The Negative Spillover Effect of Electronic Prescribing for Controlled Substances (EPCS) on Opioid Epidemic

The opioid epidemic is a widespread societal problem. Electronic Prescription of Controlled Substances (EPCS) was introduced to reduce opioid overdose by enabling prescribers to detect doctor shoppers through patients’ comprehensive prescription history. However, there is potential that limited access to opioids after EPCS mandates may cause drug users to travel to other locations without EPCS. Using a US county-level dataset from 2010 to 2020 with a difference-in-difference model, we find that EPCS mandate in a neighboring county is associated with increased opioid-related mortality and opioid dispensing rate in the focal county without EPCS. We offer relevant policy implications, demonstrating that insurance coverage moderates the effect of EPCS mandates, underscoring the importance of aligning health insurance initiatives with electronic prescribing policies. By identifying the negative spillover effect of EPCS, our work enriches discussions on the societal impacts of information sharing, prompting further research exploration

Algorithm as Boss or Coworker? Randomized Field Experiment on Algorithmic Control and Collaboration in Gig Platform

Without a doubt, the heavy use of artificial intelligence (AI) will be involved in the future of work. Pertinent to the deployment of AI in organizations, algorithmic control is the managerial use of intelligent algorithms as a means to align individual worker behaviors with organizational objectives. While algorithmic control may facilitate efficient management of workers, it also leads to intrusive and unilateral exertion of controls over workers, also known as “algorithm as boss” phenomenon. In this study, we attempt to understand the outcomes and tradeoffs that different configurations between the AI and gig workers would produce, by conducting a randomized field experiment with one of the largest delivery rider labor unions in Asia. Overall, our study suggests that providing collaborative algorithmic control not only increases gig workers’ utility in terms of monetary rewards but also enhances their intrinsic rewards, which has the potential to benefit the gig platform as well

The Effect of Broadband Adoption on the Labor-market Inclusion of the Disabled: An Empirical Analysis

Although a significant amount of research has examined the effect of broadband on the rise of employment, the majority of this work has been focused on general population\u27s employment, with little attention paid to the effect of broadband may have on social minority employment, i.e., disabled. Motivated from this research gap, we empirically examine the effect of the broadband use on disabled employment in the United States during 2013–2016 using a county level panel data set. We find evidence that, on average, broadband use increases the disabled employment. Our empirical analysis results also provide evidence supporting the argument that this association is attributable to the role of the broadband in increasing teleworking disabled. This research contributes to the literature addressing the positive effect of Information Systems (IS) on labor market, by addressing how the broadband reshapes the disabled employment

How Information Technology Can Help in the Fight Against an Opioid Epidemic: An Empirical Analysis of the Effect of E-Prescribing on Opioid Overdoses

The United States is experiencing an opioid overdose epidemic. E-prescribing has gained significant attention as a breakthrough way to reduce opioid overdoses, because it enhances control over the prescribing of drugs by providing patients’ hospital visit and medication record to prescribers at the point of care. However, there is little empirical evidence on how e-prescribing affects opioid overdoses. In this paper, we begin to bridge this gap by reporting on an empirical investigation of the effect of e-prescribing on opioid overdoses in the United States during 2009–2013 using a panel data set. We find that e-prescribing decreases opioid overdoses, on average, as well as overall drug overdoses. Interestingly, these effects are strongly moderated by two social factors: health insurance coverage and narcotic drug accessibility. Overall, our results shed light on one of the many health care benefits gained from increased adoption of information technology in the health care sector

A Depressing Internet Tale: Empirical Analysis of the Internet’s Impact on Suicide

Has widespread diffusion of the internet influenced suicide, and if so, how? To answer this important question, we examined the association between internet access and suicide in the United States between 2009 and 2013. The empirical evidence shows that the association varies depending on the type of internet services accessed, and that the spread of mobile access is positively associated with an increase in suicide. We did not, however, find evidence that fixed internet use has any effect on suicide. We also examined the conditions under which a positive association with mobile internet access is greater. We find that a positive impact of mobile internet on suicide is most evident in communities with low social capital, low ethnic density level, and high urbanization level. By demonstrating that access to the mobile internet is positively associated with suicide, we contribute to the literature addressing the dark side of internet proliferation

Automation of membrane capacitive deionization process using reinforcement learning

Capacitive deionization (CDI) is an alternative desalination technology that uses electrochemical ion separation. Although several attempts have been made to maximize the energy efficiency and productivity of CDI with conventional control methods, it is difficult to optimize the CDI processes because of the complex correlation between the operational conditions and the composition of feed water. To address these challenges, we applied deep reinforcement learning (DRL) to automatically control the membrane capacitive deionization (MCDI) process, which is one of the representative CDI processes, to accomplish high energy efficiency while desalinating water. In the DRL model, the numerical model is combined as the environment that provides states according to the actions. The feed water conditions, that is, the input state of the DRL, were assumed to have a random salt concentration and constant foulant concentration. The model was constructed to minimize energy consumption and maximize desalted water volume per cycle. After training of 1,000 episodes, the DRL model achieved a 22.07% reduction in specific energy consumption (from 0.054 to 0.042 kWh m???3) and 11.60% increase in water desalted water volume per cycle (from 1.96??10???5 to 2.19??10???5 m3), achieving the desired degree of desalination, compared to the first episode. This improved performance was because the trained model selected the optimized operating conditions of current, voltage, and the number and intensity of flushing. Furthermore, it was possible to train the model depending on demand by modifying the reward function of the DRL model. The fundamental principle described in this study for applying the DRL model in MCDI operations can be the cornerstone of a fully automated water desalination process

Walk for Whom? The Effectiveness of Egoistic and Philanthropic Incentive Designs for Mobile Health Interventions

Financial incentives are widely used to promote health behavior. Among others, egoistic and philanthropic incentives are the two most representative types of financial incentives: While the former is directly given to individuals performing health behavior, the latter ties such behavior to charitable contributions. Despite their prevalence, an understanding of the optimal incentive design for health behavior is limited. In this study, we conducted a randomized field experiment in collaboration with a leading mobile health app provider in order to identify (1) the relative effectiveness of egoistic and philanthropic incentives in promoting health behavior, and (2) how they are moderated by the two common design factors: incentive easiness and incentive value. We found that egoistic incentives usually outperform philanthropic incentives unless incentives have high easiness and low value. In addition, while incentive easiness is a major driver of effective philanthropic incentives, incentive value is a key driver of effective egoistic incentives

An open-source deep learning model for predicting effluent concentration in capacitive deionization

To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technol-ogy, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R2 >= 0.968), and the accuracy increased with the data size. This model was based on the open-source language, Python, and the code has since been distributed with proper instructions for general use. Owing to the nature of the data-oriented deep learning model, the findings of this study are not only applicable to conventional CDI but also to various types of CDI (membrane CDI, flow CDI, faradaic CDI, etc.). Therefore, by referring to the examples shown in this study, we hope that this open-source deep learning code will be widely used in CDI research