A medical consumable whole-process traceability management system

Abstract

This invention relates to the field of medical informatics and supply chain management technology, specifically a full-process traceability management system for medical consumables. The system achieves automatic identification of consumables through an intelligent sensing and data collection layer. It uses a trusted data chain layer based on a consortium blockchain to store the binding relationship between the "unique device identifier - internal material code - medical insurance settlement code" and all process events. Management services are provided through intelligent applications and a decision-making layer. The method includes: initializing the binding relationship on the blockchain, collecting multi-node events and associating them with clinical use on the blockchain, making intelligent decisions based on on-chain data and automatically settling medical insurance claims, and providing trusted traceability queries across the entire chain. This invention achieves seamless data collection, tamper-proof data storage, and automated business settlement, effectively improving the security, accuracy, and efficiency of medical consumable management.

Description

Technical Field

[0001] This invention relates to the field of medical information technology and supply chain management, specifically to a full-process traceability management system for medical consumables. Background Technology

[0002] Currently, the management of medical consumables suffers from several problems, including fragmented information across multiple stages, reliance on error-prone manual barcode scanning for clinical usage records, and difficulty in accurately matching national medical insurance settlement codes with physical consumables. Existing technical solutions often employ centralized database records or single-stage barcode scanning, which suffers from high risks of data tampering, chaotic mapping relationships between different coding systems, and an inability to support automated verification for medical insurance payments. This makes it difficult to achieve rapid and accurate recall in the event of quality and safety incidents, and also fails to meet the high standards of data authenticity and accuracy required by medical insurance's "code-based payment" system. Therefore, there is an urgent need for an intelligent traceability solution that can achieve automatic and reliable data collection, ensure the immutability of key mapping relationships, and connect clinical and settlement processes. Summary of the Invention

[0003] The purpose of this invention is to provide a medical consumables full-process traceability management system to realize the automated collection, tamper-proof evidence storage, and intelligent business closed loop of consumables circulation data, thereby improving the security, compliance, and efficiency of medical management.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a medical consumables full-process traceability management system, comprising: The intelligent sensing and acquisition layer includes an AI visual recognition module deployed at clinical use nodes, which is used to non-contactly acquire images of consumables through cameras and automatically identify the UDI of consumables based on a deep learning model. The trusted data chain layer is built on consortium blockchain technology and includes a mapping relationship storage module. The mapping relationship storage module is configured to receive and store the binding relationship data between the UDI of the consumable and the corresponding hospital internal material management code and national medical insurance settlement code, and write the binding relationship data into the blockchain. The intelligent application and decision-making layer is used to synchronize data from the trusted data chain layer and provide inventory management, expiration date warning and flow tracking services. The system is configured such that when the AI ​​visual recognition module detects that a consumable is being used, it automatically associates the UDI of the consumable with the anonymous patient identification information in the current medical event, and submits the association information to the trusted data chain layer to generate a new data block.

[0005] Furthermore, the intelligent sensing and acquisition layer also includes batch data acquisition devices deployed at warehouse nodes. The batch data acquisition devices are fixed barcode scanning platforms of model SK6080 or SK3390, or handheld intelligent data terminals of model SK9028C.

[0006] Furthermore, the deep learning model of the AI ​​visual recognition module is deployed on an edge AI computing device of model GWB-6030H or GWF-4059A1.

[0007] Furthermore, the consensus nodes of the consortium blockchain include medical institutions, medical insurance regulatory agencies, and consumable production or distribution companies.

[0008] Furthermore, the intelligent application and decision-making layer also integrates a time-series prediction model for predicting consumable demand based on historical consumption data in the trusted data chain layer.

[0009] Furthermore, the system is connected to a medical-grade smart camera, model SC3000-MED, for acquiring images of consumables in a clinical environment.

[0010] Secondly, this invention provides a method for full-process traceability management of medical consumables, applied to the system described above, comprising the following steps: S1: Initialization and storage of mapping relationship: Generate binding relationship data between the UDI of consumables and the corresponding hospital internal material management code and national medical insurance settlement code, and submit the binding relationship data to the consortium blockchain network for storage; S2: Multi-node intelligent data collection and event on-chain: Collect status data at each node of consumable circulation and upload it to the blockchain; at the clinical use node, automatically identify the UDI of consumables through AI visual recognition, and upload the UDI as the use event data after associating it with the patient's diagnosis and treatment event information. S3: Intelligent Decision-Making and Business Closed Loop: Automatically execute inventory management and expiration date warning based on on-chain data; In the settlement process, automatically query the binding relationship data stored on the chain, and convert the UDI of consumables into the corresponding medical insurance settlement code to complete the cost verification; S4: Full-chain trusted traceability query: Responds to query requests, extracts and returns complete traceability chain information from the blockchain.

[0011] Furthermore, in step S2, the deep learning model used for AI visual recognition is trained using a dataset of consumable images that includes scenes of metal reflections and blood stains in an operating room environment.

[0012] Furthermore, in step S3, the operation of converting the consumable UDI into a medical insurance settlement code is automatically triggered by a smart contract that listens for consumable usage events on the blockchain.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) A trusted data foundation has been built: the core mapping relationship of consumables and the whole process events are solidified through consortium blockchain technology, which fundamentally eliminates the possibility of data tampering and ensures the ultimate credibility of traceability information.

[0014] (2) Achieved seamless clinical data collection: High-precision AI visual recognition replaces traditional manual scanning, automatically and accurately recording consumable usage information in complex clinical environments, greatly improving data collection efficiency and eliminating human error.

[0015] (3) The automated medical insurance settlement link has been opened up: Based on the authoritative "one item, three codes" binding relationship on the chain, the automatic and accurate conversion from clinical use of consumables to medical insurance expense settlement has been realized, providing key technical support for medical insurance payment.

[0016] (4) Improved management intelligence: The system is driven by trusted data across the entire chain and can realize advanced functions such as intelligent inventory prediction and proactive expiration date warning, enabling consumable management to shift from passive recording to proactive decision-making. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the system architecture of the present invention.

[0018] Figure 2 This is a logical diagram illustrating the process of multi-code binding relationship notarization and data on-chaining in this invention.

[0019] Figure 3 This is a timeline diagram of the entire process of consumables in this invention, from warehousing, verification, use to settlement triggering. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1-3 This invention provides a technical solution: a medical consumables full-process traceability management system, comprising: The intelligent sensing and acquisition layer includes an AI visual recognition module deployed at clinical use nodes. The deep learning model of the AI ​​visual recognition module is deployed on an edge AI computing device of model GWB-6030H or GWF-4059A1, used to non-contactly acquire images of consumables through a camera and automatically identify the UDI of the consumables based on the deep learning model. The intelligent sensing and acquisition layer also includes a batch data acquisition device deployed at warehouse nodes. The batch data acquisition device is a fixed barcode scanning platform of model SK6080 or SK3390, or a handheld intelligent data terminal of model SK9028C. The trusted data chain layer is built on consortium blockchain technology and includes a mapping relationship storage module. The consensus nodes of the consortium blockchain include medical institutions, medical insurance regulatory agencies, and consumable production or distribution companies. The mapping relationship storage module is configured to receive and store the binding relationship data between the UDI of the consumable and the corresponding hospital internal material management code and national medical insurance settlement code, and write the binding relationship data into the blockchain. The intelligent application and decision-making layer is used to synchronize data from the trusted data chain layer and provide inventory management, expiration date warning and flow tracking services; the intelligent application and decision-making layer also integrates a time series forecasting model for predicting consumable demand based on historical consumption data in the trusted data chain layer. The system is configured such that when the AI ​​visual recognition module detects that a consumable is being used, it automatically associates the UDI of the consumable with the anonymous patient identification information in the current medical event, and submits the association information to the trusted data chain layer to generate a new data block. The system communicates with the SC3000-MED model medical-grade smart camera to acquire images of consumables in clinical environments. A method for full-process traceability management of medical consumables, applied to the system described above, includes the following steps: S1: Initialization and storage of mapping relationship: Generate binding relationship data between the UDI of consumables and the corresponding hospital internal material management code and national medical insurance settlement code, and submit the binding relationship data to the consortium blockchain network for storage; S2: Multi-node intelligent data acquisition and event on-chain: Status data is collected and uploaded to the blockchain at each node of the consumable circulation; at the clinical use node, the UDI of the consumable is automatically identified by AI visual recognition, and the UDI is associated with the patient's diagnosis and treatment event information and uploaded to the blockchain as the use event data; the deep learning model used by the AI ​​visual recognition is trained using a consumable image dataset that includes scenes of metal reflection and blood stains in the operating room environment. S3: Intelligent Decision-Making and Business Closed Loop: Automatically execute inventory management and expiration date warning based on on-chain data; in the settlement process, automatically query the binding relationship data stored on the chain, and convert the UDI of consumables into the corresponding medical insurance settlement code to complete the cost verification; the operation of converting the UDI of consumables into the medical insurance settlement code is automatically triggered by the smart contract that listens to the consumable usage event on the blockchain; S4: Full-chain trusted traceability query: Responds to query requests, extracts and returns complete traceability chain information from the blockchain.

[0022] I. System Architecture and Core Data Initialization like Figure 1 As shown, the system adopts a three-layer architecture. The intelligent sensing layer may include high-resolution medical intelligent cameras (such as the SC3000-MED model) for operating rooms and connected edge AI computing devices (such as the GWB-6030H model), as well as fixed batch scanning platforms (such as the SK6080 model) for warehouses. The trusted data chain layer is a consortium blockchain network composed of multiple participating nodes. The intelligent application layer is deployed on backend servers (such as the NF5270M6 model).

[0023] Trusted initialization of core data (combined with) Figure 2 Taking a batch of intraocular lenses as an example: The manufacturer first assigns a unique, standard-compliant device identifier (e.g., a UDI code conforming to the GS1 standard) to each smallest sales unit of the batch of consumables. Subsequently, the system creates and associates this UDI code with a dedicated hospital internal supplies management code and a corresponding national medical insurance settlement classification code. This "UDI-internal code-medical insurance code" binding relationship, along with the basic specifications of the consumables, constitutes the digital identity credential for the consumables. After the manufacturer node digitally signs this credential data, it submits it as the first transaction to the consortium blockchain network. After the consensus nodes of the network verify that there are no errors, they package this transaction into the first data block (e.g., block height 1001), thus completing the initial trusted on-chain storage of the consumables' digital identity and its core mapping relationship.

[0024] II. Intra-hospital circulation and intelligent identification (combined) Figure 3 ) Hospital Central Warehouse Inbound: Consumables are delivered to the hospital's logistics center. Warehouse staff use a fixed barcode scanning platform (SK6080 model) to read the UDI codes on the packaging in batches. The inbound system automatically verifies the validity and integrity of the digital identity credentials corresponding to the batch of UDI codes (query block 1001) by calling the on-chain smart contract. After successful verification, the system generates inbound event data containing information such as operator, inbound time, and storage location. After signing, this data is recorded on the blockchain as a new transaction (which can be marked as Tx-In) (e.g., stored in block 1002).

[0025] Preoperative AI Verification in the Operating Room: Consumables are delivered to the operating room. During preoperative preparation, nurses place the consumables in a designated identification area. A medical smart camera (SC3000-MED) captures images of the consumables, and a deep learning model deployed in an edge AI computing device (GWB-6030H) analyzes the images. This model, based on an improved convolutional neural network architecture, has been specifically optimized for common interference factors in the operating room environment, such as glare from metal instruments and liquid obstruction. It can accurately locate and identify the appearance features and UDI codes of consumables, with an accuracy rate exceeding 99.5%. The system automatically compares the identified UDI codes with the list of consumables to be used in the current surgical plan. After confirmation, a "Preoperative Inventory Confirmation" event is generated and uploaded to the blockchain (e.g., stored in block 1003).

[0026] III. Clinical Use Association and Automatic Billing Trigger On-chain usage event: The aforementioned intraocular lens was successfully implanted into the patient during the surgery. Post-surgery, the system automatically performs a key operation: strongly associating the used consumable UDI code with the anonymized patient identifier (e.g., a unique string P_hash_9a8b7c generated from the patient's medical record number using a hash algorithm) and the surgical record number to generate a structured usage event record. This record, after being digitally signed by the operating room node, is submitted as a consumable usage transaction (which can be labeled Tx-Use) and stored on the blockchain (e.g., in block 1004).

[0027] The automated settlement process is initiated: A smart contract deployed on the blockchain continuously monitors consumable usage events such as Tx-Use. Upon capturing such a transaction, the contract automatically executes predefined logic: extracting the consumable's UDI code from the transaction data, then tracing back through historical blocks on the chain (locating block 1001, which stores the initial mapping relationship), to obtain the authoritative national medical insurance settlement code bound to that UDI code. Subsequently, the smart contract automatically generates a settlement instruction containing the medical insurance code, anonymous patient identifier, and surgical information, and pushes it to the hospital's billing and medical insurance settlement system, thus seamlessly triggering an automated closed-loop process from clinical use of consumables to medical insurance payment settlement.

[0028] IV. Full-chain trusted traceability query The system provides traceability query services to various authorized users. For example, when a supervisor enters a UDI code, the traceability engine will sequentially retrieve all data blocks related to that code from the blockchain (including the initial mapping relationship block 1001, and subsequent event blocks such as warehousing, stocking, and usage), and reconstruct a complete, continuous, and publicly verifiable flow trajectory with digital signatures at every stage. Any illegal tampering with on-chain data will cause hash verification to fail, thus ensuring the absolute credibility of the traceability report.

[0029] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A medical consumables full-process traceability management system, characterized in that, include: The intelligent sensing and acquisition layer includes an AI visual recognition module deployed at clinical use nodes, which is used to non-contactly acquire images of consumables through cameras and automatically identify the UDI of consumables based on a deep learning model. The trusted data chain layer is built on consortium blockchain technology and includes a mapping relationship storage module. The mapping relationship storage module is configured to receive and store the binding relationship data between the UDI of the consumable and the corresponding hospital internal material management code and national medical insurance settlement code, and write the binding relationship data into the blockchain. The intelligent application and decision-making layer is used to synchronize data from the trusted data chain layer and provide inventory management, expiration date warning and flow tracking services. The system is configured such that when the AI ​​visual recognition module detects that a consumable is being used, it automatically associates the UDI of the consumable with the anonymous patient identification information in the current medical event, and submits the association information to the trusted data chain layer to generate a new data block.

2. The medical consumables full-process traceability management system according to claim 1, characterized in that, The intelligent sensing and acquisition layer also includes batch data acquisition devices deployed at warehouse nodes. These batch data acquisition devices are fixed barcode scanning platforms of model SK6080 or SK3390, or handheld intelligent data terminals of model SK9028C.

3. The medical consumables full-process traceability management system according to claim 1, characterized in that, The deep learning model of the AI ​​visual recognition module is deployed on an edge AI computing device of model GWB-6030H or GWF-4059A1.

4. The medical consumables full-process traceability management system according to claim 1, characterized in that, The consensus nodes of the consortium blockchain include medical institutions, medical insurance regulatory agencies, and consumable production or distribution companies.

5. The medical consumables full-process traceability management system according to claim 1, characterized in that, The intelligent application and decision-making layer also integrates a time-series prediction model, which is used to predict consumable demand based on historical consumption data in the trusted data chain layer.

6. The medical consumables full-process traceability management system according to claim 1, characterized in that, The system communicates with the SC3000-MED model medical-specific smart camera to acquire images of consumables in a clinical environment.

7. A method for full-process traceability management of medical consumables, applied to the system as described in any one of claims 1-6, characterized in that, Including the following steps: S1: Initialization and storage of mapping relationship: Generate binding relationship data between the UDI of consumables and the corresponding hospital internal material management code and national medical insurance settlement code, and submit the binding relationship data to the consortium blockchain network for storage; S2: Multi-node intelligent data collection and event on-chain: Collect status data at each node of consumable circulation and upload it to the blockchain; at the clinical use node, automatically identify the UDI of consumables through AI visual recognition, and upload the UDI as the use event data after associating it with the patient's diagnosis and treatment event information. S3: Intelligent Decision-Making and Business Closed Loop: Automatically execute inventory management and expiration date warning based on on-chain data; In the settlement process, automatically query the binding relationship data stored on the chain, and convert the UDI of consumables into the corresponding medical insurance settlement code to complete the cost verification; S4: Full-chain trusted traceability query: Responds to query requests, extracts and returns complete traceability chain information from the blockchain.

8. The method for full-process traceability management of medical consumables according to claim 7, characterized in that, In step S2, the deep learning model used for AI visual recognition is trained using a dataset of consumable images that includes scenes of metal reflections and blood stains in an operating room environment.

9. The method for full-process traceability management of medical consumables according to claim 7, characterized in that, In step S3, the operation of converting the consumable UDI into a medical insurance settlement code is automatically triggered by a smart contract that listens for consumable usage events on the blockchain.

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