Lu (Lucy) Yan

Areas of Expertise

Social Media in Healthcare, Social Networks and Large-Scale Data Analysis, Business Analytics and Data-driven Decision Making

Academic Degrees

• Ph.D. Information Systems, University of Washington, 2012
• M.S.B.A. Information Systems, University of Washington, 2012
• M.S. Computer Science, San Diego State University, 2005
• B.E. Computer Science, Nanjing University of Aeronautics and Astronautics, 2001

Professional Experience

• Editorial Review Board Member, Journal of IEEE Transactions on Engineering Management (IEEE-TEM), Sep 2016 - present
• Editorial Review Board, Journal of Management Information Systems, 2014 – present
• Associate Editor, Palgrave Journal of Health Systems Special Issue, 2012
• Associate Editor, International Conferences on Information Systems (ICIS), 2015, 2016, 2017, 2018
• Track Chair, Americas Conference on Information Systems (AMCIS), 2015, 2016, 2017, 2018
• Program Committee Member, Workshop on Information Technologies and Systems (WITS), 2014, 2015, 2016, 2017, 2018
• Program Committee Member, INFORMS Conference on Information Systems and Technology (CIST), 2011, 2012, 2014, 2015, 2016, 2017, 2018
• Program Committee Member, International Conference for Smart Health, 2018, Wuhan, China
• Program Committee Member, The Second International Conference on Health Information Technology Advancement, ICHITA-2013
• Session Chair, INFORMS Healthcare, 2015
• Session Chair, INFORMS, 2012, 2013, 2018
• Session Chair, INFORMS Conference on Information Systems and Technology (CIST), 2012
• Session Chair, Workshop on Information Systems and Economics (WISE), 2012

Awards, Honors & Certificates

• Nominated for Indiana University Outstanding Junior Faculty Awards, 2018
• ODT Faculty Scholar Award, Indiana University, 2017
• Best Student Paper Award Runner-up, the 8th Conference on Health IT and Analytics, 2017
• Best Paper Award Runner-up, Humanitarian Operations & Crisis Management Track, POMS 2016
• Young Researcher Award, Workshop on Economics of Healthcare IT, 2012
• Bertauche Endowed Fellowship Dissertation Funding, University of Washington, 2010 – 2011
• Best Paper Nomination, Conference on Information Systems and Technology, 2011
• Best Student Paper Nomination, Conference on Information Systems and Technology, 2011
• Best Paper Nomination, Conference on Information Systems and Technology 2010
• Global Business Center''s Summer Doctoral Fellowship University of Washington, 2010
• International Conference on Information Systems Doctoral Consortium Fellow, 2010

Selected Publications

• Yan, L. (2018), “Good Intentions, Bad Outcomes: The Effect of Mismatches in Social Support and Health Outcomes in an Online Weight Loss Community,” Production and Operations Management, 27(1): 9-27 (Lead Article).

Abstract Although social support has been recognized for its effectiveness in promoting health, that social support may not always lead to good outcomes. By analyzing participants of an online weight‐loss community, Lucy shows that providing and receiving support affects weight‐loss outcomes in different ways. While providing support is positively associated with weight‐loss progress, receiving support could hinder the weight‐loss outcome for a person with high self‐efficacy. She finds evidence that the match between needed and received social support also influences individuals’ performance in the weight‐loss process, and a mismatch of social support could affect weight‐loss outcomes negatively. These findings can help maximize the usefulness of social support for participants, clinicians who refer individuals to online weight‐loss communities, and for the online community designers.

• Yan, L., Yan, X., Tan, Y., and Sun, S. (2018), “Shared Minds: How Patients Use Collaborative Information Sharing via Social Media Platforms,” Accepted at Production and Operations Management.
• Stauffer, J., Pedraza-Martinez, A., Yan, L., and Van Wassenhove, L. N. (2018), “Asset Supply Networks in Humanitarian Operations: A Combined Empirical-Simulation Approach,” Journal of Operations Management, forthcoming.
• Yan, L., and Y. Tan (2017), "The Consensus Effect in Online Healthcare Communities," Journal of Management Information Systems, 34(1): 11-39 (Lead Article).
• Yan, L., Peng, J.P., and Tan, Y. (2015), "Network Dynamics: How Can We Find Patients Like Us?" Information Systems Research, 26(3): 496-512.

Abstract Social networks have been shown to affect health. Because online social networking makes it easier for individuals to interact with experientially similar others in regard to health issues and to exchange social support, there has been an increasing effort to understand how networks function. Nevertheless, little attention has been paid to how these networks are formed. In this paper, we examine the driving forces behind patients’ social network formation and evolution. We argue that patients’ health-related traits influence their social connections and that the patients’ network layout is shaped by their cognitive capabilities and their network embeddedness. By studying longitudinal data from 1,322 individuals and their communication ties in an online healthcare social network, we find that firsthand disease experience, which provides knowledge of the disease, increases the probability that patients will find experientially similar others and establish communication ties. Patients’ cognitive abilities, including the information load that they can process and the range of social ties that they can manage, however, limit their network growth. In addition, we find that patients’ efforts to reach out for additional social resources are associated with their embeddedness in the network and the cost of maintaining connections. Practical implications of our findings are discussed.

• Yan, L., and Tan, Y. (2014), “Feeling Blue? Go Online: An Empirical Study of Online Supports among Patients,” Information Systems Research, 25(4): 690-709.

Abstract In this paper, we investigate whether social support exchanged in an online healthcare community benefits patients’ mental health. We propose a nonhomogeneous Partially Observed Markov Decision Process (POMDP) model to examine the latent health outcomes for online health community members. The transition between different health states is modeled as a probability function that incorporates different forms of social support that patients exchange via discussion board posts. We find that patients benefit from learning from others and that their participation in the online community helps them to improve their health and to better engage in their disease self-management process. Our results also reveal differences in the influence of various forms of social support exchanged on the evolution of patients’ health conditions. We find evidence that informational support is the most prevalent type in the online healthcare community. Nevertheless, emotional support plays the most significant role in helping patients move to a healthier state. Overall, the influence of social support is found to vary depending on patients’ health conditions. Finally, we demonstrate that our proposed POMDP model can provide accurate predictions for patients’ health states and can be used to recover missing or unavailable information on patients’ health conditions.