Problem definition: This work aims to examine the role of emergency department (ED) operational status related to congestion in fast-track (FT) routing decisions and the subsequent effects on patient outcomes. Methodology/results: In this paper, we utilize a two-year data set from two hospital EDs in Alberta, Canada, and adopt an instrumental variable approach to examine the effects of FT routing decisions on patient outcomes. Based on the empirical findings, we utilize a data-calibrated simulation to compare the performance of different routing policies. First, our study reveals that FT routing decisions are not purely clinically driven, and ED operational status is also associated with FT routing decisions. Second, being routed to FT can improve ED efficiency by reducing the average length of stay and left without being seen rates. However, this efficiency improvement comes at the cost of potential quality decline. In particular, being routed to the FT leads to an 8.2% increase in the 48-hour revisit rate for the high-complexity group and a 2.3% increase for the medium-complexity group. Third, we delve into the mechanisms behind observed patient outcomes and find that physicians in the FT area may prioritize expediting patient flow by simplifying patient diagnosis and treatment procedures. Consequently, the quality of care may be compromised for high- and medium-complexity patients. Finally, our simulation findings highlight the importance of selecting the “right” patients to be routed to the FT unit. To this end, the complexity-based classification method and dynamic routing policies emerge as promising avenues. Managerial implications: Our findings call for immediate attention from healthcare practitioners to carefully balance the trade-off between emergency care efficiency and quality, emphasizing the necessity of selecting the right patients for routing. Funding: This study is partially supported by the Hong Kong Research Grants Council [Grants GRF 11508921 and CRF C7162-20G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0440 .

In the face of a significant public health event, consumers may either increase their panic buying or decrease their willingness to make purchases. This study focuses on the impact of a significant public health event on offline store sales and consumer consumption, utilizing data from chain convenience stores in Hefei and Wuhu during early 2019 and early 2020 in China. Employing a difference-in-differences model, the study investigates the effect of the significant public health event outbreak on weekly store sales, order numbers, and consumer consumption in terms of product quantities, transaction amount, average amount per order, and transaction frequency. Different from prior literature that finds hoarding behavior of consumers online, the findings of this paper indicate a significant reduction in stores’ offline weekly sales and order numbers, as well as consumers’ offline weekly consumption across the four dimensions, as a result of the significant public health event outbreak. Additionally, employing a mediation model, the study explores the pathway of population mobility through which the significant public health event adversely affects offline consumption. Furthermore, subset analysis is conducted for stores located in different areas and consumers with varying characteristics, revealing that the aforementioned conclusions predominantly apply to stores situated in office areas and residential areas, as well as consumers with either no apparent preference for different product categories or a noticeable preference for food.

The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

The sudden emergence of epidemics, such as COVID-19, entails economic and social challenges requiring immediate attention from policy makers. An essential building block in implementing mitigation policies (e.g., lockdowns, testing, and vaccination) is the identification of potential hotspots, defined as locations that contribute significantly to the spatial diffusion of infections. During the initial stages of an epidemic, information related to the pathways of spatial diffusion of infection is not fully observable, making the detection of hotspots difficult. This work proposes a data-driven framework to identify hotspots using advanced analytical methodologies, specifically, a combination of interpretable long short-term memory (LSTM) model, multi-task learning, and transfer learning. Our methodology considers mobility within- and across-locations, which is the primary driving factor for the diffusion of infection over a network of connected locations. Additionally, to augment the signals of infection diffusion and the emergence of hotspots, we use transfer learning from past influenza transmission data, which follow a similar transmission mechanism as COVID-19. To illustrate the practical importance of our framework in deciding on lockdown policies, we compare the hotspots-based policy with a pure infection load-based policy and the state-wide lockdown policy used in practice. We show that the hotspots-based lockdown policy can achieve up to 21% improvement in reducing new infections as compared to an infection-based lockdown policy. In addition, we illustrate that locking down only top few hotspot counties can achieve almost similar performance as a state-wide lockdown policy used in practice. Finally, we demonstrate that the inclusion of transfer learning improves hotspot prediction accuracy by 53.4%. We also compare our model performance with the commonly used compartmental epidemiological model and demonstrate the superior prediction performance. Our paper addresses a practical problem with hotspot identification framework, which policy makers can use to improve mitigation decisions related to the control of epidemics.

In this paper, we provide a survey of recent developments in the fintech (financial technology) industry, focusing on the operational structures, the technologies involved, and the operational risks associated with the new systems. In particular, we discuss payment systems, algorithmic trading, robo-advisory, crowdfunding, and peer-to-peer lending. In the conclusion section, we discuss various promising research directions.