Healthcare Supply Chain Research Articles

Opportunities and challenges of drones and internet of drones in healthcare supply chains under disruption

This study investigates the use of drones and the Internet of Drones (IoDs) in healthcare supply chains (HSCs), highlighting the opportunities and challenges of integrating them to respond to HSCs disruptions, an area that is currently under-examined in supply chain literature. A mixed methods approach is employed in this research. The article establishes important contributions. Firstly, it evaluates and integrates current research to provide new insights for dealing with pandemics and other similar emergencies. Secondly, the article framework reveals relevant political, economic, social, technological, environmental, and legal influencing factors to respond to disruptions. Thirdly, the analysis through the SCOR provided a hierarchical process model for HSCs enabled by drones and IoDs. Fourthly, the paper provides a framework for a shift from people-dependent HSCs to technology-dependent HSCs. Promising future research directions are indicated to advance the supply chain research on this topic.

Evaluation and Selection of Supplier in A Healthcare Supply Chain using Analytic Hierarchy Process under Fuzzy Environment

The aim of the supplier selection process is to reduce purchasing risks, boost customer profitability, and cultivate enduring, close relationships between suppliers and buyers. This process stands as a pivotal component of supply chain management (SCM), making supplier selection a critical concern for establishing an effective supply chain system. In this instance, a case study has been presented for supplier selection employing Analytic Hierarchy Process (AHP) in conjunction with the Multi-Criteria Decision Making (MCDM) technique. We have explained it using fuzzy data. It not only contributed on utility of fuzzy AHP methodology, but also provided comprehensive literature review of MCDM problems. Furthermore, by outlining the steps of fuzzy AHP in a straightforward numerical manner, this study can serve as a guide for implementing the methodology in other (MCDM) problems. In this study, we evaluate four suppliers using fuzzy AHP. This method can be used when one has to select one particular supplier from a number of suppliers in a short time span. It will be also helpful to the Pharmacists to select the best supplier who can fulfil all their needs. Despite the abundance of studies on supplier selection, there has been relatively less investigation into the evaluation and selection of suppliers using specific measures tailored to the healthcare sector. To bridge this gap, this research puts forth a guideline for selecting suppliers of baby hygiene products.. KEYWORDS :Supplier, AHP, Fuzzy, Evaluation, Supply chain management (SCM).

The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management

This paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.

Sustainable supply chain management in U.S. healthcare: Strategies for reducing environmental impact without compromising access

The U.S. healthcare sector’s supply chain operations are responsible for about 8.5% of all national greenhouse gas (GHG) emissions. In healthcare, traditional supply chain management has focused on cost efficiency to the detriment of environmental considerations leading to higher emissions, waste, and resource depletion. The focus of this study is to investigate strategies for making sustainable supply chain management (SSCM) in the U.S. healthcare system, while reducing environmental impact at minimal cost to patient care quality. The objectives of the study are to evaluate the current state of healthcare supply chains, explore sustainable practices, evaluate emerging technologies, propose an SSCM implementation framework, and explore policy implications. Sustainable supply chain management can be achieved by forming the key strategies: comprehensive waste reduction and recycling programs, adoption of energy efficient transportation and storage solutions, green energy sourcing, and use of data driven inventory management that would help optimize inventory, along with other technologies like blockchain, IoT and artificial intelligence that could achieve transparency and efficiency. The research wouldn't go into specific case studies but would likely look at institutions that have already been able to implement these practices. The findings indicate that considerable environmental footprint reduction of the U.S. healthcare sector is possible without sacrificing access to high quality care through adoption of SSCM practices. But it requires joint efforts from policymakers, healthcare providers and industry partners. The long-term goal is a sustainable, resilient healthcare system that will integrate environmental stewardship with high quality care, that will help mitigate climate change and acknowledge the interdependence of environmental and human health.

The Value of Applying Big Data Analytics in Health Supply Chain Management

This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

Equity in healthcare supply chain access: a narrative review of challenges and strategies in the U.S.

Equity in healthcare supply chain access: a narrative review of challenges and strategies in the U.S.

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing.

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Supply-Side Barriers in Accessing Human Papillomavirus Screening for Cervical Cancer Prevention in Rural India: Evidence From a Cross-Sectional Study.

Cervical cancer is a leading cause of cancer-related mortality among women worldwide, particularly in low- and middle-income countries. In India, rural areas face a disproportionate burden of cervical cancer cases. Effective screening methods, such as Human Papillomavirus (HPV) testing, are recommended by the World Health Organization (WHO) for early detection and prevention. However, the uptake of these screening methods in rural India is significantly low due to various supply-side barriers. This study aims to evaluate the readiness and capacity of PHCs in rural India to conduct HPV screening tests. This cross-sectional study in Pondicherry, India, examined cervical cancer screening and management at 15 primary health centers (PHCs) through a structured questionnaire. It assessed personnel training for HPV screening, availability of screening tests, healthcare infrastructure functionality, supply chain efficiency, infection prevention practices, medicine and diagnostic supply availability, referral protocols, data management practices, policy adherence, and equipment availability. Visual inspection with acetic acid was available in all PHCs, while more advanced tests like cytology, HPV testing, loop electrosurgical excision procedure (LEEP), colposcopy, and histology/pathology were generally absent. Only one PHC had personnel trained for cytology processing, with none trained for other advanced procedures or HPV testing. The study identified significant gaps in healthcare infrastructure, trained personnel, and supply chain logistics. Enhancing cervical cancer screening and treatment in PHCs necessitates investments in infrastructure, training, and data systems. Key priorities include upgrading equipment like colposcopes and implementing a robust Health Management Information System (HMIS). Collaboration with stakeholders is essential for effective resource allocation and capacity building.

Optimizing last-mile delivery of essential medicines in Nigeria: Insights from Niger State’s health-care supply chain

Last-mile delivery of health commodities is a critical component of efficient supply chains, and third-party logistics (3PL) providers are pivotal in ensuring cost-effective and reliable product distribution. This study explores the implementation of last-mile delivery outsourcing by government-operated warehouses and drug management agencies (DMAs). This research focuses on the relationship between 3PL utilization and key performance metrics, such as cost reduction, timely delivery, and end-user satisfaction. This study combines a literature review, surveys, and interviews conducted with employees of the DMAs in Minna, Niger State, to establish significant correlations. The findings show that using 3PLs significantly reduced operational costs, improved the frequency of timely deliveries, and enhanced end-user satisfaction. These findings underscore the need for continued engagement with 3PL providers to optimize supply chain performance and improve service delivery outcomes. Managers are recommended to maintain and expand such collaborations to sustain these benefits.

A novel healthcare 4.0 system for testing respiratory diseases based on nanostructured biosensors and fog networking

New digital healthcare models based on advanced bio-sensing technologies are regarded as a possible solution to improve the screening and prevention processes and the overall performance of healthcare supply chains. This is particularly relevant for respiratory diseases, which are currently among the first causes of death and medical expenditures in industrialized countries. This research proposes a new e-health model based on a fog architecture and a smart device, enabling remote diagnostics of respiratory diseases and allowing for decentralized patient testing and self-testing. According to such a model, the patients’ testing is executed through the analysis of the exhaled breath collected using a smart device based on a novel nanostructured sensor and transferring relevant information to the medical staff involved in the diagnosis process. This research proposes an original testing method and system, validated in the lab through a comparative analysis of culture media samples collected from healthy patients, and subsequently exposed to cigarette smoke extract (CSE, an inducer of oxidative stress). The preliminary results obtained demonstrate the validity of the approach proposed, encouraging further experimental analyses on human patients for the implementation into clinical practice.

THE EFFECT OF AI-DRIVEN INVENTORY MANAGEMENT SYSTEMS ON HEALTHCARE OUTCOMES AND SUPPLY CHAIN PERFORMANCE: A DATA-DRIVEN ANALYSIS

There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance. The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

Assessing the Dynamic Capabilities and Sustainable Performance in Public Hospital Supply Chains Management

In the volatility, uncertainty, complexity, and ambiguity (VUCA) era, unexpected disasters are rising. The Coronavirus disease 2019 (COVID-19) pandemic severely disrupted healthcare supply chains, with resource shortages significantly impacting the sustainable provision of public healthcare services. This highlighted the importance of supply chain management for public healthcare sustainability operations. An online survey was conducted among 127 public hospital employees and managers, adopting the SEM-PLS method to identify critical dynamic capabilities of supply chain management capabilities and their effect on sustainable development. The results indicate that strategic formulation capabilities, leadership, and digital technology competence significantly enhance the sustainable performance of public hospitals. This finding enriches the literature on sustainable supply chain management in the healthcare sector and provides policymakers and healthcare administrators with decision-making support.

Blockchain in Healthcare: Overview of Applications, Challenges, and Future Directions

There are numerous healthcare issues which show the potential of blockchain technology such as transparency, interoperability and security of data. Through this study, it tries to offer a very deep explanation of where the Blockchain applications today stand for in the healthcare domain emphasising on possible benefits from the technology and some known challenges that are being faced while actuating those. We then use blockchain applications in the above-mentioned aspects to explain different aspects of use of Blockchain in healthcare like supply chain management, clinical trials, secure medical data exchange and patient empowerment. We also talk about Blockchain from implementation point of view – smart contracts, consensus methods and data privacy. In this publication, we determine whether deploying Blockchain in healthcare raises ethical and legal issues and how existing issues can be addressed. In conclusion, we describe future directions for research and opportunities to use Blockchain to improve healthcare delivery and outcomes.

Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic.

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

Avoidance of Medicine Wastage in Private Clinics In Hong Kong: Practitioners’ Perspectives

Hong Kong with its well-established healthcare and medicine system and professional health services is one of the renowned healthiest places in the world. In fact, Hong Kong's healthcare system is running on a dual-track basis comprising public and private sectors. This study intends to explore medicine waste management and sustainability. First, we examine the existing phenomenon of Hong Kong medicine waste. Also, we discuss the causes of medical waste from a private clinic doctors' perspective. In addition, we identifies possible policies and recommendations to minimize the medicine wastage in private clinic in Hong Kong as well as improve the healthcare supply chain practices. Qualitative research with in-depth interviews of doctors and medical practitioners have been carried out to gather their views and opinion of medication wastage, including the current medicine waste situation and the means of handling the expired medicines. Also, the study analyzes the centralized procurement platform effectiveness and suggests some possible policies for government to adopt. Interviewees mentioned that medication waste has occurred over decades. Medicine wastages are not only financial burdens, but also social burdens. If the medication waste problem cannot be alleviated, it will affect the population health and environment in the long run. An all-rounded healthcare supply chain with efficient logistics operations would help the government to collect excess medicines from private clinics and redistribute to some NGOs. In fact, all parties including manufacturers, distributors, prescribers, and patients have the responsibilities to maintain and implement suitable policies to prevent bulk medicine waste. This research study provides the foundation of medicine wastage in private clinics in Hong Kong. Future research can investigate the medicine wastage in other aspects as well as consolidate the literatures in both industrial and public perspectives.

The Value of Applying Big Data Analytics in Health Supply Chain Management

The use of big data analytics (BDAs) allows for the collection, management, and analysis of large volumes of data, which helps in making real-time decisions. This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP). Through a systematic literature review, the research explores how BDA enhances efficiency within healthcare supply chains (HCSCs) and identifies the critical factors enabling successful BDA implementation. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs' efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges.

The effect of visibility on forecast and inventory management performance during the COVID-19 pandemic

During the COVID-19 pandemic, healthcare facilities faced significant shortages of critical supplies like personal protective equipment, with dire repercussions. This study evaluates the potential role of decreased data visibility on these shortages, analysing different forecasting methods integrated with a periodic review inventory system (i.e. a base stock policy) on semi-simulated data encompassing several visibility issues. The forecasting methods chosen pertain to different data types: Holt and naïve methods are used as demand-based predictors, while a modified epidemiological model utilises pandemic data for demand forecasting. We scrutinise three prevalent data visibility issues through specifically crafted scenarios examining the effects of delayed, temporally aggregated, and erroneous data on system performance. Generally, our research illustrates that data visibility issues have a detrimental impact on the healthcare supply chain's efficacy. Interestingly, the system performance sees an uptick when these issues spur significantly oversized over-forecasts, e.g. when employing an epidemiological compartmental model for predictions.

Impact of organizational culture on healthcare supply chain resilience in Jordan: Moderating role of technology integration

This study aims to investigate the impact of organizational culture (i.e., rational, hierarchical, and group) on healthcare supply chain resilience (SCR) in Jordan. This paper further examines the moderating role of technology integration on the relationship between organizational culture and healthcare SCR. Cross-sectional research was conducted, and participants were recruited from different hospitals in Jordan. An electronic survey was employed to collect the responses from 304 participants, including senior professionals designated as doctors, nurses, ray technicians, physical therapists, procurement officers, pharmacists, and lab technicians with more than three years of work experience. There was no statistically significant influence of rational culture on healthcare SCR (p-value = 0.156) and an adverse impact of hierarchical culture on healthcare SCR (p-value = 0.030). Group culture had a statistically significant impact on healthcare SCR (p-value = 0.007). Technology integration had an influential moderating influence on the association between rational culture and healthcare SCR (p-value = 0.042) and the association between hierarchical culture and healthcare SCR (p-value = 0.0129). However, technology integration had no moderating influence on the association between group culture and healthcare SCR (p-value = 0.331). The analysis revealed that group culture has an influential impact on healthcare SCR, while hierarchical culture has a negative impact on healthcare SCR. Moreover, technological integration was observed to improve the beneficial influence of rational culture and the negative effects of hierarchical culture on healthcare SCR. However, the technology integration was not observed to moderate the relationship between group culture and healthcare SCR. Acknowledgment The author acknowledges all the associated personnel who, in any reference, contributed to the completion of this study.