Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions.

ABSTRACT:

Objective

Amid electronic health records, laboratory tests, and other technology, office-based patient and provider communication is still the heart of primary medical care. Patients typically present multiple complaints, requiring physicians to decide how to balance competing demands. How this time is allocated has implications for patient satisfaction, payments, and quality of care. We investigate the effectiveness of machine learning methods for automated annotation of medical topics in patient-provider dialog transcripts.

Materials and methods

We used dialog transcripts from 279 primary care visits to predict talk-turn topic labels. Different machine learning models were trained to operate on single or multiple local talk-turns (logistic classifiers, support vector machi

SUBMITTER: Park J 

PROVIDER: S-EPMC6857514 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature