COVID-19: Recovery Models for Radiology Departments.

The coronavirus disease 2019 (COVID-19) pandemic has greatly affected demand for imaging services, with marked reductions in demand for elective imaging and image-guided interventional procedures. To guide radiology planning and recovery from this unprecedented impact, three recovery models were developed to predict imaging volume over the course of the COVID-19 pandemic: (1) a long-term volume model with three scenarios based on prior disease outbreaks and other historical analogues, to aid in long-term planning when the pandemic was just beginning; (2) a short-term volume model based on the supply-demand approach, leveraging increasingly available COVID-19 data points to predict examination volume on a week-to-week basis; and (3) a next-wave model to estimate the impact from future COVID-19 surges. The authors present these models as techniques that can be used at any stage in an unpredictable pandemic timeline.

Modeling workflows: Identifying the most predictive features in healthcare operational processes.

Limited resources and increased patient flow highlight the importance of optimizing healthcare operational systems to improve patient care. Accurate prediction of exam volumes, workflow surges and, most notably, patient delay and wait times are known to have significant impact on quality of care and patient satisfaction. The main objective of this work was to investigate the choice of different operational features to achieve (1) more accurate and concise process models and (2) more effective interventions. To exclude process modelling bias, data from four different workflows was considered, including a mix of walk-in, scheduled, and hybrid facilities. A total of 84 features were computed, based on previous literature and our independent work, all derivable from a typical Hospital Information System. The features were categorized by five subgroups: congestion, customer, resource, task and time features. Two models were used in the feature selection process: linear regression and random forest. Independent of workflow and the model used for selection, it was determined that congestion feature sets lead to models most predictive for operational processes, with a smaller number of predictors.

Robotic metamorphosis by origami exoskeletons.

Changing the inherent physical capabilities of robots by metamorphosis has been a long-standing goal of engineers. However, this task is challenging because of physical constraints in the robot body, each component of which has a defined functionality. To date, self-reconfiguring robots have limitations in their on-site extensibility because of the large scale of today's unit modules and the complex administration of their coordination, which relies heavily on on-board electronic components. We present an approach to extending and changing the capabilities of a robot by enabling metamorphosis using self-folding origami "exoskeletons." We show how a cubical magnet "robot" can be remotely moved using a controllable magnetic field and hierarchically develop different morphologies by interfacing with different origami exoskeletons. Activated by heat, each exoskeleton is self-folded from a rectangular sheet, extending the capabilities of the initial robot, such as enabling the manipulation of objects or locomotion on the ground, water, or air. Activated by water, the exoskeletons can be removed and are interchangeable. Thus, the system represents an end-to-end (re)cycle. We also present several robot and exoskeleton designs, devices, and experiments with robot metamorphosis using exoskeletons.