Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
On the frontline: Engaging health workers to mitigate the last-mile stock-out of health commodities in developing countries
Amir Karimi1, Anant Mishra2, Karthik Natarajan2, Kingshuk Sinha2
1Alvarez College of Business, University of Texas at San Antonio, United States of America; 2Carlson School of Management University of Minnesota, United States of America
We rigorously investigate whether and to what extent variations in the (i) the physical context where training is administered (i.e., onsite vs. offsite training); (ii) the familiarity of the trainer who administers the training (i.e., familiar vs. unfamiliar trainer); and (iii) the timing of the week when training is administered (i.e., early-week vs. mid-week vs. late-week training) impact the learning outcomes of health workers and subsequently the likelihood of health commodity stock-outs.
Service chains' operational strategies: standardization or customization? Evidence from the nursing home industry
Lu Kong1, Kejia Hu2, Rohit Verma3
1University of South Florida, United States of America; 2Vanderbilt University, United States of America; 3Cornell University, United States of America
We investigate how the Degree of Standardization across service chain-belonging units impacts performance outcomes. We find that nursing homes that customize service delivery and standardize customer mix tend to experience improved financial performance; those that standardize customer mix tend to experience improved clinical outcomes, and those that customize service delivery tend to experience enhanced resident welfare.
Modeling strategic walk-in patients in appointment systems: equilibrium behavior and capacity allocation
E. Lerzan Ormeci, Feray Tuncalp, Evrim Didem Gunes
Koc University, Turkey
We develop a queueing model to represent a clinic with two types of strategic patients who choose between making an appointment, incurring type-dependent indirect wait cost, and walking in, bearing an inconvenience cost and a risk of being rejected. We focus on the clinics' decisions to allocate slots to walk-ins and appointments to maximize their revenues. The system is analyzed under observable and unobservable settings. The model assumptions and results are examined via a simulation platform.
Service speed under multi-dimensional workload in Emergency Departments
Hao Ding, Sokol Tushe, Donald K. K. Lee
Goizueta Business School, Emory University, Atlanta, Georgia 30322
This study improves our understanding of how workload affects service speed by analyzing patient flow through the ED at a high resolution. We exploit a novel dataset and a nonparametric ML method to track multiple dimensions of workload in realtime. We find the service rate resembles the hazard of a log-normal distribution, nurse load has a greater impact on service speed than physician load, which in turn has a greater impact than system load, and a clearer picture of the system workload.
Safely bridging offline and online reinforcement learning
Wanqiao Xu1, Yecheng Jason Ma2, Kan Xu2, Hamsa Bastani3, Osbert Bastani2
1Stanford University; 2University of Pennsylvania; 3Wharton School
A key challenge to deploying reinforcement learning in practice is safe exploration. We propose a reinforcement learning algorithm that provably satisfies a safety constraint where it uniformly improves performance at each iteration while achieving sublinear regret. We experimentally validate our results on a sepsis treatment task and an HIV treatment task, demonstrating that our algorithm can learn while ensuring good performance compared to the baseline policy for every patient.
