Verily‘s visit to last week’s Health 2.0 conference had an odd-but-fun tack, comparing the data received from human bodies to the billions of data points generated by an average late-model automobile in normal operations. We generate a lot less (ten orders of magnitude difference, according to Verily Chief Technology Officer Brian Otis), but Verily wants to maximize the output by wiring us to multiple sensors and to use the data in a predictive health model. Some of the Verily devices this Editor predicts will be non-starters (the sensor contact lens developed with Alcon) but others like the Dexcom partnership to develop a smaller, cheaper continuous blood glucose monitor and Liftware, the tremor-canceling silverware company Google acquired in 2014, appear promising. Key to predictive health is the Study Watch, which is a wearable that collects a lot of data but is easy to wear for a long time. Mobihealthnews

But what to do with this All That Data? Where this differs from a car is that the operational data goes into feedback loops that tune the engine’s performance, perform long-term monitoring, electrical system, braking, and more. (When the sensors go south or the battery’s low, watch out!) It’s not clear from the talk where this overwhelming amount of healthcare data generated goes to and how it becomes useful to a person or a doctor. This has its own feedback loop this Editor dubbed a few years ago as the Five Big Questions (FBQs): who pays, how much, who’s looking at the data, who’s actioning it, how data is integrated into patient records. That’s not answered, but presumably these technologies will incorporate machine learning and AI to Crunch That Data into bite-sized parts.

Which leads us back to Verily’s parent, Alphabet a/k/a Google. All that data into Verily devices could be monitored by Google and fed into other Google programs like their search engines and Adwords. Another privacy problem? 

Perhaps health systems are arriving at the realization that they have to crunch the data, not avoid it. For the first time, this Editor has observed that a CMIO of a small health system in Illinois and Sanford Health‘s executive director of analytics are actually welcoming patient data and research. Startups in this area such as PreventScripts labor on that “last mile” of clinical decision support, preventative medicine. EHRs are also into the act. Epic launched Share Everywhere, where patients can grant access to their data and clinicians can send updates into the patient portal (MyChart). What’s needed, CMIO Goel admits, is software that combines natural language processing and algorithms to track by disease and specialty–once again, machine learning. Healthcare IT News