INTRODUCTION: The COVID-19 pandemic is an unprecedented global crisis profoundly affecting oncology care delivery. PURPOSE: This study will describe the occupational and personal consequences of the COVID-19 pandemic on oncologist well-being and patient care. MATERIALS AND METHODS: Four virtual focus groups were conducted with US ASCO member oncologists (September-November 2020). Inquiry and subsequent discussions centered on self-reported accounts of professional and personal COVID-19 experiences affecting well-being, and oncologist recommendations for well-being interventions that the cancer organization and professional societies (ASCO) might implement were explored. Qualitative interviews were analyzed using Framework Analysis. RESULTS: Twenty-five oncologists were interviewed: median age 44 years (range: 35-69 years), 52% female, 52% racial or ethnic minority, 76% medical oncologists, 64% married, and an average of 51.5 patients seen per week (range: 20-120). Five thematic consequences emerged: (1) impact of pre-COVID-19 burnout, (2) occupational or professional limitations and adaptations, (3) personal implications, (4) concern for the future of cancer care and the workforce, and (5) recommendations for physician well-being interventions. Underlying oncologist burnout exacerbated stressors associated with disruptions in care, education, research, financial practice health, and telemedicine. Many feared delays in cancer screening, diagnosis, and treatment. Oncologists noted personal and familial stressors related to COVID-19 exposure fears and loss of social support. Many participants strongly considered working part-time or taking early retirement. Yet, opportunities arose to facilitate personal growth and rise above pandemic adversity, fostering greater resilience. Recommendations for organizational well-being interventions included psychologic or peer support resources, flexible time-off, and ASCO and state oncology societies involvement to develop care guidelines, well-being resources, and mental health advocacy. CONCLUSION: Our study suggests that the COVID-19 pandemic has adversely affected oncologist burnout, fulfillment, practice health, cancer care, and workforce. It illuminates where professional organizations could play a significant role in oncologist well-being.

PURPOSE: Recent data suggest that the rate of burnout among oncologists has risen over time. In addition to the negative effect of burnout on individuals, widespread burnout may prevent the oncology workforce from meeting patient care needs. METHODS: ASCO surveyed US-based oncologists in patient care roles about their professional well-being and satisfaction and the effect of both on their career plans. Burnout was assessed using measures of emotional exhaustion and depersonalization from the Maslach Burnout Inventory Human Services Survey. Data from the 2023 survey were compared with data from Shanafelt et al (JCO, 2014) collected about oncologist burnout/well-being in 2013. RESULTS: In all, 328 responses to the 2023 survey were analyzed. Burnout was significantly higher compared with 2013, 45% in 2013 versus 59% in 2023 ( P < .01). In 2023, <25% of oncologists reported feeling satisfied with their work-life integration, compared with nearly 35% in 2013 ( P < .01). Burnout was significantly associated with being a caregiver in one’s personal life, being under age 50 years, and working more than 60 hours per week (all P < .05). Over 20% of respondents reported that it was likely or definite that they would reduce their clinical hours in the next 12 months. Additional practice/administrative support and reducing work hours were identified as potential solutions to improve job satisfaction. CONCLUSION: Oncologists in 2023 are experiencing higher rates of burnout compared with a decade ago. System-based interventions to reduce burnout are necessary to maintaining an adequate workforce to provide care for current and future patients with cancer.

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14–0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56–50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.