Title: Standardization vs. Exploration: How Knowledge Spillovers Shape Healthcare Practices and Innovation

Abstract:
This study employs a difference-in-differences (DID) approach to examine how knowledge spillovers, facilitated by China’s Urban Medical Alliance (UMA) policy, impact clinical practices and innovation. Using a unique dataset of radiology reports and prescription records from over 310,000 patients across 13 secondary hospitals, we apply machine learning algorithms to match each radiology report with the closest reference from a leading tertiary hospital, quantifying convergence in prescribing practices. Our findings show that the UMA policy significantly enhanced standardization in clinical decision-making, reducing overall medical spending through more consistent care. However, this standardization has come at the expense of innovation, as secondary hospital doctors’ academic output and citation impact declined after the policy’s implementation. These results highlight the trade-off between achieving uniformity in healthcare practices and fostering creative exploration, offering valuable insights for future policy development.

Speaker: Assistant Professor Liu Yinan, School of Applied Economics, Renmin University of China
Date & Time: Wednesday, November 6, 2024, 13:00–14:30
Location: Conference Room 347, Qiushi Building
Language: Chinese and English
Moderator: Professor Zhao Liqiu
Audience: All faculty and students are warmly invited to attend.

Speaker Bio:
Liu Yinan is an Assistant Professor at the School of Applied Economics, Renmin University of China. She earned her Ph.D. in Economics from the University of Southern California in 2021. Her main research areas include health economics, labor economics, and public economics. Her recent work focuses on how individuals respond to changes in healthcare service provision, including the expansion of online healthcare services and the introduction of new medical technologies. Her research has been published in the Journal of International Money and Finance.