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Building a Blueprint: Solving Data Integration at 10.10.10

In March I attended a unique event called 10.10.10. This inaugural gathering brought together tech entrepreneurs and health care experts to mull solutions to health care’s most vexing problems: health data, Alzheimer’s, childhood obesity, and others. The basic construct: ten entrepreneurs, ten wicked problems, ten days to work on them. The gathering yielded what you often get when you bring a diverse group of people together: different viewpoints, and resulting insights into one’s own area by seeing it through different eyes.

The problem I presented for CIVHC was the extraction of data of different types (e.g., clinical vs. financial), and combining them into a nuanced and detailed record of a clinical event. One of the entrepreneurs took a particular interest in this “wicked problem”. His background is in gathering and normalizing disparate data types. He then uses the combined data to produce insights that companies and individuals can use in novel ways. In a sense, he typifies the entrepreneur in the age of big data: bring together the flood of information in today’s world, re-order it, and produce insight into a problem that can’t be had with any of the separate silos of information from which the combined dataset is derived.

It turns out that he took this problem back to his company, and now has an initial round of funding of a quarter of a million dollars to explore solutions. As you may gather, getting someone to put up a quarter million dollars based on an idea isn’t all that easy! But the fact that someone did put up that kind of money tells me that he might be on to something. Here’s why.

As we’ve talked about on this site extensively, CIVHC administers the All Payer Claims Database for the state of Colorado. That database is getting progressively more powerful and complete as time goes on. But even when we have claims records from 3.5 million Coloradans, as we expect to by the end of the year, there are limits to the insights one can gain from only this type of data alone. Essentially, we’re trying to recreate what a house looks like from the bills the builder submits to the eventual owner of the house. We can tell what kinds of materials he’s using, even maybe the color of the exterior, but we can’t get a mental image of the finished house without other data. So the entrepreneur in this blog post is metaphorically working on getting a floor plan, blueprints, pictures of the construction in progress, etc.: all the other components one might use to reconstruct a picture of the whole house.

A computer engine that can do that in health care could be very, very valuable. As Americans trend toward buying health care in the same way they buy other things, based on the value received, someone who can combine financial, clinical, and experiential data into something that approximates a value index for average people can be tremendously helpful. We look forward to working with this entrepreneur, and others like him, to enable Coloradans to buy health care that fits them better, and delivers greater value, by their definition.

About the Author: Dr. Jay Want is CIVHC's Chief Medical Officer. Contact him at

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