To identify patient flow patterns in Emergency Department (ED) and to assess cross consultation patterns of three consult areas with slightly different functions of an ED in Singapore, through analyzing patient-level data.
A retrospective descriptive study was carried out using patient data from an ED of an acute 1200-bed hospital in Singapore. At ED, there are different functional areas including triage, consult area, resuscitation area, decontamination area, observation room, and isolated area for febrile cases. Three consult areas, called AUC, EDC, and EDX consult, operate in ED. Generally, AUC is for ambulatory urgent care; EDC and EDX are both for ED urgent care while the latter is specifically for febrile cases. For each patient, individual movements at various areas within ED were identified using touch points and location change to exhaustively map patient flow patterns. Consultations crossing the consult areas were estimated based on the established flow patterns.
Three-month data of 41,231 patients (approximately 448.2 daily visits) were reviewed. The patients were prioritized into 4 patient acuity categories (PAC). 270 different patient flow patterns were identified. There were 204 patient flow patterns for PAC2 patients, 134 patterns for PAC3 patients, 88 patterns for PAC1 patients, and 16 patterns for PAC4 patients. Overall, the top 15 flow patterns accounted for 91.8% of total attendances, ranging from 2.5 to 117.1 visits per day. Among all consultations in ED, 7.6% were cross consultations between the three consult areas, in which 63.3% were from AUC to either EDC or EDX consult and 30.7% were from EDC to the other two areas.
This study helps clinicians and managers better understand the complexity of ED patient flows and consultation patterns. The obtained results concerning cross consultations can be helpful to improve the care process especially at triage. Emergency repeat requests always tax resources in a stochastic network. Describing patient flow in ED provides a database for further analytical or discrete event simulation models. We seek to continue studying ED diagnosis or case mix linking to patient flows. This will help to predict the resource impact if there is a slow change in presenting population trend due to ageing or sharp surges during casualty or novel flu epidemics.