Novel Computational Linguistic Measures, Dialogue System and the Development of SOPHIE: Standardized Online Patient for Healthcare Interaction Education

TL;DR

This paper presents SOPHIE, an online virtual patient system designed to improve healthcare communication skills through computational linguistic analysis and feedback, validated by qualitative evaluation with medical professionals.

Contribution

It introduces novel computational linguistic measures for analyzing patient-physician conversations and integrates them into a virtual training system for healthcare communication.

Findings

Automated detection of lecturing and positive language patterns correlates with patient prognosis understanding.

SOPHIE received favorable reviews from practicing physicians.

The system demonstrates potential for improving sensitive healthcare communication.

Abstract

In this paper, we describe the iterative participatory design of SOPHIE, an online virtual patient for feedback-based practice of sensitive patient-physician conversations, and discuss an initial qualitative evaluation of the system by professional end users. The design of SOPHIE was motivated from a computational linguistic analysis of the transcripts of 383 patient-physician conversations from an essential office visit of late stage cancer patients with their oncologists. We developed methods for the automatic detection of two behavioral paradigms, lecturing and positive language usage patterns (sentiment trajectory of conversation), that are shown to be significantly associated with patient prognosis understanding. These automated metrics associated with effective communication were incorporated into SOPHIE, and a pilot user study identified that SOPHIE was favorably reviewed by a user group of practicing physicians.