Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients’ data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years (‘older adults’), males, current smokers, and BMI classified as ‘overweight’ and ‘obese’ were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models’ interpretability were from the ‘sociodemographic characteristics’, ‘pre-hospital comorbidities’, and ‘medications’ categories. However, ‘pre-hospital comorbidities’ played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients’ conditions when urgent treatment plans are needed during the surge of patients during the pandemic.

Stress and burnout are significant concerns in nursing academia, as students balance clinical and academic demands, faculty manage teaching and research responsibilities, and staff navigate heavy workloads. Traditional wellness initiatives often address these groups separately, overlooking the value of shared engagement across the life course. This case study examined the development, implementation, and outcomes of an evidence-based wellness program within a College of Nursing. The life course framework reflected the continuum of human development, spanning students entering nursing directly from high school, registered nurses returning to advance their education, staff balancing work and family responsibilities, and faculty navigating the demands of early- to late-career stages. Guided by Yin's embedded case study methodology, data sources included pre-surveys, post-program feedback, and written interview guides from one student, one faculty member, and one staff member. Analysis combined within- and cross-case synthesis to identify similarities and differences across roles. Participants reported reduced stress, stronger wellness practices, improved coping, and greater feelings of belonging, with role-specific areas: students focused on coping and consistency, faculty on role modeling, and staff on recognition and sense of belonging. The intergenerational space was experienced as supportive and nonhierarchical, with participants sustaining at least one wellness practice beyond the program. These findings suggest that nursing academia can function as a whole multigenerational community for holistic wellness, normalizing wellness practices, fostering reciprocal learning, and cultivating an environment that strengthens well-being across the life course.