Analysis of 'One in a Million' primary care consultation conversations using natural language processing.

ABSTRACT:

Background

Modern patient electronic health records form a core part of primary care; they contain both clinical codes and free text entered by the clinician. Natural language processing (NLP) could be employed to generate these records through 'listening' to a consultation conversation.

Objectives

This study develops and assesses several text classifiers for identifying clinical codes for primary care consultations based on the doctor-patient conversation. We evaluate the possibility of training classifiers using medical code descriptions, and the benefits of processing transcribed speech from patients as well as doctors. The study also highlights steps for improving future classifiers.

Methods

Using verbatim transcripts of 239 primary care consultation conversations

SUBMITTER: Pyne Y 

PROVIDER: S-EPMC10151863 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature