Stories from Slate Stories from Slate Too Much of a Good Thing <p><em>On Thursday, March 26, Future Tense—a partnership of <strong>Slate</strong>, New America, and Arizona State University—will hold an event on medical device security and privacy at the New America office in Washington, D.C. For more information and to RSVP, </em><a href=""><em>visit the New America website</em></a><em>.</em></p> <p>How many lives could be saved if smartphones gave medical workers in remote Kenyan villages access to a suite of accurate, mobile diagnostic tools? Colorimetrix is tackling that question. It’s an application in development that can <a href="">help smartphones diagnose</a> diabetes, kidney disease, and urinary tract infections, and the hope is that it will eventually detect HIV, malaria, and tuberculosis. Worldwide, academics and businesses are creating apps that they hope will provide in-the-field diagnoses and treatment recommendations where <a href="">access to reliable, portable, and cheap medical technology is scarce</a>.</p> <p>But to fulfill those promises, mobile health care technologies, known as mHealth, have to overcome some major challenges related to data collection and usage. Right now treating patients using mHealth isn’t important or even very realistic—what matters is the data collected from these patients. But researchers don’t know exactly what types of information they need. And consequently, so much data is collected that the gatherers don’t know how to use it all.</p> <p>Advancements in mobile biological sensors are providing opportunities to <a href="">gather unprecedented amounts of clinical-quality data</a>. With the correct algorithms, that information can revolutionize researchers’ understanding of chronic illnesses and answer questions that have puzzled healers for generations. Data could reveal the driving factors behind <a href="">leading causes of death</a>, like cancer, heart disease, and stroke. Someday, users could be alerted about an imminent heart attack hours, days, or even months before it happens.</p> <p>But to create that predictive future, scientists and medical researchers need copious amounts of granular data. And Euan Thomson, the CEO of AliveCor, a mobile electrocardiogram manufacturer and heart disease research company, says detailed data is not what researchers currently have.</p> <p>Chronic diseases react differently within each person. That makes discovering the root cause of these diseases challenging. For instance, although the symptoms might be similar, viral strains within people are going to send slightly different signals. Doctor visits and traditional clinical trials, meanwhile, can offer only snapshots of patients’ health at that particular moment (or month). This approach inhibits collecting data on the environmental factors that affect diseases in somebody’s day-to-day life, and makes discovering why an illness is there and how it arrived much harder, says Alexander Hoffmann, the director of the Institute for Quantitative and Computational Biosciences at UCLA. The method also leads to subjects being placed in large subsets in which individual distinctions get lost in the background. But until technology catches up, that’s among the most efficient methods for reliable data collection.</p> <p>That’s where mobile health care comes in—by its very nature, it adds in missing data points. Mobile health care offers longitudinal data, which eliminates snapshot irregularities by showing what signals might correlate with health or disease over a long period of time. And if we figure out what the outside disease drivers are in a chronic illness—the genotype, the phenotype, and the environment—then we can understand how the body works, and we can help all of society manage and avoid risk factors for these diseases.</p> <p>For example, Thomson’s company focuses on <a href="">atrial fibrillation</a>, a chronic heart disease affecting about 3 million Americans. The average cardiologist treating this disease might review electrocardiograms, or ECGs, at the rate of four or five per day for his entire career, Thomson says. And he might not be able to relate the fine detail of one to the fine detail of the next. But a computer algorithm, such as the <a href="">artificial scientist Eve</a>, can sift through more than 10,000 data points a day, spotting minuscule changes and connecting them. That’s because inside a large data set are statistical clusters. Within these correlating clusters you can figure out which (if any) data points have any clinical meaning. The more data you have, the better chance you have to find granular detail around the cluster.</p> <p>Thomson says data cluster analytics give insights about a person’s ECGs and lifestyle data over a long period of time. Algorithms then identify key clusters of heartbeats, which can eventually show how factors like lifestyle, habits, and medications can change his or her heart. AliveCor has collected more than 1.5 million ECGs and has hundreds of thousands that are categorized as medically abnormal. Thomson says in the next 10 years he expects mHealth data to show atrial fibrillation is no longer one singular disease, which opens up new treatment opportunities.</p> <p>But Thomson doesn’t see this change occurring before the next decade. <a href="">Several kinks</a> will prevent the mHealth hype train from leaving the station anytime soon. Reade Harpham, the director of human-centric design at Battelle Memorial Institute, says one of the big problems is an important and somewhat forgotten question: <em>We have all this data. Now what do we do with it?</em> The simple answer is that researchers are still trying to figure that out.</p> <p>A couple of years ago, developers and researchers might get 200 data points a day about parameters such as somebody’s diet, exercise, glucose levels, heartbeat, and how he or she reacts to certain treatments. Now some pharmaceutical companies are staring down 18 million data points a day about <a href="">genetics, reactions to drugs, and environmental stimuli</a>, Harpham says. &nbsp;</p> <p>That’s a lot of information, but for some organizations it’s still not enough to satiate the needs of their mHealth algorithms. Until those needs are met, how useful a certain data set is remains a mystery.</p> <p>Businesses, researchers, and academics in the mHealth industry are racing to get the largest data set possible so they can become valuable, says Nigam Shah, an assistant professor of biomedical informatics at Stanford University. It’s relatively easy and cheap to collect the data from users, but developing algorithms to figure out how that information correlates and who can benefit from it is expensive and time-consuming. To complicate matters more, Hoffmann says two analysis methods are widely used to create these algorithms. And the two methods’ results can be drastically different.</p> <p>One is purely qualitative. The algorithm looks at whether a type of mobile health signal correlates with the user’s health, or a known disease or medication. That’s the most basic approach available. It’s a completely unbiased method, but the algorithm requires a much larger sample size to glean something useful from the data.</p> <p>The slightly more sophisticated but more powerful approach is to have an understanding of the causal relationship between a disease and its symptoms, Hoffmann says. For example, we know that eating a lot of sugar affects insulin balance and metabolism. For diabetics, this is dangerous. That a priori knowledge allows researchers to pinpoint what the pathways are that connect these things, which becomes a quantitative relationship. An algorithm can be made based off these biochemical pathways that allows researchers to analyze data sets based on proving or disproving a specific end goal. It gives predictive power. A problem with this approach is that it depends on the reliability of the knowledge that the algorithm is based on—if the information is incorrect, then the predictions are also wrong.</p> <p>The ideal scenario combines the two methods. First, use a qualitative algorithm to discover causal relationships among diseases and symptoms. Then, use that data in a quantitative algorithm to create now-accurate predictions and inferences. However, the problems of big data collection in health care aren’t solely limited to the algorithms. The doctor’s office also plays a big role.</p> <p>Because these algorithms are still years off, the average doctor <a href="">doesn’t have the ability</a> to make mHealth data mean anything. Even if they did, the law isn’t clear on if they’re allowed to use that information to treat patients. Today, if you use an ECG monitor like AliveCor’s and give its data to your doctor, he will have to figure out whether that should be part of your medical record. If it is, is it covered by HIPAA? Is your doctor required to archive it for seven years? Is it identifying information, or not identifying information? Is it protected health information or not PHI? That’s a policy decision. And the law is always lagging behind technology.</p> <p>Mix the policy challenges alongside the technological challenges, and right now the entire process is basically chaos.</p> <p>Mobile health care is in a tricky but crucial spot. Device developers and industry researchers are caught in a looping cycle of needing users to give more data to improve medicine, but not knowing yet if that data is even useful or reliable. Developers and researchers are in dire need of better algorithms to make millions of data points tell a story. But with more data and time, these issues will solve themselves. If early adopters stay willing to offer their personal data, despite the risks, the future of medicine will be marvelous.</p> Fri, 20 Mar 2015 12:08:00 GMT Travis McKnight 2015-03-20T12:08:00Z What to do with all of the data generated by medical devices? Technology What to Do With All of the Data Generated by Medical Devices? 100150320002 technology mhealth medicine Travis McKnight Future Tense false false false Researchers don’t know what to do with all of the data generated by medical devices: What to Do With All of the Data Generated by Medical Devices? Illustration by Mark Alan Stamaty Can a New Video Format Save Netflix From Future ISP Shakedowns? <p>The tussle over costs associated with video streaming bandwidth is becoming ridiculous. Netflix is crying extortion, because the big ISPs began allegedly <a href="">throttling Netflix download speeds</a> mere weeks after the <a href="">FCC lost its net neutrality court case against Verizon</a>, and the ISPs are saying that because Netflix is responsible for 30 percent of peak Internet bandwidth usage, it should partially pay for the necessary infrastructure upgrades. Transit providers like <a href="">Level 3 and Cogent argue</a> that the IPSs knew this problem was coming and should invest in fixing the situation—after all, many of the required infrastructure components are already in place, but have yet to be turned on. As a stopgap, Netflix is paying Comcast <a href="">an estimated $25 million to $50 million</a> to install their servers in Comcast’s infrastructure. Industry analysts say the video giant is looking at making similar deals with Verizon and AT&amp;T.</p> <p>In the meantime, this of course makes it so Netflix customers have to deal with the hassle of ridiculously long buffering—even though compared with other highly developed countries, we Americans pay <a href="">an exorbitant amount</a> for high-speed Internet. But what will happen in the long term?</p> <p>I bet if the amount of data required to stream a video over an ISP'’s network decreases, these debates would diminish: ISPs wouldn’t have enough of an argument regarding bandwidth usage to demand additional payments.</p> <p>This is where <a href="">high-efficiency video coding</a><a href=""> (HEVC), or H.265</a>, comes into play. The video format has been in development for the last couple of years and compresses a video data packet up to as little as 50 percent of the current <a href="">MPEG-4, or H.264</a>, format, all while maintaining or improving standard or high-definition video quality. For example, a 720p movie transmitting in HEVC will only require 600 Kbps to crisply stream, a connection speed drastically lower than the current average of 4.5 Mbps.</p> <p>This essentially means that if Netflix makes it a priority to adapt its library to HEVC, the ISPs currently squeezing it for cash will benefit immensely from this transition, and with very little upfront cost.</p> <p>To me, the solution is simple. Since HEVC slashes bandwidth requirements for HD streaming so heavily, ISPs should incentivize Netflix to convert its library to HEVC. If videos required less download data to stream, the ISPs infrastructure wouldn’t be overloaded during peak hours from Netflix customers, and thus the problem is solved.</p> <p>True, converting H.264 to H.265 is time-consuming and expensive. But Netflix is already adopting H.265 as a progression from H.264, which they use today, said Joris Evers, the vice president and head of European communications for Netflix, in an email. Surely with some fiscal incentives this process can be expedited.</p> <p>Amazon is also getting in on HEVC but won’t discuss its plan past using the compression method to stream <a href=";p=irol-newsArticle&amp;ID=1884896&amp;highlight=">ultra HD television shows</a>, said Jenna Snyder, an Amazon spokesperson.</p> <p>While HEVC-encoded videos will certainly be the future of streaming, there are still a few hurdles left before it’s widely implemented. Alongside conversion costs, companies might hold off on rolling out video using H.265 until consumers have access to enough HEVC-compatible hardware and software.</p> <p>H.265 is three to four times more complex to process than H.264, thus requiring newer systems to support it. <a href="">TV manufactures are currently working toward that</a>, but built in HEVC support will take a few product generations. However, because the process is being streamlined by developers and product manufacturers&nbsp;the adoption will occur much faster than it did&nbsp;with H.264, says John Held, a <a href="">DivX </a>spokesperson. H.264's initial development was finalized in 2003, but it wasn’t until 2009 that it actually began to get a foothold in the market. It took until late 2011 for H.264 to cross an <a href="">80 percent industry-wide adoption&nbsp;</a><a href="">threshold</a>. But Held says because of the coordination between developers and manufacturers, HEVC could potentially replace H.264's market superiority within the next few years.&nbsp;</p> <p>Another potential snag: Google is pushing its own super compression format, <a href="">VP9</a>, which is currently being used in Chrome and Mozilla Firefox and on YouTube. But companies looking to implement a new compression system might not have to choose between VP9 and H.265. Like many of Google’s projects, VP9 is open-source and free to use, so it’s reasonable that companies will implement both formats—assuming VP9 keeps HEVC’s royalty costs low once its licensing fees are established.</p> <p>Despite these challenges, major ISPs working together with Netflix to adapt its massive library into a data-friendly format is a better long-term solution for every party involved. Assuming Netflix’s customer base continues growing, failing to make this change will only postpone the issue until the new infrastructure upgrades also become overloaded.</p> Thu, 27 Mar 2014 21:45:29 GMT Travis McKnight 2014-03-27T21:45:29Z Technology Can a New Video Format Save Netflix From Future ISP Shakedowns? 203140327003 netflix internet streaming streaming video Travis McKnight Future Tense Future Tense false false false Can a New Video Format Save Netflix From Future ISP Shakedowns? Can a New Video Format Save Netflix From Future ISP Shakedowns?