Assessing the accuracy of electronic health record gender identity and REaL data at an academic medical center.

Background: Collection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution.

The I-STOP Program and Narcotic Prescriptions Following Facial Reconstructive Plastic Surgeries.

Objectives: To explore the effect of e-prescribing requirements on narcotic dispersion in New York State. Slicer Dicer was used to identify patient records based on CPT codes.

Methods: We investigated the influence of New York State e-prescribing requirements on narcotic dispersion following five common facial plastics procedures.