Towards The Automatic Coding of Medical Transcripts to Improve Patient-Centered Communication

TL;DR

This paper explores automatic coding of medical transcripts using machine learning to enhance patient-centered communication, aiming to reduce manual effort and improve coding accuracy.

Contribution

It introduces an approach employing Naive Bayes, Random Forest, and SVM algorithms for automatic coding of physician-patient dialogues, demonstrating their potential effectiveness.

Findings

Machine learning algorithms can distinguish communication codes effectively.

Automatic coding can support and train human annotators.

The approach reduces labor costs and minimizes human errors.

Abstract

This paper aims to provide an approach for automatic coding of physician-patient communication transcripts to improve patient-centered communication (PCC). PCC is a central part of high-quality health care. To improve PCC, dialogues between physicians and patients have been recorded and tagged with predefined codes. Trained human coders have manually coded the transcripts. Since it entails huge labor costs and poses possible human errors, automatic coding methods should be considered for efficiency and effectiveness. We adopted three machine learning algorithms (Na\"ive Bayes, Random Forest, and Support Vector Machine) to categorize lines in transcripts into corresponding codes. The result showed that there is evidence to distinguish the codes, and this is considered to be sufficient for training of human annotators.