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Telehealth is a valuable tool for primary health care (PHC), where depressionis a common condition. PHC is the first point of contact for most people withdepression, but about 25% of diagnoses made by PHC physicians are inaccurate.Many other barriers also hinder depression detection and treatment in PHC.Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC andimprove overall diagnosis and treatment outcomes. Telehealth consultationsoften have video issues, such as poor connectivity or dropped calls. Audio-onlytelehealth is often more practical for lower-income patients who may lackstable internet connections. Thus, our study focused on using audio data topredict depression risk. The objectives were to: 1) Collect audio data from 24people (12 with depression and 12 without mental health or major healthcondition diagnoses); 2) Build a machine learning model to predict depressionrisk. TPOT, an autoML tool, was used to select the best machine learningalgorithm, which was the K-nearest neighbors classifier. The selected model hadhigh performance in classifying depression risk (Precision: 0.98, Recall: 0.93,F1-Score: 0.96). These findings may lead to a range of tools to help screen forand treat depression. By developing tools to detect depression risk, patientscan be routed to AI-driven chatbots for initial screenings. Partnerships with arange of stakeholders are crucial to implementing these solutions. Moreover,ethical considerations, especially around data privacy and potential biases inAI models, need to be at the forefront of any AI-driven intervention in mentalhealth care.

Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care