We investigate the impact of delay announcements on the coordination within hospital networks using a combination of empirical observations and numerical experiments. We show that patients take delay information into account when choosing emergency service providers and that such information can help increase coordination in the network, leading to improvements in performance of the network, as measured by Emergency Department wait times. Our numerical results indicate that the level of coordination that can be achieved is limited by the patients ’ sensitivity to waiting, the load of the system, the heterogeneity among hospitals, and, importantly, the method hospital use to estimate delays. We show that delay estimators that are based on historical average may cause oscillation in the system and lead to higher average waiting times when patients are sensitive to delay. We provide empirical evidence which suggests that such oscillations occurs in hospital networks in the US.

In customer support contact centers, every service interaction involves a messaging dialogue between a customer and an agent; together, they exchange information, solve problems, and collectively co-produce the service. Because the service progression is shaped by the history of conversation thus far, we propose a bivariate marked Hawkes process cluster model of the customer-agent interaction. To evaluate our stochastic model of service, we apply it to an industry contact center data set containing nearly 5 million messages. Through both a novel residual analysis comparison and several Monte Carlo goodness-of-fit tests, we show that the Hawkes cluster model indeed captures dynamics at the heart of the service and surpasses classic models that do not incorporate the service history. Furthermore, in an entirely data-driven simulation, we demonstrate how this history-dependent model can be leveraged operationally to inform a prediction-based routing policy. We show that widely used and well-studied customer routing policies can be outperformed with simple modifications according to the Hawkes model. Through analysis of a stylized model proposed in the contact center literature, we prove that service heterogeneity can cause this underperformance and, moreover, that such heterogeneity will occur if service closures are not carefully managed. This paper was accepted by Elena Katok, operations management. Funding: The authors are grateful for the generous support of this work by the National Science Foundation Division of Graduate Education [Grant DGE-1650441] (A. Daw), the Israel Science Foundation [Grant 336/19] (G. B. Yom-Tov), and the United States-Israel Binational Science Foundation [Grant 2022095] (A. Daw, G. B. Yom-Tov). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04060 .