With antimicrobial resistance being one of the top global public health threats, integrated antimicrobial resistance surveillance systems are critical in gathering data, understanding resistance trends, creating stewardship plans and accurately quantifying resistance at national and local levels. We report on the Washington Integrated Surveillance for Antibiotic Resistance (WISAR) database that houses data from human and animal data from hospitals, laboratories, and clinics in Washington State, as well as human and animal data from the US National Antibiotic Resistance Monitoring System. This analysis used two datasets from the WISAR database to look at outpatient human antimicrobial susceptibility testing (AST) data for E. coli from October 2017 (n=1311) and bovine AST data for E. coli from 2002-2017 (n=253) in an attempt to analyze resistance trends between E. coli in humans and bovine in Washington state. A panel of 5 antibiotics were used for this analysis to allow conclusions and resitotypes to be developed. We found the odds of resistance between humans and bovine for individual antibiotics as well as developed resistotype plots to compare resistotypes between humans and bovine isolates. Using Clinical Laboratory Standards Institute (CLSI) breakpoints, the data showed the odds of resistance for 3rd generation cephalosporins and aminoglycosides (OR: 2.90, p<0.001) to be greater for bovine than for humans. The odds of resistance to fluoroquinolones and trimethoprim sulfa were respectively 33% less (OR: 0.33, p<0.001) and 21% less (OR: 0.21, p<0.001) in bovines than for humans. We found the same statistically significant directionality of results using ECOFF breakpoints. This proof of concept analysis highlights the challenges in using local surveillance data and comparing human and animal strains for AMR as well as provides recommendations for moving forward with this type of data. Integrated antimicrobial susceptibility testing data creates an opportunity for a collaborative effort to discuss the next stages for local efforts in antimicrobial stewardship across human, animal, and environmental sectors as well as gaining an understanding of: 1) what conclusions can be made between data sets? 2) how valid are these conclusions? 3) what data is needed to make this type of comparison in resistance across sectors?