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Taming the Human Data Stream
Daniel Faggella   Aug 10, 2016   Ethical Technology  

“Big Data” is more of an opportunity than it is a benefit in and of itself. This might hold even more true for data gleaned from the human body itself as it does from the information streams from stock markets and eCommerce.

Charles Fracchia of MIT-based startup BioBright is of the belief that health and medicine  worldwide would be bettered not just by more data on heart rate, genomics, blood pressure, etc., but by a better way to aggregate and make sense of this data. BioBright’s idea is to go beyond a software to make sense of data and create a protocol allowing anyone to add to and make sense of real-time human data streams.

The project is called the “Sensor Data Interoperability Protocol” or SDIP, and it’s one of the first ardent attempts to lay out a common protocol to connect and make sense of human biological data from all angles. Fracchia makes the analogy of diagnosing problems with an automobile. “It’s like with a car, instead of just looking at what’s going on with the car when it’s making funny noises, what if we always tracked the various metrics and factors of a car’s functioning? Then we’d be able to detect problems in advance and understand what patterns or conditions are leading to the funny noises.” Our doctor visits, Charles would argue, have the same limited scope as a trip to the mechanic, and the same predicaments with respect to understanding deeper causes.

Fracchia’s vision is to allow people to leverage globally collected data, alongside their own health information, and to take personal responsibility for making (hopefully) better health decisions. For example, we may wonder if the air quality in our workplace has been a cause of our recent grogginess, or whether or not our caffeine habits have had any effect on our blood pressure, etc. To take a snippet from the SDIP problem statement: “...all of the data floating around out there should be easily visualized, integrated and cross-correlated” and advancements in the IoT makes this a more real possibility.

Healthcare has not had the unified foresight necessary to keep up with the technology that would allow for the stream of information that the BioBright team envisions. While data collection/analysis paired with AI and the IoT has been leveraged in an array of other industries, healthcare is still trying to assemble the individual pieces that would be necessary to join this evolution.

Charles jokes about his initial expectations of working in the top bio labs at MIT and Harvard. “I thought ‘Oh, surely, I’m coming to these awesome labs here in Boston…I’m sure I’ll come in and have an awesome dashboard to work with, like the movie Iron Man. I’ll have my cell growth rate here, heart rate here…you know, something fantastic’.” Even at the finest institutions on earth, his anticipation quickly turned to disappointment: “Then I’d get this blank look, like ‘’s your notebook...and your timer’.”

Though the technological processes of going from “bio to bits” (gleaning meaningful information from biological processes, human or otherwise) has undeniably increased by orders of magnitude in the past half-century, much of the data collection is still done with relatively primitive equipment that was available in the 1950s. Charles references cell phones, Fitbits, and videos games, and comments how far in the stone age much of modern healthcare remains.

From air quality to dietary changes to sleeping patterns, we live in a world where data can (and in many instances is) being collected, but we’re unable to correlate it, to notice changes, or to pick up on trends that could make a meaningful impact on our personal health.  “We have very few tools to understand and tease out these data streams into one language that’s open, that everybody can look at, that’s free, that’s available…so that you can start to ask your own questions.”

The idea apparently has appeal, and the team at BioBright presented their SDIP concept to the National Institute of Standards and Technology (NIST) at the White House in 2014 (no word yet on outcomes). Regardless, Fracchia predicts a future where healthcare will fundamentally change. “The information collection will no longer be the value-add; the value-add will be in the analysis and understanding of this data,” he says. But there’s a problem: “Right now, no business has any incentive to open up the data collection side of things. We really think that the future is where the companies will open up the data streams and be able to tease out those insights from this pooled data.”

In his eyes, the future of much of the entrepreneurial opportunity in making sense of biological data is either in creating a great device to extract the information (maybe a wristband that can tell us when we’ve had one too many cups of coffee), and the analysis and visualization of this data. Charles doesn’t see the value-add of the future being in what he calls “the middle of the pipe” or the mere data collection, which he believes should be opened up for experimentation and research.

Fracchia’s predicted paradigm shift is just that - a prediction - but there’s no denying the surge of business in both the collection and analysis of biological data. Companies like Tute Genomics are coming on the map to help pharmaceutical companies predict the impact of new drugs on patients with different genetic profiles. Others, like uBiome and 23 and Me, are offering services to analyze different segments of biological data (our microbiome and segments of our DNA, respectively), in addition to providing us with an analysis of exactly what this data might mean for our health and well-being.

It would seem, in some respects, that the mission of analytics companies like Tute Genomics may in fact be at odds with freely “open-sourcing” biological data. If any old pharmaceutical company with access to Google can bring up the same research findings and important genetic correlations as they could with Tute’s proprietary software, a good chunk of their revenue model goes away.

The good news for those of us interested in the future of healthcare is that the approaches seem to be different arrows fired at the same target: improving human health and utilizing the influx of human biological data productively. The data is starting to pile up either way, which should bode well for Charles and future efforts like BioBright, whether for or non-profit, and should help propel healthcare out of the stone age and into the digital age over the next decade.

Daniel Faggella is the founder of TechEmergence, and blogs at

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