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We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. Aspects relating to communicating forecast uncertainty to patients, and implementing this methodology in practice, are also discussed.
Emergency department (ED) crowding has been an increasing problem worldwide. Prior research has identified factors that contribute to ED crowding. However, the relationships between these remain incompletely understood. This studys objective was to a
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a
In 2020, California required San Francisco to consider equity in access to resources such as housing, transportation, and emergency services as it re-opened its economy post-pandemic. Using a public dataset maintained by the San Francisco Fire Depart
Trauma mortality results from a multitude of non-linear dependent risk factors including patient demographics, injury characteristics, medical care provided, and characteristics of medical facilities; yet traditional approach attempted to capture the
We investigate the efficacy of surgical versus non-surgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instrumental variable design with surgeons tendencies to operate as an inst