia Begasse De Dhaem, MD, neurologist and headache specialist, Hartford HealthCare Headache Center and author of Headache: What Do I Do Now?

    Resources:

    • Fujitsu Study:  Migraine in the workplace
    • Find a Migraine Specialist: American Migraine Foundation
    • The Atlantic: It’s the Best Time in History to Have a Migraine — So why doesn’t it feel that way?
    • Harvard Business Review: Migraines Are a Serious Problem. Employers Can Help.

      •  

      34 min

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