RESUMO
In service of particularly vulnerable populations, safety net healthcare systems must nimbly leverage health information technology (IT), including electronic health records (EHRs), to coordinate the medical and public health response to the novel coronavirus (COVID-19). Six months after the San Francisco Department of Public Health implemented a new EHR across its hospitals and citywide clinics, California declared a state of emergency in response to COVID-19. This paper describes how the IT and informatics teams supported San Francisco Department of Public Health's goals of expanding the safety net healthcare system capacity, meeting the needs of specific vulnerable populations, increasing equity in COVID-19 testing access, and expanding public health analytics and research capacity. Key enabling factors included critical partnerships with operational leaders, early identification of priorities, a clear governance structure, agility in the face of rapidly changing circumstances, and a commitment to vulnerable populations.
RESUMO
We present a new external memory multiresolution surface representation for massive polygonal meshes. Previous methods for building such data structures have relied on resampled surface data or employed memory intensive construction algorithms that do not scale well. Our proposed representation combines efficient access to sampled surface data with access to the original surface. The construction algorithm for the surface representation exhibits memory requirements that are insensitive to the size of the input mesh, allowing it to process meshes containing hundreds of millions of polygons. The multiresolution nature of the surface representation has allowed us to develop efficient algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification of massive meshes. The empirical performance of these algorithms demonstrates that the underlying data structure is a powerful and flexible tool for operating on massive geometric data.