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Distributed guidelines (DiG): a software framework for extending automated health decision support to the general population.

This article presents a framework and methodology to create personal health record (PHR) systems able to transform raw health data into meaningful information for the general population. By bridging the semantic gap between an individual and his or her health data, it is expected that better care will ensue through consumer empowerment. An important challenge for the realization of this vision is the lack of available expert knowledge in a format that is concomitantly easy to codify, share, and be used by the general population. To address this challenge, we developed a novel approach to encode expert knowledge into machine-interpretable, reusable components called "consumer guidelines." Once encoded, guidelines are easily shared, extended, and modified. These guidelines can exist as distributed documents on the Internet and be executed by our processing engine (Health-Guru) to provide an individual with personalized assessment against various health risks based on the evidence data stored in a PHR.