Forecasting local hospital bed demand for COVID-19 using on-request simulations.

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Assessing the clinical accuracy of a hand hygiene system: Learnings from a validation study.

There is a need to establish validation standards that allow for comparison of automated hand hygiene systems. To assess the accuracy of an innovative monitoring tool (Sani nudge), 2 test nurses performed clinical standard tasks while being observed by 2 infection preventionists. Data from the direct observations were compared with data obtained from the hand hygiene system (Sani nudge) using an independent-event approach. We identified 54 true-positive events (100% system accuracy) and 4 true-negative events (100% system accuracy). No false-positive or false-negative events were identified. We found this approach to be feasible and clinically useful to validate hand hygiene systems in the future.