Here are some suggestions and resources for faculty at SJSU. 
Remote patient monitoring (RPM) is a rapidly growing healthcare technology that enables healthcare providers to remotely monitor and manage patients' health outside of traditional healthcare settings. This article explores the value of RPM, including its potential to improve patient outcomes and reduce healthcare costs, and provides an overview of the RPM market, including market size, growth trends, and business models. The article also discusses the challenges and opportunities facing the RPM industry, including regulatory and reimbursement issues, privacy and security concerns, and the need for robust evidence of the effectiveness of RPM technologies. The potential for effective RPM has been accelerated as a result of new technologies. We also examine these technologies and their impact on RPM. The article concludes by highlighting the key drivers of RPM adoption and the future outlook for the RPM market, including the impact of COVID-19 and the growth of digital health more broadly. By providing a comprehensive overview of the RPM market and its business model, this article will be of interest to healthcare providers, investors, policymakers, and other stakeholders interested in the growth and development of the RPM industry.
In this paper, we study a two-stage model in which a manufacturer expands to a new market through a local retailer and has private information on the advertising effectiveness. The manufacturer chooses the information sharing format with the retailer, either no information sharing or mandatory information sharing. Under no information sharing format, the manufacturer and the retailer play a signaling game. We derive both separating and pooling equilibria and conduct equilibrium refinements for the signaling game. Under mandatory information sharing format, the manufacturer simply informs the retailer the advertising effectiveness. We also establish the stylized model and derive the optimal advertising effort. By comparing the manufacturer’s ex ante profit under the two information sharing formats, we find that the manufacturer always prefers mandatory information sharing, under which both the advertising effort and profit can be higher. We also observe that unlike the common case that the channel members may have different preference over the information sharing formats, the manufacturer and the retailer can actually achieve alignment. While some previous studies suggest that the manufacturer and the retailer may have different preference over the information sharing formats, we find that they can actually achieve alignment with asymmetric information on advertising effectiveness.
The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively.
Smart supply chain management (SSCM) has recently attracted significant attention from both industry and academia, particularly in light of the COVID pandemic. This article reviews current literature on information and integration, process automation, advanced analytics, and related business curriculum in SSCM. Our survey results demonstrate a concerning lack of adoption of SSCM in business curriculum and reveal several major challenges faced by educators when teaching SSCM, including a scarcity of available teaching resources, a need for cross-disciplinary knowledge, and a lack of industry experience among students. These findings lead to our exploration of SSCM application in various supply chain and operations functions (e.g., product design, sourcing and procurement, production, quality management, warehouse management and distribution, delivery, and supply chain risk management) during the COVID-19 pandemic. Drawing on our analysis, we conclude by offering several recommendations for promoting SSCM in business education.
COVID-19 best reminds us of how every pain that is felt by supply chains goes to impact production. An aggregated framework of supply chain risk analysis is needed. This research endeavoured to theoretically structure the inter-relationships among supply chain vulnerabilities as well as corresponding capacity factors to mitigate supply chain risks and sustain supply chain resilience. An inter-relationship structure among risk vulnerabilities was mapped. Capacity factors were matched with each vulnerability category using anecdotal evidence found from various news and literature databases relevant to supply chain resilience concepts. The derived capacity-vulnerability typology revealed links between vulnerability and capacity factors and a structural model for risk prioritisation in managing low-probability, high-impact risks (LPHIRs). Production businesses and professionals shall find our findings a focused guideline for efficiently and effectively building risk resilient capacities. An extensive empirical study of our model with more comprehensive databases will better validate our theoretical results.
Purpose
A key feature of human rights in health is access to safe, effective and affordable medicines. Pharmacovigilance is advocated for monitoring intended/unintended effects of medicines to assure their safety. The purpose of this paper is to synthesize knowledge about supply chain impediments to safe medicines in developing nations and contribute to future development of research in this field.
Design/methodology/approach
This paper conducts a structured literature review based on Preferred Reporting Items for the Systematic Reviews and Meta-Analyses guidelines. It aims at profiling supply chain impediments to safe medicines in developing nations by reviewing 46 recent pharmacovigilance-specific papers published between 2005 and 2020.
Findings
Analysis of reviewed articles identified criticality of supply chain impediments that affect constituents across pharmaceutical in developing nations, which still struggle to maintain robust national pharmacovigilance systems due to lack of awareness, policy and practices.
Research limitations/implications
Research results can be applied by pharmaceutical industry decision-makers and drug safety professionals in developing nations. Because the review is qualitative in nature, its implication ought to be tested after actual implementation.
Practical implications
This review can help identify underinvestigated impediments and methods to aid in developing new pharmacovigilance knowledge areas in developing nation context.
Social implications
The review uncovers gaps in global health equity dialogue in developing nations. It also recognizes that macrolevel supply chain impediments exist due to unfair disease burden and health inequities in developing nations.
Originality/value
The paper examines supply chain impediments to safe medicines in developing nations with insights for future pharmacovigilance research. Identifying and classifying supply chain impediments through this review is the first step toward creating effective interventions for these impediments to safe medicines.
We consider an e-commerce model with two competing retailers (retailers A and B) and one intermediary, and study the decisions of retailer A on augmented reality (AR) advertising given that the other retailer (retailer B) has adopted AR advertising. We investigate both retailers’ optimal AR advertising level under two channel modes: the reseller mode and the marketplace mode. We find that, under both channel modes, retailer A will follow retailer B in the use of AR advertising only if the set-up cost for AR advertising is below a certain threshold that decreases in competition intensity. Also, retailer A's adoption of AR advertising as a follower can always benefit retailer B and the intermediary. We also identify the impact of AR advertising on the supply chain performance under different channel modes. Under the reseller mode, AR advertising weakens the intermediary's channel power such that the intermediary may not benefit from a fierce upstream competition; whereas, under the marketplace mode, AR advertising moderates the mitigation of channel mode on double marginalization so that the intermediary does not necessarily benefit from a higher proportional fee. Our comparison of the supply chain members’ performance under the two channel modes reveals that the preferred channel mode depends on the magnitude of competition intensity and proportional fee.
We study a green investment problem of a sustainable supply chain with one manufacturer who decides whether to implement blockchain and one retailer who has emotional fairness concerns. Although blockchain adoption is costly, it can raise customers’ green sensitivity level and exempt a firm from environmental taxes. A fair-minded retailer cares about disadvantageous inequity in addition to monetary payoffs. We compare the supply chain performance with and without blockchain, each in combination with and without emotional fairness concerns. We show that the manufacturer has the incentive to adopt blockchain if the operational cost is below a certain threshold, and this threshold increases in both tax rate and the blockchain-driven increment in customers’ green sensitivity. We find that fairness can encourage blockchain implementation if and only if the blockchain-driven increment in customers’ green sensitivity level is nominal. Moreover, blockchain adoption may weaken the positive impact of fairness on the supply chain performance such as the mitigation of double marginalization and consumer welfare improvement. We also find that blockchain can encourage green investment and benefit the retailer only if the blockchain-driven increment in customers’ green sensitivity level is relatively high; that is, an “all-win” situation for the supply chain can be achieved by blockchain adoption. Extended studies on consumption-based emission taxes and blockchain setup cost are also provided.
Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.
Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.