BACKGROUND: The use of drones in the health care sector is increasingly being discussed against the background of the aging population and the growing shortage of skilled workers. In particular, the use of drones to provide medication in rural areas could bring advantages for the care of people with and without a need for care. However, there are hardly any data available that focus on the interaction between humans and drones.
OBJECTIVE: This study aims to disclose and analyze factors associated with user acceptance of drone-based medication delivery to derive practice-relevant guidance points for participatory technology development (for apps and drones).
METHODS: A controlled mixed methods study was conducted that supports the technical development process of an app design for drone-assisted drug delivery based on a participatory research design. For the quantitative analysis, established and standardized survey instruments to capture technology acceptance, such as the System Usability Scale; Technology Usage Inventory (TUI); and the Motivation, Engagement, and Thriving in User Experience model, were used. To avoid possible biasing effects from a continuous user development (eg, response shifts and learning effects), an ad hoc group was formed at each of the 3 iterative development steps and was subsequently compared with the consisting core group, which went through all 3 iterations.
RESULTS: The study found a positive correlation between the usability of a pharmacy drone app and participants\' willingness to use it (r=0.833). Participants\' perception of usefulness positively influenced their willingness to use the app (r=0.487; TUI). Skepticism had a negative impact on perceived usability and willingness to use it (r=-0.542; System Usability Scale and r=-0.446; TUI). The study found that usefulness, skepticism, and curiosity explained most of the intention to use the app (F3,17=21.12; P<.001; R2=0.788; adjusted R2=0.751). The core group showed higher ratings on the intention to use the pharmacy drone app than the ad hoc groups. Results of the 2-tailed t tests showed a higher rating on usability for the third iteration of the core group compared with the first iteration.
CONCLUSIONS: With the help of the participatory design, important aspects of acceptance could be revealed by the people involved in relation to drone-assisted drug delivery. For example, the length of time spent using the technology is an important factor for the intention to use the app. Technology-specific factors such as user-friendliness or curiosity are directly related to the use acceptance of the drone app. Results of this study showed that the more participants perceived their own competence in handling the app, the more they were willing to use the technology and the more they rated the app as usable.

The management of health supplies in public hospitals has been a major concern of national and European institutions over time, often being a field of reforms and regulatory interventions. Health procurement systems constitute complex decision-making and supply chain management mechanisms of public hospitals, involving suppliers, health providers, administrators and political bodies. Due to this complexity, the first important decision to be taken when designing a procurement system, concerns the degree of centralization, namely to what extent the decision-making power on the healthcare procurement (what, how and when) will be transferred either to a central public authority established for this purpose, or to the competent local authorities. In this perspective, we attempt to analyse the types of public procurement in the healthcare sector of the European Union, in terms of degree of centralization. Employing a narrative approach that summarizes recent interdisciplinary literature, this perspective finds that the healthcare procurement systems of the EU Member States, based on the degree of centralization, are categorized into three types of organizational structures: Centralized, Decentralized and Hybrid procurement. Each structure offers advantages and disadvantages for health systems. According to this perspective, a combination of centralized and decentralized purchases of medical supplies represents a promising hybrid model of healthcare procurement organization by bringing the benefits of two methods together.

BACKGROUND: The shortage of medicines and medical supplies remains to be a major issue that is facing public health facilities in Tanzania. This situation has been influenced by lack of consistency in the supply chain, increase in healthcare demand, poor regulatory system, insufficient funds, and lack of conducive infrastructure. Formerly, the Government initiatives such as engagement with the Prime Vendor System (PVS) demonstrated great assistance in getting rid of this challenge. Despite the operation of PVS, a recent shortage of medicines and medical supplies has been noticed.
OBJECTIVE: This study aimed to assess the effectiveness of PVS on the availability of medicine and medical supplies in the selected public health facilities in Arusha District Council.
METHODS: The study used a case study design with mixed research approach. The study involved 77 respondents which included 25 health facility in-charges, 50 patients, 1 District Pharmacist and 1 Prime Vendor. Questionnaires, interviews, and observation methods were used to collect data. Data collected covered a period of 2021-2022. Thematic analysis was used to analyze the qualitative data whereas descriptive analysis was used to analyze the quantitative data with the help of Excel and the Statistical Package for Social Sciences (SPSS) version 28.0.
RESULTS: The analysis indicates that PVS is not completely effective in supplying medicines and medical supplies due to its low capacity to conform to the orders placed by the public health facilities, a lack of supply competition, and a failure to adhere to contractual terms. Furthermore, at the time of data collection, the average availability of medicines and medical supplies at the selected public health facilities was 74.8%, while 80% of the selected public health facilities reported having a scarcity of medicines and medical supplies, and 92% of the interviewed patients reported having no full access to medicines.
CONCLUSIONS: Despite the shortcomings associated with the operation of the PVS, the system still seems to be very important for enhancing the availability of medicines and medical supplies once its effectiveness is strengthened. This study recommends a routine monitoring of PVS operations and timely interventions to reinforce an adherence to the contracted terms and improve PVS effectiveness.

For three years, a large amount of manufactured pollutants such as plastics, antibiotics and disinfectants has been released into the environment due to COVID-19. The accumulation of these pollutants in the environment has exacerbated the damage to the soil system. However, since the epidemic outbreak, the focus of researchers and public attention has consistently been on human health. It is noteworthy that studies conducted in conjunction with soil pollution and COVID-19 represent only 4 % of all COVID-19 studies. In order to enhance researchers\' and the public awareness of the seriousness on the COVID-19 derived soil pollution, we propose the viewpoint that \"pandemic COVID-19 ends but soil pollution increases\" and recommend a whole-cell biosensor based new method to assess the environmental risk of COVID-19 derived pollutants. This approach is expected to provide a new way for environmental risk assessment of soils affected by contaminants produced from the pandemic.

The outbreak of Coronavirus disease 2019 (COVID-19) highlights the importance of sufficient medical supplies stockpiling at the pre-event stage. In contrast, the potential disadvantages of maintaining adequate items at strategic locations (i.e., reserves) are considerable inventory-related costs. Unpredicted demand leads to a high degree of uncertainty. Efforts to mitigate the uncertainty should rely not only on prepositioning supplies at reserves but also on integrating various channels of medical materials. This paper proposes multi-mitigation strategies in medical supplies to ensure uninterrupted supply for hospitals and significant savings by introducing two-type suppliers, reserving and manufacturing suppliers. Thus, each hospital with uncertain demand is enabled to be served by various channels during pandemics: prepositioning in reserves, backups served by reserving suppliers, and medical commodities produced by manufacturing suppliers. Stochasticity is also incorporated into the raw materials available to produce. This research aims to develop an emergency response application that integrates preparedness action (reserve location, inventory level, and contract supplier\'s selection) with post-event operations (allocating medical materials from various channels). We formulate a two-stage stochastic mixed integer program to determine prepositioning strategy, including two-type suppliers\' selection, and post-event allocation of multiple sources. A branch-and-Benders-cut method is developed for this problem and significantly outperforms both the classical Benders decomposition and Gurobi in the solution time. Different-sized test instances also verify the robustness of the proposed method. Based on a realistic and typical case study (inspired by the COVID-19 pandemic in Wuhan, China), significant savings, an increase in inventory utilization and an increase in demand fulfilment are obtained by our approach.

In the early days of the COVID-19 pandemic in Wuhan, there was an unreasonable allocation between hospitals and a lack of timely transportation of medical supplies, which reduced the cure rate of infected cases. To solve the problem, this research proposes a method for scheduling medical supplies in major public health emergencies to develop a rapid and accurate supply scheme for medical materials, including the allocation of medical materials per vehicle to each hospital and the supply sequence per vehicle to each hospital. Specifically, this paper solves the following two sub-problems: (1) calculating the shortest transportation times and the corresponding routes from any distributing center(s) to any hospital(s); (2) calculating the medical supplies per vehicle transporting to each hospital. The method of solving sub-problem 1 is performed by multiple iterations, each of which calculates the shortest route from a distributing center, through one or more hospitals, and back to the distributing center. According to sub-problem 2, this research proposes a distribution model of medical supplies in major public health emergencies. A multiple dynamic programming algorithm which is a combination of some separated dynamic programming operations is proposed to solve this model. This algorithm also realizes the rapid updating of the scheme in the context of the changing number of vehicles. The first sub-problem can be solved in normal times, while the second one should be solved on the premise of obtaining the corresponding data after the occurrence of a major public health emergency. In the case study section, the whole method proposed in this research is employed in the medical supplies scheduling in the early stage of the COVID-19 outbreak in Wuhan, which proves the availability of the method. The main innovation of the method proposed in this research is that the problems can obtain the optimal solution while the time complexity is within an acceptable range.

In the COVID-19 pandemic, it is essential to transport medical supplies to specific locations accurately, safely, and promptly on time. The application of drones for medical supplies delivery can break ground traffic restrictions, shorten delivery time, and achieve the goal of contactless delivery to reduce the likelihood of contacting COVID-19 patients. However, the existing optimization model for drone delivery is cannot meet the requirements of medical supplies delivery in public health emergencies. Therefore, this paper proposes a bi-objective mixed integer programming model for the multi-trip drone location routing problem, which allows simultaneous pick-up and delivery, and shorten the time to deliver medical supplies in the right place. Then, a modified NSGA-II (Non-dominated Sorting Genetic Algorithm II) which includes double-layer coding, is designed to solve the model. This paper also conducts multiple sets of data experiments to verify the performance of modified NSGA-II. Comparing with separate pickup and delivery modes, this study demonstrates that the proposed optimization model with simultaneous pickup and delivery mode achieves a shorter time, is safer, and saves more resources. Finally, the sensitivity analysis is conducted by changing some parameters, and providing some reference suggestions for medical supplies delivery management via drones.

In this paper, we construct the first stochastic Generalized Nash Equilibrium model for the study of competition among countries for limited supplies of medical items (PPEs, ventilators, etc.) in the disaster preparedness and response phases in the Covid-19 pandemic. The government of each country is faced with a two-stage stochastic optimization problem in which the first stage is prior to the pandemic declaration and the second stage is post the pandemic declaration. We provide the theoretical constructs, a qualitative analysis, and an algorithm, accompanied by convergence results. Both illustrative examples are presented as well as algorithmically solved numerical examples, inspired by the need for N95 masks and ventilators. The results reveal that, in addition to the preparedness of countries before the pandemic declaration, their ability to adapt to the conditions in different scenarios has a significant impact on their overall success in the management of the pandemic crisis. The framework can capture competition for other medical supplies, including Covid-19 vaccines and possible treatments, with modifications to handle perishability.

Background: Analyzing purchased health commodities based on their budgetary consumption and importance is crucial for efficient utilization of a hospital\'s budget. However, it is rarely seen when hospitals, mostly in developing countries, conduct such kinds of analyses and make an informed decision, including to utilize their limited budget efficiently. Therefore, the purpose of this study was to analyze a 3-year inventory of health commodities (medicines, medical supplies, and laboratory reagents and chemicals) in Saint Paul Hospital Millennium Medical College (SPHMMC). Methodology: The study was conducted in SPHMMC located in Addis Ababa, Ethiopia. It is one of the largest specialized public hospitals in the country. It is a huge teaching hospital in the country where a large amount of budget is utilized. Three years of data were collected and ABC, VEN, and ABC-VEN matrix techniques were applied for the analysis. The data collection period was from March to April 2017. Results: An average of 296 medicines, 194 laboratory commodities, and 105 medical supplies were purchased over 3 years. Class A medicines, which consume 80% of the total annual pharmaceutical expenditures (APE) account, are 17.8%-20% of the total medicines by quantity. Antibiotics (ceftriaxone 1 gm injection and metronidazole), IV fluids (sodium chloride 0.9% injection and dextrose 40% injection), and antidiabetic medication (insulin zin suspension and metformin) are among the top 10 medications by value that consume significant amounts of the budget of the hospital. On VEN analysis, an average of 24% of the items were vital, 67% were essential, and 4-8-8.9% were nonessential. Nonessential items consumed 0.49%, 9.9%, and 1.1% of Annual Expenditures (AEs) in 2013/14, 2014/15 and 2015/16, respectively. On ABC-VEN matrix analysis, a single expensive and nonessential medicine (valganciclovir HCL 450 mg tablet) consumed 9.4% of expenditure in 2014/15. Class A laboratory commodities, which consume 70%-80% of the total laboratory expenditures represented 8.5%-20% of the total laboratory commodities analyzed for the 3 years. From class A items, antimonoclonal antibodies in 2013/14, hemocue glucose 201 4 × 25 tests in 2014/15, and glucose tests in 2015/16 consumed the highest percentages: 9.2%, 8.2%, and 23.7% of the AEs, respectively. There were laboratory commodities procured out of the VEN list, and these accounted for 6.8%-31.2% of the total laboratory expenditures over the 3 years. Class A medical supplies, which consumed 80% of the total medical supply expenditures, represented only 8.2%-15.8% of the total items over the 3 years studied. Surgical gauze 90 cm × 100 m, surgical gloves sterile latex number 7.5, and examination gloves were the top three based on expenditures in all the studied years. In 2015/16, examination gloves alone consumed 71.9% of the total expenditure. Conclusion: SPHMMC manages large numbers of health commodities (more than 500 excluding program commodities) which necessitate efficient inventory management practice in place. However, the purchase of the commodities particularly those products used for laboratory diagnosis is not strictly based on the hospital\'s VEN list, indicating the need for better communication of the laboratory unit with the Drug and Therapeutic Committee (DTC) of the hospital. The DTC of the hospital should update the VEN list of the health commodities and strictly enforce the hospital procurement to adhere to the agreed upon list of medicines. In addition, the hospital should prioritize and decide the quantity and frequency of ordering health commodities based on regular ABC-VEN results.

In this paper, we study the competition of healthcare institutions for medical supplies in emergencies caused by natural disasters. In particular, we develop a two-stage procurement planning model in a random environment. We consider a pre-event policy, in which each healthcare institution seeks to minimize the purchasing cost of medical items and the transportation time from the first stage, and a recourse decision process to optimize the expected overall costs and the penalty for the prior plan, in response to each disaster scenario. Thus, each institution deals with a two-stage stochastic programming model that takes into account the unmet demand at the first stage, and the consequent penalty. Then, the institutions simultaneously solve their own stochastic optimization problems and reach a stable state governed by the stochastic Nash equilibrium concept. Moreover, we formulate the problem as a variational inequality; both the discrete and the general probability distribution cases are described. We also present an alternative formulation using infinite-dimensional duality tools. Finally, we discuss some numerical illustrations applying the progressive hedging algorithm.