Rahlyn Gossen

	Google handles over 1 trillion searches a year. That’s more than 33 searches a month for each person worldwide who uses the Internet. Search engine queries provide a window into people’s thoughts and questions. Using aggregate search query data, we can better understand potential clinical trial participants. And understanding is essential to improving trial education and awareness. A number of tools provide access to search data, but Google’s tools are most popular. 

	To illustrate the value of search engine data, I decided to have a little fun with Google’s autocomplete. Autocomplete is the real-time suggestions Google provides as you type a query. These suggestions are based on various factors, including prior queries from other people. Because prior queries inform autocomplete, it can provide a view of collective consciousness around different topics.

	This exercise is purely for “infotainment.” If you really want to analyze search engine data, there are better, more sophisticated ways. But this exercise is a quick and easy method for getting some high-level insights into the general public’s thoughts and questions about clinical trials.

	I’ve structured the following query examples as questions. The question format helps to elicit biases people have about clinical trials. Hence, autocomplete will provide clues not only to what people want to know, but also their preconceptions with regard to clinical studies.

	There’s much we can learn from these and other autocomplete suggestions. People’s preconceptions and questions about clinical trials can be addressed with two main types of education: basic and protocol-specific. Autosuggest provides insights into educational themes we might need to address within each of these categories. Basic themes found in the autosuggest exercise include:

		What’s involved in participation (e.g., length)?

	In recent years, clinical researchers have turned to social listening to understand patients, but social data is not the only online data game in town. As this exercise shows, search data provides a unique view into people’s thoughts and questions. Regardless of type, online data can be a quick and inexpensive form of market research.

 is Founder/Digital Marketing Strategist, Rebar Interactive, and Applied Clinical Trials Editorial Advisory Board Member

- This article is adapted from a post that appeared on the Rebar Interactive blog.

Articles

Search-engine data offers window to public consciousness around clinical trial research, participation.

What Google Can Teach Us About Trial Education, Awareness

Editor's Note: This post originally appeared on Medidata's blog 

 of this article, I described a collaboration between 

, which resulted in a very interesting proof-of-concept. Heinz created an app that serves as a bridge between Medidata Rave and popular quantified self devices like the 

So what might this proof-of-concept mean for the future of clinical trials?

The Quantified Self-Enabled Virtual Trial

Personally, I find the most exciting potential use case for this proof-of-concept to be in 

As Heinz pointed out during our discussion, Phase I trials are currently the most “quantified” of clinical trial phases. By pulling quantified self data into Medidata, we could bring this same level of quantification (or better) to other phases as well. And we could do it at far greater convenience for patients.

 to a quantified self-enabled virtual trial, which I’ll discuss in more depth below. 

An agile and efficient clinical trial model 

Each benefit described is an extension of, and enabled by, the previous one.

Benefit 1: Access to Rich High-Quality Data

Perhaps the most straightforward and obvious benefit of a quantified self-enabled virtual trial is access to rich high-quality data. 

Rather than being dependent upon site visits for data, we’d have access to a continual, or at least regular, feed of it. The collection process would require little, if any, human intervention, eliminating the possibility for data entry error. Furthermore, the data collected would be more “real,” since collection would occur in the patient’s natural surroundings, rather than an artificial site environment.

Perhaps most interestingly, we’d have access to new kinds of data. 

This article has focused on quantified self data because that’s the focus of Heinz’s proof-of-concept. But this 

 to serve as a bridge between Medidata Rave and other types of data as well. 

The big advantage of application programming interfaces (APIs) like those provided by Medidata is the relative ease of doing exactly what I’ve described. For example, you could 

 into the patient’s virtual trial record. Incidentally, 

, which has an API similar to that of Medidata’s, to advance genomic testing in clinical trials. 

 into a virtual clinical trial, which brings me to the second benefit.

Benefit 2: An Agile and Efficient Clinical Trial Model

Why stop at EHR data? Why stop at quantified self data? The virtual clinical trial of the future could 

incorporate multiple disparate data sources

This virtual clinical trial would be more 

automated, collaborative, modular, and personalized

. Given the need for pharma to shift from blockbuster to 

, the personalization aspect should be of particular interest.

Extensive discussion of such a clinical trial model is beyond the scope of this article. But to illustrate the possibilities, here’s one example of a powerful capability enabled by these improvements. Using a 

 approach, the system could be programmed to perform 

, potentially alerting us to serious health issues before they occur. 

Weight fluctuation, for instance, can signal a serious cardiac condition, as Heinz noted during our discussion. If patients regularly monitored their weight at home using a “connected” scale, that data could be continuously fed into and analyzed by a virtual trial system. Should that system detect weight fluctuation indicative of a cardiac condition, it could alert us to the potential issue.

This example is just one of many opportunities we have to create a more agile and efficient clinical trial model. 

Last but certainly not least, the virtual trial of the future could provide a better patient experience in

. This model would reduce or eliminate the need for site visits. Furthermore, the seamless data collection enabled by such a trial would eliminate the more onerous tasks that we require of patients.

. I don’t think it’s a stretch to suggest that a more automated, collaborative, modular, and personalized clinical trial has the potential to provide higher quality for patients.

. As discussed at the beginning of this article, many patients view clinical trials as being disconnected from their broader health (and human) interests. Once we have the ability to connect the pieces of a formerly disparate patient puzzle, we can better align clinical trials with these interests. 

Our goal should be to align clinical trials and patient interests so well that the distinction between each becomes almost invisible. With the convergence of clinical research and clinical practice, the 

This final benefit also creates a desirable byproduct. 

If we provide a superior clinical trial experience for patients, that stubborn patient recruitment 

Charting the Course to Future Clinical Trials: What Now?

The primary purpose of this article was to describe Heinz’s proof-of-concept and what it might suggest for clinical trials of the future. I’ve taken an admittedly ambitious and optimistic bias in this description, largely because the opposite bias can easily be found elsewhere.

 the clinical trial model I’ve described. Substantial technological, regulatory, institutional, cultural, and operational barriers exist.

However, the difficulty of overcoming these barriers shouldn’t prevent us from trying.

 of our existing clinical trial model. For example, quantified self devices aren’t currently advanced enough to collect all of the data needed for clinical trials. However, activity trackers like the Fitbit, which work with Heinz’s proof-of-concept, still have potential value.

. Though patients are often asked not to change their exercise routine during a clinical trial, we have no objective measure of compliance. Activity trackers could change that.

Alternatively, activity data could provide a 

 for some trials. For studies related to pain or mental health, an increase in activity might be suggestive of meaningful quality of life improvement. Interestingly, 

 published a study indicating that Fitbit data could be used to predict surgery recovery time.

Experimentation with our existing clinical trial model won’t be without challenges or failures. But in my experience, very little worth doing is.

What I’ve described is just one person’s thoughts on a future clinical trial model, and I have no doubt these thoughts will change over time. So I’d like to hear your vision. What might the clinical trial of the future be like?

Put your comment below or contact me directly via the 

In Part 1 of this article, I described a collaboration between Brock Heinz from Spaulding Clinical and Joe Dustin from Medidata, which resulted in a very interesting proof-of-concept.

What a New Proof-of Concept Might Mean for Clinical Trials of the Future (Part 2)

This post originally appeared on Medidata's blog 

As clinical researchers, it’s natural for us to think of patients first and foremost as research participants (potential or current). 

But that’s not how patients think of themselves.

For most patients, clinical trials seem rather disconnected from their broader health (or human) interests. And until we meaningfully connect clinical trials to these larger interests, 

Drawing this connection is a challenge not just because of our natural tendency to classify patients as research participants. It’s also an issue of access. 

Historically, our access to patient data has been mostly confined to the scope of clinical trials. And without access to a more holistic view of the patient, it’s difficult to connect clinical trials to larger patient interests.

Thanks to technology advancements, new access is opening up. 

Electronic health record (EHR) data, for instance, can provide us with a better understanding of the patient. As a result of this understanding, we have new ability to strengthen the connection between clinical trials and the patient’s broader health interests. 

EHRs provide information about patient interaction with the healthcare system, which is far from a complete patient picture. Furthermore, this incomplete picture tends to be painted primarily from one perspective, that of the healthcare provider. 

Meanwhile, the provider perspective is losing the significance it once had. 

Though physicians and providers remain an important source of health information, they are no longer the only source. Patients are shifting from passive participants operating at the periphery of their care to active participants operating at the center of their care. 

In essence, quantified selfers gather and monitor data about their daily functioning in an effort to improve it. 

, for example, recently announced that over 18 million people use its quantified self technology to track their exercise. 

A common misconception about quantified self is that only the fitness fanatics, gadget geeks, and worried well practice it. 

Though mainstream consumers may not be familiar with the term “quantified self,” most are participants in the movement. According to 

, 7 in 10 US adults track a health indicator for themselves or a loved one. Metrics and tools may range from simple and nontechnical (e.g. recording weight in a notebook) to complex and highly technical.

As the technology to capture health data evolves, it will become more sophisticated, unobtrusive, and inexpensive. And in many cases, quantified self technology will allow for nearly effortless passive data collection. 

Writing weight in a notebook, for example, will become an unappealing health tracking option for most people. As a result, mainstream consumers will embrace what are by today’s standards quite advanced quantified self devices. The data enabled by this technology, of course, can be stored and moved around.

And that’s where quantified self gets quite interesting for clinical trials.

Bridging Quantified Self and Clinical Trial Data

Perhaps most notably, quantified self data could be pulled into EDC systems, providing us with unprecedented rich data about our patients. I just used the word “could,” but actually, it can. 

 to do exactly what I’ve described. Heinz created an app that serves as a bridge between Medidata Rave and popular quantified self devices like the 

.
   
  During a discussion I had with Dustin and Heinz, Dustin noted that the technical feasibility of this integration is not what presents the real challenge, as the proof-of-concept illustrates. 

The big challenge, which is something we often struggle with in clinical trials, is how do we use this new ability? 

In Part 2 of this blog, we’ll talk about that question.

Rahlyn Gossen is a former clinical research coordinator and the owner of 

, a digital media company serving the clinical research industry. You can find her on twitter 

As clinical researchers, it?s natural for us to think of patients first and foremost as research participants (potential or current). But that?s not how patients think of themselves.

What a New Proof-of Concept Might Mean for Clinical Trials of the Future (Part 1)

You know those moments when you come across a really exciting idea, and you say to yourself, “why didn’t I think of that?!”

No, this moment was not inspired by the creation of 

(though I wouldn’t blame you for that assumption, given SnapCat’s brilliance).

Lilly Clinical Open Innovation (LCOI) team

 blog post a few months back, which teased at an upcoming clinical trial challenge.

Lilly has since announced the details of this challenge, which is 

Before describing Lilly’s challenge, let’s talk about a big problem in clinical trials. The fact that most clinical trial information is patient unfriendly is pretty well-documented.

As a former clinical research coordinator, I’m intimately familiar with this unfortunate reality. An important part of my job as a coordinator was to translate dense clinical trial jargon to more patient centric language. However, the fact that site staff are available as translators does not mean we should continue to rely on them for that purpose.

I could talk about the fact that our industry invests relatively little in providing basic clinical trial education. I could talk about the fact that most online clinical trial registries are incomplete and/or poorly designed. I could talk about the fact that informed consent forms require an absurdly high reading level for comprehension.

These points are all important. But I think they’re also suggestive of a larger issue.

I’d argue that this quote is very relevant to clinical trial participation, particularly when discussing patients who are sick.

How patients are made to feel will dramatically impact their willingness to consider, initiate, and continue clinical trial participation. That’s been my experience, but there is also

So how does clinical trial information, as it’s currently presented, often make patients feel?

The use of patient centric clinical trial information is an important step in overcoming negative feelings about participation.

Clinical Trial Visualization Redesign Challenge

Clinical trials are complex. The task of succinctly translating that complexity into layman’s terms is not a trivial one. Nor is it a task our industry has meaningfully progressed on.

Creative solutions are needed. Lilly’s 

 is intended to spark such creative solutions.

If you are not familiar with challenges, here’s some background. In the last few years, challenges have become a popular way to crowdsource solutions to difficult problems. Typically the sponsor will offer prizes to the winners. Such challenges are open to the public, so they are a great way to break out of “insider” thinking about a problem. (Incidentally,

For the Lilly challenge, competitors are being provided sample clinical trial documents, including a protocol, informed consent form, and 

 page. Using these documents, they are to submit one or more image or browser viewable files that include particular data fields. These submissions are to be designed so that they are ultimately implementable using HTML, CSS, and JavaScript.

Participants are competing for a total of $75,000 in cash prizes, and submissions are dueOctober 2. Designs are being judged according to the following criteria:

Appropriateness for mobile or web application

As I mentioned at the opening of this blog post, I was really excited about the challenge when I first became aware of it. Little did I know that the Lilly COI team would later ask me to serve on the judging panel.

I am truly honored to have the opportunity. For one, I have great respect for the Lilly COI team’s work. Second, Lilly has assembled a very high caliber panel of judges, and I’m honored to be in their company.

Last but not least, I feel privileged to serve as a judge for what I believe to be a very notable development in the evolution of clinical trials.

Here’s what makes this challenge unique and important:

It addresses the big clinical trial problem I discussed previously, which has long served as a barrier to clinical trial awareness, engagement, and recruitment.

To my knowledge, this is the first challenge of this nature that addresses a clinical trial problem.

Lilly’s challenge is one of only a few crowdsourcing examples in clinical trials.

Challenge competitors must agree to a Creative Commons Attribution 3.0 Unported License, which means the design will be released to the public for reuse.

I certainly cannot predict what the outcome of this challenge will be. However, a previously completed challenge is illustrative of the possibilities.

Last year the Office of the National Coordinator for Health Information Technology (ONC) created a challenge inviting competitors to redesign the patient health record.

The screenshots above are a taste of what this challenge produced, but by no means do these images do the submitted designs justice. To get a better sense of the quality and diversity of designs, view them 

Whatever the Lilly challenge ultimately produces, my hope is that it will go a long way inovercoming the negative feelings patients often have when viewing clinical trial information.

When viewing clinical trial information in the future, hopefully patients can feel:

Rahlyn Gossen is a former clinical research coordinator and the owner of Rebar Interactive, a digital media company serving the clinical research industry. You can find her on twitter 

You know those moments when you come across a really exciting idea, and you say to yourself, "why didn't I think of that?"

Re-imagining Clinical Trial Info in a Patient-Centric Way

Do you have any competing clinical trials?

This question was common at site selection visits during my time as a clinical research coordinator. By competing, what the sponsor typically meant was a similar trial enrolling concurrently with theirs.

It’s a logical question, but this question can obscure an important point about patient enrollment.

Every clinical trial that is not yours is a competing clinical trial.

Clinical research sites have finite resources. And with research sites being asked to take on more and more duties, those resources are only getting more strained. Here’s what this reality means for patient enrollment.

If research site staff are working on other clinical trials, they are not working on your clinical trial. Nor are they working on patient recruitment for your clinical trial. To excel at patient enrollment, you need to maximize the time and energy that sites spend recruiting patients foryour clinical trial.

That’s pretty obvious, right? But how do you do that?

Understanding the Research Site Perspective

First, you have to understand an important part of the research site perspective. For research sites, the allocation of patient recruitment resources is about far more than patient recruitment itself.

Each time a research site enrolls a patient for a clinical trial, they are making a commitment to see that subject through to the end of the trial. Which means that they are also making a commitment to work with the sponsor on the trial over that period of time (and beyond).

If the prospect of making such a commitment is exciting, sites will prioritize patient recruitment for your trial. If the prospect of making that commitment is unpleasant, sites aren’t exactly going to jump through hoops to enroll patients.

Make research sites prefer working on your trial

This advice is my single best recommendation to maximize patient enrollment. If you relentlessly focus on this goal, 

You minimize the chance that you have to work with 

 or another patient recruitment company.

 company, research sites will prioritize the referrals generated for your clinical trial. Or in other words, you will get better ROI.

This strategy also has a host of secondary benefits:

The overall quality of site work will be better.

So what tactics can we use to achieve this end?

I recently came across a series of blog posts that are excellent guidance.

As I was reading these blog posts, it was evident that they were written by a very experienced CRA. And more importantly, it was evident that they were written by a CRA who understands the needs and motivations of research sites (not all experienced CRAs do).

 wrote a 3-part series on improving relationships with sites and investigators. Read it. Bookmark it. Print it. It’s great stuff.

Every Decision Ultimately Impacts Patient Enrollment

Most decisions you make, as a sponsor, effect how enthusiastic sites are about working on your clinical trial. And that enthusiasm will determine how many resources sites allocate to patient recruitment for your clinical trial.

Is your drug one that PIs don’t feel excited to present to patients?

Do research sites dislike working with the vendors you have selected?

Is your inclusion/exclusion criteria impossibly difficult and discouraging?

Do your CRAs have poor communication skills?

Is your EDC system user unfriendly, slow, and buggy?

Do research sites continually have to hunt you down to collect payment according to the timelines in their contract?

If you find yourself often answering yes to questions like these, you’ve got problems. Forget 

 or whatever trendy solutions you are considering. That’s like rearranging deck chairs on the Titanic. It’s time to get back to basics.

Just about every decision, regardless of whether it appears directly tied to recruitment, will impact patient enrollment in one way or another. Early trial decisions that may seem cheap or easy in the beginning can prove costly or complex in the end.

simple habit that will set you up for success

When making these decisions, ask yourself how they might impact site motivation to work on your trial. And while you’re at it, ask yourself how they might impact patient motivation to participate in your clinical trial.

Getting into that habit will pay big dividends.

To be clear, I am not suggesting that sponsors cater to every site and patient whim. What Iam suggesting is that sponsors view sites and patients as partners. And that they consider what these partners need so that everyone can succeed.

A Systems Approach to Clinical Operations

This approach has been adopted in the wider US healthcare system with great results. Back in 2011, I wrote about what we could learn from the US healthcare system’s adoption of a systems approach.

If you’d like to read more on that topic, check it out here:

What Healthcare Reform Can Teach Us About Patient Recruitment

Rahlyn Gossen is a former clinical research coordinator and the owner of Rebar Interactive, a digital media company serving the clinical research industry. This blog was originally posted on Rebar Interactive, and can also be seen 

The Patient Recruitment Secret (for Sponsors and CROs)

Clinical Trials. Games. One of these things is not like the other. How can they possibly go together? Clinical trials are a very serious business. And games are, well, games.

Do games really have serious applications? Here’s a quick story.

The success of Linkedin, as a social network, hinges on its ability to encourage user profile completion.

Individual users will only find value in Linkedin when many other people have completed their profile. Yet unless individual users see value in the network, they are not likely to engage in the unpleasant activity of completing their profile.

Before gaining traction, young social networks have to overcome this difficult chicken and egg problem. The phenomenon that I’ve described is known as the 

So you see in the earlier days of Linkedin, it was very important that the social network find a way to encourage profile completion. In fact, it was 

. So how would Linkedin encourage people to complete their profiles?

simple, inexpensive, and highly effective

. In fact, as a result of this solution, profile completion went up 20%. Not too shabby.

The progress bar you see above was the source of that improvement. 

But what about this progress bar is “gamified?”

 about performance – The progress bar tells you how you are performing relative to how you could be performing. Linkedin even provides a suggestion on how you can improve your performance, making the progress bar even more effective.

 – In the same way that you might “level up” in a game, there is a sense of progression each time you complete a new portion of your profile.

 – Games usually have some kind of completion goal. Linkedin is providing a completion goal and encouraging users to reach it.

gamification in pharma and clinical trials

 back in 2011. In that post I used Boehringer’s Syrum game as an example of gamification in pharma, and potentially, clinical trials. At the time, Syrum was still under development, so the information I had was limited.

A European release of Syrum occurred in August of 2012. But as of January 2013, the game has not been released here in the US. Based on the information now available, it appears that my original classification of Syrum as “gamification” was stretching it.

Gamification is the use of game design elements and 

game design techniques to solve non-game problems

According to Boehringer, the “non-game problem” they are attempting to solve with Syrum is education about drug development. But from what I understand, the game is a bit light on education and heavy on game. That’s why I think it’s questionable whether Syrum could reasonably be classified as “gamification.” (However, I want to emphasize that opinion is based on limited information. Because I live in the US, I do not have access to Syrum.)

So what are some more clear cut examples of gamification in healthcare?

I’ll describe a few. And then I’ll hint (hey, I can’t give away all the goods on this blog) at some potential applications of gamification in clinical trials.

One of the most visible examples of healthcare gamification is activity trackers that are becoming increasingly popular. I happen to own the Nike Fuelband. Its features, some of which I’ve listed below, are very game-like.

With the band and its accompanying software, I can set a daily activity 