Streaming in the emergency department : an innovative care delivery design

• Main Author: Morrish, Sherri
• Language: English
• Published: University of British Columbia 2012
• Online Access: http://hdl.handle.net/2429/43213

Emergency departments (EDs) in urban settings are experiencing extreme challenges such as overcrowding, long wait times, and patient dissatisfaction (Kelley, Bryant, Cox, & Jolley, 2007). Streaming in the Emergency Department is an innovative care delivery design that is designed to reduce ED overcrowding and improve access to care. Streaming enables moderately acute ambulatory Triage Level 3 patients, who often wait the longest, to be cared for in a separate area embedded within the main ED. These patients receive care to the same standards but sit in a chair for most of their stay, only lying in a bed for examinations, treatments, and tests. This care approach often closely matches the way fast track or minor treatment clinics are run for lower acuity Triage Levels 4 and 5 patients in many urban EDs. This study aims to create a deeper understanding of “streaming.” The research examined what factors influenced the successful streaming of moderately acute ambulatory patients within the streaming unit of Hospital X (British Columbia, Canada). A qualitative descriptive design using a single site case study was employed. The data analysis involved reducing data into meaning units, then reducing them to sub-themes which were finally grouped into four major themes (Graneheim & Lundman, 2003; Yin, 2004). These themes were: “facing health care realities,” “doing it right,” “maintaining flow,” and "what matters?” From this research, it is clear that streaming is improving care, patient outcomes, and staff satisfaction in Hospital X’s emergency department. This innovative care delivery design for moderately acute ambulatory patients is challenging the traditional paradigm of ED care, bringing positive changes in a complex health care environment. Timely care for ED patients, through such innovative models as a streaming unit, can save lives (Devkaran, Parsons, Van Dyke, Drennan, & Rajah, 2009).