Tailored health information is important for generating patient-specific recommendations in clinical decision support systems and for crafting health education materials that are specifically customized to a patient. Many previous attempts to generate tailored information require complex representations, lack general applicability, and are inflexible to content alterations. In this article, we describe a simple, yet flexible approach for tailoring health communication. This generalized and scalable approach relies on a flexible state representation of each individual and an expandable rule drafting and processing engine. It utilizes a relational database schema and a simple table structure to maintain each individual's past and current health information. Content for tailored communication is represented in a single table which stores predefined logic describing the rules for selecting content applicable to specific individuals. The flexibility, scalability, and simplicity of this approach are demonstrated by describing two diverse projects. One project has provided patient-tailored decision support for physicians for over 82,000 patient encounters and the other generates tailored health questions and messages for patients through a tool developed in less than 4 months.