First, a set of syndromes in an existing JHU-APL CC parser used in civilian versions of ESSENCE were selected for evaluation. The CC parser was then used to categorize the ROV from 3 months of records from all DoD facilities (about 12 million records total). From the records matched to each syndrome, 2000 were selected; the 1000 most common strings and an additional 1000 strings at random. Two analysts evaluated the sample strings independently and identified key decisions they each used to decide whether the match between the text string and the syndrome was accurate. They then attempted to reconcile those cases where they disagreed. Unresolved differences were reviewed by a DoD consultant who offered revised rules based on clinical experience and known practice patterns among DoD providers. The modified rules were incorporated into the CC parser and tested on miniature data samples for accuracy, and then the CC parser was rerun on the full record set. The cycle of independent analysis and review was repeated with additional modifications to correct any remaining errors followed by a final full run. Before and after contingency tables were used to compare CCbased versus diagnosis-based classifications. The final product was a modified CC parser input file tailored for use with DoD ambulatory healthcare records.