The quantified self movement has certainly seemed to gather some steam in recent months as more and more people begin experimenting with various methods of tracking oneself to generate meaningful and useful data.
The company was founded by physicians associated with Harvard Medical School following a 2008 study by researchers at the Center for Connected Health, a division of Partners HealthCare, which tracked individuals with hypertension who worked for the computing company EMC. Employees were asked to keep track of their blood pressure at home and participants received feedback on their progress.
The program proved quite successful at lowering blood pressure. Not only did an independent auditor of the study suggest that some EMC staffers would avoid heart attacks and strokes as a result of the feedback they received, the program was also estimated to have a 3:1 financial return.
An example of the machine learning technology, referenced several times by, Mr Zobol, would be the platform asking an individual whether they want to lower their blood pressure through diet and exercise or with the help of medication. Users who respond that they prefer to do it naturally can be informed that people who exceed 10,000 steps per day have a greater likelihood of bringing systolic blood pressure down to healthy levels.
Users would then try to steadily increase the amount they walk and wear pedometers to assess whether they are succeeding–with the software providing encouragement in a variety of different ways.
According to a Technology Review profile of the company, Healthrageous already has 1,600 users generating $500,000 in annual revenue across at least seven institutional clients. The company is poised to release the platform to the general public by the end of 2012.
Below you can read my interview with Greg Zobel, VP of Business Development for the 18 month old company, who explains to me a little bit about their vision for the quantified self business model.
BTE: What is the inspiration behind Healthrageous?
Greg Zobel: Essentially, to find a way to use machine learning to make meaningful sense of the data being collected from all the various sensor devices you see around the floor here.
BTE: Which devices are currently part of your ecosystem?
Greg Zobel: We are very device agnostic. The devices that are currently plugged in are mostly fitness or A&E devices, but literally any wireless device can plug into this platform.
BTE: What is the business model for this platform?
Greg Zobel: Essentially, this is a machine learning platform that is marketed through various channels to consumers. Not directly to consumers, but through channels, for instance health plans, employers, and what we call “health adjacencies”, such as the large weight loss programs around the country (i.e. Jenny Craig). Anybody really that has an engagement problem, that needs to hold onto their clients and become more engaged with their customers.
BTE: So who is the client in this mixture? Who is paying you?
Greg Zobel: Typically, or at the moment, clients are health plans who is embedding our product either into their wellness offering or into their disease and care management offering. I don’t know how much you know about what is going on in that space right now, but there are a lot of things like like high deductible health plans who are using our platform to reduce premiums for those who are on the healthier side.
BTE: So lower premiums for people who are active, and you guys are contracted to monitor and support the data?
Greg Zobel: Right, and we do it objectively, which is very important.
BTE: So I assume there is a consumer web app that gives users access to their data?
Greg Zobel: Correct. Virtually every person using the system is a consumer, like you or I, and they have access to their data both through the web and via all three mobile platforms. We use wearable wireless devices to capture the data and smartphone apps provide the interface to the consumer.
BTE: So very little computing actually takes place on the smartphone? It serves as more of an application interface for the cloud-based data captured by wearable sensors?
Greg Zobel: Correct. We are a machine learning platform, taking in massive amounts of information, asking users questions to conduct a health risk assessment, building a profile of each user and then pushing messages back out to you as the user which we know are relevant to you and will help you improve whatever aspect of your health you are working on. Its like the Pandora or Netflix for health care.
BTE: How many team members do you have today?
Greg Zobel: Currently, Healthrageous has 20 folks working in our Boston headquarters. When you lift up the veil its basically a bunch of MIT engineers, Harvard behavioral scientists and a few business development specialists like myself.
From a customer standpoint, we have six self insured employers on the platform, and our landscape changes very significantly next year because we are in the process of implementing three very large Blue Cross Blue Shield plans in California, Pennsylvania and another in the middle of the country, so it is going to be a big year for us.
BTE: Have you raised venture funding or are you self-funded?
Greg Zobel: We were officially “born” as a venture backed firm in June 2010. We have three major venture backers, North Ridge, Long River and Egan-Managed Capital, and that was our Series A round. We are in the process of raising a Series B round right now.
BTE: What is the next big step for Healthrageous? Can you share any of your business development plans as the business development director?
Greg Zobel: Well, the platform itself is designed to be stand alone–or in other words–the intel behind these other devices and services, so we are working with a lot of the big telco’s you see on the floor here who are looking for a way to figure out how to absorb the massive amounts of patient data that can be collected and make sense out of the data for the consumer. That is the layer we provide.
BTE: So you envision being able to provide the service to the consumer free of charge indefinitely?
Greg Zobel: Correct.