Comparing Electronic Health Record Domains' Utility to Identify Transgender Patients.

Purpose: Earlier literature has reported on the utility of diagnostic codes and demographic information for identifying transgender patients. We aim to assess which method identifies the most transgender patients utilizing readily available tools from within the electronic health record (EHR). Methods: A de-identified patient database from a single EHR that allows for searching any discrete data point in the EHR was used to query International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) diagnostic codes and demographic data specific to transgender patients from January 2011 to April 2019. Results: Demographic data and ICD-10 codes yielded 1494 individual EHRs with transgender-specific data domains. ICD-10 diagnostic codes alone identified 942 (63.05%) unique EHRs. Demographics alone identified 218 (14.59%) unique EHRs. A total of 334 (22.36%) unique EHRs had both ICD-10 and demographic identifiers. Of those identified by transgender-specific demographic data (552), 294 (53.26%) were trans masculine, 215 (38.95%) were trans feminine, and 43 (7.79%) were nonbinary. Of the 552 demographic-identified transgender patients, 141 (25.86%) were identified by a two-part gender identity demographic question. Conclusions: ICD-10 diagnostic codes, not demographic data, identified the most transgender patient records, but neither diagnostic codes alone nor demographic data captured the full population. Only 26.36% of the charts identified as transgender patients had both ICD-10 codes and demographic data. We recommend that when identifying transgender populations through EHR domains, a combination of diagnostic codes and demographic data be used. Furthermore, research is needed to optimize disclosure and collection of demographic information for gender minority populations.

Social deviance activates the brain's error-monitoring system.

Social psychologists have long noted the tendency for human behavior to conform to social group norms. This study examined whether feedback indicating that participants had deviated from group norms would elicit a neural signal previously shown to be elicited by errors and monetary losses. While electroencephalograms were recorded, participants (N = 30) rated the attractiveness of 120 faces and received feedback giving the purported average rating made by a group of peers. The feedback was manipulated so that group ratings either were the same as a participant's rating or deviated by 1, 2, or 3 points. Feedback indicating deviance from the group norm elicited a feedback-related negativity, a brainwave signal known to be elicited by objective performance errors and losses. The results imply that the brain treats deviance from social norms as an error.

Neural and behavioral measures of error-related cognitive control predict daily coping with stress.

This study tested the hypothesis that individual differences in cognitive control can predict individual differences in emotion regulation. Participants completed color-word and emotional Stroop tasks while an electroencephalogram was recorded, and then they reported daily stressful events, affect, and coping for 14 days. Greater posterror slowing in the emotional Stroop task predicted greater negative affect in response to stressors and less use of task-focused coping as daily stressors increased. Participants whose neural activity best distinguished errors from correct responses tended to show less stress reactivity in daily self-reports. Finally, depression levels predicted daily affect and coping independent of cognitive control variables. The results offer qualified support for an integrated conception of cognitive and emotional self-regulation.

Cognitive control in the intertrial interval: evidence from EEG alpha power.

This study used electroencephalogram (EEG) power spectrum analyses to characterize neural activity during the intertrial interval, a period during which online cognitive adjustments in response to errors or conflict are thought to occur. EEG alpha power was quantified as an inverse index of cerebral activity during the period between each response and the next stimulus in a Stroop task. Alpha power was significantly reduced following error responses compared to correct responses, indicating greater cerebral activity following errors. Reduced alpha power was also observed following Stroop conflict trials compared to no-conflict trials, suggesting that conflict engages processes of mental adjustment. Finally, hemispheric differences in alpha power during the intertrial interval supported the complementary roles of the left and right hemispheres in behavioral activation and inhibition.