A care/clinical pathway (CP) is a standardized care process where temporal and data constraints of clinical activities are defined to ensure quality of care. In actual care practice, various situations of compliance and non-compliance with CPs can be observed. Analysis of these CP variation patterns (CPVPs) can help improve care quality and enhance decision support. In this paper, we propose an automatic method to detect CPVPs in electronic medical records (EMR), and statistically examine their correlation with patient outcomes. From each CP constraint, we first derive a CPVP tree, where each pattern is represented using first-order linear temporal logic and translated into a Büchi automaton for pattern detection. Then we identify the CPVPs that are evidently correlated with a patient outcome by examining the odds ratios. The method has been applied to a CP for congestive heart failure and real world EMR to demonstrate the effectiveness.