Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with it's detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective data from a large Nordic ED with a LightGBM model. We provide predictions for the whole ED and individually for it's different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using anonymous administrative data is feasible.

Across a sterile white table in a windowless room, I'm introduced to a woman in her forties. She has a square jaw and blonde hair that has been pulled back from her face with a baby-blue scrunchie. "The girls call me Marmalade," she says, inviting me to use her prison nickname. Early on a Wednesday morning, Marmalade is here, in a Finnish prison, to demonstrate a new type of prison labor. The table is bare except for a small plastic bottle of water and an HP laptop.

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.

GACHA will begin operating for the general public in the city of espoo in april 2019, before rolling out to hämeenlinna, vantaa, and helsinki later in the year. MUJI and sensible 4 say that the inspiration for the design came from a toy capsule, a universal shape that'embodies joy and excitement, bringing peace and happiness to those who encounter it.' 'the GACHA development got started when sensible 4 team, working back then with the first generation of robot buses, noticed that they just don't perform at all even in light rain, not to mention the typical winter conditions in finland,' says harri santamala, CEO of sensible 4. 'completely autonomous self-driving technology is not here yet.