Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning

Article

Rana, Rajib, Higgins, Niall, Haque, Kazi Nazmul, Burke, Kylie, Turner, Kathryn and Stedman, Terry. 2024. "Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning." Nursing Reports. 14 (4), pp. 4162-4172. https://doi.org/10.3390/nursrep14040303
Article Title

Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning

Article CategoryArticle
AuthorsRana, Rajib, Higgins, Niall, Haque, Kazi Nazmul, Burke, Kylie, Turner, Kathryn and Stedman, Terry
Journal TitleNursing Reports
Journal Citation14 (4), pp. 4162-4172
Number of Pages11
Year2024
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2039-439X
2039-4403
Digital Object Identifier (DOI)https://doi.org/10.3390/nursrep14040303
Web Address (URL)https://www.mdpi.com/2039-4403/14/4/303
Abstract

Ensuring accurate call prioritisation is essential for optimising the efficiency and responsiveness of mental health helplines. Currently, call operators rely entirely on the caller’s statements to determine the priority of the calls. It has been shown that entirely subjective assessment can lead to errors. Furthermore, it is a missed opportunity not to utilise the voice properties readily available during the call to aid in the evaluation. Incorrect prioritisation can result in delayed assistance for high-risk individuals, resource misallocation, increased mental health deterioration, loss of trust, and potential legal consequences. It is vital to address these risks to guarantee the reliability and effectiveness of mental health services. This study delves into the potential of using machine learning, a branch of Artificial Intelligence, to estimate call priority from the callers’ voices for users of mental health phone helplines. After analysing 459 call records from a mental health helpline, we achieved a balanced accuracy of 92%, showing promise in aiding the call operators’ efficiency in call handling processes and improving customer satisfaction.

Keywordsartificial intelligence; automated distress screen; deep learning; distress; mental health; spontaneous speech; triage; voice computing
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461103. Deep learning
Byline AffiliationsSchool of Mathematics, Physics and Computing
West Moreton Health, Australia
Metro North Health, Australia
University of Queensland
Australian Research Council Centre of Excellence for Children and Families over the Life Course
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