METHODS: Possible terms with HbA1c data were identified from ambulatory EHR data (N=69,832 possible HbA1c results) first by using simple regex logic and second by using keyword neighbor’s regex model (python pandas and string) and clinical knowledge to obtain more terms involving HbA1c. Grammatical errors (i.e., misspelling, punctation, etc.) and relative names (hgb, eag, mean plasma glucose, etc.) were accounted for. The possible term list was reviewed by clinicians to determine validity as HbA1c data. Using clinical expertise and regular expressions, inclusion (i.e aic in the presence of %) and exclusion criteria (i.e., waived, ‘not taken’) were added to strengthen model accuracy. The model was adapted to conform the alias terms corresponding result values and units. The model then uses units and lab result values to calculate and standardize HbA1c with a unit of percentage. If the result/unit combination could not be standardized, then the lab name was not included.