Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic.

OBJECTIVE: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift. MATERIALS AND METHODS: We obtained four datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-COVID (Jan 1, 2019-Feb 1, 2020) and COVID-era (May 15, 2020-Feb 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than four hours was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for two experiments: 1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, 2) we evaluated the impact of spatial drift by testing models trained at Location 1 on data from Location 2, and vice versa. RESULTS: The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at Location 2) to 0.81 (COVID-era at Location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs. 0.78 at Location 1). Models that were transferred from Location 2 to Location 1 performed worse than models trained at Location 1 (0.51 vs. 0.78). DISCUSSION AND CONCLUSION: Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift.

The impact of coronavirus disease 2019 (COVID-19) on the antimicrobial stewardship pharmacist workforce: A multicenter survey.

Objective: The coronavirus disease 2019 (COVID-19) pandemic has required healthcare systems and hospitals to rapidly modify standard practice, including antimicrobial stewardship services. Our study examines the impact of COVID-19 on the antimicrobial stewardship pharmacist. Design: A survey was distributed nationally to all healthcare improvement company members. Participants: Pharmacist participants were mostly leaders of antimicrobial stewardship programs distributed evenly across the United States and representing urban, suburban, and rural health-system practice sites. Results: Participants reported relative increases in time spent completing tasks related to medication access and preauthorization (300%; P = .018) and administrative meeting time (34%; P = .067) during the COVID-19 pandemic compared to before the pandemic. Time spent rounding, making interventions, performing pharmacokinetic services, and medication reconciliation decreased. Conclusion: A shift away from clinical activities may negatively affect the utilization of antimicrobials.

COVID-19: An Emerging Threat to Antibiotic Stewardship in the Emergency Department.

While current research efforts focus primarily on identifying patient level interventions that mitigate the direct impact of COVID-19, it is important to consider the collateral effects of COVID-19 on antimicrobial resistance. Early reports suggest high rates of antibiotic utilization in COVID-19 patients despite their lack of direct activity against viral pathogens. The ongoing pandemic is exacerbating known barriers to optimal antibiotic stewardship in the ED, representing an additional direct threat to patient safety and public health. There is an urgent need for research analyzing overall and COVID-19 specific antibiotic prescribing trends in the ED. Optimizing ED stewardship during COVID-19 will likely require a combination of traditional stewardship approaches (e.g. academic detailing, provider education, care pathways) and effective implementation of host response biomarkers and rapid COVID-19 diagnostics. Antibiotic stewardship interventions with demonstrated efficacy in mitigating the impact of COVID-19 on ED prescribing should be widely disseminated and inform the ongoing pandemic response.