Medical Logistics Research Articles

Enhancing HIV/AIDS and TB medication logistics: A comprehensive approach to global healthcare distribution

The distribution of HIV/AIDS and tuberculosis (TB) medications is critical to the success of global healthcare initiatives, particularly in regions with limited infrastructure and resources. This review examines the logistical challenges hindering the efficient delivery of these life-saving medications, including supply chain inefficiencies, poor infrastructure, and geographical barriers. It also explores innovative approaches to optimizing logistics, such as the use of blockchain, drones, and artificial intelligence (AI), alongside public-private partnerships (PPPs) and supportive policy frameworks. These emerging technologies have the potential to enhance the availability and effectiveness of HIV/AIDS and TB treatments by improving supply chain transparency, reducing stockouts, and overcoming transportation challenges in remote areas. The paper further assesses the impact of these logistical improvements on healthcare outcomes, demonstrating how they contribute to improved patient adherence, reduced disease transmission, and cost savings. Finally, recommendations are provided for policymakers, healthcare organizations, and stakeholders to adopt these innovations, and future research directions are proposed to expand these models to other medications and diseases.

Bloodstream: notes towards an anthropology of digital logistics in healthcare.

Based on ethnographic research in northern Ghana, this article explores the complex logistics of blood and the ways in which the availability of blood has been transformed through the introduction of drones. We explore how drone services affect this ecosystem of supply and contribute to reshaping the practices of physicians, nurses, facility pharmacists and stock managers, as well as the expectations and experiences of patients and their families. Situated at the interface of medical anthropology, critical studies of infrastructure and anthropological studies of digital innovations in healthcare, our paper attends to the emerging anthropological research on medical logistics as a means of connecting people with medical resources. It demonstrates the fundamentally ambivalent nature of technological innovation: on the one hand, drones have fueled health workers' hopes and transformed access to blood. On the other hand, their introduction has also led to connectivity without stock. In line with STS scholarship, we highlight the important role of the physical properties of objects such as blood in shaping their circulation.

Current Advancements in Drone Technology for Medical Sample Transportation

Background: The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. Methods: This paper provides a narrative review of current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Drawing from recent studies and case reports, the review highlights the role of technologies such as artificial intelligence (AI)-driven navigation systems, real-time monitoring, and secure payload management in mitigating logistical barriers like traffic congestion and geographical isolation. Results: Based on findings from various case studies, the review demonstrates how drones can significantly reduce transportation time and costs, while improving accessibility to healthcare services in underserved areas. Conclusions: This paper concludes that, while challenges such as regulatory hurdles and privacy concerns remain, ongoing technological advancements and the development of supportive regulatory frameworks have the potential to revolutionize medical logistics, ultimately improving patient outcomes and healthcare delivery.

The Influencing Mechanism of Robustness of Emergency Medical Logistics: Mediating Role of Knowledge Integration

Drawing on the social capital theory, the research examines the impact of network size, network centrality, trust, and regulation on the knowledge integration and robustness of emergency medical logistics. Additionally, the research seeks to provide deeper insight into the link between the variables by studying how knowledge integration mediates the relationship between independent variables and the robustness of emergency medical logistics. The study utilized structural equation modeling to assess the underlying assumptions of the research model. A total of 465 valid questionnaires were collected from government departments, hospitals, social teams, and enterprises. The data processing and analysis were conducted using SPSS 23.0 and AMOS 24.0 software. The study’s outcome indicated that network size and network centrality have indirect effects on the robustness of emergency medical logistics through the intermediate variable of knowledge integration, but neither has a direct effect. Moreover, knowledge integration has a significant positive impact on the robustness of emergency medical logistics. Both trust and regulation have positive effects on the robustness of emergency medical logistics, and they also have positive effects on the robustness of emergency medical logistics through knowledge integration. This study is the inaugural exploration of the correlation between knowledge integration and the robustness of emergency medical logistics. It adds to the literature by providing evidence that knowledge integration is an essential emergency organization’s aide in promoting the robustness of emergency medical logistics. The findings of this study establish a strong theoretical foundation and practical significance for ensuring and improving the level of effectiveness in emergency medical logistics management.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

A-123 Comparative Assessment of Unmanned High-Speed Aerial Vehicles in Medical Logistics: Blood Sample Transportation Amidst Weather Variability

Abstract Background Drones, as an innovative technology, hold transformative potential in the field of medical logistics, especially in the transportation of sensitive medical materials like blood samples. This study aimed to assess and compare the impact of high-speed drone transportation with conventional car transportation on the integrity and preanalytics of blood samples, taking into account various blood materials and analytes. Methods Blood samples, including EDTA whole blood, serum, and plasma anticoagulated with Citrate and Lithium-Heparin transported as whole blood, were subjected to transportation via both drone and car. Drone flights were tested under diverse weather conditions (ranging between 4 and 20 degrees Celsius), and results were compared to samples stored without transportation. The maximum drone speed exceeded 100 km/h. A total of 28 analytes were tested on Serum, 20 on EDTA, 26 on Lithium-Heparin, and 5 on Citrate blood. Statistical analyses were performed using Passing Bablok and Bland-Altman methods. Results For Serum samples, the correlation coefficient (r) ranged from 0.830 to 1.000, and slopes varied from 0.913 to 1.111. Five analytes (total bilirubin, Calcium, Ferritin, Kalium, and Sodium) exhibited discrepancies between the negative control and the transported sample, with r lower than 0.800, slopes not between 0.8 and 1.2, and mean (%) not between +/- 10%. However, no significant differences were noted between drone and car transportation. Similar patterns emerged for EDTA, Lithium-Heparin, and Citrate samples, with r ranging from 0.829 to 0.997, 0.939 to 0.998, and 0.830 to 1.000, respectively. Slopes ranged from 0.956 to 1.051, 0.938 to 1.085, and 0.913 to 1.111. Despite identifying a few discrepancies in specific analytes, no significant differences were observed between transportation methods. Conclusions n comparison to transportation by car, drone transportation of blood samples did not lead to alterations in concentrations of commonly used analytes. Notably, instances of differences before and after transportation were attributed to the transportation process itself rather than the mode of transportation. This suggests that the use of drones for medical sample transport can be deemed comparable to, if not superior to, traditional car transportation methods.

A-124 Sustainable Transport: Assessing Environmental Impact in Medical Sample Transit via Drones and Vehicles

Abstract Background This research explores the ecological implications associated with different methods of transporting medical samples, specifically focusing on drones, combustion cars, and electric cars within two laboratories in central European countries. With the primary goal of assessing and comparing CO2 consumption, delivery time, and overall environmental impact, the study aims to provide a basis for adopting sustainable practices in healthcare logistics. Methods The transportation of medical samples occurred between two laboratories in the Principality of Liechtenstein and Switzerland, utilizing authorized aerial and road routes. Various weather conditions and distances were considered for sample transports using different vehicles (8 combustion cars, two electric cars, and one drone model). Energy consumption was documented, and the CO2 footprint was calculated based on data from the Swiss Federal Office for the Environment (FOEN) for both imported and renewable electricity. Comparative analyses were conducted to quantify the environmental impact of each transportation method. Results In terms of CO2 footprint, combustion cars exhibited a CO2 release of 159.5 g/km, while electric cars showed 3.43g (2.15 to 4.08% of combustion cars). Drones demonstrated a minimal 0.09g CO2 emission per kilometer (0.066% of combustion car CO2 emission; 2.6% of electric car CO2 emission). The distance traveled by drones was significantly shorter (16.7% to 50%). Additionally, the efficiency of drone transport was emphasized by its ability to avoid traffic jams and associated detours, resulting in time savings ranging from 31.25 to 50% per delivery when comparing transport times during rush hour and regular hours in the selected setting. Conclusions The integration of drone technology emerges as a crucial strategy for establishing a “green laboratory.” The substantial reduction in CO2 emissions and enhanced delivery efficiency position drone transport as a sustainable and operationally effective solution for medical sample logistics, offering shorter delivery times. These findings highlight the potential for transformative change in healthcare logistics, aligning with the global shift towards sustainability and green practices.

Preanalytic Integrity of Blood Samples in Uncrewed Aerial Vehicle (UAV) Medical Transport: A Comparative Study

The integration of unmanned aerial vehicles or uncrewed aerial vehicles (UAVs)—commonly known as drones—into medical logistics offers transformative potential for the transportation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation. Blood samples, including EDTA whole blood, serum, lithium-heparin plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing–Bablok regression and Bland–Altman plots. The results demonstrated that drone transportation maintains blood sample integrity comparable to traditional car transportation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, lithium-heparin, and citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges.

Design of a reverse logistics network for medical waste considering location and routing decisions

Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective Mixed-Integer Linear Programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots).This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed.Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlántico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the Mixed-Integer Linear Programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the Mixed-Integer Linear Programming model.This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the issue from a bi-objective standpoint, jointly considering economic factors and biomedical safety. Additionally, the article introduces realistic and pertinent assumptions that have not been explored in previous literature.

Improving Medical Logistics Resilience in Earthquake Disasters: A Qualitative Case Study from Indonesia

Improving Medical Logistics Resilience in Earthquake Disasters: A Qualitative Case Study from Indonesia

A Serious Game for Enhancing Rescue Reasoning Skills in Tactical Combat Casualty Care: Development and Deployment Study.

Serious games (SGs) have emerged as engaging and instructional digital simulation tools that are increasingly being used for military medical training. SGs are often compared with traditional media in terms of learning outcomes, but it remains unclear which of the 2 options is more efficient and better accepted in the process of knowledge acquisition. This study aimed to create and test a scenario-based system suitable for enhancing rescue reasoning skills in tactical combat casualty care. To evaluate the effectiveness of the SGs, a randomized, observational, comparative trial was conducted. A total of 148 members from mobile medical logistics teams were recruited for training. Pre- and posttraining assessments were conducted using 2 different formats: a video-based online course (n=78) and a game simulation (n=70). We designed 3 evaluation instruments based on the first 2 levels of the Kirkpatrick model (reaction and learning) to measure trainees' satisfaction, knowledge proficiency, and self-confidence. There were 4 elements that made up the learning path for the SGs: microcourses (video-based online courses), self-test, game simulation, and record query. The knowledge test scores in both groups were significantly higher after the intervention (t154=-6.010 and t138=-7.867, respectively; P<.001). For 5 simulation cases, the average operation time was 13.6 (SD 3.3) minutes, and the average case score was 279.0 (SD 57.6) points (from a possible total of 500 points), with a score rate of only 44% (222/500 points) to 67% (336/500 points). The results indicated no significant difference in trainees' satisfaction between the 2 training methods (P=.04). However, the game simulation method outperformed the video-based online course in terms of learning proficiency (t146=-2.324, P=.02) and self-perception (t146=-5.492, P<.001). Despite the high satisfaction reported by trainees for both training methods, the game simulation approach demonstrated superior efficiency and acceptance in terms of knowledge acquisition, self-perception, and overall performance. The developed SG holds significant potential as an essential assessment tool for evaluating frontline rescue skills and rescue reasoning in mobile medical logistics teams.

Automation in Healthcare: Advantages, Prospects, Perceptual Barriers

Introduction. In healthcare, automation refers to the use of technology to replace manual labor with mechanized processes. It offers solutions to some of the biggest challenges in this field by increasing work efficiency and improving the quality of medical care. The aim of the study was to explore the potential impact of automation on healthcare practices and to investigate the perceptions of healthcare professionals towards these innovations. Materials and methods. The study utilized general scientific research methodologies, including analysis and synthesis. Through the examination of secondary data,, the findings of a sociological survey conducted by Google Cloud commissioned by Harris Poll were analyzed and presented in the context of the studied topic. The questionnaire was filled in by 300 doctors across the United States. Results. Over the next years, global automation in healthcare is expected to see a significant growth due to various growth factors including the continuous improvement of healthcare automation technologies, growing public investment, as well as recognition of the benefits of automation in medical logistics processes, laboratory research and other types of medical and related activities. Automation, such as information technology and control systems, reduces individual efforts in multiple procedures.

Developing a responsive medical logistics network during Covid‐19: a study on outbreak in India

Developing a responsive medical logistics network during Covid‐19: a study on outbreak in India

Experiences of Deployed Physicians in Support of Operation Allies Refuge/Operation Allies Welcome: Lessons to Inform Improvements in Training.

Stability operations, including humanitarian assistance and disaster relief missions, are key functions of U.S. Military medicine and the Military Medical Humanitarian Assistance Course (MMHAC) is a 2-day course widely used to prepare military medical personnel for such missions. It focuses on caring for those most vulnerable in the wake of disasters, particularly children. The large-scale humanitarian deployment of military medical providers in support of Operation Allies Welcome/Operation Allies Refuge (OAW/OAR) presents an opportunity to evaluate the preparedness of these providers to care for the needs of the Afghan travelers, so we explored the experiences of military medical providers deployed in support of OAW/OAR to inform improvements in the MMHAC. We conducted a qualitative study of military medical providers who were deployed in support of OAW/OAR using a series of three virtual focus groups. Focus group questions were structured around the main topics covered in the MMHAC (patient care, ethical considerations, logistical concerns, and preventive and public health) and explicitly asked about adaptive leadership challenges faced and strategies used to overcome them. We analyzed transcripts using inductive thematic analysis within a constructivist paradigm, with adaptive leadership as a sensitizing concept. The study was approved by the Institutional Review Board of Uniformed Services University. We constructed 4 themes from participant responses, each addressing challenges that medical providers faced during their mission: (1) Medical providers navigated tension between medical and public health priorities and military mission priorities; (2) Chronic and complex care needs posed unique challenges for medical personnel; (3) Challenges in patient care were compounded by logistical and system-based barriers; and (4) Cultural barriers led to ethical dilemmas that physicians felt inadequately prepared to handle, most notably with respect to gender-related concerns. Within each theme, participants described which aspects of MMHAC training were most helpful and which areas were inadequate. Physicians found the OAR/OAW mission meaningful but also identified challenges related to medical care provision, public health, logistics, and ethical dilemmas that hindered their ability to carry out their medical mission. Lessons learned from OAW/OAR highlight several areas in which the MMHAC training could be augmented and improved to further mitigate these challenges.

Design and analysis of a lower limb assistive exoskeleton robot.

In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

Unmanned high speed aerial vehicles in medical logistics: A comparative analysis of blood sample transportation with weather variability

Unmanned high speed aerial vehicles in medical logistics: A comparative analysis of blood sample transportation with weather variability

Frontline health workers’ experiences of providing care for people living with non-communicable diseases during the COVID-19 pandemic in Ghana: a qualitative study

BackgroundThe COVID-19 pandemic has significantly impacted frontline health workers. However, a neglected dimension of this discourse was the extent to which the pandemic impacted frontline healthcare workers providing non-communicable diseases...

Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand

With the development of the healthcare industry, the demand for medical services and protective equipment is boosted, causing the generation rate of infectious medical waste to increase rapidly. Therefore, it is of utmost importance for decision makers to effectively predict the potential risks and propose corresponding solutions. This paper investigates the reverse logistics network optimization for medical waste under the conditions of an uncertain proposal demand. Firstly, a prediction model of medical waste based on the SEIR epidemiological dynamics is constructed, in which both routine and public health emergency scenarios are simultaneously considered. Secondly, a bi-objective location-routing optimization model for a medical waste reverse logistics network is proposed, by simultaneously optimizing the total economic cost and potential risk throughout the entire logistics process. Subsequently, an NSGA-II algorithm is designed for a model solution in response to the model’s characteristics. The epidemiological dynamics-based prediction model is validated by the real case to be scientifically effective in predicting the amount of generated medical waste with a mean absolute percentage error (MAPE) of 18.08%. The constructed reverse logistics network model and the NSGA II algorithm provide a medical waste process center location, transportation routing, and vehicle selection solutions for both routine and emergency public health cases of Xi’an city with large, medium, and small scales. The above results indicate that the research scheme proposed in this paper could significantly reduce the medical waste logistics-related risks and costs and provide decision makers with more safe and reliable logistical solutions.

Cyber-Physical Internet (CPI)-enabled logistics infrastructure integration framework in the greater bay area

The emerging digital economy has facilitated the digital transformation of the logistics industry toward a standard and collaborative state. The logistics infrastructure has accordingly attracted much attention as the basis for supporting digital economy implementation. Moreover, the initiatives of regional collaborative development, such as in the Greater Bay Area (GBA) of China, have accelerated the integration of logistics infrastructure and operation-level collaboration. Fortunately, the Cyber-Physical Internet (CPI) has created new opportunities for promoting infrastructure integration through the cyber, physical and internet dimensions. However, regional infrastructure integration in the GBA faces various challenges associated with CPI network operations. Thus, a bottom-up approach is proposed to facilitate logistics infrastructure integration in the CPI environment based on the conceptual fusion of computer networks. Considering the fundamental difference in network fusion and operation mechanisms between CPI and computer networks, a CPI-LI2 (logistics infrastructure integration) framework is designed as an integrated solution. Finally, a simulation study is conducted using real-life medical logistics data from the GBA to verify the effectiveness of the CPI-LI2 framework. Experiments show that the framework is well adapted to large-scale logistics networks with dynamically changing links.

AN ALGORITHM FOR SOLVING A TWO-STAGE CONTINUOUS-DISCRETE LOCATION PROBLEM FOR MEDICAL LOGISTICS OPTIMIZATION

The research paper focuses on logistics optimization, a critical component in supply chain management across various sectors, including healthcare. Efficient coordination of medical logistics is essential for maintaining public health and welfare, particularly during global emergencies where quick and effective distribution of medicine is crucial. This study aims to create and analyze a model and algorithm for a two-stage continuous-discrete location problem within medical logistics applications. We present a mathematical model tailored for a two-stage continuous-discrete location problem in medical logistics, considering the unique aspects of this field. The solution algorithm combines genetic methods with the optimal partition of sets theory. Additionally, we demonstrate the algorithm's effectiveness through a software application, using it to solve a representative model problem.