Service line care delivery model for COVID-19 patient-centric care.

OBJECTIVES: The COVID-19 pandemic has caused hospitals around the world to quickly develop not only strategies to treat patients but also methods to protect health care and frontline workers. STUDY DESIGN: Descriptive study. METHODS: We outlined the steps and processes that we took to respond to the challenges presented by the COVID-19 pandemic while continuing to provide our routine acute care services to our community. RESULTS: These steps and processes included establishing teams focused on maintaining an adequate supply of personal protection equipment, cross-training staff, developing disaster-based triage for the emergency department, creating quality improvement teams geared toward updating care based on the most current literature, developing COVID-19-based units, creating COVID-19-specific teams of providers, maximizing use of our electronic health record system to allocate beds, and providing adequate practitioner coverage by creating a computer-based dashboard that indicated the need for health care practitioners. These processes led to seamless and integrated care for all patients with COVID-19 across our health system and resulted in a reduction in mortality from a high of 20% during the first peak (March and April 2020) to 6% during the plateau period (June-October 2020) to 12% during the second peak (November and December 2020). CONCLUSIONS: The detailed processes put in place will help hospital systems meet the continuing challenges not only of COVID-19 but also beyond COVID-19 when other unique public health crises may present themselves.

Motion based markerless gait analysis using standard events of gait and ensemble Kalman filtering.

We present a novel approach to gait analysis using ensemble Kalman filtering which permits markerless determination of segmental movement. We use image flow analysis to reliably compute temporal and kinematic measures including the translational velocity of the torso and rotational velocities of the lower leg segments. Detecting the instances where velocity changes direction also determines the standard events of a gait cycle (double-support, toe-off, mid-swing and heel-strike). In order to determine the kinematics of lower limbs, we model the synergies between the lower limb motions (thigh-shank, shank-foot) by building a nonlinear dynamical system using CMUs 3D motion capture database. This information is fed into the ensemble Kalman Filter framework to estimate the unobserved limb (upper leg and foot) motion from the measured lower leg rotational velocity. Our approach does not require calibrated cameras or special markers to capture movement. We have tested our method on different gait sequences collected from the sagttal plane and presented the estimated kinematics overlaid on the original image frames. We have also validated our approach by manually labeling the videos and comparing our results against them.

Markerless identification of key events in gait cycle using image flow.

Gait analysis has been an interesting area of research for several decades. In this paper, we propose image-flow-based methods to compute the motion and velocities of different body segments automatically, using a single inexpensive video camera. We then identify and extract different events of the gait cycle (double-support, mid-swing, toe-off and heel-strike) from video images. Experiments were conducted in which four walking subjects were captured from the sagittal plane. Automatic segmentation was performed to isolate the moving body from the background. The head excursion and the shank motion were then computed to identify the key frames corresponding to different events in the gait cycle. Our approach does not require calibrated cameras or special markers to capture movement. We have also compared our method with the Optotrak 3D motion capture system and found our results in good agreement with the Optotrak results. The development of our method has potential use in the markerless and unencumbered video capture of human locomotion. Monitoring gait in homes and communities provides a useful application for the aged and the disabled. Our method could potentially be used as an assessment tool to determine gait symmetry or to establish the normal gait pattern of an individual.