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How to Design and Manage Clinical Trials with Data Analytics

In general, technology has improved significantly in the past 15 years. And in the process, it has improved human life, communication and access to information and knowledge.

Radical innovative thinking, fueled by abundance of computing power, skills and device connectivity have become the DNA of modern business models, process models and technology landscape.

Yet, many people have made an important point that the last, best-known innovation that happened in the clinical trials world was the introduction of EDC systems.

I wonder, in a world where some of the most advanced technologies fuel our social media platforms, micro-blogging platforms, movie streaming platforms and serious (and casual) dating platforms, how come we are not using any of these technologies to design, conduct and manage clinical trials in a better way?

This article is based on a talk that I gave in May 2016 during a Clinical Trials Innovation Summit in Boston, and it begs the question:

How can mobile, social, big data and analytics be used to radically change the way we work with clinical trials?

Impact of mobility

One single technology that has the potential to impact how the general healthcare ecosystem will work in the future is mobile technologies.

This include mobile devices, connected devices and other seen and unseen mobile hardware. It also includes mobile software frameworks and accelerators that vendors like Apple and Google are producing for healthcare.

Mobile can be used in several ways with respect to clinical trials. It can be a data consumer endpoint, and it can be a data producer endpoint. In my view, we take full advantage of mobile technologies in clinical trials when we use mobile as both data consumer and data producer.

One of the biggest – if not THE biggest – problems with designing and executing successful clinical trials is finding the right kind of participants and then recruiting them in a cost-effective way. This issue eventually makes the entire trial – plus the treatment that the trial is focused on – extremely expensive and slow.

It is only a matter of time before mobile will be considered a profound patient identification, selection and recruitment channel and will be preferred over other channels such as print and TV.

The reason is simple and powerful. There are billions of people out there with compatible mobile devices. Using mobile technologies, these people can be reached in a very personalized way (with the help of a big data application at the backend). The cost of designing an app that can do this for trial sponsors will be much less than spending endless dollars on print ads, only to later realize that those print ads are not as effective as they were expected to be.

A mobile app powered by a big data backend can expedite the speed of recruitment strategy adjustment and optimization. Usage data will be collected continuously, and it can be used by human as well as computer algorithms to identify and adjust recruitment information and strategies much faster than current way. This will enable content personalization, recommendations and early screenings of potential participants to be fast and cost effective.

Additionally, mobile apps have a profound impact on keeping participants engaged with the trial by sending reminders, personalized messages and trial progress reports.

Most mobile devices have sensors that can produce important health data, which could be vital for the success of a particular trial. These data points can be directly collected from a participant’s mobile device upon a consent from the participant. This collection can be done several times a day (instead of the traditional method where a participant needs to come to the clinic twice a week), thus making trial less expensive and more effective.

Impact of social media

Can the thing called Facebook be used for anything more than posting personal status and eating preferences? Certainly it can be, as there are thousands of businesses in hundreds of verticals that leverage social media to make their businesses more effective.

What if we could use social media platforms to make clinical trials more effective? Like mobile devices and apps, social media can also play a role of data producer and data consumer for clinical trials.

Consider a scenario where a sponsor needs to run a trial based on a specific condition globally. They are trying to figure out which locations make the most sense with respect to putting their marketing dollars to work.

Could they mine Twitter data on an aggregate level to see what the top 10 locations in the world are where people talk most about their target conditions? Can they do a sentiment analysis with the help of established and proven big data and data analytics techniques to determine what geographical areas they should focus on with respect to recruiting efforts?

How about creating a participant engagement page using Facebook that not only keeps participants current about trial progress but also asks for feedback from participants to see what is working and what is not working? Can this information be used to adjust the trajectory of an ongoing trial?

How about using social platform such as PatientsLikeMe to listen to actual patients, their concerns and suggestions to not only identify more accurate eligibility criteria for clinical trials but also move away from research-based trials to patient-centric trials?

Possibilities are endless, and technology and resources are available. We need desire to make it happen.

Certainly, there are privacy concerns around using social media platforms for designing and conducting clinical trials. But in my view, regulations, like always, will have to catch up with technology.

So strong are the benefits of using some of these mechanisms to improve clinical trials that using regulation and compliance to not innovate in this area cannot be an excuse.

Big data and data analytics

Social media, mobile, internet and other technologies that we have discussed so far demonstrate the power of connectivity. They make additional, constant data collection possible. They also provide a way to send data, messages and information to clinical trial participant, researchers, investigators and research institutes.

But what happens with all the collected data? Who is collecting, managing and then parsing these datasets to make timely decisions, either for human consumers or machines and devices?

That is where big data and data analytics add value to the entire equation.

In general, big data and data analytics can be used to consume incoming streams of data from various channels (mobile, sensors, Facebook) and can run algorithms in real time to process data. Machine learning and AI can be used to identify and determine patterns in this data that is either hard to see or impossible to recognize in a manual way.

Graph and network techniques can be used to connect various research sites, investigators and trial endpoints to find the cause and effect relationships.

Distributed search methods can be used to index all current and upcoming trials and allow physicians and patients to search trials.

Smart matching algorithms can be used on hosted and flowing data to match patient eligibility with existing trial protocols to identify the right kinds of participants to speed up the trial recruitment process, which is too slow and expensive today.

All of this can be done where most people hang out – on mobile and Facebook.

There are so many great things that can be done by leveraging today’s talent pool and technology pool that not innovating how we design, execute and manage clinical trials and continue with status quo should not be accepted as an option.

For added visuals, here's a look at my presentation from the Clinical Trials Innovation Summit:

Exploring new ways

Clearly, it is challenging to explore enough about these individual topics in one article. Each of these points deserve a detailed discussion of their own.

My hope is that I have communicated the key drivers behind exploring new ways of making clinical trials more effective with the help of data, mobility and social media.

Drug discovery is a long, arduous process. Anything that can be done to reduce the cost and increase the delivery time and quality will make a real difference in the lives of many patients who look forward to these treatments.

Article written by Manoj Vig
Image credit by Getty Images, Image Source, REB Images
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