Tan Lekwijit is a fifth-year doctoral student in the Operations Management track. He holds a Master of Science in Management Science and Engineering from Columbia University.
Tan’s research goals are to understand how healthcare providers and managers can make operational improvements to achieve better health and operational outcomes and provide actionable, data-driven insight toward how to maximize effectiveness and efficiency given organizational constraints. He utilizes data analytics and econometrics to analyze large-scale datasets and use operations management and business knowledge to interpret and communicate the results.
Prior to joining Wharton, Tan worked as a research assistant at Columbia Business School, focusing on healthcare operations management. Beyond his academic career, he worked as a process engineer at P&G and as a management consultant at Accenture, advising clients on operations strategy and business process transformation. During his Master’s, he served as a part-time consultant for Bill & Melinda Gates Foundation and BTQ Financial.
Suparerk Lekwijit, Carri Chan, Linda Green, Vincent Liu, Gabriel Escobar (2020), The Impact of Step-Down Unit Care on Patient Outcomes After ICU Discharge, Critical Care Explorations.
Suparerk Lekwijit, Christian Terwiesch, David Asch, Kevin Volpp (Under Review), Evaluating the Efficacy of Connected Healthcare: An Empirical Examination of Patient Engagement Systems and Their Impact on Readmission.
Abstract: Connected healthcare is a form of health delivery that connects patients and providers through connected health devices, allowing providers to monitor patient behavior and proactively intervene before an adverse event occurs. Unlike the costs, the benefits of connected healthcare in improving patient behavior and health outcomes are usually difficult to determine. In this study, we examine the efficacy of a connected health system that aimed to reduce readmissions through improved medication adherence. Specifically, we study 1,000 patients with heart disease who received electronic pill bottles that tracked medication adherence. Patients who were non-adherent received active social support that involved different types of feedback such as text messages and calls. By integrating data on adherence, intervention, and readmission, we aim to (1) investigate the efficacy of connected healthcare in promoting medication adherence, (2) examine the relationship between medication adherence and readmission, and (3) develop a dynamic readmission risk-scoring model that considers medication adherence and use the model to better target non-adherent patients. Our findings suggest that patients are more likely to become adherent when they or their partners receive high levels of intervention that involve personalized feedback and when the intervention is escalated quickly and consistently. We also find that long-term adherence to two crucial heart medications, statins and beta-blockers, is strongly associated with reduced readmission risk. Lastly, using counterfactual simulation, we apply the dynamic readmission risk-scoring model to our setting and find that, when using an intervention strategy that prioritizes high-risk patients, we obtain 10% fewer readmissions than we would obtain without considering readmission risk while using the same effort level from the patient support team.
Carri Chan, Linda Green, Suparerk Lekwijit, Lijian Lu, Gabriel Escobar (2019), Assessing the Impact of Service Level when Customer Needs are Uncertain: An Empirical Investigation of Hospital Step-Down Units, Management Science.
Abstract: Many service systems have servers with different capabilities and customers with varying needs. One common way this occurs is when servers are hierarchical in their skills or in the level of service they can provide. Much of the literature studying such systems relies on an understanding of the relative costs and benefits associated with serving different customer types by the different levels of service. In this work, we focus on estimating these costs and benefits in a complex healthcare setting where the major differentiation among server types is the intensity of service provided. Step-down units (SDUs) were initially introduced in hospitals to provide an intermediate level of care for semicritically ill patients who are not sick enough to require intensive care but not stable enough to be treated in the general medical/surgical ward. One complicating factor is that the needs of customers is sometimes uncertain—specifically, it is difficult to know a priori which level of care a particular patient needs. Using data from 10 hospitals from a single hospital network, we take a data-driven approach to classify patients based on severity and empirically estimate the clinical and operational outcomes associated with routing these patients to the SDU. Our findings suggest that an SDU may be a cost-effective way to treat patients when used for patients who are post-ICU (intensive care unit). However, the impact of SDU care is more nuanced for patients admitted from the emergency department and may result in increased mortality risk and hospital length of stay for patients who should be treated in the ICU. Our results imply that more study is needed when using SDU care this way.
Suparerk Lekwijit and Daricha Sutivong (2018), Optimizing the Liquidity Parameter of Logarithmic Market Scoring Rules Prediction Markets, Journal of Modelling in Management.