Diabetes Data Bus

There’s a rising notion of a diabetes data bus. A system which integrates data collected from a variety of systems, and communicates that data to authorized users. In addition, this infrastructure would support agents from an expert systems presenting analyses and simulations of expected results.

Diabetics own multiple mobile computers that record biometric data on a regular basis. This typically includes a menagerie of glucometers, of which I own at least 5, 2 of which are in active rotation at any given time. I also use an insulin pump, like many diabetics, and it keeps logs of insulin given, as well as performs opaque simulations on expected results. In addition, there are ancillary devices that measure interstitial glucose levels on a real-time basis, as well as pedometers, sleep monitors, and the list goes on ad nauseum.

With so many sources of data critical to managing medical therapy, it is impossible to predict the new sources of data that will arise. It’s also impossible to replace all the existing devices with new devices that are designed to cooperate with one another. However, all existing devices have a serial port with which an authorized agent can communicate with the device in order to audit therapeutic details. Therefore, it’s much easier to adapt existing devices into a common framework that knows how to present data to expert systems, knows how to store data over time, and knows how to keep the user connected to that data in ways that allow better decision making.

Despite all the data currently logged by devices, how much of it is leveraged to drive ongoing decisions? The proprietary software offered by medical industry offers snapshots of interesting data from the past, and then asks the user to manually fill in any missing data. Each manufacturer offers a perspective that their software knows everything about managing diabetes, and in so doing fails to offer a holistic perspective on therapy.

Instead, a data bus accepts input from a variety of sources, aggregates it with other available sources, and makes it available to the user at any time and any place. The user can choose which applications can subscribe to data, as well as re-route and transform data into those applications. Indivo already provides the container for aggregating a user’s data with customizable schema types. Cube offers a great presentation engine for arbitrary data. When the two are tweaked to manage the data from diabetic therapy, we have a diabetic data bus.

Many parts, loosely coupled.