John Silberholz Directory Page
John Silberholz
Assistant Professor of Business Analytics
PhD in Operations Research, MIT
John Silberholz an Assistant Professor of Business Analytics in the Decision, Operations & Information Technologies group. His current research interests lie in healthcare analytics. He is particularly interested in questions surrounding designing and learning from clinical trials, and his research relies on both analytical modeling and empirical tools to dig into this topic. He is interested not only in how to most efficiently run a clinical trial to compare a set of treatments, but also how to best select what should be compared within that clinical trial as well as how to derive as much value as possible from the results of the trial. John received his undergraduate degree from the University of Maryland and his PhD from MIT. Prior to joining the faculty at the R.H. Smith School of Business, he was an assistant professor at the University of Michigan Ross School of Business, where he won the Neary Teaching Excellence Award for his teaching within the Master of Business Analytics program.
News
Workshop Looks at Using AI and Analytics for Social Good
Academic Publications
Can Employees' Past Helping Behavior Be Used to Improve Shift Scheduling? Evidence from ICU Nurses
Management Science, November 2025
Employees routinely make valuable contributions at work that are not part of their formal job description, such as helping a struggling coworker. These contributions, termed organizational citizenship behavior, are studied from many angles in the organizational behavior literature. However, the degree to which the past helping behavior of employees scheduled to a shift impacts that shift’s operational outcomes remains an underexplored question. We define two measures of past helping behavior for members of a shift -- the total past helping of each employee and the past helping between each pair of employees -- and hypothesize that they are associated with shift performance. We empirically confirm our hypotheses with detailed scheduling and patient outcome data from six intensive care units (ICUs) at a large academic medical center, using the hospital’s electronic medical records to identify cases of one nurse helping another. Our empirical results indicate that both measures of past helping are predictive of patient length of stay (LOS), more so than the broadly studied notion of team familiarity. Counterfactual analysis shows that relatively small changes in shift composition can yield significant reduction in total LOS, indicating the managerial significance of the results. Overall, our study suggests the potential value of shift scheduling using data on past helping behaviors, and this may have promise far beyond the selected application to ICU nursing.
Zhaohui (Zoey) Jiang (CMU), John Silberholz (UMD), Yixin (Iris) Wang (UIUC), Deena Kelly Costa (Yale), Michael Sjoding (University of Michigan)
Cost-Saving Synergy: Energy Stacking in Battery Energy Storage Systems
Management Science
Despite the great potential benefits of battery energy storage systems (BESSs) to electrical grids, most standalone uses of BESS are not economical due to batteries’ high upfront costs and limited lifespans. Energy stacking, a strategy of providing two or more services with a single BESS, has been of great interest to improve profitability. However, some key questions, for example, the underlying mechanism by which stacking works or why and how much it may improve profitability, remain unanswered in the literature. Using two popular battery services, we analytically show that there often exists cost-saving synergy -- the cost of performing both services at the same time (simultaneous stacking) is smaller than the sum of individual costs if we had performed each service alone -- which allows for bigger profits. Furthermore, we perform comparative statics on the optimal mix of the services to systemically characterize grid/market conditions that maximize/minimize this synergy. We also derive a theoretical upper bound on simultaneous stacking’s benefits, showing that it can approximately double the profit of the best standalone service. Several generalizations of the base model not only show that the main lessons continue to hold but also that stacking’s benefits may become even stronger.
Joonho Bae (Indiana University), Roman Kapuscinski (University of Michigan), John Silberholz (UMD)