Design and Development of a Proactive Rapid Response System

Abstract

Timely identification of patient deterioration can prompt intervention and prevent the escalation of care and unplanned intensive care admissions. However, both personal experience and professional literature reveals that staff nurses in the acute care setting may not notice subtle signs of patient deterioration or may be reluctant to activate the rapid response system. To overcome these barriers, a proactive rapid response system with early warning signs was created and studied. Using a quasi-experimental design, data were collected from two medical-surgical nursing units at one large tertiary medical center over a 6-month period. One unit used the new rapid response system and early warning sign criteria with real-time data entry and trigger activation. A second unit served as the control and relied on the nurse for rapid response system activation. Findings revealed that the use of the newly developed rapid response system demonstrated significantly greater sensitivity to subtle signs of patient deterioration and prompted early evaluation and intervention.

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes

“…Two studies used randomised controlled designs24 30 and a further three studies compared control and intervention group patients using prospective cohort designs 27 31 32. Twelve studies used quasi-experimental designs using either retrospective or prospective28 33 data collection.…”

“…Three studies used data from multiple sites,26 35 36 with the remaining 15 occurring at a single site. Sample size was disclosed for all but two studies27 37 and ranged from 571 to 3 74 838 patient episodes.…”

“…Six studies adopted models already developed and available in the literature 25 28 32 35 38 39. The remaining seven studies selected items using a combination of literature and clinical judgement27 34 37 or did not describe the method for selecting alert thresholds 36 40–42…”

“…One study used the systemic inflammatory response syndrome criteria 40. A multisite study used a variety of EWS across various sites,36 while five studies reported customising an existing EWS for their institutions 27 31 32 34 37. Three studies used logistic regression models with rolling feature windows based on the last 12 hours or 24 hours 24 26 30.…”

“…Search calibration was performed for PubMed using the Bond University Systematic Review Accelerator 23. Five studies were identified as seed studies by an initial PubMed search24–28 and used to refine the search to exclude terms that added many studies without including relevant articles (eg, ‘predict*’, ‘model’), and include relevant terms (eg, ‘algorithm’).…”