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Objectives/Hypothesis Follow‐up care in head and neck cancers (HNC) is critical in managing patient health. However, social determinants of health (SDOH) can create difficulties in maintaining follow‐up care. The study goal is to explore how SDOH impacts maintenance of HNC follow‐up care appointments. Methods A systematic retrospective chart review of 877 HNC patients diagnosed in the past 10 years a safety‐net tertiary care hospital with systems to help reduce care disparities. Cohort groups were identified and compared against protocols for follow‐up. Data were analyzed using analysis of variance, chi‐square tests, Fisher's exact tests, two‐sample t‐tests, and simple linear regression. Results The average length of follow‐up time in months and average total number of follow‐ups over 5 years were 32.96 (34.60) and 9.24 (7.87), respectively. There was no significant difference in follow‐up care between United States (US) versus non‐US born and English versus non‐English speaking patients. Race/ethnicity, county median household income, insurance status, and county educational attainment were not associated with differences in follow‐up. However, living a greater distance from the hospital was associated with lower follow‐up length and less frequency in follow‐up (P < .0001). Conclusion While income, primary language, country of birth, race/ethnicity, insurance status, and markers of educational attainment do not appear to impact HNC follow‐up at our safety‐net, tertiary care institution, and distance from hospital remains an important contributor to disparities in care. This study shows that many barriers to care can be addressed in a model that addresses SDOH, but there are barriers that still require additional systems and resources. Laryngoscope, 132:1022–1028, 2022

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed enormous inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. -image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging strategies for mitigating bias via decentralized learning, disentanglement, and model explainability.

Objective: This study aims to identify clinical and socioeconomic factors associated with long-term, post-surgical opioid use in the head and neck cancer population. Methods: A single center retrospective study was conducted including patients diagnosed with head and neck cancer between January 1, 2014 and July 1, 2019 who underwent primary surgical management. The primary outcome measure was continued opioid use 6 months after treatment completion. Both demographic and cancer-related variables were recorded to determine what factors were associated with prolonged opioid use. Univariate analysis was performed using chi-squared test for categorical variables and 2-sample t-test for continuous variables. Multivariate analysis was performed using logistic regression. Results: A total of 359 patients received primary surgical management. Forty-five patients (12.53%) continued to take opioids 6 months after treatment completion. Using univariate analysis, patients less than 65 years of age ( P = .0126), adjuvant chemoradiation (n = 25, P < .001), and overall length of hospital stay (8.60 ± 8.58 days, P = .0274) were significantly associated with long term opioid use. Multivariate logistic regression showed that adjuvant chemoradiation (OR = 3.446, 95% CI [1.742, 6.820], P = .0004) and overall length of hospital stay (OR = 0.949, 95% CI [0.903, 0.997], P = .0373) to be significantly associated with opioid use 6 months after head and neck cancer treatment. Conclusion: Long-term postoperative opioid use in head and neck cancer patients is significantly associated with adjuvant chemoradiation, and patients with longer length of hospital stay. Therefore, future research should focus on interventions to better manage opioid use during the acute treatment period to decrease long-term use.

Background: This study compares select social determinants of health (SDOH) with treatment modality selection and treatment completion in head and neck cancer (HNC) patients, to better understand disparities in health outcomes.Methods: A retrospective cohort study of HNC (n = 1428) patients was conducted. Demographic and disease-specific variables were recorded, including treatment modality selection and completion. Data were analyzed using twosample t tests, chi-square, and Fisher's exact tests. Results: Primary language was significantly associated with treatment choice, where non-English speakers were less likely to choose treatment as recommended by the Tumor Board. Lower mean distance from the hospital (37.38 [48.31] vs. 16.92 [19.10], p < 0.0001) and a county-based higher mean percentage of bachelor degree or higher education (42.16 [8.82] vs. 44.95 [6.19], p < 0.0003) were associated with treatment selection. Conclusion:Language, distance from the hospital, and education affected treatment selection in this study and may be useful in understanding how to counsel patients on treatment selection for HNC.

Objective The objective of this study was to compare the rates of spontaneous labor onset and its progression in obese and nonobese women after 37 weeks. Study Design We performed a secondary analysis of a retrospective cohort of all women who were admitted for delivery at ≥ 37 weeks of gestation at a university-based tertiary care center between 2004 and 2010. The cohort was stratified by weeks of gestation at which the patient presented for delivery. The rates of spontaneous labor, vaginal delivery, and augmentation with oxytocin were compared between obese (body mass index [BMI] ≥ 30) and nonobese (BMI < 30) women. Results Obese women had lower rates of spontaneous labor than nonobese women at every gestational week (37 weeks, 6.1 vs. 9.3%, p < 0.001; 38 weeks, 12.8 vs. 19.2%, p < 0.001; 39 weeks 26.0 vs. 37.0%, p < 0.001; 40 weeks, 39.6 vs. 50.2%, p < 0.001; 41 weeks, 30.8 vs. 38.0%, p < 0.012). Among women who presented in spontaneous labor, obesity was associated with higher rates of augmentation with oxytocin and lower rates of vaginal delivery. Conclusion Obese women at or beyond 37 weeks are less likely to experience spontaneous labor compared with nonobese women. In addition, obese women presenting in spontaneous labor are less likely that nonobese women to have a vaginal delivery at 37 to 40 weeks, even after oxytocin augmentation.

Background Modern innovations, like machine learning, genomics, and digital health, are being integrated into medical practice at a rapid pace. Physicians in training receive little exposure to the implications, drawbacks, and methodologies of upcoming technologies prior to their deployment. As a result, there is an increasing need for the incorporation of innovation and technology (I&T) training, starting in medical school. Objective We aimed to identify and describe curricular and extracurricular opportunities for innovation in medical technology in US undergraduate medical education to highlight challenges and develop insights for future directions of program development. Methods A review of publicly available I&T program information on the official websites of US allopathic medical schools was conducted in June 2020. Programs were categorized by structure and implementation. The geographic distribution of these categories across US regions was analyzed. A survey was administered to school-affiliated student organizations with a focus on I&T and publicly available contact information. The data collected included the founding year, thematic focus, target audience, activities offered, and participant turnout rate. Results A total of 103 I&T opportunities at 69 distinct Liaison Committee on Medical Education–accredited medical schools were identified and characterized into the following six categories: (1) integrative 4-year curricula, (2) facilitated doctor of medicine/master of science dual degree programs in a related field, (3) interdisciplinary collaborations, (4) areas of concentration, (5) preclinical electives, and (6) student-run clubs. The presence of interdisciplinary collaboration is significantly associated with the presence of student-led initiatives (P=.001). “Starting and running a business in healthcare” and “medical devices” were the most popular thematic focuses of student-led I&T groups, representing 87% (13/15) and 80% (12/15) of respondents, respectively. “Career pathways exploration for students” was the only type of activity that was significantly associated with a high event turnout rate of >26 students per event (P=.03). Conclusions Existing school-led and student-driven opportunities in medical I&T indicate growing national interest and reflect challenges in implementation. The greater visibility of opportunities, collaboration among schools, and development of a centralized network can be considered to better prepare students for the changing landscape of medical practice.

The fast onset and extensive impact of COVID-19 necessitated strict public health measures and temporary diversion of personnel and resources from other types of medical care. This study examined the prevalence of such disruptions and their impacts on patient-perceived well-being using an untargeted survey. The majority of surveyed patients experienced changes in their routine medical care. Of those whose appointments were postponed or canceled, most patients indicated an overall negative impact on their emotional and physical well-being. We highlighted the impact of disruptions in nonurgent medical care during a large-scale public health emergency.