Click here for Part I

Transforming mental health care through an anonymous, scalable group therapy platform

Shrenik Jain is the founder of Beacon (formerly Grüp). Labeled “Most Disruptive Startup”, by the American Psychiatric Association, Beacon is a mobile therapy software-as-a-service platform that grows through natural language processing. Beacon seeks to fill the current gaps in mental health care by providing users with anonymous, persistent communities of like-minded individuals. The application gives users the opportunity to connect with others experiencing issues such as PTSD and depression without fear of stigma. Aside from his role at Beacon, Shrenik is also a trained first responder (EMT) who volunteers with the Montgomery County Volunteer Fire/Rescue Association.

Q. What inspired you to found Beacon?

SHRENIK JAIN: We looked at institutions, on a larger scale. Take the VA, for example. They spend $7 billion a year—and rising quickly—on therapists. They are literally throwing money at the problem, because they’re losing over 20 veterans a day to suicide and a host of other issues. But the problem is these institutions are structurally flawed in how they approach this problem, because they keep spending money on therapists, because therapy is shown to be clinically effective—but that’s not having much effect in reaching their populations, because, for one, they’re highly stigmatized. More veterans who died by suicide last year weren’t receiving any form of therapy. The majority of veterans who took their own lives weren’t actually using the services that we’re spending billions of dollars on. That’s a completely unsound way to approach things. It doesn’t matter how many therapists you have.

The second thing was because so few people are actually seeking care for these conditions, the current infrastructure that we spend so much money in building is only really built to treat a very small fraction of the people who actually seek care. If we got what we wanted, and we reduced the stigma around mental health and everybody who needed help tried to get help, there’s no way that our current standard of just one-on-one therapy would actually work. They would overwhelm the infrastructure in days.

We realized that there needed to be a way to make things more cost effective for the institutions that are paying for these providers, while at the same time making things more accessible for patients to deal with issues of stigma, and a distrust of therapists, and all of that.

That’s when I came to Hopkins and I met my cofounder, Ravi, who had similar thoughts on this space. He had watched a very close friend suffer through isolation, basically, between therapy sessions. At the time, he had the idea for a basic chat up. Since we both studied computer science, we realized that you could use natural language processing here to really make things more scalable.

We got together and we built out this platform for anonymous, group-based text therapy—text and voice therapy, because the idea was that if you make the platform anonymous, people can reach out under a pseudonym, kind of like a screen name you’d see on Reddit. And then they wouldn’t feel judged for sharing their issues.

The other component is, by giving them this peer dynamic, this constant community, you really a) make things more scaleable for the provider, because you’re adopting this group model of support; but b) you give people access to others who they can always empathize with. In certain demographics—say, veterans, the military, or firefighters—it’s a very tight-knit group, and oftentimes when these people go to reach out for support, they feel alienated because the therapist doesn’t relate to them. This way, we could combine sort of the clinical efficacy of therapy with the persistence that you see in peer support.

And then we use natural language processing to make the whole thing scaleable. We use natural language processing, for example, to automatically detect suicidal or malicious intent that takes place within these groups. We can also use these same algorithms to track this group’s overall sentiment, whether it’s positive or negative, going over time. We can see if individual users are getting better or worse over a month-long period.

That’s something that really hasn’t been approached before in behavioral health, because right now the average person drops out of therapy in less than three sessions. There is a total lack of tracking and quantitative data—any of that—so it was our idea to use technology to track these people, make moderating groups more efficient. Now, the therapists can focus their time and energy on the users most in need of help, when they need help, and do that all through a mobile platform, so that the users could reach out to help easily from the privacy of their phones.

Connect with Shrenik on LinkedIn

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