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In services where teams come together for short collaborations, managers are often advised to strive for high team familiarity so as to improve coordination and consequently, performance. However, inducing high team familiarity by keeping team membership intact can limit workers’ opportunities to acquire useful knowledge and alternative practices from exposure to a broader set of partners. We introduce an empirical measure for prior partner exposure and estimate its impact (along with that of team familiarity) on operational performance using data from the London Ambulance Service. Our analysis focuses on ambulance transports involving new paramedic recruits, where exogenous changes in team membership enable identification of the performance effect. Specifically, we investigate the impact of prior partner exposure on time spent during patient pickup at the scene and patient handover at the hospital. We find that the effect varies with the process characteristics. For the patient pickup process, which is less standardized, greater partner exposure directly improves performance. For the more standardized patient handover process, this beneficial effect is triggered beyond a threshold of sufficient individual experience. In addition, we find some evidence that this beneficial performance impact of prior partner exposure is amplified during periods of high workload, particularly for the patient handover process. Finally, a counterfactual analysis based on our estimates shows that a team formation strategy emphasizing partner exposure outperforms one that emphasizes team familiarity by about 9.2% in our empirical context., Deloitte Institute of Innovation and Entrepreneurship

This paper studies pricing and rationing strategies of luxury retailers and identifies exclusivity-seeking consumer behavior as another rational behind price markdowns in seasonal conspicuous goods.

Early infant diagnosis (EID) programs in many resource-limited settings are aimed at diagnosing infants born to HIV-infected mothers. Because of the complexity of the diagnostic technology, EID programs are often highly centralized with few laboratories testing blood samples from a large network of health facilities. This leads to long diagnostic delays and consequent failure of patients to collect results in a timely manner. Several point-of-care (POC) devices that provide rapid diagnosis within the health facilities are being developed to mitigate these drawbacks of centralized EID networks. We study the decision of which facilities should receive the POC device (the placement plan) using the EID program in Mozambique as a case study. We argue that the choice of an appropriate plan is critical to maximizing the public health impact of POC devices in the presence of tight budget constraints. To formalize this argument, we develop a detailed simulation model to evaluate the impact of a placement plan. It comprises two parts: an operational model that quantifies the impact of a POC placement plan on the diagnostic delay and a behavioral part that quantifies the impact of diagnostic delay on the likelihood of result collection by infants’ caregivers. We also develop an approximate version of these operational and patient behavior dynamics and embed them in an optimization model to generate candidate POC placement plans. We find that the optimization-based plan can result in up to 30% more patients collecting their results compared to rules of thumb that have practical appeal. Finally, we show that the effectiveness of POC devices is much higher than other operational improvements to the EID network such as increased laboratory capacity, reduced transportation delay, and more regularized transport.

This paper studies a model of community-based healthcare delivery for a chronic disease. In this setting, patients periodically visit the healthcare delivery system, which influences their disease progression and consequently their health outcomes. We investigate how the provider can maximize community-level health outcomes through better operational decisions pertaining to capacity allocation across different patients. To do so, we develop an integrated capacity allocation model that incorporates clinical (disease progression) and operational (capacity constraint) aspects. Specifically, we model the provider's problem as a finite horizon stochastic dynamic program, where the provider decides which patients to schedule at the beginning of each period. Therapy is provided to scheduled patients, which may improve their health states. Patients that are not seen follow their natural disease progression. We derive a quantitative measure for comparison of patients' health states and use it to design an easy-to-implement myopic heuristic that is provably optimal in special cases of the problem. We employ the myopic heuristic in a more general setting and test its performance using operational and clinical data obtained from Mobile C.A.R.E. Foundation, a community-based provider of pediatric asthma care in Chicago. Our extensive computational experiments suggest that the myopic heuristic can improve the health gains at the community level by up to 15% over the current policy. The benefit is driven by the ability of our myopic heuristic to alter the duration between visits for patients with different health states depending on the tightness of the capacity and the health states of the entire patient population.