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

• 成果类型: Article; Early Access
• 署名作者: Wang, Kanix; Mai, Feng; Shan, Zhe; Zhang, Dawei (David); Peng, Xiaosong (David)
• 署名单位: University System of Ohio; University of Cincinnati; University of Iowa; University System of Ohio; Miami University; Lehigh University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251318885
• 发表日期: 2025
• 关键词: Professional Ethics; Social media; Healthcare; Machine Learning; Natural Language Processing

摘要

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 multilabel 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.