Research Area
Patient preferences and personalized care
How can services respond to what patients and families need and value? We study preference elicitation, demand for care, and the tailoring of prevention and family support.
Overview
How can services respond to what patients and families actually need and value? Our research applies preference elicitation, behavioral economics, and empirical methods to study demand for care and the design of interventions and information that are tailored to what people want.
Key areas of investigation include:
- Preference elicitation: Measuring what patients and families value when they choose care, content, or support
- Demand for care: Understanding what drives engagement with preventive and family-support services
- Tailored prevention: Designing nudges and curricula personalized to preferences, context, and constraints
- Personalized family support: Adapting home-visit and program content to what individual families need
Active Projects
Healthy Future 2.0
We study how AI-informed recommendations and family preferences can help prioritize maternal and child health home-visit content in rural China. CareLoom is our open-source platform for scheduling visits and delivering curricula.
In development. No enrollment, comparative results, or Kenya implementation claims.
Patient preferences and diabetes prevention
With Humana Foundation support, we study how patient preferences can inform text-message nudges for Diabetes Prevention Program participation. Formative research and preference-survey development are guiding preparation for a feasibility pilot.
Formative work and pilot preparation. No efficacy claim; no learned personalized or adaptive policy.
Related Publications
View all publicationsEpidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review
Adhikari, S. P., Meng, S., Wu, Yuju, Mao, Yu-Ping, Ye, R., Wang, Qingzhi, Sun, Chang, Sylvia, Sean Yuji, Rozelle, S., Raat, H., Zhou, Huan (2020)
Infectious Diseases of Poverty
DOI: 10.1186/s40249-020-00646-xViolence against health care workers in China, 2013–2016: evidence from the national judgment documents
Cai, Ruilie, Tang, Ji, Deng, Chenhui, Lv, Guofan, Xu, Xiaohe, Sylvia, Sean Yuji, Pan, Jay (2019)
Human Resources for Health
DOI: 10.1186/s12960-019-0440-yThe impact of pay-for-percentile incentive on low-achieving students in rural China
Chang, Fang, Wang, Huan, Qu, Yaqiong, Zheng, Qiang, Loyalka, P., Sylvia, Sean Yuji, Shi, Yaojiang, Dill, Sarah-Eve, Rozelle, S. (2020)
Economics of Education Review
DOI: 10.1016/j.econedurev.2020.101954Structural Determinants of Child Health in Rural China: The Challenge of Creating Health Equity
Chen, Yunwei, Sylvia, Sean Yuji, Dill, Sarah-Eve, Rozelle, S. (2022)
International Journal of Environmental Research and Public Health
DOI: 10.3390/ijerph192113845Primary Care Quality and Inappropriate Community Antibiotic Use: A Double Machine Learning Instrumental Variable Approach
Chen, Y., Yi, H., Rao, S., Weber, A., Hassmiller-Lich, K., Sylvia, Sean Yuji (2026)
medRxiv
DOI: 10.64898/2026.08.26.26361459Evaluation of a Village-Based Digital Health Kiosks Program: Evidence from Two Cluster-Randomized Controlled Trials
Cheng, Fei, Yip, Winnie, Zhu, Yue, Sylvia, Sean (2022)
Health Policy and Planning
DOI: 10.1093/heapol/czac072Evaluation of a village-based digital health kiosks program: A protocol for a cluster randomized clinical trial
Cheng, Weibin, Zhang, Z., Hoelzer, Samantha, Tang, Weiming, Liang, Yizhi, Du, Yumeng, Xue, Hao, Zhou, Qiru, Yip, W., Ma, Xiaochen, Tian, Junzhang, Sylvia, Sean Yuji (2022)
Digital Health
DOI: 10.1177/20552076221129100Innovative Approaches of Measuring Care Quality in China's Market for Telemedicine
Cheng, Fei, Zeng, Tongxin, Sylvia, Sean, Chen, Xi (2024)
China Economic Review
DOI: 10.1016/j.chieco.2024.102320Research Themes
Preference Elicitation
Measuring what patients, caregivers, and families value when they choose care, curricula, or program content.
Demand for Prevention
Understanding what drives engagement with preventive services like diabetes prevention programs and home-visit curricula.
Personalized Family Support
Tailoring content, nudges, and delivery to what individual patients and families need, rather than a one-size-fits-all design.
Collaborate With Us
We partner with health systems, community organizations, and researchers to design and evaluate personalized, preference-informed interventions.
Get in Touch