Describing and predicting frequent callers to an ambulance service: Analysis of 1 year call data

Jason Scott*, Annette Patricia Strickland, Karen Warner, Pamela Dawson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Aims: Ambulance services in England receive around 8 million calls a year, and no known studies have explored characteristics of frequent callers. This study aimed to identify the characteristics of the most frequent callers to Yorkshire Ambulance Service (YAS) between April 2010 and March 2011.

Methods: Top 100 frequent callers to YAS were analysed using population comparison, case control and multiple regression methods. 7808 calls were made by the frequent callers, and data were analysed to predict total number of calls made, and explore characteristics of frequent callers.

Results: Six call codes were associated with a higher number of calls. Frequent callers were assigned slower response levels, or often no call code. Calls increased during the times of 4:00-9:00, 16:00-20:00 and 22:00-2:00, and in the months of December, January and February. Men and patients with all but the very highest conveyance rates had a higher number of different reasons for calling. Patients with a medical diagnosis were more likely to be conveyed, while patients with a psychiatric classification had a higher number of different reasons for calling, were older and were more likely to call for 'assault/sexual assault' or 'haemorrhage/laceration'.

Conclusions: Frequent callers to YAS were a heterogeneous group that differed from the overall population served, resulting in numerous implications for the delivery of services for this group of patients. Further research is required to determine if and how frequent callers differ from frequent attenders at emergency departments.

Original languageEnglish
Pages (from-to)408-414
Number of pages7
JournalEmergency Medicine Journal
Volume31
Issue number5
Early online date14 Feb 2013
DOIs
Publication statusPublished - 1 Jan 2014

Fingerprint

Dive into the research topics of 'Describing and predicting frequent callers to an ambulance service: Analysis of 1 year call data'. Together they form a unique fingerprint.

Cite this