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   Kazemi, Fatemeh ; Kurz, Sascha ; Soljanin, Emina ( June 2020 , 2020 IEEE International Symposium on Information Theory (ISIT'20))

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   https://doi.org/10.1080/10410236.2022.2051349  

   Park, Sung-Yeon ; Hill, Kylie ; Yun, Gi Woong ; Friedman, Sarah ; Coppes, Max J. ( March 2022 , Health Communication)

   Direct-to-consumer advertisements for healthcare services constitute a rare channel of public communication where consumers see and hear directly from their local providers and healthcare organizations. Although spending on these advertisements has increased drastically during the past decades, research on their content and effects remains rare. To fill this gap, we analyzed primetime television advertisements for healthcare services directly targeting consumers. The advertisements were collected from the two largest media markets in Nevada for one month. In total, 795 advertisements were identified, and 106 of them were non-duplicates. Analysis revealed that the advertisements focused on patients’ good health outcomes by showing them smiling, going out and about, having fun with others, and enjoying rigorous physical activities. On the other hand, the advertisements focused less on the providers. Although the advertisements often showed providers in clinical settings, basic information about their professional degrees was often missing. Mentions of providers’ other qualifications and professional experiences were even scarcer. Also, a substantial number of advertisements failed to show providers interacting with patients. Additional analysis of patient and provider characteristics revealed under-representation of racial or ethnic minority and older adult patients. Representation of women and minorities as providers was even more uncommon. We discussed the implications of these findings from the perspective of patient expectation and made suggestions to help providers improve their direct-to-consumer advertisements. 

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   Yu, Xian ; Shen, Siqian ; Wang, Huizhu ( September 2021 , Service Science)

   In this paper, we consider an integrated vehicle routing and service scheduling problem for serving customers in distributed locations who need pick-up, drop-off, or delivery services. We take into account the random trip time, nonnegligible service time, and possible customer cancellations, under which an ill-designed schedule may lead to undesirable vehicle idleness and customer waiting. We build a stochastic mixed-integer program to minimize the operational cost plus expected penalty cost of customers’ waiting time, vehicles’ idleness, and overtime. Furthermore, to handle real-time arrived service requests, we develop K-means clustering-based algorithms to dynamically update planned routes and schedules. The algorithms assign customers to vehicles based on similarities and then plan schedules on each vehicle separately. We conduct numerical experiments based on diverse instances generated from census data and data from the Ford Motor Company’s GoRide service, to evaluate result sensitivity and to compare the in-sample and out-of-sample performance of different approaches. Managerial insights are provided using numerical results based on different parameter choices and uncertainty settings. 

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