RESUMO
Background Rates of COVID-19 hospitalization are an important measure of the health system burden of severe COVID-19 disease and have been closely followed throughout the pandemic. The highly transmittable, but often less severe, Omicron COVID-19 variant has led to an increase in hospitalizations with incidental COVID-19 diagnoses where COVID-19 is not the primary reason for admission. There is a strong public health need for a measure that is implementable at low cost with standard electronic health record (EHR) datasets that can separate these incidental hospitalizations from non-incidental hospitalizations where COVID-19 is the primary cause or an important contributor. Two crude metrics are in common use. The first uses in-hospital administration of dexamethasone as a marker of non-incidental COVID-19 hospitalizations. The second, used by the United States (US) CDC, relies on a limited set of COVID-19-related diagnoses (i.e., respiratory failure, pneumonia). Both measures likely undercount non-incidental COVID-19 hospitalizations. We therefore developed an improved EHR-based measure that is better able to capture the full range of COVID-19 hospitalizations. Methods We conducted a retrospective study of ED visit data from a national emergency medicine group from April 2020 to August 2023. We assessed the CDC approach, the dexamethasone-based measure, and alternative approaches that rely on co-diagnoses likely to be related to COVID-19, to determine the proportion of non-incidental COVID-19 hospitalizations. Results Of the 153,325 patients diagnosed with COVID-19 at 112 general EDs in 17 US states, and admitted or transferred, our preferred measure classified 108,243 (70.6%) as non-incidental, compared to 71,066 (46.3%) using the dexamethasone measure and 77,399 (50.5%) using the CDC measure. Conclusions Identifying non-incidental COVID-19 hospitalizations using ED administration of dexamethasone or the CDC measure provides substantially lower estimates than our preferred measure.
RESUMO
Introduction Previous studies have shown that risk attitudes and tolerance for uncertainty are significant factors in clinical decision-making, particularly in the practice of defensive medicine. These attributes have also been linked with rates of physician burnout. To date, the risk profile of emergency medicine (EM) physicians has not yet been described. Our goal was to examine the risk profile of EM residents using a widely available risk tolerance and attitude assessment tool. Methods First-, second-, and third-year residents of Thomas Jefferson University Hospital's EM residency program completed the commercially available, unmodified Risk Type Compass, a validated instrument offered by Multi-Health Systems (MHS Inc, New York, USA). Scored reports included information on residents' risk type (one of eight personality types that reflect their temperament and disposition); risk attitudes (domains where residents are more likely to engage in risky behaviors); and an overall risk tolerance indicator (RTi) (a numerical estimate of risk tolerance). RTi scores are reported as means with 95% confidence intervals (CIs). Results There was no significant change in RTi scores in residents across different years of their post-graduate year (PGY) training. PGY-one residents trended towards risk aversion; PGY-two residents were more risk-taking; and PGY-three residents scored in the middle. Conclusion Our pilot assessment of risk types in EM residents highlighted shifts across the years of training. Variations between members of each PGY cohort outweighed any outright differences between classes with regards to absolute risk tolerance. There was an increase in the frequency of health and safety risk-taking attitude with higher PGY class, and this was also the risk attitude that was the prominent domain for resident risk tolerance. The study was limited by sample size and single cross-sectional evaluation.