Telecritical Care Clinical and Operational Strategies in Response to COVID-19

Abstract

Background: The cororavirus disease 19 (COVID-19) pandemic has strained intensive care unit (ICU) material and human resources to global crisis levels. The risks of staffing challenges and clinician exposure are of significant concern. One resource, telecritical care (TCC), has the potential to optimize efficiency, maximize safety, and improve quality of care provided amid large-scale disruptions, but its role in pandemic situations is only loosely defined. Planning and Preparation Phase: We propose strategic initiatives by which TCC may act as a force multiplier for pandemic preparedness in response to COVID-19, utilizing a tiered approach for increasing surge capacity needs. The goals involved usage of TCC to augment ICU capacity, optimize safety, minimize personal protective equipment (PPE) use, improve efficiencies, and enhance knowledge of managing pandemic response. Implementation Phase: A phased approach utilizing TCC would involve implementing remote capabilities across the enterprise to accomplish the goals outlined. The hardware and software needed for initial expansion to cover 275 beds included $956,670 for mobile carts and $173,106 for home workstations. Team role deployment and bedside clinical care centering around TCC as critical care capacity expand beyond 275 beds. Surge capacity was not reached during early phases of the pandemic in the region, allowing refinement of TCC during subsequent pandemic phases. Conclusions: Leveraging TCC facilitated pandemic surge planning but required redefinition of typical ICU staffing models. The design was meant to workforce efficiencies, reduce PPE use, and minimize health care worker exposure risk, all while maintaining quality care standards through an intensivist-led model. As health care operations resumed and states reopened, TCC is being used to support shifts in volume and critical care personnel during the pandemic evolution. The lessons applied may help health care systems through variable phases of the pandemic.

Identification of high-risk COVID-19 patients using machine learning

“…As a response to this international public health crisis, scientists and clinicians have made enormous efforts in the last few months to generate new knowledge and to develop technological tools that may help in combating this infectious disease and mitigate its effects. Some of these efforts include the development of drugs and vaccines [ 1 – 4 ], the construction of epidemiological models to forecast the dynamics of disease spreading in the population [ 5 – 8 ], the development of mobile-device applications for tracking infected patients and new cases [ 9 – 11 ], and the development of strategies and the application of new technologies to manage the resources and capacities in hospitals [ 12 – 14 ].…”

Telemedicine Intensive Care Unit (Tele-ICU) Implementation During COVID-19: A Scoping Review

“…In the Atrium Health study performed by Singh et al, virtual critical care augmentation was found to be more cost-effective than structural changes, though the initial investment in home workstations required extensive set-ups and support from their Information Technology department [ 22 ]. The Singh study found that teleintensivists worked well with triaging interfacility transfer decisions and resource utilization and noted that this was well received by physicians who had an intimate knowledge of hospital resources [ 22 ]. The role changes inherent in a hybrid virtual and in-person team initially created confusion, which was eventually overcome with frequent and intense communication strategies [ 22 ].…”

“…The Singh study found that teleintensivists worked well with triaging interfacility transfer decisions and resource utilization and noted that this was well received by physicians who had an intimate knowledge of hospital resources [ 22 ]. The role changes inherent in a hybrid virtual and in-person team initially created confusion, which was eventually overcome with frequent and intense communication strategies [ 22 ].…”

Identification of high-risk COVID-19 patients using machine learning

“…As a response to this international public health crisis, scientists and clinicians have made enormous efforts in the last few months to generate new knowledge and to develop technological tools that may help in combatting this infectious disease and mitigate its effects. Some of these efforts include the development of drugs and vaccines [1][2][3][4], the construction of epidemiological models to forecast the dynamics of disease spreading in the population [5][6][7][8], the development of mobile-device applications for tracking infected patients and new cases [9][10][11], and the development of strategies and the application of new technologies to manage the resources and capacities in hospitals [12][13][14].…”