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Electronic health records (EHR's) are only a first step in capturing andutilizing health-related data - the problem is turning that data into usefulinformation. Models produced via data mining and predictive analysis profileinherited risks and environmental/behavioral factors associated with patientdisorders, which can be utilized to generate predictions about treatmentoutcomes. This can form the backbone of clinical decision support systemsdriven by live data based on the actual population. The advantage of such anapproach based on the actual population is that it is "adaptive". Here, weevaluate the predictive capacity of a clinical EHR of a large mental healthcareprovider (~75,000 distinct clients a year) to provide decision supportinformation in a real-world clinical setting. Initial research has achieved a70% success rate in predicting treatment outcomes using these methods.

Data Mining and Electronic Health Records: Selecting Optimal Clinical Treatments in Practice