Leveraging Data to Address Mental Health-Related Service Calls
Transforming unstructured service call data into actionable insights to guide mental health response strategies.
A municipal organization sought to better understand the prevalence of mental health-related calls received by its public service teams. Existing records were dispersed across audio files and reporting systems, making it challenging to identify trends or measure the scale of the issue. A clear, evidence-based report was needed to guide policy decisions and improve service delivery.
The call data was largely unstructured—comprising thousands of audio recordings and disparate system records. Manual review was not scalable, and the lack of consistent classification for mental health-related incidents made it difficult to quantify their prevalence or identify actionable patterns.
As a data consultant on the project, I developed and implemented an automated analysis pipeline to process, classify, and report on the data:
Audio Transcription: Utilized Python-based tools to convert service call audio files into accurate text transcriptions.
Text Analysis & Classification: Applied natural language processing (NLP) techniques to analyze transcriptions and structured records, identifying key terms and flags indicating potential mental health involvement.
Data Synthesis & Reporting: Compiled findings into a comprehensive report that quantified the scale of mental health-related calls and presented key trends to decision-makers.
The report provided a clear, data-driven foundation for decision-making. It enabled leadership teams to better understand the scale of mental health-related calls, identify areas for intervention, and inform strategies to improve community response and resource allocation.
This project demonstrates the power of data science and NLP in transforming unstructured, real-world data into actionable insights that can shape public policy and service delivery.
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