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The Quarterly eBook of Clinical Informatics News’ Most Trending Articles

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The Quarterly eBook of Clini-cal Informatics News’ Most Trending Articles

In This Issue

A Note from Aaron Krol

HelpAround Explores the Social Aspects of Mobile Health

Why Digital Health is Harder Than You Might Think: A Reality Check From the Clinical Trenches

Social Media Insights Company Launches App for Social Listening

Amplifying the Patient Voice

Rare Genomics Institute Quantifies Crowdsourcing, Weighs in on Patient Engagement

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About Clinical Informatics NewsClinical Informatics News reports on innovative technologies from clinical trials to medical informatics. Technology continues to permeate all aspects of clinical trials and the patient experience, and the tools to support these efforts are maturing rapidly. ClinicalInformaticsNews.com and the Clinical Informatics News email newsletter provide authoritative news, views, and insights on the vast landscape of innovation between clinical trial management and delivery of care.

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2 In This Issue

3 Note from Science Writer Aaron Krol

4 HelpAround Explores the Social Aspects of Mobile Health

6 Why Digital Health is Harder Than You Might Think: A Reality Check From the Clinical Trenches

11 Social Media Insights Company Launches App for Social Listening

13 Amplifying the Patient Voice

17 Rare Genomics Institute Quantifies Crowdsourcing, Weighs in on Patient Engagement

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Patients are more connected than ever before—taking charge of their data, their health, and advocating for themselves in ways that were never possible before social media. We are also more data-rich than ever, with millions of people tracking their health, talking about their symptoms and treatments, and collecting data points to share with their health care teams.

Mining this mountain of data is crucial for trial sponsors and drug developers. There’s an impression that answers are out there, if we can just learn to effectively listen and process the information we can now gather.

Thankfully, there are researchers and companies working on those very problems, using machine learning and user interface research to organize the data we are receiving and add to it.

We are just laying the groundwork for all we’ll be able to learn from social media and mobile health. It’s an exciting time at the forefront of health research.

Aaron Krol Science Writer, Bio-IT World and Clinical Informatics News

Social Media& Mobile Health

Aaron Krol

“There’s an impres-sion that answers

are out there, if we can just learn to effectively listen and process the

information we can now gather. ”




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By Clinical Informatics News Staff | July 27, 2015

A new app called Alert is taking an unusually direct approach to mobile health: its aim is to make it as easy as possible for users to call their family members and caregivers in case of a medical emergency. With the push of a button on their smart phone’s lock screen, or by shaking the phone when the app is open, users of Alert can start a conference call with three preselected contacts, send information on their current location, and quickly escalate to a 911 call if things deteriorate.

HelpAround ExploresTHE SOCIAL ASPECTS OF MOBILE HEALTH




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Most mHealth tools on the market today have concentrated on the longitudinal use of continuous health data ― tracking useful metrics like activity, blood pressure, and glucose levels over time to produce an overall picture of users’ health. But the creators of Alert, at a small company called HelpAround, are more interested in making tools with real-world functions in the moment. “The last 90 days of graphs are really less interesting than the last 90 seconds,” says CEO Yishai Knobel.

At HelpAround, Knobel has focused on social aspects of care for people with chronic diseases. His company’s previous app is Diabetes Helpers, which puts diabetes patients in contact with other patients in their area to share supplies when they run low or their testing equipment breaks down. The app has found most of its users around metropolitan hubs, especially among people with type 1 diabetes, for whom disease management is a stringent and lifelong process.

“Our vision was, we’re going to find the right resource for the chronic patient at the right time,” Knobel says. “The level of solidarity, and the willingness of people to step up for each other, in chronic conditions is very, very high. So the ability to start aggregating that kind of knowledge on a local level is priceless.”

The philosophy of both Diabetes Helpers and Alert is that patients prefer to deal with their medical needs outside of the healthcare

environment whenever possible. Minor emergencies can usually be handled by a personal network of caregivers, rather than through a lengthy and expensive hospital visit. Diabetes patients, for instance, who may become woozy and disoriented when their blood sugar is too high or too low, much prefer to reach out to their families rather than going straight to 911. That becomes easier if they don’t have to fumble through multiple layers of UIs on their phones.

“We have people right now who have epilepsy, people with dementia and Alzheimer’s, or caregivers who might get stuck with an elderly parent and need help,” says Knobel. “We have people who suffer from panic attacks, and maybe you’re out of breath and can’t describe where you are… We wanted to make the pre-911 circle as easy and accessible as possible.”

The idea behind Alert is simple, but its designers are touting one feature they consider cutting edge. For diabetics who use the app on their iPhones, Alert is able to communicate with Apple’s HealthKit, an mHealth platform that aggregates medical data from many devices. While most apps that connect to HealthKit use the platform to chart and analyze data, Alert uses it as a real-time feed on a patient’s condition. If HealthKit picks up a blood glucose reading outside a specified range, Alert can automatically open and begin a call within 5 seconds unless the user overrides it. “This is the first time that any

iPhone app is taking advantage of HealthKit in real time,” Knobel says.

Several brands of glucose monitors have been programmed to share readings with HealthKit. In fact, Knobel began his mHealth career writing apps to work with one of these monitors, the iBGStar produced by AgaMatrix.

At the moment, the real-time character of Alert is more an ancillary feature than a core part of the app’s value. Blood glucose meters like iBGStar must be operated manually, so Alert is unlikely to be anyone’s first warning that their blood sugar is out of range. But manufacturers of continuous glucose monitors, which read glucose levels without intervention, may one day choose to plug their devices into HealthKit, in which case Alert will be able to keep tabs on users who haven’t been thinking about their glucose levels and reach out for help if they veer off balance.

To Knobel, access to up-to-minute data on patients’ conditions is one of the most exciting developments in mHealth. Central platforms like HealthKit are also making it easier for companies like his to reach large numbers of users, to take full advantage of that new wealth of information. “We don’t have to connect to this meter and that meter,” he says. “The beauty of HealthKit is that it just grabs the data and shares it with all your other apps. We want to set the tone for many other apps to follow, and start taking advantage of that data in real time.”

“THE LEVEL OF SOLIDARITY, AND THE WILLINGNESS OF PEOPLE TO STEP UP FOR EACH OTHER, IN CHRONIC CONDITIONS IS VERY, VERY HIGH. SO THE ABILITY TO START AGGREGATING THAT KIND OF KNOWLEDGE ON A LOCAL LEVEL IS PRICELESS.”Yishai Knobel. , HelpAround CEO




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Why Digital Health is Harder Than You Might Think:GUEST COMMENTARY | Brennan Spiegel, MD, MSHS | August 13, 2015

W e often hear that digital health is transforming medicine at a breathtaking pace. Each day brings

news of an innovation poised to alter medicine in ways we could never have conceived just years ago. Technophiles announce that digital health advances such as electronic health records

A REALITY CHECKFROM THE CLINICAL TRENCHES




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(EHR), mobile health (mHealth) applications, and wearable biosensors permit inexpensive and seamless data collection and processing, allowing previously unimaginable delivery of meaningful data from patients to healthcare providers, administrators and analysts. Put simply: We’re told that modern technologies are dramatically transforming healthcare for the better (example).

We need a reality check. This grand vision is not yet clear for many of us testing the science of digital health and seeking to understand how it will improve care. Digital health is at its infancy. For those who might disagree with this assessment, I would pose a simple litmus question:

Have you ever placed a digital device on an actual patient?

I ask this question because our research team at Cedars-Sinai spends a lot of time placing digital devices on patients. We’ve seen what happens next, and it’s not always what we expect. In fact, we are constantly surprised and perplexed by what we learn. I’d like to share some of those examples. These vignettes illustrate what we don’t know, and also what we don’t know we don’t know yet. None of this means we shouldn’t pursue digital health with vigor, because we should. But it does mean we need to study, test, observe, measure and update our assumptions with real-life experiences in actual patients. Digital health is hard. Here’s why:

Four “Real-Life” Examples of Why Digital Health is Really Hard

#1: Missteps with step counts

Our research team has been conducting a study to measure activity metrics in patients with rheumatoid arthritis undergoing medical treatment. The idea is to evaluate the correlation between activities – like step counts, maximum and minimum velocity and distance travelled – and patient report outcomes (PROs) – like fatigue, pain and joint stiffness. If patients’ self-reported health improves, then so should their step counts and mobility, right?

So, we obtained high-tech sensors from our colleagues at the UCLA Wireless Health Institute and put them on the limbs of patients undergoing treatment. Simple. Just wear them, keep them charged up and report the PROs daily; then we’ll see what we get.

Our very first patient threw us for a loop. She is a 72-year-old caregiver to her disabled husband. She refuses to let her disease stop her from doing what she has to do, but regrets how it keeps her from her true passion: writing. She finally found relief during the treatment course in our study, and, sure enough, her pain, fatigue, and joint stiffness all improved dramatically over the course of 30 days. Take a look at her actual pain data, right here:

It’s easy to see that her pain improved. I’m not showing her other data, but her fatigue, joint stiffness, physical function, and other key outcomes also improved. No doubt about it: she felt better.

So, then what do you make of her step count data, measured during the exact same period and shown below?




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You’re reading that right: she nearly stopped walking. She apparently put the brakes on basic ambulation at just the moment she felt gloriously better.

What gives? Our immediate reaction was that the data were wrong. It made no sense. She must not have used the sensors correctly. Or she must have taken them off for long periods during the day. Or maybe she put them on someone else. Or, perhaps, she really wasn’t feeling better, and we should trust the step counts but not the PRO results. There had to be some explanation.

What happened? Why did she report steady symptom improvement yet moved less and less? We figured out the answer only after speaking with the patient: the symptom improvement allowed her to return to her true work, writing. Stiffness and pain in her knees and ankles had previously prevented her from sitting at her computer for long periods of time, a necessary prerequisite for the type of work she was passionate about. Now, sitting at her desk for many hours and registering little movement beyond typing with her fingers was an “activity” she could return to with gusto. Diminished pain and stiffness in her knees and ankles allowed her to be less physically active, and achieve a greater quality of life in the process. Here is a telling quote from her interview:

Now when I sit down to work, I’ll work like 11 hours straight. You know, I’ll take bathroom breaks and go to eat, but then I go back to it, because when you’re hot, you’re hot, you know, you do it, and it was always sort of heartbreaking to have to stop.

Only by contextualizing her PRO and sensor data within our face-to-face interview did the data streams give a clear picture of her progress and its effects on her quality of life.

Had we been monitoring this patient from a remote e-coordination hub, keeping an eye

on her steps as a surrogate measure of illness severity, we’d have been duped. Totally duped. Instead, her inactivity was evidence of her improvement. Our assumptions were violated – with the very first patient in our study.

Conclusion: Our most fundamental assumptions about digital health data can be laid to waste with a single patient. Who would have thought that walking and moving less is a good thing for a patient suffering from debilitating arthritis. In this case it was.

#2: “I’m on house arrest”

Not long after we recruited our first patient in the arthritis study, we began to find more unexpected results from

others wearing the sensors. In particular, the sensor, which holds a small gyroscope in housing that’s the size of a small matchbox, caused most of our patients to cringe. One said: “I feel like I’m on house arrest.” He explained that having the device around his ankle felt like being on parole. This was not an isolated belief. In fact, four out of the first six patients in our trial agreed that there was a psychological burden to wearing the sensor on their ankles, and some took it off because they felt stigmatized and judged. Some offered preemptive explanations, anticipating that others would make assumptions and draw negative conclusions about them.

One patient made the obvious point that she could not wear the sensor over her affected joints, no matter how small the sensor was. Having anything near a “hot joint” was enough to make it unacceptable. No chance. Just not going to wear it there.

Conclusion: be prepared for unexpected consequences of putting devices on people. These findings may be obvious in retrospect, but they weren’t obvious going in. Again, our presuppositions about how “easy” or unobtrusive a device might be are easily violated.

#3: The “Virtual” virtual reality study

We’re conducting another study at Cedars-Sinai that involves using virtual reality (VR) goggles in

hospitalized patients. The idea is this: offer patients in pain or distress the opportunity to experience a fully immersive, three-dimensional, peaceful world that can distract them from the boredom, isolation and discomfort of their hospital bed. If you’ve ever used VR goggles, then you know they can overtake your brain and transport you to compelling and striking worlds you’ve never experienced.

For this study, we bring patients to beautiful landscapes in Iceland, transport them to tranquil ocean scenes, tour them through a virtual art studio, and even place them on stage with Cirque du Soleil performers. The visualizations are pretty amazing and transformative. It’s hard to get them out of your mind once you’ve been there. There is extensive evidence that VR interventions like these can help people in distress, so we’ve been excited to offer VR to our own patients.

After months of finalizing our protocol and obtaining approvals, we finally hit the wards with goggle in hand. Our first patient didn’t really get it. “No thanks,” he said. Our second patient wasn’t interested. “No thanks,” she said. Our third patient thought it was a weird psychology experiment. “Sorry, not interested in your experiment,” he said. Our fourth patient got it, but said he was too busy. Our fifth patient had used VR before and understood the concept, but said he didn’t want to get dizzy. Our sixth patient… you get the point.

It took 10 patients on the first day before we found a taker. And even that patient wanted to first watch the Republican Presidential Debate before using the goggles. “Could you come back later, maybe tomorrow?”




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It seemed like our virtual reality study had become, well, virtual.

The good news is that most people who actually use the VR goggles really like it and we’ve seen great responses. We believe VR will indeed be effective for inpatients, but it’s not for everyone (no matter how carefully, thoughtfully, or respectfully we present the offer). What sounded like an awesome idea wasn’t immediately awesome to many patients in distress, who are suffering in ways we cannot easily understand unless and until we’re in a hospital bed like theirs.

Conclusion: Some digital health use cases look great on paper, but until you try them in actual people with distress, we can’t know if the application is feasible or acceptable.

#4: Murphy’s Law

You know Murphy’s Law: “What can go wrong, will go wrong.” I promise you this is true with digital health. It’s surprising to

see what really happens when digital device and patient are united. Here are some highlights from our own hands-on experiences:

Lost devices: If a patient gets a digital device, you can bet it will be lost before long. In just the first six patients in our arthritis study we had two devices lost forever. Just gone. In another recent example we used a FitBit device around a patient’s wrist. She reported that it fell off her wrist one day and she couldn’t find it. She said this while in tears and with great remorse – this wasn’t made up. Somehow, someway, a FitBit just fell off her wrist and disappeared into the abyss.

Can’t charge: We are using a device in one study that uses an inductive charge pad. It’s simple: place the device on the pad, look for the light to turn green, and leave it there until it’s recharged. We’ve learned that some patients are not comfortable with inductive charging. One subject called our research coordinator and said the sensors just weren’t working. Sure enough, our

remote monitoring data came back empty – she wasn’t using the sensors. An extensive telephone conversation couldn’t troubleshoot the problem, so we sent a team member out to her home to replace the device. It turned out that the device was working fine. The patient was placing the device near the charge pad, but not directly onto of the pad. She didn’t get it.

This may seem like an isolated or extreme example. We might even wonder how this patient could be so easily confused by something as “simple” as an inductive charge pad. But that

would be misguided. People are people. And technology is technology. If we’re going to use digital technologies to monitor something as vital as, well, vital signs, then they better be foolproof. We’re not yet there.

Ouch!: We developed a device, called AbStats, that non-invasively measures your belly. The disposable sensor sticks to the abdominal wall with Tegaderm – a common medical adhesive – and has a small microphone on the inside that monitors the sounds of digestion. A computer analyzes the results and helps doctors make

SOME DIGITAL HEALTH USE CASES LOOK GREAT ON PAPER, BUT UNTIL YOU TRY THEM IN ACTUAL PEOPLE WITH DISTRESS, WE CAN’T KNOW IF THE APPLICATION IS FEASIBLE OR ACCEPTABLE.




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decisions about whether and when to feed someone recovering from surgery (for example). We’re using AbStats in our patients to direct feeding decisions and, so far, the device is performing well. But for whom? If you’re a patient who has to pull the sensor off your belly, then it’s not performing very well at that particular moment. We quickly learned that it hurts like crazy to pull Tegadarm off the abdominal wall. Moreover, if you’re a hairy man (or even just a slightly hairy man), it gets worse. Ouch. We all know that the form factor really matters. But even when we know that, we still learn it over and over again.

Even colors matter: We developed an mHealth app called My GI Health that allows patients to measure their gastrointestinal (GI) symptoms, obtain tailored education, and communicate their information efficiently with their doctor. The system works great and has been shown, in peer-reviewed research, to even outperform doctors in obtaining a proper medical history. All good. But we learned that some users did not like the color scheme of our data visualizations. We present symptom scores over a colored

gradient, where green means “no symptom” and varying shades of red mean “you have the symptom.” We had to review at least 10 different color gradient schemes before we finally settled on the one users like the most. But even then, after meticulously documenting our decision, we found that some users wanted a “yellow” area between the green and red – like a stoplight. Now we’re trying out more schemes to get it right. The point is this: even when a digital health intervention is mature, it still needs ongoing validation and refinement. Nothing rolls off the assembly line ready to go. And even when it’s working, it’s still not quite working in some cases. This stuff is hard to do.

Conclusion: Expect things to go wrong when device meets human. All the time.

What does this all mean?This essay might come across as naysaying. It is – to a degree. I’ve become more and more hesitant to blow the digital health trumpet when I see, first hand, that patients lose their devices, can’t charge them, feel stigmatized by wearing them, or just have no interest at all in using them. I also know

that for every pessimistic example I’ve described here there are positive examples where digital health is shining. I get it. But we need balance.

We also need hard-fought, meticulous, systematic research to figure out whether, how, and when digital health will provide value. Consider how much evidence a pharmaceutical company must amass before getting a drug approved. Then think about how little we need for a digital device. The bar right now is pretty low for promoting adoption. It also appears there are a hundred-fold more essays pronouncing the success of digital health than there are peer-reviewed studies evaluating their true impact on living, breathing, and oftentimes suffering patients.

We should spend more time with patients. We should interview our patients about their experiences with digital health, improve our devices based on their feedback, and continuously test our technologies in everyday care settings. It’s also crucial to understand how patients interpret their own data. The case of the misleading step count is illustrative: what we presuppose is important may not be important to a patient. This is all pretty obvious stuff. So let’s do it!

Next time you read a forward-reaching statement about the glory of digital health, ask yourself whether the author has ever placed a digital device on an actual patient. Digital health is a contact sport—a hands-on science. Let’s keep connecting patients to devices and studying, carefully, thoroughly, and directly, what works and doesn’t work. We’ll get this right in time.

Brennan Spiegel, MD, MSHS, is Director of Health Services Research, Cedars-Sinai Health System, and Co Founder & Co Chief Medical Officer of MyGiHealth.

THE POINT IS THIS: EVEN WHEN A DIGITAL HEALTH INTERVENTION IS MATURE, IT STILL NEEDS ONGOING VALIDATION AND REFINEMENT.





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By Maxine Bookbinder | July 6, 2015

Treato, a social media insights company that describes itself as the world’s largest “patient voice” source, has announced it will apply its big data

infrastructure and technology to social media networks, giving patients a platform to discuss treatments and allowing the medical and business communities to gauge real-time patient attitudes, view social trends, and gain insights into their—and their competitors’—advertisements and products.

Social Media Insights Company Launches App for

SOCIAL LISTENING




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“Real change for improvement of the healthcare system will come from the outside. It’s where IT meets health. We are one of many companies forcing new thinking,” says Ido Hadari, CEO of the healthcare insight company Treato, which has collected, indexed, and analyzed more than 2.1 billion patient and caregiver online conversations.

Treato recently announced the launch of the Social Media Application, an addition to its core platform, which applies the company’s big data infrastructure to social media listening and analyzes social media trends, favored networks, top influencers, and company and competitor brands. It also allows viewers to gauge patient sentiment, combining algorithms with social listening and determining which topics are trending which way.

Hadari tells Clinical Informatics News that Treato is the “first company to create the technology to give a voice for the health consumer.” He states that, to date, Treato has analyzed billions of conversations on public health forums and blogs, regarding 26,000 drugs, 14,000 medical conditions, and hospitals, clinics, tests and procedures worldwide. The company, with offices in Israel and New Jersey, is expanding online to non-English speaking countries and recently launched the Hispanic insight solution, offering pharma marketers insight into the health concerns, experiences, and consumer interests of the Spanish-speaking population.

Specifically, the announced social media

monitoring application is a supplement to its focus on cyber blogs, forums, and communities to track and analyze why a sentiment is trending a certain way, what therapy, medical advertisements, or healthcare issues are praised or panned, and what consumers think and feel. “Was it (an ad or drug) working or not?” says Hadari. “Was the response positive or negative? The Internet knows first.”

The company delivers insights separately to consumers and the healthcare industry. Treato.com is a free, online portal offering consumers a single resource to access patient thoughts, experiences, and attitudes. According to Hadari, more than 2.5 million patients and caregivers worldwide access Treato.com monthly to find out what others are saying about their symptoms, conditions, and treatments. Tracked discussions include consumer attitudes on topics ranging from vaccine controversies to cancer treatments.

Medical professionals can follow trends and gain insight into consumer attitudes on Treato IQ, a “patient intelligence platform” that helps companies to understand the “why” and “what” behind patient behavior in real-time and allow pharma to formulate business and marketing decisions; the Treato IQ platform can be customized to fit a particular business’s needs. “The trajectory of a conversation is a great way to monitor what’s going on,” says Hadari. Currently, thirteen out of the top 50 pharmaceutical companies use Treato services, the company says.

One example was a pharma’s slogan, “feeling refreshed in the morning,” for a weak bladder product. On social networks, patients complained that they were sleep-deprived and did not care about or relate to “feeling refreshed”; they simply wanted more sleep. The company revamped the campaign.

Treato collects approximately one million conversations daily, filtering for spam, ads, inappropriate comments, and third-party “plants” and then analyzes and organizes them. Algorithms, statistical analysis, and human reviewers strive for an accuracy rate in the high 90s.

Treato helps the business community decipher patient lexicon, understand patients’ unmet needs in clinical trials, gain input into marketing strategies, monitor brand “hits,” target specific demographics, and track consumer responses to treatments.

The Treato website states that information should be regarded as a qualitative, not quantitative tool, giving companies insight into consumer attitudes. It also notes that, according to the international law firm Alston & Bird LLP, pharmaceutical companies are not required to report adverse findings “due to the non-specific nature of the information” and patient anonymity.

However, a pharma might choose to explore a unique comment about, for example, a new treatment for a rare disease. Only public forums are monitored, no identifying information is revealed, and conversations are quoted from the sites.

Hadari says the company, which launched commercially in 2011, has spent “over 100 man-years pulling data on everything health and then analyzes it. We are the world’s most comprehensive source of patient insights from billions of online conversations. You can go to WebMD to find out what pharma says. You go to Treato to find out what people like you say.”

“REAL CHANGE FOR IMPROVEMENT OF THE HEALTHCARE SYSTEM WILL COME FROM THE OUTSIDE. IT’S WHERE IT MEETS HEALTH. WE ARE ONE OF MANY COMPANIES FORCING NEW THINKING.” Ido Hadari, Treato CEO





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By Aaron Krol | May 29, 2015

P atientsLikeMe’s centerpiece is its social network: a site where patients living with major chronic illnesses can find other people

dealing with the same symptoms to share treatment experiences, ways of coping, and research opportunities. Facebook was just two years old when PatientsLikeMe launched its first “community” in 2006, for patients with amyotrophic lateral sclerosis (ALS), yet the organization’s founders already had an intuitive sense for the strengths of social media, which would quickly make the service a popular meeting space for patients.

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Key to the website’s success is its search function. Members of PatientsLikeMe communities enrich their profiles by recording symptoms they’ve experienced, along with other information like their medications and side effects. They can then search the entire site for other members who have dealt with the same or similar symptoms. These matches are the beginning of the personal contacts members forge among themselves, the core of PatientsLikeMe’s mission.

At the time PatientsLikeMe was building its platform, this kind of user-defined tagging of content was seen as a new feature of the social web, and a big departure from traditional search functions that relied on top-down data structures. Letting members enter their experiences with disease in their own words allowed PatientsLikeMe to build its entire data scheme on what the organization calls “the patient voice.”

However, says Sally Okun, PatientsLikeMe’s Vice President for Advocacy, Policy and Patient Safety, the same democratic approach to patient data that made the platform so valuable to its users would later prove to be an obstacle to connecting more formally with physicians and researchers. “The tagging was useful in a social media perspective, or a social networking perspective,” she says, “but it wasn’t going to be particularly useful in a clinical perspective.”

No one will be surprised to hear that the words patients use to describe their illnesses are imperfect matches for the terms preferred by clinicians. Take, for example, a patient who has recently developed tremors that interfere with her ability to walk. “As a clinical concept that might be considered ‘gait disturbance,’” says Okun. “And yet our patients might tell us about that in a variety of ways. They might say, ‘I was walking like a drunk,’ or, ‘I was tripping over my feet.’”

There are plenty of reasons, good and bad, for this language barrier. It’s helpful for clinicians to

have highly precise terms, so it’s possible to tell at a glance when two cases have overlapping presentations. Broad analyses across hospitals or research groups also benefit from a standard vocabulary, which makes it easier for computers to reconcile data collected in different locations. And no doubt any doctor would hesitate to say a patient was “walking like a drunk” in a clinical report or scientific journal.

Yet, says Okun, the scientific community can be missing a lot more than a flair for colorful expressions when it tries to force the patient voice into a chart-shaped box. The standard scales for pain or fatigue, for instance, where patients are asked to rate their symptoms from one to ten or choose between words like “moderate” and “severe,” can obscure the patient experience as easily as clarify it.

“Even ‘severe fatigue’ does not measure the kind of fatigue many people with fibromyalgia may experience after having a particularly bad day,” Okun says. “They may have to say something like ‘overwhelming fatigue’ in order to get the actual severity that makes sense for them.” That nuance can mean the difference between aggressively treating a symptom, and failing to appreciate its debilitating effect on a patient’s life.

“The problem with these standardized terminologies is they really weren’t created to help improve care,” she says. “Many of them were created to help improve reimbursement. So they’re not tied to some true experience that a patient might be having.”

VOCABULARY BUILDING

One of the first occasions PatientsLikeMe had to confront the messiness of its patient data concerned a vocabulary called MedDRA, the Medical Dictionary for Regulatory Activities. The PatientsLikeMe team had always hoped that its work bringing communities of patients together could be used to support clinical trials of new therapies, or extended research of treatments already on the market. To communicate with the pharmaceutical companies who would sponsor that research, PatientsLikeMe would have to learn to speak the same language.

“As we were starting to work with the pharmaceutical industry, we had an important responsibility to help them meet their regulatory

NO ONE WILL BE SURPRISED TO HEAR THAT THE WORDS PATIENTS USE TO DESCRIBE THEIR ILLNESSES ARE

IMPERFECT MATCHES FOR THE TERMS

PREFERRED BY CLINICIANS.




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obligations around adverse event reporting,” says Okun. That meant digging into the side effects that members were recording in their profiles, and squaring them with MedDRA, a formal vocabulary for adverse events that helps both industry and regulators analyze how patients are responding to drugs.

Mapping between the patient voice and a clinical taxonomy is not a straightforward task. Sometimes, as in the example of “gait disturbance,” there’s a fairly clear one-to-one correspondence: on the front end, members can still see the terms they entered, but behind the scenes those terms have been translated to a common formal taxonomy. That way, not only can a member’s data be usefully analyzed outside the PatientsLikeMe network, but when users search for one another, the patient who wrote that he’s tripping over his feet can be linked with another who said she’s always stumbling.

Other times, however, users might supply information that has no easy equivalent in a dictionary like MedDRA. Some expressions, like “cramps,” are too general; others are more specific than medical taxonomies are equipped to handle. If symptoms are felt only in a certain body part, at particular times of day, or in

association with other symptoms (like “headache after coughing fits”), that information is likely to be lost in translation. These nuances can be important, both to the patients themselves and to researchers trying to understand their diseases.

Even changes in emphasis and shades of meaning can reveal real clinical differences. If two patients both rate their pain as a “five” on a numerical scale, but one describes a “hammering pain” and the other a “shooting pain,” they could be results of different pathologies. The same can be said for broader statistical analyses: fibromyalgia patients and ALS patients might score themselves similarly on fatigue scales, but if the fibromyalgia population is more likely to use adjectives like “overwhelming” or “debilitating,” that probably says something about the disease itself.

“We already know that patients will rate their symptomology differently from their clinicians,” says Okun. “If all we’re asking as clinicians is ‘tell me about your pain,’ we’re not getting in deep enough to say, ‘tell me about that icepick pain that you described.’”

Despite the translation issues, PatientsLikeMe was able to render a large number of patient-supplied symptoms in the language of MedDRA,

often reaching out to members to check whether certain formal terms were accurate descriptions of their experience. At the same time, the PatientsLikeMe team decided to link their “patient taxonomy” to several other formal vocabularies that were becoming standards in electronic health records (EHRs), including SNOMED, ICD, and LOINC. “In 2008 that was a relatively forward-thinking approach that we took,” says Okun. “And I’m glad that we did, because now all of our data can map to any one of those taxonomies.”

Over the next several years, federal incentives for EHRs created with the Affordable Care Act would make these vocabularies near-universal across American hospitals and care centers. While the correspondence between member-supplied terms in PatientsLikeMe and the taxonomies in EHRs is still far from exact, PatientsLikeMe now has a fairly strong base by which to share or compare data across these systems.

WORD CHOICE

In its internal research, PatientsLikeMe has taken advantage of surprising patient-supplied symptoms to contribute to our understanding of chronic diseases. In the ALS community, frequent yawning turned out to be a common characteristic of one unique subtype of the disease. In the Parkinson’s community, member surveys lent support to the now well-established notion that dopamine agonists, a common class of Parkinson’s medication, can lead to addictive gambling ― and also suggested the side effect could be more widespread than previously believed.

So far, these studies that begin with hints in the PatientsLikeMe data have had to be followed up with more rigorous surveys and better definitions of the symptoms being examined. The organization is still debating what sort of work would have to be done to validate that the connections between its patient taxonomy

EVEN CHANGES IN EMPHASIS AND

SHADES OF MEANING CAN REVEAL

REAL CLINICAL DIFFERENCES.




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and formal vocabularies are accurate. The PatientsLikeMe members are also in many ways a highly unusual population, and few conclusions drawn from their ranks can be safely generalized to patients as a whole.

If, however, the healthcare community can settle on a way to reliably link patient-supplied terms with clinical taxonomies, this kind of analysis could one day be applied across the healthcare system, and perhaps even automated. Physicians already collect subjective notes based on patients’ descriptions of their symptoms, and often include these in the medical record; a taxonomic system that makes even a portion of that information machine-readable would allow clinical informatics programs to draw broader conclusions about a disease’s pathology.

“The need is for us to find electronic and digital ways to start capturing this and depositing it into a more patient-focused and patient-centric data collection environment… where data’s going in and being subjected to some kind of analysis,” says Okun. This will become even more

important as PatientsLikeMe starts to connect more directly with the care centers where patient data is collected. Just this week, the organization announced a partnership with the Partners HealthCare hospital network in Massachusetts, to offer patients a direct portal between the Partners Patient Gateway health management tool and the PatientsLikeMe site.

Meanwhile, as PatientsLikeMe refines its patient taxonomy to make closer matches to the language of EHRs, the creators of some formal vocabularies are interested in making their dictionaries a little more like the language of PatientsLikeMe. Okun says that MedDRA, for instance, has discussed including some of the terms that PatientsLikeMe members commonly use, to ensure its dictionary of side effects corresponds as closely as possible to drugs’ real-world impact on patients.

She is also encouraged by a recent movement in oncology to take the patient voice into account when describing symptoms associated with chemotherapy, whose effects can be hard to

manage and have a huge impact on quality of life. Side effects of cancer treatments are often under-addressed, and a growing number of advocates believe that communication breakdowns between patients and their physicians are partly responsible.

Emerging technologies like natural language processing could make the transition to using the patient voice much easier in the future, opening up free text fields in both EHRs and platforms like PatientsLikeMe to much more analysis. “The time has come,” says Okun. “Patient voice, and patient-generated data broadly, has become something that people are talking about all the time.”

The focus on the patient voice is about much more than research and data collection, she adds. Even at the individual level of the doctor-patient relationship, a shared understanding of the language used to describe health can change the way care is delivered. “The goal is truly for us to better understand each other.”

IN ITS INTERNAL RESEARCH, PATIENTSLIKEME HAS TAKEN ADVANTAGE OF SURPRISING

PATIENT-SUPPLIED SYMPTOMS TO CONTRIBUTE TO OUR UNDERSTANDING OF

CHRONIC DISEASES.





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By Allison Proffitt | July 30, 2015

I f you ask Jimmy Lin to describe his role in patient engagement, he may well compare himself to a jet pack. The president of the Rare Genomics Institute sees himself as an empowering accessory for the true heroes: parents of children with rare diseases.

The Rare Genomics Institute is an international nonprofit organization born about four years ago whose mission is to connect patients and their families with the tools, knowledge, and experts necessary to understand the cause of their rare diseases. RGI believes understanding starts with sequencing, and so it partners with research facilities able to perform sequencing and provide a clinician or scientist who can interpret the data. RGI also hosts a fundraising platform to help families raise funds to access those resources.

Last month Lin was appointed by the Patient-Centered Outcomes Research Institute (PCORI) as a member of its Advisory Panel on Patient Engagement. The panel’s goal is to help PCORI refine research funding priorities and ensure that PCORI supports outcomes that matter to patients and other healthcare decision makers.

“It’s no longer researchers at the top and subjects at the bottom. We’re now calling them participants and they should have a role, and

they should be able to get back their data,” Lin says. “They should be at the table from the very beginning… They should be acting as co-PI’s [principle investigators].”

That’s very much the case for RGI’s work, Lin says. Each family that applies to work with RGI gets a personal patient advocate. The advocate walks the family through the process to apply for sequencing and raise funds if necessary. While RGI does everything it can to help, a bulk of the work does fall to patients—or in most cases, a young patient’s family.

“We see the parents as the true heroes. They work day and night to see the child through the ups and downs. We see ourselves as the cheerleaders, as the jetpacks to the families, to help these amazing parents access the resources they need to.”

PEER REVIEWING CROWDSOURCING

While RGI can help with one of the biggest

RARE GENOMICS INSTITUTE QUANTIFIES CROWDSOURCING,

Weighs in on Patient Engagement




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hurdles—access to experts—it does not have the funding to pay all of the fees associated with genomic sequencing and analysis. To bridge the gap, RGI set up a crowdfunding platform for its families.

The funding model is particularly interesting to Lin. He wanted to delve more into how crowdsourcing works to raise funds, but also how it can, “create a community, and educate that community about the scientific impact of genomic medicine.”

With a grant from the Templeton Foundation, in May RGI announced the Amplify Hope Initiative, a study of how crowdfunding can promote scientific research to help rare disease patients. In the first phase of the study, RGI interviewed experts in crowdfunding—individuals who have raised millions for their own children, companies who host crowdfunding platforms, and professional fundraisers—and gathered best practices.

Phase two launched in mid-June. Patients for whom RGI has determined medical need are eligible for the Amplify Hope Initiative. These patients need a physician referral and must show that they have exhausted other genetic panels and microarrays, and thus would benefit from exome sequencing. Two existing RGI sites, Ambry

Genetics and Baylor Miraca Genetics Laboratories, will conduct the sequencing. Crowdfunding partners to the project include CrowdRise, Indiegogo Life and YouCaring.

Once enrolled, these patients will begin a free, 30-day Crowdfunding Bootcamp. “We’ve created a curriculum that includes everything we’ve learned,” Lin says.

The program will help families understand available fundraising platforms, plot campaigns, reach out to their networks, and leverage social media more effectively with eye-catching images and videos. Top fundraising experts will be conducting free webinars and offering remote support to showcase the most effective email templates and messaging strategies.

Meanwhile, RGI will be comparing incentive mechanisms and determining how various metrics predict the success of fundraising. When the study is complete, all of the findings will be shared. “At the end, we’ll be posting all of the best practices and making that free and available to everyone who’s interested,” Lin says.

MR. LIN GOES TO WASHINGTON

Lin will bring what he’s learned to the PCORI advisory panel on patient engagement as well.

He already has a recommendation for how to better serve patients, particularly those with rare diseases for whom clinical trials are always N=1.

“As we move into the genomic era of medicine… we shouldn’t practice genetic exceptionalism in thinking that this is a super special test. Every other test we give back raw results. We should be able to give [genomic test] raw results.”

Lin acknowledges the challenges and advocates for education along with the returned data, but believes especially for rare disease patients having the raw data is crucial.

“For our patients who are on this diagnostic odyssey, the first interpretation may not give them the answer. If they want to take that data to another researcher they often are prohibited from doing that… Clinical genomicists are not doing reinterpretation most of the time. The onus is really on the patient.”

Pressed to clarify what he means by “raw results”, he advocates sharing what patients want—VCFs, aligned BAM files, even FASTQ files. “I think if patients really want their FASTQ files… that could be very helpful,” but he clarifies: “I don’t think all patients are going to ask for their raw data.”

THE PROGRAM WILL HELP FAMILIES UNDERSTAND AVAILABLE FUNDRAISING PLATFORMS, PLOT CAMPAIGNS, REACH OUT TO THEIR NETWORKS, AND LEVERAGE SOCIAL MEDIA MORE EFFECTIVELY WITH EYE-CATCHING IMAGES AND VIDEOS.





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