An Optimization Model for Managing the Drug Logistics Process in a Public Hospital Supply Chain Integrating Physical and Economic Flows

Research on contactless intelligent medication pickup mode selection based on a hospital in China under COVID-19

BACKGROUND

During an outbreak such as COVID-19, hospital staff needs to be in close contact with all types of patients visiting the hospital and the risk of cross-infection is extremely high. Payment and medication pickup is a mandatory part of a patient's hospital visit, with direct contact between healthcare workers and patients, and long waiting times in the hospital area, which can easily lead to the spread of disease infection.

OBJECTIVE

This paper designed the prototype of a contactless smart medicine cabinet based on RFID technology and optimized the patient consultation and medication pickup process to address these problems.

METHODS

We conducted a 50-day field observation of patients for consultation and medication pickup at the First Hospital in H city, Jiangsu Province, China, and randomly timed 1600 sets of data from Surgery (ophthalmology) and Internal patients, then we designed the prototype of a contactless smart medicine cabinet based on RFID technology, optimized the patient consultation and medication pickup process, comparing the traditional and intelligent models using AnyLogic.

RESULTS

The results show that this contactless medicine cabinet was able to reduce the time taken by patients in consultation and medicine pickup by 18.74 minutes, increasing the overall efficiency of the consultation by 32.20%. The simulation revealed that this contactless intelligent medication pickup model was able to reduce the time taken by patients in consultation and medicine pickup, increasing the overall efficiency of the consultation, effectively reducing the frequency of contact between healthcare workers and patients, and reducing the risk of disease infection.

CONCLUSION

The proposed technical model provides a new idea to solve the problems of long queues, low efficiency and high risk of infection for patients to consult and get medicine during epidemics. Especially within hospitals it has important theoretical and practical implications for epidemic prevention and control as well as future hospital management.

Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19

As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.

Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain

Drug shortage is always a critical issue of inventory management in healthcare systems since it potentially invokes several negative impacts. In supply chain management, optimization goes hand-in-hand with inventory control to address several issues of the supply, management, and use of drugs. However, it is difficult to determine a shortage situation in a hospital due to multiple unpredictable reasons, such as manufacturing problems, supply and demand issues, and raw material problems. To avoid the shortage problem in a hospital, efficient inventory management is required to operate the system in a sustainable way and maximize the profit of the organization in the Hospital Supply Chain (HSC). In this work, we study a drug refilling optimization problem, a general model for drug inventory management in a hospital. We then investigate a Deep Reinforcement Learning (DRL) model to address this problem under an online solution that can automatically make a drug refilling decision in order to prevent a drug shortage. We further present a numerical result to verify the performance of the proposed algorithm, which outperforms the baselines (e.g., over-provisioning, ski-rental, and max-min) in terms of the refilling cost and the shortage rate.