EXPRESS: Analyzing Professional Ethics of Physicians Using Online Patient Reviews: a Machine Learning Approach

Kanix Wang; Feng Mai; Zhe Shan; Dawei (David) Zhang; Xiaosong (David) Peng

doi:10.1177/10591478251318885

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EXPRESS: Analyzing Professional Ethics of Physicians Using Online Patient Reviews: a Machine Learning Approach

Journal article

Peer reviewed

EXPRESS: Analyzing Professional Ethics of Physicians Using Online Patient Reviews: a Machine Learning Approach

Kanix Wang, Feng Mai, Zhe Shan, Dawei (David) Zhang and Xiaosong (David) Peng

Production and operations management

01/31/2025

DOI: 10.1177/10591478251318885

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Abstract

The erosion of professional ethics in medicine has severe consequences for patients and society. Existing approaches often rely on retrospective analysis and lack the precision and timeliness needed to effectively identify and mitigate risks. Although patient online reviews offer a unique opportunity to proactively detect ethical issues by providing candid, unsolicited feedback on healthcare experiences, few studies have empirically established the link between patient reviews and ethical breaches in medicine. This research introduces a novel machine learning framework to derive text-based indicators of physicians’ professional ethics using online patient reviews. Our approach leverages large language models to extract ethics-related comments and employs few-shot contrastive learning to train multi-label classifiers. Empirical validation studies suggest that the ethical indicators can help predict a wide range of adverse outcomes including drug-related deaths, disciplinary actions, malpractice claims, and rent-seeking behaviors. Our framework offers promising avenues for proactively managing ethical risks in healthcare and other professional services.

Professional Ethics

Social Media

Healthcare

Machine Learning

Natural Language Processing

Details

Title: Subtitle: EXPRESS: Analyzing Professional Ethics of Physicians Using Online Patient Reviews: a Machine Learning Approach
Creators: Kanix Wang - University of Cincinnati
Feng Mai - University of Iowa
Zhe Shan - Miami University
Dawei (David) Zhang - Lehigh University
Xiaosong (David) Peng - Lehigh University
Resource Type: Journal article
Publication Details: Production and operations management
Publisher: SAGE PUBLICATIONS INC
DOI: 10.1177/10591478251318885
ISSN: 1059-1478
eISSN: 1937-5956
Language: English
Electronic publication date: 01/31/2025
Academic Unit: Business Analytics
Record Identifier: 9984786441302771

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