COVID-19 Impact on Intern Exposure to Common Inpatient Diagnoses

OBJECTIVE We sought to understand the impact of the coronavirus disease 2019 (COVID-19) pandemic on the clinical exposure of pediatric interns to common pediatric inpatient diagnoses. METHODS We analyzed electronic medical record data to compare intern clinical exposure during the COVID-19 pandemic from June 2020 through February 2021 with the same academic blocks from 2017 to 2020. We attributed patients to each pediatric intern on the basis of notes written during their pediatric hospital medicine rotation to compare intern exposures with common inpatient diagnoses before and during the pandemic. We compared the median number of notes written per intern per block overall, as well as for each common inpatient diagnosis. RESULTS Median counts of notes written per intern per block were significantly reduced in the COVID-19 group compared with the pre–COVID-19 group (96 [interquartile range (IQR): 81–119)] vs 129 [IQR: 110–160]; P < .001). Median intern notes per block was lower in the COVID-19 group for all months except February 2021. Although the median number of notes for many common inpatient diagnoses was significantly reduced, they were higher for mental health (4 [IQR: 2–9] vs 2 [IQR: 1–6]; P < .001) and suicidality (4.5 [IQR: 2–8] vs 0 [IQR: 0–2]; P < .001). Median shifts worked per intern per block was also reduced in the COVID-19 group (22 [IQR: 21–23] vs 23 [IQR: 22–24]; P < .001). CONCLUSIONS Our findings reveal a significant reduction in resident exposure to many common inpatient pediatric diagnoses during the COVID-19 pandemic. Residency programs and pediatric hospitalist educators should consider curricular interventions to ensure adequate clinical exposure for residents affected by the pandemic.

VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.