Browsing by Author "Mehta, Shivani"
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Item Collection and Validation of Patient Self-Reported Race, Ethnicity, and Language (REL) Information In a Postpartum Setting(University of Delaware, 2023-05) Mehta, ShivaniAs healthcare organizations move toward accountable care agreements, and move away from fee-for-service, there is a greater need for healthcare organizations to have stronger data to support population-based interventions. Moreover, literature has highlighted the discrepancies in collecting identity-based information from patients, including information regarding patient race, ethnicity, and language (REL). REL data has multiple policy and clinical implications as it is utilized to not only determine the allocation of funds for programs but also is used to create evidence-based interventions to decrease health disparities. If the core of this data is incorrect, then resource allocation is futile. More importantly, there is a potential that the resources and interventions that are being created using this data are now not effectively reaching and impacting these communities. Prior research demonstrates that there are large disparities in women’s health, especially by race.1 Given what we know about the nature of flawed data, these disparities are potentially increased or inadequately captured by current interventions. In an effort to assess organizational capacity to collect REL data and identify where discrepancies in the documentation of REL data may occur, this quality and safety improvement project assesses the practices of collecting REL data from patients, as well as concurrence or discrepancy in how REL data is documented within the patient’s chart and how they choose to self-identify. These concurrences and discrepancies were measured with a two-pronged approach where one prong involved patient survey of self-identified REL information and the second involved collection and validation from the Electronic Health Record (EHR). Study results demonstrated an overall concordance between the two data corpuses; however, the discrepancies and variation in certain minority groups were noteworthy. Given the results, the main finding is the need for an EHR with broader fields and/or allowing patients to self-identify their demographic data to allow for the validation of patient identities, create accurate data corpuses, and improve patient health outcomes. Once modified, we expect researchers to have more accurate and credible data to identify health disparities from, driving the eventual closure of the inequities seen within multiple minority populations.Item “I Think There's Only Two Fields for That”: Hospital Registrar Attitudes and Practices for Collecting Patient Gender Identity Data(Transgender Health, 2022-04-22) Mehta, Shivani; Waad, Alex; Brooks, Madeline; Siegel, Scott D.Purpose: This study aimed to understand the experiences of hospital registrars in collecting gender identity data. Methods: A qualitative study that thematically analyzed key informant interviews with 37 registrars regarding their attitudes and practices in collecting gender identity data. Results: Collection of gender identity is influenced by (1) system-level barriers, (2) discrepancies in source of truth for documentation, and (3) registrars' underlying attitudes and behaviors. Conclusions: Findings demonstrate that person- and system-level barriers can interfere with the accurate and respectful collection of gender identity data, which is critical for tracking and addressing lesbian, gay, bisexual, transgender, and queer health disparities.