A coordination mechanism between the emergency department and inpatient unit to mitigate hospital crowding
Jie Wang et al.
Abstract
Emergency department (ED) crowding is a typical problem in hospitals across many countries, leading to numerous grievous consequences. One of the major causes of ED crowding is ED boarding , which refers to cases in which patients are delayed being admitted to inpatient wards (IW) due to bed shortages. Our research is motivated by the recent developments in hospital information systems and the introduction of a new operational unit to hospital management, known as the capacity command center or coordination center . This unit is tasked with monitoring real‐time performance metrics across different hospital units and recommending operational actions. Enabled by this advancement in hospital information systems, our research considers a patient streaming strategy and proposes a coordination mechanism between ED and IW. With our proposed system visibility , both the system status and information about the operations are shared between the two units. To capture the dynamic updates of individual patient statuses, mixed‐integer linear programming models are developed. Our proposed methodology is shown to be effective in improving computational efficiency. Coordination effects are evaluated with numerical experiments. Our results suggest that the coordination mechanism can improve the efficiency of both units significantly in terms of patient waiting time, boarding time, and length of stay. Lastly, we extend it to more complex scenarios and find that the coordination mechanism continues to perform well when treatment time is uncertain and dependent on patient's type and physician's workload, and streaming accuracy varies. The impacts of various factors are also investigated to derive managerial insights.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.