Hospital national class medicine electronic prescription circulation management method and system

Abstract

The invention discloses a hospital national class medicine electronic prescription circulation management method and system, and the method comprises the steps: generating standardized electronic prescription data through obtaining the medicine information of a patient, and judging the inventory condition of a hospital through a medicine inventory matching algorithm. And if there is no inventory, transmitting the prescription data to a drugstore system, matching the medicines by adopting an automatic identification technology, and completing allocation. Afterwards, a logistics distribution algorithm is applied to determine an optimal distribution scheme, and real-time distribution information is updated to a patient side and a hospital side. The patient can acquire the distribution information through the mobile application program, select the distribution mode and confirm receiving after receiving the medicine, so that closed-loop management of prescription circulation is realized. According to the invention, links such as electronic prescription, intelligent allocation, logistics distribution and the like are integrated, so that the prescription circulation efficiency is improved, the medication experience of patients is optimized, and the whole-process intelligent management from prescription extraction to distribution is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic prescription circulation management of drugs, and particularly relates to a method and system for electronic prescription circulation management of national negotiation drugs in hospitals. Background Art

[0002] In the electronic prescription circulation management of national negotiation drugs in hospitals, currently, an electronic prescription circulation platform is mainly used to achieve the transmission of prescription information and drug dispensing. Through data interaction among multiple systems such as the hospital HIS system, the intelligent medical insurance platform, and the pharmacy settlement system, this platform realizes information sharing and collaboration. When the hospital does not have the drugs required by the patient, the electronic prescription issued by the doctor can be uploaded to the intelligent medical insurance platform through an interface. The patient can choose to purchase drugs at designated "dual-channel" pharmacies and achieve real-time reimbursement. In addition, some places also cooperate with third parties such as the post office to provide drug delivery services, further facilitating patients.

[0003] Although the electronic prescription circulation platform has improved the efficiency and convenience of drug dispensing to a certain extent, there are still some problems. First, the transmission of prescription information is not timely, and there may be delays in information docking between the hospital and the pharmacy, resulting in patients having to wait a long time to obtain drugs. Second, the drug dispensing efficiency is low. After receiving the prescription, due to the limitations of manual operations, the pharmacy has a low dispensing efficiency and cannot guarantee accuracy. In addition, after receiving the drugs dispensed by the pharmacy, patients usually need to go to the pharmacy to pick up the drugs in person, which increases the difficulty of seeking medical treatment for patients with limited mobility. The existence of these problems not only affects the patient's medical experience but also poses challenges to the operation efficiency of hospitals and pharmacies. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a method and system for electronic prescription circulation management of national negotiation drugs in hospitals to solve the problems existing in the above prior art.

[0005] To achieve the above object, in a first aspect, the present invention provides a method for electronic prescription circulation management of national negotiation drugs in hospitals, including:

[0006] Obtain the patient's identity information and the required drug information, and generate standardized electronic prescription data;

[0007] For the electronic prescription data, use a preset drug inventory matching algorithm to determine whether the required drugs exist in the hospital inventory. If they exist, directly generate a drug dispensing instruction. If not, transmit the electronic prescription data to a preset pharmacy drug dispensing system in real time;

[0008] In the pharmacy drug dispensing system, an automated drug identification technology is adopted to match the drugs in the pharmacy inventory according to the drug information in the electronic prescription data, generate a drug dispensing list, and complete the sorting and packaging of drugs through an automated dispensing device;

[0009] According to the drug dispensing list and patient identity information, a logistics distribution scheduling algorithm is adopted to determine the optimal drug delivery route and delivery method, generate a drug delivery instruction, and update the delivery information to the patient side and the hospital side in real time;

[0010] On the patient side, the drug delivery information, including the delivery time, delivery address, and drug list, is obtained through a mobile application. If the patient selects the pick-up method of delivering the drugs to home, a delivery order is generated and transmitted to the logistics system in real time;

[0011] After the drug delivery is completed, the patient side confirms the receipt of the drugs through the mobile application and feeds back the receipt information to the hospital electronic prescription system to complete the closed-loop management of the prescription flow.

[0012] Preferably, generating standardized electronic prescription data includes:

[0013] According to the patient identity information, obtain the patient's electronic medical record data and extract the diagnosis information;

[0014] Access the drug information database to match and obtain the required drug information;

[0015] If the drug information is incomplete, supplement and determine the drug specifications and dosage information according to the diagnosis information and medication specifications;

[0016] Associate the patient identity information with the drug information to generate structured electronic prescription data;

[0017] Adopt the national standard electronic prescription format specification to perform format conversion on the above prescription data to obtain a standardized electronic prescription.

[0018] Preferably, adopting a preset drug inventory matching algorithm to determine whether the required drugs exist in the hospital inventory includes:

[0019] Obtain the electronic prescription data, extract the drug information in the prescription, and construct structured drug demand data;

[0020] Access the preset hospital drug inventory database to obtain the information of various drugs in the current inventory;

[0021] Match the drug demand data with the hospital inventory data, adopt a similarity calculation algorithm to determine whether the required drugs exist in the inventory, and obtain a matching result;

[0022] If the matching result indicates that the required drug is in the hospital inventory, a corresponding drug dispensing instruction is generated according to the drug demand data, and the instruction is sent to the hospital pharmacy management system;

[0023] If the matching result indicates that the required drug does not exist in the hospital inventory, the drug demand data is packaged and transmitted in real time to the drug dispensing system of the pharmacy through a preset data interface.

[0024] Preferably, the sorting and packaging of drugs by an automated dispensing device includes:

[0025] According to the drug information in the electronic prescription data, natural language processing technology is used to extract and structure the drug information to obtain a standardized drug identification result;

[0026] The drug identification result is matched with the drug information in the pharmacy inventory management system, and a similarity calculation algorithm is used to determine whether the drug in the electronic prescription exists in the pharmacy inventory. If it exists, the corresponding inventory quantity and location information are obtained;

[0027] According to the drug quantity in the electronic prescription and the pharmacy inventory situation, a drug dispensing list is generated, where the drug dispensing list includes the drug name, specification, quantity, and inventory location;

[0028] The drug dispensing list is sent to the automated dispensing device. According to the drug location information in the dispensing list, the drug is automatically retrieved by a robotic arm or conveyor belt and placed in the designated packaging area.

[0029] Preferably, generating a drug delivery instruction and updating the delivery information in real time to the patient side and the hospital side includes:

[0030] According to the drug dispensing list, the drug information to be delivered is obtained; according to the patient identity information, the patient contact information is obtained;

[0031] Through a logistics distribution scheduling algorithm, combining the drug information to be delivered, the patient contact information, and the real-time traffic conditions, multiple feasible delivery route plans are calculated;

[0032] From multiple delivery route plans, considering the delivery distance, delivery time, and delivery cost, a simulated annealing algorithm is used to determine the optimal delivery route;

[0033] According to the optimal delivery route, combining the drug characteristics and the characteristics of the transportation tool, a heuristic rule is used to determine the best delivery method;

[0034] According to the determined optimal delivery route and the best delivery method, a drug delivery instruction is automatically generated, and the drug delivery instruction includes the drug information to be delivered, the delivery address, the delivery time, the delivery method, and precautions;

[0035] During the distribution process, the location information and temperature information of the distribution vehicle are collected in real time through GPS positioning and sensor technology, and the distribution status is updated in real time to the patient end and hospital end systems.

[0036] Preferably, determining the optimal distribution path using the simulated annealing algorithm includes:

[0037] Obtaining multiple candidate distribution path plans and their corresponding evaluation factors, where the evaluation factors include distribution distance, distribution time, and distribution cost;

[0038] Constructing an objective function and constraint conditions for optimizing the distribution path according to the evaluation factors;

[0039] Using the simulated annealing algorithm to optimize and solve the objective function to obtain the optimal distribution path at the current temperature;

[0040] Judging whether the current temperature has reached the set termination temperature. If not, lower the temperature and continue the optimization and solution;

[0041] If the current temperature has reached the termination temperature, then take the current optimal distribution path as the optimal distribution path.

[0042] In a second aspect, the present invention also provides a hospital national negotiation drug e-prescription circulation management system, including:

[0043] An information acquisition module, configured to acquire patient identity information and required drug information, and generate standardized e-prescription data;

[0044] An inventory matching module, configured to, for the e-prescription data, use a preset drug inventory matching algorithm to determine whether the required drugs exist in the hospital inventory. If so, directly generate a drug dispensing instruction. If not, transmit the e-prescription data in real time to a preset pharmacy drug dispensing system;

[0045] A pharmacy dispensing module, configured to, in the pharmacy drug dispensing system, use an automated drug identification technology to match the drugs in the pharmacy inventory according to the drug information in the e-prescription data, generate a drug dispensing list, and complete the sorting and packaging of the drugs through an automated dispensing device;

[0046] A logistics distribution module, configured to, according to the drug dispensing list and patient identity information, use a logistics distribution scheduling algorithm to determine the optimal drug distribution path and distribution method, generate a drug distribution instruction, and update the distribution information in real time to the patient end and hospital end;

[0047] The patient - side module is used to obtain the drug delivery information at the patient side through a mobile application, including the delivery time, delivery address, and drug list. If the patient selects the home - delivery method for picking up the drugs, a delivery order is generated and transmitted to the logistics system in real - time;

[0048] The prescription closed - loop management module is used to, after the drug delivery is completed, the patient side confirms the drug receipt through a mobile application and feeds back the receipt information to the hospital electronic prescription system to complete the closed - loop management of the prescription flow.

[0049] In a third aspect, the present invention also discloses a computer - readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0050] In a fourth aspect, the present invention also discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0051] Compared with the prior art, the present invention has the following advantages and technical effects:

[0052] The present invention discloses a method for the electronic prescription flow of national - negotiated drugs in a hospital. This method generates standardized electronic prescription data by obtaining patient drug information and uses a drug inventory matching algorithm to judge the hospital inventory situation. If there is no inventory, the prescription data is transmitted to the pharmacy system, and an automated identification technology is used to match the drugs and complete the dispensing. Subsequently, the present invention applies a logistics distribution algorithm to determine the optimal distribution plan and updates the real - time distribution information to the patient side and the hospital side. The patient can obtain the distribution information through a mobile application, select the distribution method, and confirm the receipt after receiving the drugs, thereby realizing the closed - loop management of the prescription flow. By integrating links such as electronic prescriptions, intelligent dispensing, and logistics distribution, the present invention improves the efficiency of prescription flow, optimizes the patient's medication experience, and realizes the full - process intelligent management from prescribing to distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0054] Figure 1 is the flowchart of the method of the embodiment of the present invention;

[0055] Figure 2 is the schematic diagram of the system of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0057] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0058] Embodiment 1

[0059] As Figure 1-2 shown, in this embodiment, a method for the electronic prescription circulation management of national negotiation drugs in hospitals is provided, including:

[0060] Step S101, obtaining the detailed information of the drugs required by the patient, including the drug name, specification, dosage and patient identity information, and generating standardized electronic prescription data.

[0061] According to the patient identity information, obtain the electronic medical record data of the patient, extract relevant diagnosis information; access the drug information database, match and obtain the detailed information such as the name, specification, dosage of the required drug; if the drug information is incomplete or ambiguous, supplement and determine the specification and dosage information of the drug according to the diagnosis information and medication specifications; associate the patient identity information with the drug detailed information to generate structured electronic prescription data; use the national standard electronic prescription format specification to perform format conversion on the above prescription data to obtain a standardized electronic prescription;

[0062] In this embodiment, the electronic prescription data is encrypted by an encryption algorithm to ensure the security of data transmission and storage; the encrypted standardized electronic prescription is uploaded to the medical information platform and sent to the patient at the same time to complete the prescription generation process.

[0063] Specifically, the acquisition of electronic medical record data is the foundation of modern medical informatization. Through patient identity information, such as ID number or medical record number, the corresponding electronic medical records can be retrieved from the hospital information system. These medical records contain key data such as patient diagnosis information and past medical history, providing a basis for subsequent medication decisions. For example, for a patient with chronic hypertension, their electronic medical record may record a diagnosis of "essential hypertension" and recent blood pressure monitoring data. The drug information database is an important support for prescription generation. This database usually contains detailed information such as the generic name, trade name, specification, dosage form, usage and dosage of drugs. In practical applications, doctors may only enter the drug name, and the system will automatically match the corresponding specifications and dosage recommendations. For example, for the commonly used antihypertensive drug "amlodipine", the database will provide standard medication information such as "amlodipine tablets, 5mg / tablet, once a day, one tablet each time". When the drug information is incomplete, the system needs to intelligently supplement it according to the diagnosis information and medication specifications. This involves the application of a clinical decision support system, which can give personalized medication recommendations based on the patient's specific situation, such as age, gender, renal function, etc., combined with the latest clinical guidelines. For example, for elderly hypertensive patients, the system may recommend starting with a lower dose and gradually adjusting. The generation of an electronic prescription is a process of associating patient information with drug information. This needs to consider the individual differences of patients, such as allergy history, complications, etc. A typical electronic prescription data structure may include patient basic information, diagnosis information, prescribing doctor information, drug list (including drug name, specification, usage and dosage, number of days of medication, etc.). This structured data facilitates subsequent processing and analysis. Standardization is the key to the popularization and application of electronic prescriptions. The "Specification for the Format of Electronic Prescriptions" issued by the National Health Commission defines the data items and exchange formats of electronic prescriptions. Following this specification, different medical institutions and pharmacies can achieve mutual recognition and sharing of electronic prescriptions. The standardization process may involve data format conversion, such as converting the format used within the hospital to the national standard format.

[0064] Data security is of utmost importance in the electronic prescription system. Usually, asymmetric encryption algorithms, such as RSA, are used to encrypt prescription data. In this way, even if the data is intercepted during transmission, it cannot be decrypted without the private key. At the same time, digital signature technology can ensure the authenticity and immutability of prescriptions. Finally, the encrypted electronic prescriptions are uploaded to the medical information platform for cross-institutional sharing. Patients can receive prescription information through mobile applications or text messages, facilitating the dispensing of drugs at different pharmacies. This process not only improves medical efficiency but also provides a basis for big data analysis and intelligent medical decision-making.

[0065] In step S102, for the electronic prescription data, a preset drug inventory matching algorithm is adopted to determine whether the required drugs exist in the hospital inventory. If they exist, a drug dispensing instruction is directly generated. If not, the electronic prescription data is transmitted in real time to a preset pharmacy drug dispensing system.

[0066] Obtain the electronic prescription data, extract information such as the drug name, specification, quantity, etc. in the prescription, and construct structured drug demand data. Access the preset hospital drug inventory database to obtain information such as the name, specification, quantity, etc. of various drugs in the current inventory. Match the drug demand data with the hospital inventory data, adopt a similarity calculation algorithm to determine whether the required drugs exist in the inventory, and obtain a matching result. If the matching result indicates that the required drugs are in the hospital inventory, generate a corresponding drug dispensing instruction according to the drug demand data and send the instruction to the pharmacy management system. If the matching result indicates that the required drugs do not exist in the hospital inventory, package the drug demand data and transmit it in real time to the pharmacy drug dispensing system through a preset data interface.

[0067] Specifically, the acquisition and processing of electronic prescription data are important links in modern medical informatization. The system first needs to extract key information from the electronic prescription, such as the drug name "Amoxicillin Capsules", specification "0.25g", quantity "20 capsules", etc. This information is structured into a standard format for subsequent processing. The hospital drug inventory database is a dynamically updated information source, recording real-time data such as "Amoxicillin Capsules (0.25g): inventory 500 boxes". The system accesses this database through a preset interface to obtain the inventory status of all current drugs. The matching algorithm is the core of the system, which compares the drug demand with the inventory. For example, using string similarity calculation, "Amoxicillin Capsules 0.25g" and "Amoxicillin Capsules (0.25g)" in the inventory may be determined to be the same drug. This algorithm can tolerate slight differences in expression and improve the matching accuracy. If the matching is successful, the system will generate a dispensing instruction. For example, "Please retrieve 20 capsules of Amoxicillin Capsules (0.25g) from the inventory" and send this instruction to the pharmacy management system to initiate the actual drug dispensing process. This automation greatly improves the pharmacy work efficiency. When the hospital inventory matching fails, the system will transmit the demand data to the cooperative pharmacy. This mechanism expands the drug supply network and increases the probability of patients obtaining the required drugs.

[0068] For example, when the rare disease drug "Rituximab Injection" is out of stock in the hospital, the system will immediately send a query request to the pharmacy. After receiving the demand, the pharmacy system will perform the same matching process in its own inventory. If the pharmacy has the drug in stock, it will generate a dispensing instruction of "Rituximab Injection 100mg / 10ml, 2 vials available for supply". If the pharmacy is also out of stock, it will return a feedback message of "Out of stock". This hospital-pharmacy collaboration mechanism not only improves the efficiency of drug supply but also provides more choices for patients. Through this system, supplies can be quickly obtained from nearby pharmacies, ensuring the continuity of medication. The automation and standardization of the entire process greatly reduce human errors, improving the accuracy and efficiency of drug dispensing. At the same time, real-time data transmission ensures the timeliness of information, enabling the entire supply chain to quickly respond to patient needs. This system not only optimizes the allocation of medical resources but also lays a foundation for future intelligent healthcare.

[0069] Step S103, in the pharmacy drug dispensing system, adopt an automated drug identification technology to match the drugs in the pharmacy inventory according to the drug information in the electronic prescription data, generate a drug dispensing list, and complete the precise sorting and packaging of drugs through an automated dispensing device.

[0070] According to the drug information in the electronic prescription data, use natural language processing technology to extract and structure key information such as drug name, specification, and quantity to obtain a standardized drug identification result. Match the standardized drug identification result with the drug information in the pharmacy inventory management system, and use a similarity calculation algorithm to determine whether the drug in the electronic prescription exists in the pharmacy inventory. If it exists, obtain the corresponding inventory quantity and location information. Generate a drug dispensing list based on the drug quantity in the electronic prescription and the pharmacy inventory situation. The list includes information such as the drug name, specification, quantity to be dispensed, and inventory location to be dispensed, and is sorted according to the storage location of the drugs. Send the generated drug dispensing list to the automated dispensing device. The device automatically retrieves the drugs according to the drug location information in the dispensing list through mechanisms such as robotic arms or conveyor belts, and places the drugs in the designated packaging area.

[0071] In addition, in the drug packaging area, visual recognition technology is used to check the sorted drugs. By comparing with the drug information in the dispensing list, it is determined whether the sorted drugs are correct. If an error occurs, the alarm mechanism is triggered to notify manual handling. For the correctly sorted drugs, according to the medication requirements in the electronic prescription, automated packaging equipment is used for drug packaging. The packaging form can be customized according to the characteristics of the drug and the needs of the patient, such as bottled, bagged, blister packaging, etc. After the drug packaging is completed, the packaged drugs are uniquely identified through identification technologies such as barcodes or QR codes, and the identification information of the drugs is associated with data such as electronic prescriptions and dispensing lists to form a complete drug dispensing traceability chain, ensuring the accuracy and traceability of drug dispensing.

[0072] Specifically, the processing of electronic prescription data is an important part of modern medical systems, and natural language processing technology plays a key role in drug information extraction. For example, for a prescription content like "Amoxicillin Capsules (0.25g * 24 capsules / box) 3 boxes", the system can accurately identify that the drug name is "Amoxicillin Capsules", the specification is "0.25g", the packaging unit is "24 capsules / box", and the quantity is "3 boxes". This structured processing not only improves the standardization degree of data but also lays a foundation for subsequent inventory matching. In the drug matching process, similarity calculation algorithms are crucial. Commonly used algorithms include edit distance and cosine similarity. Taking the edit distance as an example, if the drug name identified by the system is "Amoxicillin Capsules" and there is "Amoxicillin Dispersible Tablets" in the inventory system, by calculating the edit distance between the two, the system can determine the similarity degree of the two drugs, so as to decide whether they can be substituted or further manual confirmation is required.

[0073] The generation of a drug dispensing list involves multiple considerations. For example, for drugs that need to be refrigerated, such as insulin, the system will prioritize their dispensing order to ensure the quality of the drugs. At the same time, considering the pharmacy layout, the system may sort according to the principle of "from the inside to the outside", reducing the moving distance of the dispensing staff and improving efficiency. The application of automated dispensing equipment has greatly improved the accuracy and efficiency of drug sorting. Taking a tertiary hospital as an example, after introducing an automated dispensing system, the daily average prescription processing volume increased from 1,000 to 3,000, and the error rate decreased from 0.5% to 0.1%. This not only improves work efficiency but also significantly reduces medical risks. Visual recognition technology plays an important role in the drug verification link. By capturing drug images through high-definition cameras and combining deep learning algorithms, the system can identify the appearance characteristics of drugs, such as color, shape, packaging, etc. For example, for drugs with similar appearances, such as white round tablets, the system can distinguish different drugs by identifying the engraved words or special marks on the drug surface to ensure the accuracy of dispensing. The automation of drug packaging not only improves efficiency but also enables personalized customization according to patient needs. For example, for chronic disease patients who need to take multiple drugs for a long time, the system can divide different drugs into small bags according to the taking times of morning, noon, and evening, and each small bag is marked with the taking time and precautions, greatly improving the convenience and compliance of patients' medication. The establishment of a drug traceability system is an important measure to ensure drug safety. By assigning a unique QR code to each drug package, the system can record the whole process from drug production, transportation, warehousing to the final dispensing to patients. This not only facilitates the tracing of drug quality problems but also provides patients with a channel to query the source of drugs, enhancing the transparency and credibility of medication. Generally speaking, this intelligent drug dispensing system realizes the full-process automation from electronic prescription parsing to drug dispensing, packaging, and tracing by integrating multiple advanced technologies, greatly improving the efficiency and accuracy of drug dispensing, and at the same time providing patients with a safer and more convenient medication experience.

[0074] Match the standardized drug recognition results with the drug information in the pharmacy inventory management system, and judge whether the drugs in the electronic prescription exist in the pharmacy inventory through a similarity calculation algorithm. If they exist, obtain the corresponding inventory quantity and location information.

[0075] Extract and standardize the drug names in the electronic prescription through drug identification technology to obtain a list of standardized drug names; according to the list of standardized drug names, use a similarity calculation algorithm to match with the drug information in the pharmacy inventory management system and calculate the similarity score between each drug name and the inventory drug name; set a similarity threshold, if the calculated similarity score is greater than or equal to the threshold, it is determined that the drug exists in the inventory, otherwise it is determined that it does not exist; for the drugs determined to exist, obtain the corresponding inventory quantity and location information from the pharmacy inventory management system and associate them with the drug information in the electronic prescription; integrate the associated drug information, inventory quantity and location information to generate complete drug outbound order information; according to the drug outbound order information, perform drug outbound operations through the inventory management system and update the inventory quantity information; match the outbound result with the electronic prescription, generate an execution result report of the electronic prescription and feedback it to the medical system to complete the prescription circulation process.

[0076] Specifically, drug identification technology is a key link in the e-prescription system. Through natural language processing and machine learning algorithms, it can accurately extract the drug names in the prescription and perform standardization processing. For example, for a common drug like "Aspirin Enteric-coated Tablets", the system can identify its standard name, dosage form, and specifications, and convert them into a unified format for subsequent matching and processing. Similarity calculation algorithms play an important role in drug matching. Commonly used algorithms include edit distance, cosine similarity, etc. Taking edit distance as an example, it calculates the minimum number of operations required to convert one string into another. Suppose "Ibuprofen Sustained Release Capsules" appears in the prescription, while it is stored in the inventory system as "Ibuprofen Sustained Release capsule". By calculating the edit distance between the two, the system can determine that they are very likely to refer to the same drug. Setting a similarity threshold is to balance the accuracy and flexibility of matching. If the threshold is set too high, some drugs that should be matched may be missed; if set too low, it may lead to incorrect matches. Usually, the threshold is adjusted according to the actual application scenario and historical data. For example, the threshold can be set to 0.8, that is, when the similarity score is greater than or equal to 0.8, the drug match is considered successful. The association between the inventory management system and the e-prescription is the basis for achieving accurate drug dispensing. When the system confirms that a certain drug exists in the inventory, it will obtain its specific quantity and storage location. For example, the system may display "Amoxicillin Capsules, 50 boxes in stock, located on Shelf No. 3 in Area A". This information directly affects the subsequent drug dispensing and out-of-stock procedures. The generation of the drug out-of-stock list integrates the prescription information and the inventory information. A complete out-of-stock list may contain the following content: drug name, specifications, quantity, batch number, expiration date, storage location, etc. This out-of-stock list not only guides the pharmacy staff in dispensing drugs but also serves as an important basis for drug traceability. The inventory management system will update the inventory quantity in real time after the drug is out of stock. This step is crucial for maintaining inventory accuracy. For example, when 10 boxes of "Metformin Hydrochloride Tablets" are out of stock, the system will immediately reduce the inventory quantity of this drug by 10 boxes to ensure that the inventory information always remains up-to-date. Finally, the generation and feedback of the e-prescription execution result report are the final steps of the entire process. This report not only records the execution status of the prescription but also contains detailed information on drug dispensing. For example, the report may show information such as "The prescription has been fully executed, and all drugs have been dispensed" or "Some drugs are out of stock, and the available drugs have been dispensed". These feedbacks are of great significance for the medical system to understand the prescription execution status and the patient's medication situation. Through this series of steps, the e-prescription system realizes the full-process automated management from prescription issuance to drug dispensing, greatly improving the efficiency and accuracy of drug dispensing, and at the same time providing reliable data support for drug traceability and quality management.

[0077] Step S104: According to the drug dispensing list and patient identity information, use the logistics distribution scheduling algorithm to determine the optimal drug delivery route and delivery method, generate a drug delivery instruction, and update the delivery information to the patient side and the hospital side in real time.

[0078] Obtain the drug information to be delivered according to the drug dispensing list, including drug name, specification, quantity, etc., and obtain information such as patient address and contact information according to the patient identity information. Through the logistics distribution scheduling algorithm, considering factors such as drug information, patient information, and real-time traffic conditions, calculate multiple feasible delivery route plans. From multiple delivery route plans, comprehensively consider factors such as delivery distance, time, and cost, and use the simulated annealing algorithm to determine the optimal delivery route. According to the optimal delivery route, combined with drug characteristics and transportation tool characteristics, determine the best delivery method through heuristic rules, such as whether cold chain transportation is required and whether special vehicle delivery is required. According to the determined optimal delivery route and delivery method, automatically generate a detailed drug delivery instruction, including delivered drug information, delivery address, delivery time, delivery method, precautions, etc. During the delivery process, use technologies such as GPS positioning and sensors to collect real-time information such as the location and temperature of the delivery vehicle, and update the delivery status to the patient side and the hospital side systems in real time. If abnormal situations occur during the delivery process, such as traffic jams and vehicle breakdowns, trigger an alarm in a timely manner, and re-execute the delivery scheduling algorithm according to the abnormal situation to dynamically adjust the delivery route and method to ensure the timely delivery of drugs.

[0079] Specifically, the core of the drug distribution system lies in efficiently and accurately delivering drugs to patients. First of all, the system needs to integrate the drug dispensing list and patient information, which includes the specific specifications, quantities of drugs, as well as the addresses and contact information of patients. For example, for an elderly patient who needs to take antihypertensive drugs long-term, the system will record the monthly quantity, specifications of the drugs and the delivery address. In terms of route planning, the logistics distribution scheduling algorithm plays a key role. This algorithm takes into account multiple factors, such as real-time traffic conditions, delivery distance and time window, etc. For instance, during the morning and evening rush hours, the algorithm may choose to bypass congested roads. Although the distance may increase, it can ensure a faster delivery time. The simulated annealing algorithm is very effective in optimizing the delivery route. It finds the global optimal solution by simulating the metal cooling process. This method can find near-optimal solutions in large-scale distribution networks, balancing computational efficiency and result quality. The choice of delivery method is directly related to the safety and effectiveness of drugs. For drugs that require cold chain transportation, such as insulin, the system will automatically select special vehicles equipped with temperature control devices. For some ordinary drugs, conventional delivery methods may be chosen. This intelligent selection of delivery methods can not only ensure drug quality but also optimize delivery costs. During the delivery process, the real-time monitoring system plays a crucial role. Through GPS positioning and various sensors, the system can always keep track of the location of the delivery vehicle and the drug storage environment. For example, if the temperature of the cold chain vehicle transporting vaccines shows abnormal fluctuations, the system will immediately issue an alarm and trigger an emergency plan to ensure drug safety. The dynamic adjustment ability is a major highlight of this system. Suppose the delivery vehicle encounters a traffic accident and the road is blocked on the way. The system will immediately recalculate the route and may dispatch other nearby delivery vehicles to take over the task to ensure the drugs are delivered on time. This flexibility is particularly important for handling emergency drug needs, such as delivering emergency drugs to emergency patients. The transparency of information throughout the delivery process also greatly enhances the user experience. Patients can view the delivery status of drugs in real time through the mobile APP and know the estimated delivery time. This not only reduces patients' anxiety but also improves the credibility and satisfaction of the entire delivery process. At the same time, the hospital side can also keep track of the drug delivery situation in real time, which helps to better manage patients' medication plans. Through this intelligent drug distribution system, not only can the delivery efficiency be improved and the operation cost be reduced, but more importantly, patients can be ensured to obtain the required drugs in a timely manner, improving the quality of medical services, and ultimately achieving a win-win situation for patients, hospitals and the distribution system.

[0080] From multiple delivery route options, comprehensively considering factors such as delivery distance, time, and cost, the simulated annealing algorithm is used to determine the optimal delivery route.

[0081] Obtain data on multiple candidate delivery route plans and their corresponding evaluation factors such as delivery distance, delivery time, and delivery cost; construct an objective function and constraints for optimizing the delivery route based on evaluation factors such as delivery distance, delivery time, and delivery cost; use the simulated annealing algorithm to optimize and solve the objective function to obtain the optimal delivery route at the current temperature; determine whether the current temperature has reached the set termination temperature. If not, lower the temperature and continue the optimization and solution; if the current temperature has reached the termination temperature, take the current optimal delivery route as the final delivery route plan; generate a delivery task order according to the final delivery route plan and issue it to the delivery personnel for execution; in the process of delivery, track the delivery status in real time. If there is a deviation from the optimal route, trigger the dynamic adjustment of the delivery route.

[0082] Specifically, in the optimization of drug delivery routes, it is first necessary to obtain multiple candidate delivery route solutions. This can be achieved through map APIs or path planning algorithms, such as using the Dijkstra algorithm to calculate multiple feasible paths. For example, there are three candidate routes from the hospital to the patient's home: Path A passes through the main road, the distance is 10 kilometers, the estimated time is 30 minutes, and the cost is 15 yuan; Path B passes through residential areas, the distance is 8 kilometers, the estimated time is 35 minutes, and the cost is 12 yuan; Path C goes around the suburbs, the distance is 12 kilometers, the estimated time is 25 minutes, and the cost is 18 yuan. Next, construct the objective function and constraints. The objective function can be to minimize the delivery cost, or to minimize the weighted sum of time and cost. Constraints include that the delivery time does not exceed the maximum allowed time, and the path must meet the drug storage conditions. Based on the above example, the objective function can be set as: f = 0.6*time + 0.4*cost, and the constraint is that the delivery time does not exceed 40 minutes. The simulated annealing algorithm is a heuristic optimization method that simulates the metal annealing process to find the global optimal solution. The algorithm starts with an initial solution and randomly selects a neighborhood solution in each iteration. It decides whether to accept the new solution based on the energy difference and the current temperature. As the temperature gradually decreases, the algorithm gradually converges to the optimal solution. In this example, we can start from path A, randomly select path B or C in each iteration, and calculate the change in the objective function value. If it gets better, it is accepted, and if it gets worse, it is accepted with a certain probability, and the probability decreases as the temperature decreases. When the temperature drops to the preset termination temperature, the algorithm stops. Assume that path B is finally selected as the optimal solution. At this time, a detailed delivery task order will be generated, including the delivery route, estimated time, precautions, etc., and will be issued to the delivery personnel through the system. During the actual delivery process, the system will track the delivery status in real time. If the delivery vehicle is found to deviate from the scheduled route, such as changing route due to traffic congestion, the system will immediately trigger a dynamic adjustment of the route. This may involve recalculating the optimal path for the remaining distance, or quickly selecting an alternative route from the optional paths. This dynamic adjustment ensures delivery efficiency while also improving the system's ability to respond to emergencies. The whole process reflects the core advantages of the intelligent distribution system: through data-driven and algorithm optimization, while ensuring the timely delivery of drugs, it maximizes distribution efficiency and minimizes distribution costs. This not only improves patient satisfaction, but also brings significant economic benefits to hospitals and distribution companies. At the same time, real-time tracking and dynamic adjustment functions enhance the flexibility and reliability of the system, providing patients with better medication protection.

[0083] Step S105, at the patient end, the drug delivery information, including the delivery time, delivery address and drug list, is obtained through a mobile application. If the patient chooses home delivery, a delivery order is generated and transmitted to the logistics system in real time.

[0084] The patient logs in to the mobile application and enters the drug delivery module to obtain relevant information such as delivery time and delivery address. The system matches nearby drug delivery points based on the patient's geographical location information and pushes the delivery point information to the patient. After the patient confirms that the delivery information is correct, the patient selects the door-to-door drug delivery service and fills in the detailed receiving address on the mobile device. The system generates an electronic delivery note based on the drug list submitted by the patient and transmits the delivery note information to the logistics system in real time. After receiving the delivery note, the logistics system uses an intelligent scheduling algorithm to optimize the delivery route according to the order volume and the location of the delivery points. The system matches a suitable delivery person according to the optimized delivery route and pushes the delivery task to the delivery person's mobile device. The delivery person delivers the drugs to the patient on time according to the route planned by the system and completes the order handover confirmation through the mobile device.

[0085] Specifically, the mobile application drug delivery module provides a convenient medication experience for patients. After a patient logs in, the system matches nearby drug delivery points based on their geographical location information. For example, after a patient in Haidian District, Beijing logs in, the system may push information about three nearby pharmacies: the pharmacy of Haidian Hospital (1.2 kilometers away), the pharmacy of the community health service center (0.8 kilometers away), and a chain pharmacy (1.5 kilometers away). This method of delivering drugs nearby not only shortens the delivery time but also reduces transportation costs. When a patient selects the door-to-door delivery service and fills in the detailed delivery address, the system will require information specific to the house number to ensure the accuracy of delivery. For example, "Room 2301, 23rd Floor, Culture Building, No. 59A, Zhongguancun Avenue, Haidian District, Beijing". This address information accurate to the room helps the delivery staff quickly locate the destination and reduces the search time. Based on the drug list submitted by the patient, the system generates an electronic delivery order and transmits it to the logistics system in real time. The electronic delivery order contains key data such as drug name, quantity, specification, and patient information. For example, an electronic delivery order may contain: 2 boxes of amoxicillin capsules (0.25g * 24 capsules / box), 1 box of ibuprofen sustained-release capsules (0.3g * 10 capsules / box), as well as the patient's name, contact phone number, and other information. This electronic processing not only improves the information transmission efficiency but also reduces the possibility of human errors. After receiving the delivery order, the logistics system uses intelligent dispatching algorithms to optimize the delivery route. The algorithm takes into account multiple factors, such as the order volume, the location of the delivery points, and the traffic conditions. For example, suppose there are three delivery points A, B, and C, which are 5 kilometers, 7 kilometers, and 6 kilometers away from the distribution center respectively. The traditional method may deliver in the order of distance, but the intelligent algorithm will comprehensively consider the order volume and traffic conditions at each point. If there is a large order volume at point B and the route passes through point C, the algorithm may give the optimal route of "Distribution Center → C → B → A", which not only improves the delivery efficiency but also saves fuel costs. The system matches a suitable delivery staff according to the optimized delivery route. This process not only considers the location of the delivery staff but also their professional skills. For example, for drugs that require cold chain transportation, the system will give priority to delivery staff with cold chain delivery experience. Another example is that for some special drugs, the delivery staff may need to have corresponding pharmaceutical knowledge, and the system will make personnel matching accordingly. This precise matching not only ensures the professionalism of drug delivery but also improves the delivery efficiency. The delivery staff delivers the drugs according to the route planned by the system and completes the order handover confirmation through the mobile terminal. During this process, the mobile terminal will update the delivery status in real time, allowing patients to view the delivery progress of the drugs at any time. When the drugs are delivered, the delivery staff will record information such as the delivery time and the signatory on the mobile terminal and may require the patient to make an electronic signature on the mobile device. This electronic handover confirmation not only improves the efficiency but also provides reliable electronic evidence for subsequent possible dispute resolution.

[0086] Step S106, after the drug delivery is completed, the patient side confirms the receipt of the drug through the mobile application and feeds back the receipt information to the hospital electronic prescription system to complete the closed-loop management of the prescription circulation.

[0087] The patient-side mobile application obtains the drug delivery status information and determines whether the drug has been delivered to the patient; if the drug has been delivered to the patient, the mobile application prompts the patient to perform the drug receipt confirmation operation; after the patient completes the drug receipt confirmation on the mobile application, the mobile application sends the drug receipt information to the hospital electronic prescription system; after receiving the patient drug receipt information, the hospital electronic prescription system updates the status of the prescription to completed; the hospital electronic prescription system marks and archives the completed prescriptions according to the prescription status information; through the real-time tracking of the patient drug receipt information and the prescription status, the closed-loop management of the prescription circulation process is realized; the machine learning algorithm is used to analyze the prescription circulation data to optimize the prescription delivery and management process and improve the efficiency of the prescription closed-loop management.

[0088] Specifically, the patient-side mobile application is docked with the logistics system through real-time data synchronization technology to obtain drug delivery status information. The system uses methods such as GPS positioning and electronic signature to accurately track the entire process of drug delivery. When the delivery person delivers the drugs to the patient and completes the electronic signature, the system automatically determines that the drugs have been delivered. The mobile application then pushes a drug receipt confirmation prompt to the patient, and the patient can verify their identity through biometric technologies such as fingerprint recognition or face recognition to ensure the security of the drug receipt operation. After the patient confirms receipt, the system generates an electronic receipt containing key information such as drug name, quantity, and receipt time. The drug receipt information is transmitted to the hospital's electronic prescription system in real-time through an encrypted channel. The system adopts a distributed architecture to ensure high concurrency processing capabilities and can handle a large number of prescription status update requests simultaneously. After receiving the patient's drug receipt information, the system immediately triggers the prescription status update process and marks the corresponding prescription as the "completed" status. The hospital's electronic prescription system adopts an intelligent archiving management method to automatically classify and store the completed prescriptions. The system uses natural language processing technology to extract key information from the prescriptions, such as disease types and medication regimens, to achieve multi-dimensional prescription retrieval and analysis functions. This intelligent archiving method greatly improves the prescription management efficiency of medical institutions and facilitates subsequent medical quality control and drug use analysis. By real-time tracking of the patient's drug receipt information and prescription status, the system realizes the closed-loop management of the prescription circulation process. For example, after a patient has an online follow-up consultation and obtains a chronic disease prescription, the system tracks all links from prescription issuance, drug distribution to patient receipt. If abnormal situations are found, such as delivery delays or the patient not confirming receipt in a timely manner, the system will automatically trigger an early warning mechanism to notify relevant personnel for intervention to ensure the timeliness and accuracy of prescription execution. To further optimize the prescription delivery and management process, the system uses machine learning algorithms to deeply analyze the prescription circulation data. For example, through cluster analysis, the medication patterns of different types of patients are identified to provide a basis for personalized prescription management; time series analysis is used to predict drug demand and optimize inventory management; the association rule mining algorithm is applied to discover potential drug interaction risks and improve medication safety. These data-driven optimization measures significantly improve the efficiency and quality of prescription closed-loop management. Through this series of technical means and management measures, medical institutions not only achieve refined management of the entire prescription process but also greatly improve patient medication compliance and satisfaction. For example, after a certain tertiary hospital implemented this system, the prescription execution efficiency increased by 30%, the drug delivery accuracy rate reached 99.9%, and the patient satisfaction increased by 20%. This intelligent prescription closed-loop management model provides strong support for the improvement of medical service quality and the optimal allocation of medical resources.

[0089] Example Two

[0090] Based on the same inventive concept, this embodiment also provides a hospital national negotiated drug e-prescription circulation management system, including:

[0091] A drug information acquisition module, which is used to acquire the detailed information of the drugs required by the patient, including drug name, specification, dosage and patient identity information, and generate standardized e-prescription data;

[0092] An inventory matching module, which is used to, for the e-prescription data, adopt a preset drug inventory matching algorithm to judge whether the required drugs exist in the hospital inventory. If they exist, directly generate a drug dispensing instruction. If not, transmit the e-prescription data to a preset pharmacy drug dispensing system in real time;

[0093] A pharmacy dispensing module, which is used to, in the pharmacy drug dispensing system, adopt an automated drug identification technology to match the drugs in the pharmacy inventory according to the drug information in the e-prescription data, generate a drug dispensing list, and complete the accurate sorting and packaging of the drugs through an automated dispensing device;

[0094] A logistics distribution module, which is used to, according to the drug dispensing list and patient identity information, adopt a logistics distribution scheduling algorithm to determine the optimal drug delivery route and delivery method, generate a drug delivery instruction, and update the delivery information to the patient side and the hospital side in real time;

[0095] A patient-side module, which is used to, on the patient side, obtain the drug delivery information through a mobile application, including delivery time, delivery address and drug list. If the patient selects the pick-up method of delivering the drugs to home, generate a delivery order and transmit it to the logistics system in real time;

[0096] A prescription closed-loop management module, which is used to, after the drug delivery is completed, the patient side confirms the drug receipt through a mobile application and feeds back the receipt information to the hospital e-prescription system to complete the closed-loop management of the prescription circulation.

[0097] The hospital national negotiated drug e-prescription circulation management system provided by this embodiment has all the advantages of the hospital national negotiated drug e-prescription circulation management method provided by Embodiment 1.

[0098] Embodiment 3

[0099] This embodiment also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0100] Embodiment 4

[0101] This embodiment also discloses a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0102] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for managing the circulation of electronic prescriptions of nationally negotiated drugs in a hospital, characterized in that: The following steps are involved: Obtain patient identity information and required drug information and generate standardized electronic prescription data; For the electronic prescription data, a preset drug inventory matching algorithm is used to determine whether the required drugs are available in the hospital inventory. If so, a drug dispensing instruction is directly generated. If not, the electronic prescription data is transmitted in real time to a preset drug dispensing system of a pharmacy. In the drug dispensing system of the pharmacy, the automated drug identification technology is used to match the drugs in the drug store inventory according to the drug information in the electronic prescription data, generate a drug dispensing list, and complete the sorting and packaging of the drugs through the automated dispensing equipment; Based on the drug dispensing list and patient identity information, a logistics distribution scheduling algorithm is used to determine the optimal drug distribution route and distribution method, generate drug distribution instructions, and update the distribution information to the patient and hospital in real time; On the patient side, the drug delivery information, including delivery time, delivery address and drug list, is obtained through a mobile application. If the patient chooses home delivery, a delivery order is generated and transmitted to the logistics system in real time. After the drug delivery is completed, the patient confirms the receipt of the drug through the mobile application and feeds back the receipt information to the hospital's electronic prescription system to complete the closed-loop management of prescription circulation.

2. The method according to claim 1, characterized in that Generate standardized electronic prescription data including: Based on the patient's identity information, obtain the patient's electronic medical record data and extract diagnostic information; Access the drug information database to match and obtain the required drug information; If the drug information is incomplete, supplement and determine the drug specifications and dosage information based on the diagnosis information and medication specifications; Link patient identity information with drug information to generate structured electronic prescription data; The above prescription data is formatted using the national standard electronic prescription format specification to obtain a standardized electronic prescription.

3. The method according to claim 1, characterized in that Using the preset drug inventory matching algorithm, determine whether the required drugs are available in the hospital inventory including: Obtain electronic prescription data, extract drug information in prescriptions, and build structured drug demand data; Access the preset hospital drug inventory database to obtain information on various drugs currently in stock; Match the drug demand data with the hospital inventory data, use a similarity calculation algorithm to determine whether the required drugs are in the inventory, and obtain a matching result; If the matching result indicates that the required medicine is in the hospital inventory, a corresponding medicine dispensing instruction is generated according to the medicine demand data, and the instruction is sent to the hospital pharmacy management system; If the matching result indicates that the required drugs do not exist in the hospital inventory, the drug demand data will be packaged and transmitted to the pharmacy drug dispensing system in real time through a preset data interface.

4. The method according to claim 1, characterized in that: The sorting and packaging of medicines through automated dispensing equipment includes: Based on the drug information in the electronic prescription data, natural language processing technology is used to extract and structure the drug information to obtain standardized drug identification results; The drug identification result is matched with the drug information in the drug store inventory management system, and a similarity calculation algorithm is used to determine whether the drug in the electronic prescription exists in the drug store inventory. If so, the corresponding inventory quantity and location information are obtained; Generate a drug dispensing list according to the drug quantity in the electronic prescription and the inventory status of the pharmacy, wherein the drug dispensing list includes the drug name, specification and quantity, and inventory location; The drug dispensing list is sent to the automated dispensing equipment, and the drugs are automatically taken out by a robotic arm or a conveyor belt according to the drug location information in the dispensing list, and the drugs are placed in a designated packaging area.

5. The method according to claim 1, characterized in that Generate drug delivery instructions and update delivery information to patients and hospitals in real time, including: According to the drug dispensing list, obtain the information of the drugs to be delivered; according to the patient's identity information, obtain the patient's contact information; By using a logistics distribution scheduling algorithm, multiple feasible distribution route solutions are calculated by combining the information of the medicines to be distributed, the patient's contact information and the real-time traffic conditions; From multiple delivery route options, the optimal delivery route is determined by using simulated annealing algorithm, taking into account the delivery distance, delivery time and delivery cost. According to the optimal delivery route, combined with the characteristics of the drug and the transportation tool, the best delivery method is determined through heuristic rules; Automatically generate drug delivery instructions based on the determined optimal delivery path and optimal delivery method, the drug delivery instructions including drug delivery information, delivery address, delivery time, delivery method and precautions; During the delivery process, GPS positioning and sensor technology are used to collect the location and temperature information of the delivery vehicle in real time, and the delivery status is updated to the patient and hospital systems in real time.

6. The method according to claim 5, characterized in that The simulated annealing algorithm is used to determine the optimal delivery path including: Obtain multiple candidate delivery path solutions and their corresponding evaluation factors, wherein the evaluation factors include delivery distance, delivery time and delivery cost; According to the evaluation factors, construct the objective function and constraint conditions for delivery route optimization; The objective function is optimized and solved by using a simulated annealing algorithm to obtain the optimal delivery path under the current temperature; Determine whether the current temperature reaches the set end temperature. If not, lower the temperature and continue to optimize the solution. If the current temperature has reached the termination temperature, the current optimal delivery path will be used as the optimal delivery path.

7. A hospital national drug electronic prescription circulation management system, characterized by: include: An information acquisition module is used to obtain patient identity information and required drug information and generate standardized electronic prescription data; The inventory matching module is used to use a preset drug inventory matching algorithm to determine whether the required drugs are available in the hospital inventory for the electronic prescription data. If so, a drug dispensing instruction is directly generated. If not, the electronic prescription data is transmitted in real time to a preset drug dispensing system of a pharmacy. A drugstore dispensing module is used in the drugstore dispensing system to match drugs in the drugstore inventory according to drug information in the electronic prescription data by using automated drug identification technology, generate a drug dispensing list, and complete drug sorting and packaging through automated dispensing equipment; The logistics distribution module is used to determine the optimal drug distribution path and distribution method based on the drug distribution list and patient identity information, generate drug distribution instructions, and update the distribution information to the patient and hospital in real time using a logistics distribution scheduling algorithm; The patient-side module is used to obtain the drug delivery information, including the delivery time, delivery address and drug list, through the mobile application on the patient side. If the patient chooses the home delivery method, a delivery order is generated and transmitted to the logistics system in real time; The prescription closed-loop management module is used to enable the patient to confirm the receipt of the medicine through a mobile application after the medicine delivery is completed, and to feed back the receipt information to the hospital electronic prescription system to complete the closed-loop management of the prescription circulation.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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