How Suggestic plans to save the lives of millions of diabetics using machine learning

The Suggestic Team

The Suggestic Team

Data driven technologies are accelerating innovations in healthcare delivery in unparalleled ways in no news. What is indeed news is the myriad ways startups are deploying novel solutions to address the same problems. Among the many common ailments that are benefiting from such advancements is diabetes. Why is addressing diabetes so important? A WHO estimate has predicted that diabetes will be the seventh leading cause of global death by 2030. This is very likely to happen unless we are able to make amends. Thankfully, some entrepreneurs who are armed with the power of data driven predictive decision making are leveraging all their efforts to win over the ailment. One such startup is San Francisco based Suggestic. What is Suggestic? Suggestic claims to be a lifestyle intervention based Type 2 diabetes reversal program. Focusing on the concept of precision medicine (targeted treatment based on an individual’s gene, environment and lifestyle), it employs machine learning technology to extract and validate causal patterns between each person’s genes, metabolism, food and activities. Based on such calibrations, it develops a recommendation system for each individual for diabetes reversal.

Recently, I had the opportunity to interview Shai Rosen, Co-founder of Suggestic. For Shai, Suggestic is a pursuit borne both from personal experiences as well as the desire to address an urgent global healthcare need. Read on to know more about how he co-founded the startup, and what are his plans for the future.

Q: Hi Shai! Thanks for agreeing for an interview with Think Big Data. Suggestic is your first healthcare startup. Tell us how were you drawn to the idea (of Suggestic) and how has the journey been so far?
Shai: Thank you for the opportunity! I’m actually a biotechnology drop-out turned engineer. I have always been passionate about health and I’m a big self-quantification geek. I’ve looked at other health tech opportunities in the past, but never felt a strong connection to the problem being solved. Type 2 diabetes is a personal battle for me. My father passed away of diabetes-related complications after over 20 years of being diagnosed with type 2. It wasn’t until a few years ago I started learning more about it and realized there is so much that can be done about it, to the point of remission. Unfortunately, it was late for my father, but I have no doubts we’ll be able to help millions of others.

Q: We understand that Suggestic offers customized reversal programs for type 2 diabetics. Tell us more about these.
Shai: Yes! Based on the principles of Precision Medicine, we build personalized lifestyle programs from evidence-based interventions for people with Type 2 Diabetes with the goal of achieving diabetes reversal. We start by mapping the person’s health by collecting different types of data (from labs and sensors) to create those customized programs and then apply machine learning to make it actionable and practical. As we gather more data, we iterate over the recommendations and their consequential results, making them more and more personalized over time for maximum effectiveness at the individual level.

Q: In what ways has the arrival of smartphones increased the adoption and success potential of Suggestic? Tell us with reference to your coined concept of “lifestyle GPS”.
Shai: Smartphones are a key asset because not only they have sensors that we use and provide tons of useful data, but also the fact that they are constantly in the user’s hands makes the offered help more effective. The idea behind the concept of “lifestyle GPS” is that we all need help navigating our lifestyle, especially when we make changes to it or want to achieve new goals.
The common scenario is that you have an appointment with your diabetes educator, nutritionist, coach or physician and you come up with a pile of printouts, brochures, rules and homework. Then you need to memorize all those and walk around with notes and books as you start figuring out how to go about it and as the days pass, you start questioning how to apply this to your particular life. Moreover, all that is assuming you understood it and you actually want to follow it.
We have built Suggestic to help you navigate a plan created around your specific needs. We want to remove the stress and complications of following a lifestyle program by giving you actionable suggestions at the right moment and in the right place.

Q: In what ways do you think Suggestic’s machine learning algorithms improve the success rate of diabetes reversal programs?
Shai: Managing type 2 Diabetes, just as most chronic disease, might look simple on the outside but it comprises a rather complex set of processes and systems that interact with each other. It is simply impossible for a human to make sense of all that data, so we end up using “best practices” that would work for most people.
The problem is that averages based solutions are not effective. All of us are unique individuals and need our treatments to be tailored specifically to our metabolism, genetics, microbiome, psychology, preferences and so much more. That’s where machine learning can have a big impact. At Suggestic, we actually take into account all those factors in not only building your initial program but also while continuously evolving it as we adjust and improve your plan.

Q: What’s your perspective on the emerging view that machine learning algorithms will drastically reduce the dependence on physicians for healthcare delivery in the future?
Shai: Unfortunately, there are many sick people and not as many physicians, so any technology that can help us be healthier sounds good to me.

Q: Skeptics consider adherence a big issue in remotely monitoring healthcare service delivery. Do you agree? If yes, how do you plan to circumvent this issue with Suggestic?
Shai: I completely agree. Adherence is one of our main areas of focus and it is not only an issue for digital interventions, but for all of types. For example, different studies suggest that half of all patients do not take their medications as prescribed.
In our case, we tackle the adherence problem from two fronts: First, our personalization capabilities creates better plans; those translate into faster and better results for the user, which according to different studies, have over 30% more adherence. If you think about it, it makes sense. If you were on a diet that is working wonderfully, why would you stop?
Second, the experience of using Suggestic is similar to having a full time coach around, and we have built it using many concepts from psychology and habit formation that have proven successful in other places.

Q: Is the product commercially launched yet? If not, by when can we get our hands on it?
Shai: Not yet. We are currently testing with a limited number of users and expect to open the doors for beta during Q1 of 2016. I recommend signing up to our pre-release program at and we will let you know as soon as we publish on the Appstore.

Q: Tell us something about the brains “behind the scenes” – Your team, partners and advisers.
Shai: We are a passionate team of 10 entrepreneurs, scientists, engineers and designers from five nationalities, which speak seven languages and work from two different countries. It’s just awesome!

Q: Have you finalized the pricing for the service yet? Do you also offers programs for corporate clients?
Shai: Yes, the service will start free for retail users and we already have corporate plans in place that offer additional features for the enterprise partners.

There is no doubt what Suggestic plans to accomplish is both noble and urgently needed. We wish the entire team at Suggestic the best for the future!

Website: Suggestic

Anubhav, a data scientist, writes about new developments and future trends in the machine learning and data analytics domain.
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