Creating a More Inclusive and Sustainable Future
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)
Large language models and synthetic health data: progress and prospects
JAMIA Online, October 2024
There is growing interest in the application of machine learning models and advanced analytics to various healthcare processes and operations, including the generation of new clinical discoveries, development of high-quality predictions, and optimization of administrative processes. Machine learning models for prediction and classification rely on extensive and robust datasets, particularly for deep learning models common in health, creating an urgent need for large health datasets. Yet datasets can be insufficiently large due to the rapid evolution of diseases, such as coronavirus disease 2019 (COVID-19), rarity of disease, or the myriad obstacles to sharing and acquiring existing health data, including ethical, legal, political, economic, cultural, and technical barriers. Synthetic data provide a unique opportunity for health dataset expansion or creation by addressing privacy concerns and other barriers. In this paper, we review prior literature and discuss the landscape of machine learning models used for synthetic health data generation (SHDG), outlining challenges and limitations. We build on existing research on the state of the art in SHDG and prior broad explorations of the potential risks and opportunities for large language models (LLMs) in healthcare. We contribute to the literature with a focused assessment of LLMs for SHDG, including a review of early research in the area and recommendations for future research directions. Six promising research directions are identified for further investigation of LLMs for SHDG: evaluation metrics, LLM adoption, data efficiency, generalization, health equity, and regulatory challenges
Daniel Smolyak, Department of Computer Science, University of Maryland
Margret V. Bjarnadottir, Robert H. Smith School of Business, University of Maryland
Kenyon Crowley, Accenture Federal Services
Ritu Agarwal, Center for Digital Health and Artificial Intelligence, Carey Business School