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

1. Introduction

The year 2020 saw a major global outbreak of the COVID-19 epidemic. In the early stages of the epidemic, patients flocked to hospitals due to a lack of awareness of the disease, combined with a high number of infections and a shortage of medical resources, a situation that led to outbreaks gathering in hospitals from time to time. With the gradual advancement of prevention and control measures, the epidemic was effectively controlled. However, nowadays, health care workers have to deal with different patients every day in the hospital, and the risk of cross-infection with various diseases for them is extremely high. Particularly, during the severe period of the epidemic, some hospitals were turned into designated hospitals for the treatment of COVID-19, and the demand for medical treatment for residents within the coverage of the designated hospitals was compressed. After the epidemic subsided, the demand for medical care from this segment of the population was released, causing overcrowding in hospitals Although measures such as taking temperature and scanning health codes before entering the hospital can prevent new cases of COVID-19 from entering the hospital to a certain extent, there is still a risk of cross-contamination of diseases due to the overcrowding inside the hospital.

Picking up medication is a necessary part of a patient’s visit to the hospital after treatment. The general flow of medical treatment is that the patient makes an appointment at the hospital, goes to the designated consultation room, is seen by a professional physician, the physician completes the diagnosis and issues a prescription, and the patient pays for the medication. Patients need to queue up to pay for their prescriptions, queue up for their names to appear on the electronic screen in the pharmacy, and then go to the corresponding window to pick up their medications. The whole process takes a lot of time and the queuing process is crowded, with direct contact between patients and patients, and between health care workers and patients. In particular, some patients only need to be prescribed medication but still need to go through the whole process, which can take a lot of time to queue up for medication even without queuing for a consultation. The traditional way of picking up medication for a consultation creates a congregation of people, consuming a lot of time and energy for both healthcare professionals and patients, with a greater risk of cross-contamination of diseases.

Long queues, cumbersome business processes and direct contact between doctors and patients are the main problems in hospital visits, especially in the process of picking up medicines. During the consultation session, doctors need to interact with patients face-to-face. However, in the payment and medication pickup process, information technology can be used to improve the efficiency of the existing business process and achieve quick and contactless medication pickup, which will reduce the waiting time in queues and direct contact between doctors and patients. Information technology has promoted the development of medical health. Advanced medical equipment and technology have provided patients with more accurate and safe treatment plans, and improved the overall management level of hospitals in equipment, drugs, professionals and other aspects [1]. It is very important to ensure the safety of medication for patients during the COVID-19 epidemic. Exploring the relevant methods of drug supply and drug service management in hospitals during the COVID-19 epidemic can provide some experiences for epidemic prevention and control [2]. This study proposes a contactless intelligent medication pickup management model based on RFID technology to address the current problems of patients in the process of medication pickup in hospitals, and designs the smart medicine cabinet, which can effectively optimize the process of patients’ medication pickup and save patients’ waiting time in queues, also reduce crowding and the risk of spreading disease infection. It has important theoretical and practical significance for the prevention and control of epidemics and the improvement of hospital medicine management.

2. Literature review

Pharmacy pickup is one of the key aspects of drug supply management in hospitals. Inadequate inventory, distribution errors, delays in procurement and dispensing, and poor storage management are common problems in drug management. Wijegunasekara analyzed the drug supply situation in Sri Lanka health care facilities and proposed strategies for it [3], and Kumar adopted the fuzzy AHP method to study the time and cost of public drug distribution in India from the aspects of warehouse design management, supplier selection and others, which provided a reference for pharmaceutical supply chain [4]. Bam et al. used system dynamics to study inventory management and demand forecasting for a tuberculosis drug and proposed three Pareto safety stock management strategies [5], Purwaningsih et al. used ABC and VEN analysis methods to classify diabetes drugs in Insan Permata Hospital, to make rational drug planning and reduce drug shortage [6], while Kees et al. used a mixed-sum integer linear programming model to study the drug procurement and distribution problem in the hospital supply chain [7]. Drug distribution errors can lead to serious consequences, Schwartz and Kravitz conducted a study on clinical pharmacist intervention programs to minimize drug-related errors in clinical medicine [8]. Clinical drug demand is somewhat stochastic and requires certain human and material resources to achieve, Jurado et al. used an opportunity constraint model predictive control approach to meet clinical demand for drugs with limited resources [9], and Abu Zwaida et al. used the deep reinforcement learning model to study the problem of hospital drug inventory management, to solve the drug supply shortage, reduce the cost of drug filling and maximize the profit of hospital supply chain [10]. Lean management has been used more often in inventory supply chain management, and Regattieri et al. implemented lean management of the drug supply chain in the Policlinico Sant’Orsola, the largest hospital in Bologna, Italy [11]. The COVID-19 pandemic has caused a shortage of medical products, prompting countries around the world to urgently authorize the use of essential medical products such as ventilators, personal protection (facemasks) and diagnostic tests made by non-medical companies [12]. The Internet hospital drug delivery platform model is adopted in Jordan during COVID-19, Hammour et al. analyzed the running condition of the platform, which has facilitated patients’ access to prescription drugs during COVID-19 [13]. Current research at the drug supply management level is more likely to look at the problem itself, examining the causes of the problem and proposing appropriate solutions from policy, methodological and strategic perspectives.

RFID, sensors and other core technologies of the Internet of Things have provided new ideas for medication management. Liu et al. proposed a drug distribution management scheme based on the Internet of Things, using RFID electronic tags to record drug information, which can effectively improve the safety of drug distribution and reduce the cost of drug distribution management [14]. Huang and Parida et al. proposed an RFID-based medication management system capable of tracking various types of medications to solve the problem of counterfeit medications and medical errors [15, 16], and Li and Wang proposed a drug anti-counterfeiting traceability scheme based on RFID and blockchain technology, to realize the whole process of drug information traceability from production to sales and circulation [17]. Rayan and Zubair proposed an IoT-Integrated Blockchain pharmaceutical supply chain framework, to strengthen pharmaceutical supply chain management and reduce the occurrence of counterfeit drugs [18], and Jones et al. developed a Ziplock system based on RFID technology to track patients’ medication use, to reduce the occurrence of medication errors or dosage errors [19]. Wu et al. designed an inpatient care medication management system capable of automatically identifying patients and tracking their medication use through RFID tags, making medication safety more reliable [20]. Barcodes have limitations in adverse drug event management due to their small data capacity, and Houliston designed an anesthetic drug identification system based on RFID technology [21]. Adverse drug reactions are a key concern in the process of drug use and management, Jara et al. and Nakhla et al. proposed an IoT-based drug screening system and prevention system [22, 23], while Pérez et al. developed a hybrid intravenous drug administration traceability program based on RFID [24] to reduce the occurrence of adverse drug events in hospitals and improve the safety of patient’s medication use. During patient treatment, medication use requires focused monitoring, and Gupta et al. designed an IoT-based medication monitoring model to monitor the use of available medications in real time [25], while Huang et al. integrated miniaturized spiral antennas, RFID tags and medication libraries to monitor medication doses in real-time [26]. BAINI and ARAMBAWALA evaluated the impact of RFID technology on the safety and traceability of inpatient medication use [27], while Anif et al. proposed an RFID-based hospital medication tracking model-HoMeTrack to track medication use based on prescriptions and patient medical records to reduce medication errors [28]. In addition to the problems of counterfeit drugs, adverse drug reactions and medication errors, the business model of the pharmaceutical supply chain, information transmission mechanism, data collection and tracking are also important directions of concern for related scholars. Liu and Jia used the e³-value methodology to create a value-based business model of the pharmaceutical supply chain based on IoT [29], Yan and Huang proposed an IoT-based supply chain information transmission model for the pharmaceutical industry to solve the traditional pharmaceutical supply chain information transmission lag problem [30]. Meanwhile, Coustasse et al. further clarified the important role of RFID in tracking and managing the entire pharmaceutical supply chain [31], while Jones et al. integrated RFID into hospital pharmacy ERP systems to achieve inventory management of important drugs and vital medical supplies for surgical items [32]. The actual performance of RFID tags of different frequencies in the pharmaceutical supply chain can also vary, Catarinucci et al. evaluated the performance of several commercial UHF RFID tags [33], while the sensing middleware, communication authentication protocols of RFID and the integrated use of technologies such as Bluetooth and barcodes throughout the pharmaceutical supply chain [34, 35, 36, 37] are also key to achieving device management, medication tracking and patient monitoring.

Artificial Intelligence is revolutionizing the healthcare industry, with a wide range of applications in medical devices, drug management and other aspects, but it also raises ethical, privacy and regulatory issues [38]. Telemedicine and artificial intelligence can improve medication adherence, optimize drug therapy regimens, and help medical staff to guide patients to achieve optimal health [39]. Zhao et al. proposed a contactless artificial intelligence framework to detect patients’ errors in the process of drug self-administration and improve drug safety for patients [40]. At the same time, artificial intelligence technology can upgrade the quality management level of drug manufacturers, improve the potential safety problems of drug production effectively, and reduce the safety risks of drugs [41]. Chefchaouni et al. compared and analyzed the differences in human, technical and environmental risks between robotic and manual preparation of anticancer drug modes, to evaluate the role of robots in reducing the risk of drug preparation [42]. Blockchain technology is playing an important role, and has been widely used in the field of pharmaceutical management. The blockchain-based drug supply chain management system enables the monitoring and tracking of drugs at all stages [43], effectively solving the problem of counterfeit drugs and forming an intelligent healthcare ecosystem [44]. The drug tracing and supervision blockchain system-Drugledger proposed by Huang et al. ensures the authenticity and privacy of drug tracing data [45], Kumar and Tripathi use blockchain and encrypted QR (quick response) codes to trace medicines from manufacturer to consumer to address the problem of counterfeit medicines [46]. Smart cabinets have also been used in real-life, Li et al. designed a smart drug management system based on RFID, which consists of a smart monitoring cabinet and microcomputer system, to inventory, query and locate drugs [47], while del Carmen León-Araujo et al. evaluated the effectiveness of smart cabinets using RFID technology to improve cardiac thoracic surgery inventory management and work efficiency and to achieve traceability of medical materials such as drugs [48].

In summary, a series of studies have been carried out in the field of pharmaceutical management, but most of them focus on the entire pharmaceutical supply chain or pharmaceutical management within healthcare, and less on the issue of medication pick-up. In this paper, the main research content is the queuing of medication pick-up in hospitals in the context of the COVID-19 epidemic, combined with RFID technology and smart cabinets, and the previous research results provide the corresponding theoretical and methodological basis for our research work.

3. Technology and methods

4. Data source

8. Conclusion

This article investigates the problems of crowding, long queues and low efficiency in the process of patients’ traditional consultation and medication pickup, and designs an intelligent consultation and medication pickup model based on RFID technology and smart medicine cabinets. The main contributions of this article are as follows:

(1)

Based on the optimization of the flow of patients’ consultation and medication pickup in the hospital, a traditional consultation and medication pickup model and an intelligent consultation and medication pickup model were constructed. A simulation study was carried out on the patients’ consultation and medication pickup in the two models, distinguishing between internal medicine and surgical patients.

However, this article also has certain shortcomings, such as the number of patients attending the hospital and the consultation time can be disturbed by external factors, the input cost of the smart medicine cabinet and the possible economic or social benefits need to be considered comprehensively. In future research, these actual situations will be further considered and more in-depth research will be conducted to address these issues.