Background Understanding trends in surgical volumes can help Ambulatory Surgery Centers (ASCs) prevent clinician burnout and provide adequate staffing while maintaining the quality of patient care throughout the year. Health insurance deductibles reset in January each year and may contribute to an annual rhythm where the levee of year-end deductibles is breached in the last few months of every year, resulting in a flood of cases and several accompanying challenges. This study aims to identify and analyze monthly and yearly surgical volume patterns in ASCs and explore a relationship with the deductible reset. Methods De-identified, aggregate visit data for 2016–2019 were obtained retrospectively from 14 ambulatory surgery centers within the same benchmarking consortium in the Southeast. The ASCs subspecialty types consisted of orthopedics, urology, otolaryngology, and multispecialty. Kaiser Family Foundation survey data from 2016 to 2019 was used to inform deductible trends. Augmented Dickey-Fuller tests, linear regressions, and two-sample T-tests were conducted to explore and establish patterns in surgical volume between 2016 and 2019. Results Overall, average orthopedic surgical volume increased 38.04% from January to December in 2016–2019 with an average difference of 64 cases (95% CI: 47–80), while that of all ASCs combined increased 19.24% within the same timeframe with an average difference of 37 cases (95% CI: 21–52). Average health insurance deductibles rose 12% from $1476 to $1655 within the same timeframe. Regression analysis showed a stronger association between year and volume for orthopedic ASCs (R (Claxton et al., 2019) [2] = 0.796) than for all ASCs combined (R (Claxton et al., 2019) [2] = 0.645). Regression analysis also showed a stronger association between month and volume for orthopedic ASCs (R (Claxton et al., 2019) [2] = 0.488–0.805) than for all ASCs combined (R (Claxton et al., 2019) [2] = 0.115–0.493). Conclusion This study is first to identify regular and predictable yearly and monthly increases in orthopedic ASCs surgical volume. The study also identifies yearly increases in surgical volume for all ASCs. The combination of increasing yearly demand for orthopedic surgery and growing association between month and volume leads to an unnecessary year-end rush. The study aims to inform future policy decisions as well as help ASCs better manage resources throughout the year.

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Background Significant racial differences have been observed in the incidence and clinical outcomes of diffuse large B‐cell lymphoma (DLBCL) in the United States, but to the authors' knowledge it remains unclear whether genomic differences contribute to these disparities. METHODS To understand the influences of genetic ancestry on tumor genomic alterations, the authors estimated the genetic ancestry of 1001 previously described patients with DLBCL using unsupervised model‐based Admixture global ancestry analysis applied to exome sequencing data and examined the mutational profile of 150 DLBCL driver genes in tumors obtained from this cohort. Results Global ancestry prediction identified 619 patients with >90% European ancestry, 81 patients with >90% African ancestry, and 50 patients with >90% Asian ancestry. Compared with patients with DLBCL with European ancestry, patients with African ancestry were aged >10 years younger at the time of diagnosis and were more likely to present with B symptoms, elevated serum lactate dehydrogenase, extranodal disease, and advanced stage disease. Patients with African ancestry demonstrated worse overall survival compared with patients with European ancestry (median, 4.9 years vs 8.8 years; P = .04). Recurrent mutations of MLL2 (KMT2D), HIST1H1E, MYD88, BCL2, and PIM1 were found across all ancestry groups, suggesting shared mechanisms underlying tumor biology. The authors also identified 6 DLBCL driver genes that were more commonly mutated in patients with African ancestry compared with patients with European ancestry: ATM (21.0% vs 7.75%; P < .001), MGA (19.7% vs 5.33%; P < .001), SETD2 (17.3% vs 5.17%; P < .001), TET2 (12.3% vs 5.82%; P = .029), MLL3 (KMT2C) (11.1% vs 4.36%; P = .013), and DNMT3A (11.1% vs 4.52%; P = .016). Conclusions Distinct prevalence and patterns of mutation highlight an important difference in the mutational landscapes of DLBCL arising in different ancestry groups. To the authors' knowledge, the results of the current study provide the first‐ever characterization of genetic alterations among patients with African descent who are diagnosed with DLBCL.

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Background Interest in and funding for digital health interventions have rapidly grown in recent years. Despite the increasing familiarity with mobile health from regulatory bodies, providers, and patients, overarching research on digital health adoption has been primarily limited to morbidity-specific and non-US samples. Consequently, there is a limited understanding of what personal factors hold statistically significant relationships with digital health uptake. Moreover, this limits digital health communities’ knowledge of equity along digital health use patterns. Objective This study aims to identify the social determinants of digital health tool adoption in Georgia. Methods Web-based survey respondents in Georgia 18 years or older were recruited from mTurk to answer primarily closed-ended questions within the following domains: participant demographics and health consumption background, telehealth, digital health education, prescription management tools, digital mental health services, and doctor finder tools. Participants spent around 15 to 20 minutes on a survey to provide demographic and personal health care consumption data. This data was analyzed with multivariate linear and logistic regressions to identify which of these determinants, if any, held statistically significant relationships with the total number of digital health tool categories adopted and which of these determinants had absolute relationships with specific categories. Results A total of 362 respondents completed the survey. Private insurance, residence in an urban area, having a primary care provider, fewer urgent emergency room (ER) visits, more ER visits leading to inpatient stays, and chronic condition presence were significantly associated with the number of digital health tool categories adopted. The separate logistic regressions exhibited substantial variability, with 3.5 statistically significant predictors per model, on average. Age, federal poverty level, number of primary care provider visits in the past 12 months, number of nonurgent ER visits in the past 12 months, number of urgent ER visits in the past 12 months, number of ER visits leading to inpatient stays in the past 12 months, race, gender, ethnicity, insurance, education, residential area, access to the internet, difficulty accessing health care, usual source of care, status of primary care provider, and status of chronic condition all had at least one statistically significant relationship with the use of a specific digital health category. Conclusions The results demonstrate that persons who are socioeconomically disadvantaged may not adopt digital health tools at disproportionately higher rates. Instead, digital health tools may be adopted along social determinants of health, providing strong evidence for the digital health divide. The variability of digital health adoption necessitates investing in and building a common framework to increase mobile health access. With a common framework and a paradigm shift in the design, evaluation, and implementation strategies around digital health, disparities can be further mitigated and addressed. This likely will begin with a coordinated effort to determine barriers to adopting digital health solutions.