  Electronic health records (EHR's) are only a first step in capturing and
utilizing health-related data - the problem is turning that data into useful
information. Models produced via data mining and predictive analysis profile
inherited risks and environmental/behavioral factors associated with patient
disorders, which can be utilized to generate predictions about treatment
outcomes. This can form the backbone of clinical decision support systems
driven by live data based on the actual population. The advantage of such an
approach based on the actual population is that it is "adaptive". Here, we
evaluate the predictive capacity of a clinical EHR of a large mental healthcare
provider (~75,000 distinct clients a year) to provide decision support
information in a real-world clinical setting. Initial research has achieved a
70% success rate in predicting treatment outcomes using these methods.
