Healthcare Supply Chain Research Articles

Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America

This review paper critically examines the ethical considerations involved in implementing generative Artificial Intelligence (AI) in healthcare supply chain optimization across three distinct regions: India, the United Kingdom, and the United States of America. The study synthesizes findings from various case studies and academic research to highlight both common and unique ethical challenges faced in these countries. Key themes such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare solutions are explored, alongside the unique socio-cultural, legal, and regulatory challenges specific to each region. The paper proposes a set of best practices for incorporating ethical considerations into the deployment of generative AI in healthcare. These include the development of inclusive ethical frameworks, regular ethical audits, comprehensive training and education programs, public engagement initiatives, and interdisciplinary collaboration. The paper also delves into future research directions and policy development, emphasizing the need to address healthcare disparities, adapt legal and regulatory frameworks, enhance generative AI explainability, and evaluate long-term outcomes.The study concludes by underscoring the importance of ethical design and deployment of generative AI systems in healthcare, advocating for a balanced approach that aligns technological advancements with ethical standards and global healthcare needs. This comprehensive review aims to contribute to the discourse on ethical generative AI implementation, offering insights and recommendations for policymakers, healthcare professionals, and generative AI developers to foster responsible and beneficial use of generative AI in healthcare globally.

An integrated DEA/GP based approach for designing healthcare supply chains with mixed data: a real world application

PurposeAs supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.Design/methodology/approachTo achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.FindingsThis paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.Research limitations/implicationsThe outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.Practical implicationsA real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.Originality/valueThe novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

Mitigating healthcare supply chain challenges under disaster conditions: a holistic AI-based analysis of social media data

A key advantage of social media is the real-time exchange of views with large communities. In disaster situations, such bidirectional information exchange is most useful to victims and support teams, especially in communications with authorities, volunteers, and the public. This paper addresses challenges faced by the healthcare supply chain during the COVID-19 pandemic with analyses of Twitter data using an Artificial Intelligence-driven multi-step approach. We investigate tweets for information about healthcare supply chains, such as the scarcity of testing kits, oxygen cylinders, and hospital beds during the pandemic. We deployed machine learning to classify such tweets into imperative and non-imperative categories based on need severity. The study sought to predict the location of victims requesting help based on their imperative tweets if geo-tag information was missing. The proposed approach used four steps: (1) keyword-based informative tweet search, (2) raw tweet pre-processing, (3) content analysis to identify tweet trends, public sentiment, topics related to healthcare supply chain challenges, and crisis classification to label imperative and non-imperative tweets, (4) locating the point-of-crisis from imperative tweets to facilitate coordination of help operations. The pre-processing of tweets, trend analysis, and sentiment analysis relied on natural language processing and machine learning for topic modelling (K-mean clustering), crisis classification (random forest), and point-of-crisis detection (Markov chain). Results demonstrate the potential to capture significant, timely, and actionable information on healthcare supply chain challenges to respond quickly and appropriately in a pandemic.

The impact of healthcare 4.0 technologies on healthcare supply chain performance: Extending the organizational information processing theory

Increasing supply chain performance in an uncertain environment is a challenge for every industry, predominantly, healthcare sector. To address such issues, there is an urgent need for healthcare organizations like hospitals to build new capabilities. We suggest using organizational information processing theory (OIPT) as theoretical foundation for developing research model that investigates how healthcare 4.0 technologies (big data analytics, artificial intelligence and blockchain) enhance hospital supply chain processes and thereby the performance. According to our analysis of survey data from around 255 hospital managers in Indian hospitals, the three hospital supply chain processes, viz., operations, innovations, and risk management, and the supply chain performance are significantly impacted by these healthcare 4.0 technologies. Additionally, hospital supply chain operations and innovations partially mediate the association between healthcare 4.0 technologies and performance. We also found, the interaction between healthcare 4.0 technologies and hospital supply chain operations is moderated by type of healthcare organizations (private, and public hospitals). In addition to extending and validating the OIPT in context of digital hospital supply chains, these findings offer healthcare professionals empirical evidence to further maximize the benefits of healthcare 4.0 technologies for sustained hospital supply chain performance and integrate digital supply management into health system development.

Modeling the enablers of blockchain technology implementation for information management in healthcare supply chains

PurposeBlockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.Design/methodology/approachThis study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.FindingsThe results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.Practical implicationsTo implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.Originality/valueThis is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

Generalized Dombi-based probabilistic hesitant fuzzy consensus reaching model for supplier selection under healthcare supply chain framework

The process of supplier selection in supply chain management is a multi-criteria decision-making (MCDM) problem, characterized by several conflicting criteria. Despite the extensive research on MCDM in supplier selection, the healthcare sector has received limited attention compared to clinical operations research. A significant hurdle in evaluating healthcare supplier selection lies in handling uncertain information, characterized by probabilistic hesitant fuzzy (PHF) data that involves both hesitancy and probabilistic elements. Existing studies on PHF sets lack a versatile and comprehensive operator to enhance consensus and accommodate biased expert opinions. To address this gap, this study develops a model combining the merits of PHF sets, generalized Dombi operators, and a very popular MCDM method called measurement of alternatives and ranking according to compromise solution (MARCOS) for consensus-building in a group decision-making scenario. The proposed model provides higher degree of adaptability and flexibility in handling complexities in healthcare supplier selection problems. Within this model, calculation of consensus degree of each expert is facilitated through the application of correlation measures. To aggregate preferences of experts, PHF generalized Dombi weighted averaging aggregation operator has been introduced. The model further encompasses weights computation of the supplier selection criteria through an optimization model using cross-entropy and dispersion measures. A healthcare supplier selection problem is then considered to show the applicability of the developed model. Comparative analyses have been performed against existing methods to validate its robustness, ensuring consistency and effectiveness of the proposed model.

A novel hybrid sustainable healthcare supply chain model for patient safety: A case study of Cambodia

This research focuses on elucidating the attributes of a Sustainable Healthcare Supply Chain (SHSC) that play a pivotal role in enhancing patient safety, particularly in developing nations. Patient safety in healthcare supply chains is paramount, particularly in the face of inherent risks and vulnerabilities associated with disruptions and uncertainties. A hybrid selection model, integrating factor analysis, structural equation modeling (SEM), and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is introduced to rank these attributes. Twenty-two SHSC attributes are categorized into four facets: social, environmental, economic, and technological. A survey conducted across five Cambodian provinces gathered 361 valid responses from healthcare professionals, forming the basis for a structural SHSC model. This model is subsequently implemented in a Phnom Penh tertiary hospital. The findings underscore the imperative of incorporating patient safety considerations into healthcare supply chains. The hybrid selection model helps healthcare leaders figure out what factors are most important for patient safety and rank them. This gives them information they need to come up with effective strategies and long-lasting projects. The study's outcomes provide actionable insights for healthcare stakeholders. The multi-analytical approach of the model offers a comprehensive perspective to address supply chain challenges, reduce extraneous costs, and minimize environmental footprints. In essence, the effective implementation of sustainable healthcare supply chain strategies not only enhances care quality and fortifies patient safety but also aligns with the broader objectives of healthcare entities.

Assessing the Disruption Impact on Healthcare Delivery.

Health emergency outbreaks such as the COVID-19 pandemic make it challenging for healthcare systems to ration medical resources and patient care. Such disastrous events have been increasing over the past years and are becoming inevitable, necessitating the need for healthcare to be well-prepared and resilient to unpredictable rises in demand. Quantitative and qualitative based decision support systems increase the effectiveness of planning, alleviating uncertainties associated with the crisis. This study aims to understand how the COVID-19 pandemic has affected the performance of healthcare systems in different areas and to address the associated disruption. A cross-sectional online survey was conducted in the Kingdom of Saudi Arabia and the United Arab Emirates among healthcare workers who worked during the pandemic. The pandemic-related disruption and its psychometric properties were assessed using Structural Equations Modeling (SEM) with 5 latent factors: Staff Mental Health, Communication Level, Planning and Readiness, Healthcare Supply Chain, and Telehealth. Responses from highly qualified participants with many years of experience in hospital settings were collected and analyzed. Results show that the model satisfactorily fits the data with a CLI of 0.91 and TLI of 0.88. The model indicates that enhancing supply chain management, planning, telehealth usage, and communication level across the healthcare system can mitigate the disruption. However, the lack of mental health management for healthcare workers can significantly disrupt the quality of delivered care. Staff mental health and healthcare supply chain, respectively, are the highest contributors to varying degrees of disruption in healthcare delivery. This study provides a direction for more research focusing on determinants of healthcare efficiency. It also provides decision-makers insights into the main factors leading to disruptions in healthcare systems, allowing them to shape their outbreak response and better prepare for future health emergencies.

A performance management framework for smart health-care supply chain based on industry 4.0 technologies

Purpose The study aims to envisage upon conceptualizing and developing the scales of smart health-care supply chain (HCSC) performance in the era of the fourth industrial revolution. Design/methodology/approach This study has implemented structural equation modelling to analyse the survey data. To analyse the collected data from the field investigation involving a sample size of 323, the IBM SPSS AMOS 26 software package is considered to implement exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in this study. Findings The measurement model of the study developed using EFA and CFA has resulted in validating 32 items out of the 42 items. Resultantly, the analysis using the above-mentioned tools and the parsimony of items to scale development makes it more susceptible to contributing significantly to the current HCSC literature. Research limitations/implications The HC providers need to consider a holistic and systematic approach while taking into account the constructs of smart HCSC performance, specifically, the effect of HCSC responsiveness and industry 4.0 between the independent and dependent variables. The scales are validated from the perspectives of developing countries such as India, and hence, their generalizability with respect to first-world countries is practically limited. Originality/value The scales validated in this study would facilitate managers and key decision-makers to apply the various elements of HCSC practices, gauge the application of these scales and monitor the performance of health-care facilities.

Safety and Quality Within the Healthcare Supply Chain: A Great Unknown.

Quality and safety in healthcare have emerged as key factors impacting on both clinical effectiveness and clinical outcomes. While improving the healthcare supply chain has been extensively researched, the impact of the healthcare supply chain on clinical safety has received little attention in the literature, largely due to the complexity of such studies and the involvement of multiple stakeholders. This research proposes an evaluation model using key performance measurements for an electronic procurement system that enables digital transformation of the healthcare supply chain. The model will be tested before and after the introduction of an electronic procurement system in the healthcare supply chain for small and medium sized healthcare providers to provide evidence of both the usefulness of the model itself within industry and to further contribute to the knowledge base. Future use of the model may provide benchmarking and important data and insights to enable enhanced clinical safety in the healthcare supply chain.

Simulation of the Impact of Key Variables on Smart Supply Chains Efficiency in Iran’s Healthcare Industry

Background: The smartification of supply chains, which enables organizations to stay informed about crises in a timely manner and make appropriate decisions in response to the resulting pressures, has consistently been a crucial factor in the realm of organizational transformation. The healthcare industry in Iran faces more challenges and crises than other industries, especially due to its vital role in public health prevention and care.
 Objectives: According to the reasons described, this research aims to simulate and improve the efficiency of the healthcare supply chain when confronted with crises.
 Methods: In this study, the efficiency of the healthcare supply chain was simulated using the system dynamics approach and Vensim DSS.
 Results: The results indicated that healthcare supply chain efficiency in Iran is unsatisfactory and may confront challenges during crises. Therefore, this study places particular emphasis on examining scenarios for improving the current situation, which stems from the consensus of experts and stakeholders in this field. In the presented scenarios, a 2% improvement in the utilization of advanced intelligent technologies and a 5% improvement in intelligent inventory management were observed. Notably, the combined effect of these two scenarios led to an overall enhancement in the average efficiency of the healthcare smart supply chain. These improvements can increase the average efficiency levels within the pharmaceutical manufacturing segment up to 1.3%, 5.8%, and 7.7% in each of the aforementioned scenarios.
 Conclusions: It can be asserted that although the advancement of smart technologies and intelligent inventory management individually contribute to enhancing the efficiency of the healthcare supply chain in Iran, combining these changes can provide the groundwork for further increasing efficiency up to 7.7%.

Guest editorial: Health care supply chains in Africa

Guest editorial: Health care supply chains in Africa

Challenges in Healthcare Supply Chain Resilience Management : A Conceptual Framework

The healthcare supply chain (HSC) is a complex and dynamic system that plays a critical role in ensuring the delivery of essential medical products and services to patients. This system faces numerous challenges that can harm the healthcare system, leading to treatment delays, patient dissatisfaction, and increased costs. Additionally, the ageing population and the rising prevalence of chronic diseases are increasing the criticality of the HSC. Three promising avenues of future research are emerging to address these challenges in the HSC: resilience, collaboration and visibility, and the use of technology. The current research is based on a literature review of HSC challenges and opportunities, highlighting the importance of a comprehensive approach to HSC management. This research presents a conceptual framework integrating resilience, collaboration, visibility, and technologies that can provide a roadmap for future research in the field. By focusing on these aspects, organisations can create a more efficient, effective, and resilient HSC better equipped to meet the needs of patients.

Challenges and opportunities of digitalization in the healthcare supply chain: A literature review

This paper presents a literature review on the topic of digitalization in the healthcare supply chain. The healthcare industry is currently undergoing a transformation, and the supply chain is not an exception. The adoption of advanced technologies such as the blockchain is increasingly prevalent, offering improved traceability, security, trust, and privacy. However, managing the influx of new data requires highly qualified personnel. The potential benefits of digitalization are thus explored, as well as the need for collaborations to improve the present and future of the healthcare supply chain.The authors analyze the current state of digitalization in the healthcare supply chain by conducting a literature review of 81 relevant documents. The outcomes of the analyses highlight the challenges and opportunities associated with digitalization, which has the potential to revolutionize the healthcare supply chain. However, it is apparent that further studies are necessary to ensure the success of digitalization in this context.

Roles of Innovation Leadership on Using Big Data Analytics to Establish Resilient Healthcare Supply Chains to Combat the COVID-19 Pandemic: A Multimethodological Study

This article empirically examines the effect of big data analytics (BDA) on healthcare supply chain (HSC) innovation, supply chain responsiveness, and supply chain resilience under the moderating effect of innovation leadership in the context of the COVID-19 pandemic. The scanning interpretation–action–performance model and organization information processing theory are used to explain BDA, HSC innovation, responsiveness, and resilience relationships. First, the hypotheses were tested using data collected from 190 experienced respondents working in the healthcare industry. Our structural equation modeling analysis using the partial least squares (PLS) method revealed that BDA capabilities play a pivotal role in building a responsive HSC and improving innovation, which has contributed to resilience during the current pandemic situation. High innovation leadership strengthens the effect of BDA capabilities on HSC innovation. High innovation leadership also increases the effect of BDA capabilities on responsiveness. Second, we validated and supplemented the empirical research findings using inputs collected in 30 semistructured qualitative questionnaires. Our article makes a unique contribution from the perspective of innovation leaderships. In particular, we argue that the role of innovative leadership in the COVID-19 pandemic situation is critical as it indirectly affects HSC resilience when BDA is in place.

Using NFTs for Product Management, Digital Certification, Trading, and Delivery in the Healthcare Supply Chain

The COVID-19 pandemic caused disruption in the healthcare market, which resulted in shortages of essential healthcare products. The pandemic exacerbated the problems that already existed in the healthcare supply chain, such as poor data availability, transparency, and accessibility. Such problems necessitate the introduction of a solution that is capable of managing products, maintaining ownership, facilitating trading, and delivering products in a traceable, auditable, and trustworthy manner. In this article, we propose a nonfungible-token-based solution for the management of healthcare products, where the ownership of a product is maintained by using digital certification, the trade and delivery of healthcare products is facilitated by a smart contract, and disputes are settled by an arbitrator while keeping all related information on-chain for auditing purposes. We utilize the Interplanetary File System to store the metadata of healthcare products to avoid storing large-sized data on the blockchain. We present system diagrams and algorithms along with the implementation details. We conduct security testing to demonstrate that our solution is resilient and secure against common vulnerabilities and exploits. We compare our solution with the existing solutions to show its distinctive features and novelty. The smart contracts code is made publicly available on GitHub.

Blockchain Technology Applications in Healthcare Supply Chains—A Review

Blockchain Technology Applications in Healthcare Supply Chains—A Review

Corrections to “Blockchain Technology Applications in Healthcare Supply Chains – A Review”

Corrections to “Blockchain Technology Applications in Healthcare Supply Chains – A Review”

Healthcare supply chains: critical supplies during the pandemic

Healthcare supply chains: critical supplies during the pandemic

Opportunities Presented by COVID-19 for Healthcare Green Supply Chain Management and Sustainability Performance: The Moderating Effect of Social Media Usage

This research explores the opportunities presented by COVID-19 for green supply chains' environmental practices and ecological sustainability performances in the healthcare sector. This study investigates the connections between uncertainty-fear of COVID-19, healthcare green supply chain management (GSCM), and the three pillars of a firm's sustainability performance (environmental, economic, and social). Moreover, this study examines the moderating role of social media usage (SMU) on the effect of uncertainty-fear of COVID-19 on healthcare-GSCM. When conducting the empirical part, this study uses the partial least squares structural equation modeling method based on a sample of 483 healthcare managers. The findings prove that the uncertainty-fear of COVID-19 has a beneficial impact on healthcare-GSCM. Besides, SMU moderates the relationship between uncertainty-fear of COVID-19 and healthcare-GSCM, indicating the importance of SMU in gathering information for the healthcare sector during COVID-19. Likewise, when interacting with healthcare firms' sustainability performances, healthcare-GSCM positively impacts environmental and social performances, though it has a negligible impact on economic performance. This study adds to the “social cognitive theory” by introducing the concept of uncertainty-fear of COVID-19. Furthermore, this research adds to the “resource-based view theory” and the “knowledge-based view theory” by exploring the SMU's role during the outbreak.