Acoustic Analysis of Primary Care Patient–Clinician Conversations to Screen for Cognitive Impairment

Can acoustic features extracted from audio recordings of patient-physician conversations during routine primary care visits be used to screen for cognitive impairment?In this diagnostic study including 966 older adults without diagnosis of cognitive problems, machine learning models trained on acoustic features from recordings of primary care visits achieved a high degree of accuracy and generalizability for predicting cognitive impairment. The algorithm achieved a sensitivity of 68%, specificity of 64%, and positive predictive value of 30%, identifying a subset of primary care patients at higher risk for cognitive impairment in an external validation cohort.These findings suggest that short segments of naturalistic clinical dialogue may contain useful acoustic signals for passively screening patients for cognitive impairment.

Corresponding Author: Joseph T. Colonel, PhD, Icahn School of Medicine at Mount Sinai, 3 E 101st St, New York, NY 10029 (joseph.colonel@mssm.edu).

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