• Ira S Hofer, Eilon Gabel, Michael Pfeffer, Mohammed Mahbouba, and Aman Mahajan.
• From the Departments of *Anesthesiology and Perioperative Medicine and †Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California; and ‡Office of Health Informatics and Analytics, David Geffen School of Medicine at UCLA, Los Angeles, California.
• Anesth. Analg. 2016 Jun 1; 122 (6): 1880-4.

AbstractExtraction of data from the electronic medical record is becoming increasingly important for quality improvement initiatives such as the American Society of Anesthesiologists Perioperative Surgical Home. To meet this need, the authors have built a robust and scalable data mart based on their implementation of EPIC containing data from across the perioperative period. The data mart is structured in such a way so as to first simplify the overall EPIC reporting structure into a series of Base Tables and then create several Reporting Schemas each around a specific concept (operating room cases, obstetrics, hospital admission, etc.), which contain all of the data required for reporting on various metrics. This structure allows centralized definitions with simplified reporting by a large number of individuals who access only the Reporting Schemas. In creating the database, the authors were able to significantly reduce the number of required table identifiers from >10 to 3, as well as to correct errors in linkages affecting up to 18.4% of cases. In addition, the data mart greatly simplified the code required to extract data, making the data accessible to individuals who lacked a strong coding background. Overall, this infrastructure represents a scalable way to successfully report on perioperative EPIC data while standardizing the definitions and improving access for end users.

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