Project Description

Advancing analytics in systems engineering for healthcare in the digital era focuses on leveraging healthcare big data, machine learning/AI, and computational engineering to enhance healthcare delivery, efficiency, and patient outcomes. Key areas of study include prospectively assessing the impact of new medications and technologies on patient outcomes, and healthcare utilization and costs, evaluating the evolution of the healthcare system driven by technological advancements and regulatory policies, improving patient engagement and healthcare operations efficacy, promoting equitable access to healthcare, and characterizing the interconnections between human, environment, and society. In this project, we will focus on identifying patients in rural Indiana whose cardiovascular health would be mostly affected by glucagon-like peptide 1 (GLP-1) agonists (a class of type 2 diabetes drugs that not only improves blood sugar control but may also lead to weight loss), with and without the additional in-home services of community healthcare providers. This project will be well aligned with several university’s initiatives, including One Health, Purdue Computes, and Daniels School of Business, as well as the digital health research concentration, which is College of Engineering’s initiative on Engineering in Medicine. 

Start Date

August 2025

Post Doc Qualifications

PhD in Operations Research, Industrial Engineering, Operations Management, Management, Statistics, Computer Science or equivalent.

Preferred to have research experience in Healthcare Data Science, Healthcare System Analytics and Engineering, Healthcare Policy Evaluation, Generative AI for Healthcare System

Preferred to be familiar with causal inference, machine learning, deep learning, and computer simulation

Co-Advisors

Nan Kong, PhD, Professor and Interim Head, Weldon School of Biomedical Engineering, Purdue University

Qiang Liu, PhD, Associate Professor, Mitchell E. Daniels, Jr. School of Business, Purdue University

Collaborator

Yong Cai, Senior Director, Advanced Analytics, IQVIA (The largest healthcare information company) 

Bibliography

- Pareek, B., Liu, Q., Ghosh, P. (2019), “Ask Your Doctor whether This Product is Right for You: A Bayesian Joint Model for Patient Drug Requests and Physician Prescriptions,” Journal of the Royal Statistical Society: Series A, 182(1), 197-223.

- Liu, H., Liu, Q., Chintagunta, P. (2017), “Promotion Spillovers: Drug Detailing in Combination Therapy,” Marketing Science, 36(3), 382 - 401.

- Liu, Q., Gupta, S.,Venkataraman, S., Liu, H. (2016), “An Empirical Model of Drug Detailing: Dynamic Competition and Policy Implications,” Management Science, 62(8), 2321 - 2340.

- Lou, Z., M. Li, N. Kong*, N. L. Campbell, and W. Tu (2024). “An Improved Statistical Modeling Approach to Individual Anticholinergic Drug Use Trend Analysis.” IEEE Journal on Biomedical and Health Informatics. IEEE Journal on Biomedical and Health Informatics. Vol. 28, No. 2, pp. 1122–1133.

- Chen, S., N. Kong, X. Sun, H. Meng, M. Li (2019). “Claims Data-driven Modeling of Hospital Time-to-Readmission Risk with Latent Heterogeneity.” Health Care Management Science. Vol. 22, No. 1, pp. 156 – 179.