RESUS ESTIMATED TIMES OF ARRIVAL – JUST HOW ACCURATE ARE THEY?. Issue 12 (18th November 2016)

• Record Type: Journal Article
• Authors: Turner, C; Patten, H; Williams, M
• Is Part Of: Emergency medicine journal. Volume 33:Issue 12(2016)
• Page Start: 922
• Page End: 923
• Publication Date: 2016-11-18
• Subjects: Trauma; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2016-206402.42
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : Objectives & Background: Effective use of staff and facilities is one of the cornerstones of good Emergency Departments (EDs). An accurate estimated time of arrival (ETA) at the point of the alert call to the ED allows us to prepare efficiently prior to patients arriving. If the patient arrives too early the receiving team may not be fully assembled and specialist equipment not readily available; too late and team members are inadvertently wasting time that could have been allocated to other tasks, or leave the Resuscitation Area (Resus). We aimed to compare estimated versus actual time of arrival (ATA) of patients to Resus at UHCW ED in order to determine and quantify any difference. Methods: We analysed records of patients alerted into resus over a 17 day period between 22/10/15 and 07/11/15. We obtained call time and ETA from alert logbook and also noted presentation subtype: cardiac arrest, trauma, medical, FAST +ve, paediatric trauma or paediatric medical. We used individual patient Resus records for the ATA and then calculated the time difference. Ambulance PRFs were also used to identify which crew had transferred the patient. Ethical approval was waived as this was regarded as service evaluation. Results: 104 cases were analysed representing patients alerted by crews from both East and West Ambulances Services and the HeliMed Team. Across all cases the mean difference between ETA and ATA was 4.7±17.5 minutes (calculated to two standard deviations). This ranged from arriving 11 minutes early to 62 minutes late. The data demonstrates a normal distribution and as such we can calculate that the probability of a patient arriving at or before the ETA is 30%. The probability of a patient arriving within ten minutes of the ETA is 73% and by 20 minutes after the ETA we would expect 96% of patients to have arrived. Conclusion: (1) There is significant variance between ETA and ATAs (2) Most patients arrive within ten minutes of their ETA (3) If we can develop more accurate mechanisms for predicting what time alerts arrive we would potentially improve care and reduce wasted time.