Sameer Kumar

Numerous mobile health (mHealth) apps offer novel approaches to diagnosis and treatment, but their reliability and usability require thorough assessment. The purpose of this study is to create a customized Health Technology Assessment (HTA) framework that is intended exclusively for mHealth apps' certification. It also provides links to pertinent data sources. The proposed HTA framework was devised based on the EUnetHTA Core Model. The developed framework consists of 9 domains, encapsulating 52 topics and over 100 assessment related issues which were appropriately adapted to mHealth apps evaluation. An indicative AI-skin diagnosis app is subjected to high-level evaluation of the proposed HTA framework to test case the framework. Additionally, it is benchmarked against similar apps to illustrate app strengths, weaknesses, and market positioning, leading to a prescriptive approach for improvements. The proposed tailored framework establishes a holistic and rigorous evaluation process to ensure that mHealth apps are technologically robust, ethically sound, safe, effective, usable and user friendly. It can also provide a general understanding of the app's standing in the market. This adapted framework leads to the creation of a comprehensive HTA report. The HTA report can aid app developers, technology, engineering and product managers to meet the necessary HTA requirements for apps' assessment. It can also be used by healthcare decision makers, payers, and policymakers as a decision-support tool to inform decisions regarding the adoption, reimbursement, and utilization of mHealth apps.

Strategically pre‐positioning relief supplies before disasters can reduce both response times and supply costs. However, accurately estimating the probabilities of disaster occurrences is difficult due to limited historical data and cognitive biases in decision‐making. Prior research has demonstrated the effectiveness of robust‐modeling approaches to such problems. This paper expands the prior work by incorporating ‐divergence uncertainty regions into decision‐making models to account for uncertainty in scenario probability estimates. An extensive numerical study using Monte Carlo simulation studies applied to a real‐world case study of hurricane preparedness in the Southeastern United States demonstrates that our models yield lower expected response costs, compared to traditional stochastic optimization approaches. Additionally, we show that explicitly incorporating knowledge of cognitive biases in probability estimation can significantly enhance decision‐making effectiveness and cost efficiency.

Purpose: Traceability is predicted to usher in a fundamental shift in the way transactions in supply chains (SCs) are carried out. By reducing the negative aspects of trust-related issues in a SC, traceability enables improved visibility and transparency. Design/methodology/approach: We advance research on traceability adoption in the perishable products supply chain by developing and validating an integrated model that combines the technology acceptance model (TAM), the technology readiness index (TRI) and the theory of planned behavior (TPB). A quantitative approach was employed, collecting data through an online survey of 174 supply chain professionals in major Indian cities using a five-point Likert scale. Participants were selected via LinkedIn, each with at least two years of SCM experience. Nonresponse bias was assessed by comparing early and late respondents, revealing no significant differences. Structural equation modeling (SEM) was used to test various research hypotheses derived from literature. Composite reliability and discriminant validity of constructs were verified before examining the relationships among the constructs within the structural model. Findings: The study found that the TRI components of optimism and innovation did not impact perceived ease of use or perceived utility. Additionally, behavioral intention is shaped by perceived utility, attitude and perceived behavioral control. Practical implications: This research provides valuable insights for managers aiming to adopt traceability in supply chains (SCs). It helps identify critical factors for effective traceability adoption, showing that perceived ease of use (PEU) and perceived usefulness are pivotal in shaping practitioners’ intentions. Managers should prioritize developing intuitive, user-friendly traceability applications that demonstrate clear value in optimizing SC efficiency. The study also reveals that while practitioners are generally optimistic about traceability, they may feel indifferent or lack a sense of control over it. Therefore, companies should focus on marketing strategies that empower decision-makers, highlighting the ease of use and practical benefits of traceability. Additionally, the findings suggest that perceived behavioral control, combined with intention, can effectively predict traceability adoption. By understanding these dynamics, managers can better guide their firms in successfully implementing traceability, ensuring both technological acceptance and operational efficiency. Originality/value: This research offers a novel and in-depth exploration of traceability as an emerging concept in supply chains, particularly in India, where adoption remains limited. It highlights that while SC practitioners recognize traceability’s potential, they lack practical expertise, often driven by curiosity about decentralized databases. It underscores the critical role of artificial intelligence, IoT devices and big data in ensuring precise data collection and analytics, essential for successful traceability. The research also introduces a predictive model combining TAM, TRI and TPB constructs, identifying perceived usefulness, attitude and perceived behavioral control as key factors influencing traceability adoption.

A contribution to mitigate the disastrous effects of the Covid-19 pandemic has been exemplified by the development of innovative product and service applications with the use of digital technologies, such as Artificial Intelligence (AI), Machine Learning (ML) and Big Data Analytics (BDA). In this discussion paper we examine nine major application categories namely, diagnostic mechanisms; contact tracing apps; hospitalization/mortality prediction models; drug repurposing models; new vaccine models; business prediction models/robotic service apps; learning & teleconferencing systems; chatbots; and smart cities apps for environmental impact. Their analysis shows the potential long-term impact and challenges in relation to the global markets and social environment.

Purpose: The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.Design/methodology/approach: The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.Findings: The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.Originality/value: The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

A contribution to mitigate the disastrous effects of the COVID-19 pandemic has been exemplified by the development of innovative product and service applications with the use of digital technologies, such as artificial intelligence, machine learning, and Big Data analytics. Applying the transition path problem-solving framework by using the combination of TRIZ Innovation Theory and Theory of Change, this study shows the process of change, starting from the need to tackle COVID-19, through the implementation of new digital technology applications, to permanently change the functioning of societies and facilitate new global market opportunities in the post-pandemic era. From a large pool of relevant research papers and practice reports, nine major application categories were identified and analyzed through the transition path framework. The proposed framework led to the development of an intensity matrix which shows the power of the varying levels of long-term impacts of each application category studied in relation to the global markets and social environment.

One of the main characteristics of health systems and pharmaceutical supply chains is their significant costs in the public sector, which has forced governments and companies active in this field to find ways to reduce costs. In this paper, the deterioration of imported pharmaceutical items is investigated as one of the challenges of the supply chain of pharmaceutical firms. Specifically, the micro, small medium enterprise (MSME), and a collaborative strategy to reduce its costs is presented. The technical solution of the cooperative strategy is the formation of a partnership alliance between the foreign patent holder of brand drugs and a domestic manufacturer through an exclusive license contract in the local country. This leads to a significant reduction of costs in the distribution network of the pharmaceutical supply chain. On the other hand, supply chain management techniques in the cooperative strategy provide the necessary motivation for its practical implementation by splitting fair profits between producers and other members, namely local government, distributors, and pharmacies. For these purposes, a cooperative game theory-based contract is utilized to set the parameters of the license agreement, and then a profit-sharing mechanism is introduced that splits the benefits of cooperation among the supply chain members based on their afforded costs. The most important contribution of the current research is to propose an integrated framework that combines the logistics network models, valuation methods, and profit split mechanisms that embody more facts from real-world problems than separate models in this regard in previous studies. Moreover, results of the proposed strategy in the supply chain of a drug for thalassemia patients in Iran indicate the effectiveness of the proposed strategy in reducing costs and deterioration. Further, it is shown that the higher the ordering costs of the imported drugs, the lower the market share of the patent holder, and the lower the financing expenses of the cooperative alliance, the more efficient is the proposed strategy.