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The COVID-19 pandemic disproportionately impacted Asian Americans, Native Hawaiians, and Pacific Islanders (AA/NHPIs) in the United States. AA/NHPIs have historically been subjected to discrimination, which was exacerbated by the pandemic. To bring attention to their unique concerns, an AA/NHPI Interest Group of the National Institutes of Health Community Engagement Alliance Against COVID-19 Disparities (CEAL) was formed. This article highlights major concerns raised by the Interest Group: The pervasive and arbitrary practice of data aggregation by public health agencies and health-related researchers, the lack of culturally responsive services in the context of cultural safety, and leadership underrepresentation.

Objectives: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. Methods: We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. Results: A total of 27 studies underwent meta‐analysis. Findings indicated an overall mean sensitivity (recall) of 82%–87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94–0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. Conclusions: Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP‐based research to augment EM health care management. [ABSTRACT FROM AUTHOR]

A retrospective data analysis using 2000-2008 three state Medicaid Analytic eXtract was conducted to examine the prevalence and association of comorbidities (psychiatric and non-psychiatric) with healthcare utilization and expenditures of fee-for-service enrolled adults (22-64 years) with and without autism spectrum disorders (International Classification of Diseases, Ninth Revision-clinical modification code: 299.xx). Autism spectrum disorder cases were 1:3 matched to no autism spectrum disorder controls by age, gender, and race using propensity scores. Study outcomes were all-cause healthcare utilization (outpatient office visits, inpatient hospitalizations, emergency room, and prescription drug use) and associated healthcare expenditures. Bivariate analyses (chi-square tests and t-tests), multinomial logistic regressions (healthcare utilization), and generalized linear models with gamma distribution (expenditures) were used. Adults with autism spectrum disorders (n = 1772) had significantly higher rates of psychiatric comorbidity (81%), epilepsy (22%), infections (22%), skin disorders (21%), and hearing impairments (18%). Adults with autism spectrum disorders had higher mean annual outpatient office visits (32[subscript ASD] vs 8[subscript noASD]) and prescription drug use claims (51[subscript ASD] vs 24[subscript noASD]) as well as higher mean annual outpatient office visits (US$4375[subscript ASD] vs US$824[subscript noASD]), emergency room (US$15,929[subscript ASD] vs US$2598[subscrpt noASD]), prescription drug use (US$6067[subscript ASD] vs US$3144[subscript noASD]), and total expenditures (US$13,700[subscript ASD] vs US$8560[subscript noASD]). The presence of a psychiatric and a non-psychiatric comorbidity among adults with autism spectrum disorders increased the annual total expenditures by US$4952 and US$5084, respectively.

A cross-sectional analyses using Nationwide Emergency Department Sample (2006-2011) was conducted to examine the trends, type of ED visits, and mean total ED charges for adults aged 22-64 years with and without ASD (matched 1:3). Around 0.4% ED visits (n = 25,527) were associated with any ASD and rates of such visits more than doubled from 2006 to 2011 (2549-6087 per 100,000 admissions). Adults with ASD visited ED for: primary psychiatric disorder (15%[subscript ASD] vs. 4.2%[subscript noASD]), primary non-psychiatric disorder (16%[subscript ASD] vs. 14%[subscript noASD]), and any injury (24%[subscript ASD] vs. 28%[subscript noASD]). Mean total ED charges for adults with ASD were 2.3 times higher than for adults without ASD. Findings emphasize the need to examine the extent of frequent ED use in this population.