This Faculty Early Career Development (CAREER) grant promotes the national health and welfare by studying methods to improve the efficiency of resource planning and operations in healthcare and other critical services. This award extends our understanding of data-driven methods that can provide better models of patient flow and staffing. With the growing availability of data and computational powers, there is an increasing awareness and appreciation of data-backed decision making. However, challenges remain in identifying what types of predictive information are most valuable, and subsequently in quantifying system performance. The findings will provide structural insights and effective policies to improve system efficiency, consumer experience, and quality of care. The educational mission aims to expose students to the challenges of improving operational efficiency and reducing risk in service and healthcare industries. The accompanying educational plan facilitates training of engineers and researchers who will be equipped with both solid theoretical backgrounds as well as practical insights in data-informed operations research. Through the educational activities, the Principal Investigator is committed to promoting the participation of underrepresented groups in engineering.
This research contains two main threads. The first studies the use predictive information to achieve better system performance. The objective is to develop a theoretical framework to evaluate the effectiveness of different kinds of information and to quantify the impact of the information?s accuracy. Techniques from asymptotic analysis of stochastic processes will be extended to incorporate and analyze the impact of predictive information. Moreover, advanced data-informed policies that lead to substantial performance improvement and are robust to prediction errors will be constructed. The focus is on limited-resource environments where externalities need to be carefully modeled and studied. Motivated by healthcare applications, new paradigms for transient performance analysis and optimization in face of multiple scales of uncertainty will be developed. The second thread studies how to utilize data to build better stochastic models. The objective is to develop a data-driven modeling framework that provides accurate quantifications of salient tradeoffs in system operations. Key challenges in model calibration, causal inference, and performance evaluation will be addressed by combining tools from econometrics with stochastic modeling. Prescriptive solutions that can be readily implemented will be provided based on the high-fidelity nature of the models.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
