ABSTRACT
OBJECTIVES: There have been many models of providing oncology and palliative care to hospitals. Many patients will use the hospital non-electively or semielectively, and a large proportion are likely to be in the last years of life. We describe our multidisciplinary service to treatable but not curable cancer patients at University Hospitals Sussex. The team was a mixture of clinical nurse specialists and a clinical fellow supported by dedicated palliative medicine consultant time and oncology expertise. METHODS: We identified patients with cancer who had identifiable supportive care needs and record activity with clinical coding. We used a baseline 2019/2020 dataset of national (secondary uses service) data with discharge code 79 (patients who died during that year) to compare a dataset of patients seen by the service between September 2020 and September 2021 in order to compare outcomes. While this was during COVID-19 this was when the funding was available. RESULTS: We demonstrated a reduction in length of stay by an average of 1.43 days per admission and a reduction of 0.95 episodes of readmission rates. However, the costs of those admissions were found to be marginally higher. Even with the costs of the service, there is a clear return on investment with a benefit cost ratio of 1.4. CONCLUSIONS: A supportive oncology service alongside or allied to acute oncology but in conjunction with palliative care is feasible and cost-effective. This would support investment in such a service and should be nationally commissioned in conjunction with palliative care services seeing all conditions.
ABSTRACT
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.