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報告人

?陳琛助理教授?

舉辦人

閔慶飛 教授

報告時間

2022年9月21日 18:30-20:30

會議號

騰訊會議867-174-025

報告題目

A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms（使用先進的機器學習算法來解密在線問診平臺中患者的決策行為）

報告內(nèi)容和摘要

Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ～497,000 answers to ～114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ～13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings.

報告人簡介

Dr. Chen Chen is an Assistant Professor of Information Systems at The Chinese University of Hong Kong, Shenzhen. He obtained his Ph.D. in Molecular Cancer Biology from Duke University in 2014 and Ph.D. in Management from Boston University in 2020. Prior to that, he earned a Bachelor’s degree from Tsinghua University, China. His primary research interests include 1) using advanced deep learning algorithms and language models to decipher how human beings interact, behave and make decisions in both virtual and real communities; 2) understanding the dynamics of patient-doctor\'s interaction on online healthcare platforms; 3) knowledge engineering and knowledge interpolation/extrapolation vis knowledge graph and Graph Neural Networks. Dr. Chen has published at Information Systems Research (UTD24), Nature Cell Biology, etc.

來源 | 信息系統(tǒng)與商業(yè)分析研究所

撰稿 | 閔慶飛

編輯 | 臺怡