Journal
HEALTH INFORMATICS JOURNAL
Volume 25, Issue 4, Pages 1722-1738Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1460458218792668
Keywords
clustering methods; Fourier series; Fourier transform; frequency estimation; healthcare service usage; help-seeking behaviour; machine learning; mental health; mental health and well-being helpline; psychology; suicide; telephony analysis; well-being
Funding
- Samaritans Ireland
- Ireland's National Office for Suicide Prevention
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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.
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