Citation

O'Neill S, Bond RR, Grigorash A, Ramsey C, Armour C, Mulvenna MD. Health Informatics J. 2018; ePub(ePub): 1460458218792668.

Affiliation

Ulster University, UK.

Copyright

(Copyright © 2018, SAGE Publishing)

DOI

10.1177/1460458218792668

PMID

30222034

Abstract

This work presents an analysis of 3.5 million calls made to a mental health and well-being helpline, seeking to answer the question, what different groups of callers can be characterised by specific usage patterns? Calls were extracted from a telephony informatics system. Each call was logged with a date, time, duration and a unique identifier allowing for repeat caller analysis. We utilized data mining techniques to reveal new insights into help-seeking behaviours. Analysis was carried out using unsupervised machine learning (K-means clustering) to discover the types of callers, and Fourier transform was used to ascertain periodicity in calls. Callers can be clustered into five or six caller groups that offer a meaningful interpretation. Cluster groups are stable and re-emerge regardless of which year is considered. The volume of calls exhibits strong repetitive intra-day and intra-week patterns. Intra-month repetitions are absent. This work provides new data-driven findings to model the type and behaviour of callers seeking mental health support. It offers insights for computer-mediated and telephony-based helpline management.

Language: en

Keywords

Fourier series; Fourier transform; clustering methods; frequency estimation; healthcare service usage; help-seeking behaviour; machine learning; mental health; mental health and well-being helpline; psychology; suicide; telephony analysis; well-being