Background: Quality tracking and improvement are areas of importance for delivery of high value patient care. Quality Oncology Practice Initiatives (QOPI) are tools designed by ASCO to help oncology practices deliver high quality care. Specifically, QOPI measure 29 tracks whether antiemetics are administered appropriately with moderate/high emetic risk chemotherapy. In this study, we attempted to automate this quality measure using an adapted Microsoft Structured Query Language (SQL) procedure. Our primary aim was to develop an efficient and cost effective system to track appropriate anti emesis that is adaptable to other cancer centers. A secondary aim was to quantify any degree of over treatment in anti emesis in our center. Methods: We created an SQL procedure which accessed the EPIC CLARITY database and used 9 steps to identify how many chemotherapy administrations in our center for the year 2017 were given sufficient, over, or under anti emesis. We adapted our center’s antiemesis guidelines from 2017 ASCO Clinical Practice Guideline Update for antiemetics. The program identified “pass”/”fail”/”pass(-)” for appropriate/insufficient/over treatment of anti emesis, respectively. Results: 1490 patient/chemotherapy encounters were found for 2017. 862/118/510 were high/intermediate/low emetic risk, respectively. 1314/14/162 chemotherapy administrations received “pass”/”fail”/”pass (-)”, respectively. The reason for 13 of 14 “fails” was lack of steroid in high emetic risk regimens. The primary reason for “pass (-)” was use of fosaprepitant in low/intermediate risk regimens. The time required to execute the program for all 1490 encounters was 40 seconds; the average time for manual review was 3 minutes per encounter. There was no discordance between “pass/fail” status in comparing automated and manual reviews. Conclusions: We have created an automated process which rapidly and accurately verifies adherence to QOPI measure 29. It eliminates time and costs associated with manual review. Further, this program saves costs by identifying areas of over treatment in anti emesis. We have demonstrated on a large scale an adaptable and shareable process for other centers using similar EMR databases.