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. Slicer Dicer was used to identify patient records based on CPT codes.We then looked at narcotic prescription rates following those surgeries between March 2014 and March 2018 at an academic institution. RESULTS: Overall, between March 2014 and March 2018, 76.1% of the sample received a narcotic prescription following a facial reconstructive plastic surgery. Patients who underwent rhinoplasty were most likely to receive a prescription for postoperative narcotics. The implementation of ISTOP, CPT code, use of non-narcotic adjuvant, and insurance type were each significantly associated with prescription of postoperative narcotics. Surgery time and age in years were significantly associated with prescription of postoperative narcotics. Ultimately, when controlling for the aforementioned clinical and sociodemographic variables included in the study, those who underwent surgery after the implementation of ISTOP were 42.8% less likely to receive a prescription for postoperative narcotics, aOR = 0.572, 95% CI 0.356, 0.919, p = 0.021. CONCLUSIONS: New York State's ISTOP program has succeeded in reducing the number of postoperative narcotic prescriptions following facial plastic reconstructive surgeries at this academic institution. However, opioid medications can still be utilized for postoperative analgesia when clinically appropriate. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:1208-1213, 2024.

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. METHODS: A survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR. RESULTS: Race was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient's self-identity. Preferred language was 100% concordant with the EHR. CONCLUSIONS: At an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients' self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities.