The aim of the study was to explore emergency department transfer

delays and to assess the potential of using a semantic clustering approach to augment

the content analysis of transfer delay data. Data were collected over a period of 5

months from two hospitals. A set of (unique) phrases describing reasons for transfer

delays (n=333) were clustered using the k-means with 1) cluster centroids initiated

in an unsupervised fashion and 2) a semi-supervised version where the cluster

centroids were initiated with keywords. The unsupervised algorithm clustered 77 %

and the semi-supervised 86 % of the phrases to suitable clusters. We chose the better

performing approach to augment our content analysis. Three main categories for

transfer delays were found as a result. These included 1) insufficient staffing

resources, 2) transportation and bed issues, and 3) patient and care related reasons.

The findings inform the audit of organisational processes, accuracy of staffing and

workflow to reduce transfer delays. Future research should explore implications of 

semantic clustering approaches to other narrative data sets in health service research.