Modeling Crowdedness of Emergency Departments Leveraging Crowdsensing Mobility Data

Hospital emergency departments (EDs) are crucial medical facilities providing emergency healthcare. Understanding and measuring the patient flow in EDs plays a key role in maximizing the utility of scarce hospital resources. Existing approaches either require manual reporting by staff or collect sensitive data about patients via camera or hospital registration system, causing potential privacy breaches. Against this background, we propose a data-driven modeling strategy of ED crowdedness leveraging multi-source urban crowdsensing data, which automatically provides fine-grained and timely information about the crowdedness of EDs. Specifically, our model can not only accurately extract the emergency visit demand from noisy human mobility data with minimum expert knowledge using active learning and co-training techniques, but also estimate ED crowdedness by modeling the emergency service process by integrating three queueing models for general practice, internal medicine, and surgical clinics, respectively. We evaluate our method leveraging large-scale taxi and hire vehicle trajectory datasets and hospital information system datasets from the government open data portal. Results show that our approach effectively extracts the emergency visit demand and models the emergency service process to assess the crowdedness of EDs. We have deployed the system in Xiamen City by collaborating with the municipal government to provide services for citizens and health providers.