Rapid Implementation of a Complex, Multimodal Technology Response to COVID-19 at an Integrated Community-Based Health Care System.

BACKGROUND: The rapid spread of severe acute respiratory syndrome coronavirus-2 or SARS-CoV-2 necessitated a scaled treatment response to the novel coronavirus disease 2019 (COVID-19). OBJECTIVE: This study aimed to characterize the design and rapid implementation of a complex, multimodal, technology response to COVID-19 led by the Intermountain Healthcare's (Intermountain's) Care Transformation Information Systems (CTIS) organization to build pandemic surge capacity. METHODS: Intermountain has active community-spread cases of COVID-19 that are increasing. We used the Centers for Disease Control and Prevention Pandemic Intervals Framework (the Framework) to characterize CTIS leadership's multimodal technology response to COVID-19 at Intermountain. We provide results on implementation feasibility and sustainability of health information technology (HIT) interventions as of June 30, 2020, characterize lessons learned and identify persistent barriers to sustained deployment. RESULTS: We characterize the CTIS organization's multimodal technology response to COVID-19 in five relevant areas of the Framework enabling (1) incident management, (2) surveillance, (3) laboratory testing, (4) community mitigation, and (5) medical care and countermeasures. We are seeing increased use of traditionally slow-to-adopt technologies that create additional surge capacity while sustaining patient safety and care quality. CTIS leadership recognized early that a multimodal technology intervention could enable additional surge capacity for health care delivery systems with a broad geographic and service scope. A statewide central tracking system to coordinate capacity planning and management response is needed. Order interoperability between health care systems remains a barrier to an integrated response. CONCLUSION: The rate of future pandemics is estimated to increase. The pandemic response of health care systems, like Intermountain, offers a blueprint for the leadership role that HIT organizations can play in mainstream care delivery, enabling a nimbler, virtual health care delivery system that is more responsive to current and future needs.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.