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To protect both patients and staff, healthcare personnel (HCP) were among the first groups in the United States recommended to receive the COVID-19 vaccine. We analyzed data reported to the U.S. Department of Health and Human Services (HHS) Unified Hospital Data Surveillance System on COVID-19 vaccination coverage among hospital-based HCP. After vaccine introduction in December 2020, COVID-19 vaccine coverage rose steadily through April 2021, but the rate of uptake has since slowed; as of September 15, 2021, among 3,357,348 HCP in 2,086 hospitals included in this analysis, 70.0% were fully vaccinated. Additional efforts are needed to improve COVID-19 vaccine coverage among HCP.

ObjectiveThe COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions.Materials and MethodsUsing real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions.Results162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥ 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels.DiscussionPhysical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff.ConclusionSimulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

Purpose Patients scheduled for outpatient infusion sometimes may be deferred for treatment after arriving for their appointment. This can be the result of a secondary illness, not meeting required bloodwork counts, or other medical complications. The ability to generate high-quality predictions of patient deferrals can be highly valuable in managing clinical operations, such as scheduling patients, determining which drugs to make before patients arrive, and establishing the proper staffing for a given day. Methods In collaboration with the University of Michigan Comprehensive Cancer Center, we have developed a predictive model that uses patient-specific data to estimate the probability that a patient will defer or not show for treatment on a given day. This model incorporates demographic, treatment protocol, and prior appointment history data. We tested a wide range of predictive models including logistic regression, tree-based methods, neural networks, and various ensemble models. We then compared the performance of these models, evaluating both their prediction error and their complexity level. Results We have tested multiple classification models to determine which would best determine whether a patient will defer or not show for treatment on a given day. We found that a Bayesian additive regression tree model performs best with the University of Michigan Comprehensive Cancer Center data on the basis of out-of-sample area under the curve, Brier score, and F1 score. We emphasize that similar statistical procedures must be taken to reach a final model in alternative settings. Conclusion This article introduces the existence and selection process of a wide variety of statistical models for predicting patient deferrals for a specific clinical environment. With proper implementation, these models will enable clinicians and clinical managers to achieve the in-practice benefits of deferral predictions.

Publisher Summary This chapter discusses different types of manufacturing processes. All the large-chip processes use cutting tools of defined geometry, which are applied to remove metal at a predetermined rate. Turning machines embrace the wide variety of lathes and vertical boring machines, which can be controlled manually or automatically. Automatic control can be achieved using cams, sequential controllers, hydraulic copying devices, or numerical programming. Most turning processes use tools with a single cutting edge where the cutting action is characterized by a continuous chip when cutting ductile materials or short discontinuous chips when cutting brittle materials. To improve the mechanical properties of the product, while at the same time keeping the loading at a moderate level, warm processing is used. Here, the temperatures are well above ambient but, equally, well below the hot-processing range and usually slightly less than that used for re-crystallization. The increased material ductility is sufficient to reduce the power requirement of the plant.