Long-Term Effects of COVID-19: The Stories of 2 Physicians Who Became Patients.

Approximately 10% of patients who survive COVID-19 will proceed to have lasting, often debilitating effects, known as "long COVID." These symptoms can take various forms, most commonly including postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, heart palpitations, diminished sexual desire or capacity, loss of smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Here, 2 physician-patients present their own experiences with long COVID and share their perspectives on the experience. One key insight is that patients who are not familiar with long COVID may not attribute ongoing symptoms to their illness. Diagnosis requires an astute, compassionate physician who understands long COVID and can appropriately situate the symptoms within the evolving understanding of the condition, leading the patient toward recovery.

Considering Burnout and Well-Being: Emergency Medicine Resident Shift Scheduling Platform and Satisfaction Insights from a Quality Improvement Project.

Few studies explore emergency medicine (EM) residency shift scheduling software as a mechanism to reduce administrative demands and broader resident burnout. A local needs assessment demonstrated a learning curve for chief resident schedulers and several areas for improvement. In an institutional quality improvement project, we utilized an external online cross-sectional convenience sampling pilot survey of United States EM residency programs to collect information on manual versus software-based resident shift scheduling practices and associated scheduler and scheduler-perceived resident satisfaction. Our external survey response rate was 19/253 (8%), with all United States regions (i.e., northeast, southeast, midwest, west, and southwest) represented. Two programs (11%) reported manual scheduling without any software. ShiftAdmin was the most popularly reported scheduling software (53%). Although not statistically significant, manual scheduling had the lowest satisfaction score and programs with ≤30 residents reported the highest levels of satisfaction. Our data suggest that improvements in existing software-based technologies are needed. Artificial intelligence technologies may prove useful for reducing administrative scheduling demands and optimizing resident scheduling satisfaction.