CN117612683A - Surgical instrument identification management method and system based on AI technology - Google Patents
Surgical instrument identification management method and system based on AI technology Download PDFInfo
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Abstract
The invention belongs to the technical field of biomedical engineering, in particular to a surgical instrument identification management method and a surgical instrument identification management system based on an AI technology, wherein an RFID label assembling and encoding device is used for distributing and encoding an RFID label with unique information for each surgical instrument; the image recognition module is used for recognizing the type and the number of the instruments by analyzing the images of the instruments by using the deep learning and pattern recognition technology; weight measuring means for acquiring weight information of each instrument, comparing with information in the RFID tag to confirm the type and number of instruments; an anomaly detection and reporting module for detecting and reporting any possible anomalies by analyzing the information of the RFID tag, the image recognition result and the weight measurement result, recording the information of the anomalies in a database, and notifying the related personnel by an email or other notification means; and the system learning and improvement module is used for continuously learning and improving according to the new data and feedback so as to update and optimize the system.
Description
Technical Field
The invention belongs to the technical field of biomedical engineering, and particularly relates to a surgical instrument identification management method and system fusing an AI technology.
Background
In medical management, management of sterilized articles is a key element in improving medical service quality. According to statistics, about 500 thousands of patients are infected in hospitals each year, wherein the disinfection of medical equipment, instruments and the like is not strictly one of the main reasons for causing infection and causing medical accidents. Therefore, how to improve the quality of the disinfection supply and reduce the medical accidents becomes a great challenge for the current medical industry. As an important medical safety guarantee department of a hospital, the management and operation standard of a disinfection supply center is closely related to the infection rate of the hospital, and the method has the advantages of large quantity of supplied sterile articles, quick turnover, wide range of application, and plays a significant role in improving the medical care quality and controlling the infection of the hospital by monitoring and retrospecting and managing the effects of working quality and working flow. With the improvement of medical and health industry, the perfection of modern medical system and the improvement of the informatization degree of hospitals, the information management level of the disinfection supply center of the hospitals is provided with higher requirements.
The RFID technology is a contactless automatic identification technology, automatically identifies a target object through radio frequency signals and acquires related data, and adopts a wireless instant remote reading mode, so that the identification speed is high and the service life is long. The RFID technology and the informatization technology are integrated and applied to a disinfection traceability system of a disinfection supply center of a hospital, so that traceable management of the whole use process of disinfection articles of the disinfection supply center is truly realized, and the RFID technology and the informatization technology become an important research content in informatization construction of the hospital.
At present, the hardware of the RFID disinfection traceability management system mainly comprises three parts, namely an RFID electronic tag, a reader-writer and a disinfection traceability system. In practical application, by distributing unique codes for each instrument package and writing RFID tags, the codes are associated with background database information, and the electronic tags are placed in small pockets on the instrument package, so that the instrument package can be monitored by an RFID system in the whole course in the circulation process of a hospital; the reader-writer is used as a read-out and write-in device, can read the information of the electronic tag in a non-contact manner, realizes the collection, processing and transmission of the information of the instrument package through the disinfection tracing system, and simultaneously writes the information generated by the system into the RFID electronic tag through the reader-writer.
The prior art related to the use of bar codes and manual scanning devices to track and manage medical instruments. In such a system, each instrument is labeled with a bar code that can be read by a worker using a manual scanning device to track and manage the instrument.
Technical problems existing in the prior art:
1. inefficiency of manual scanning: the use of manual scanning devices requires manual operations, which can consume significant time and human resources. In busy hospital environments, such inefficiency can lead to delays and can impact the quality of medical service.
2. Scanning errors and omissions: manual scanning devices may cause scanning errors or omissions, for example, the scanning device may not read the bar code correctly, or a worker may forget to scan certain instruments. These errors and omissions may cause problems with the tracking and management of the instruments.
3. The abnormal situation cannot be automatically identified and reported: existing bar code scanning systems often fail to automatically identify and report abnormal conditions, such as missing instruments or confusion. In such a case, a worker may be required to manually check and confirm each instrument, which may consume a great deal of time and human resources.
4. Lack of deep learning and pattern recognition functionality: existing bar code scanning systems often fail to perform deep learning and pattern recognition, so they may fail to accurately identify and calculate instruments in a surgical instrument package.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a surgical instrument identification management method and a surgical instrument identification management system fused with an AI technology.
The invention is realized in such a way that the surgical instrument identification management method integrating the AI technology comprises the following steps:
RFID tag assembling and encoding means for assigning and encoding an RFID tag having unique information to each surgical instrument; the image recognition module is used for recognizing the type and the number of the instruments by analyzing the images of the instruments by using the deep learning and pattern recognition technology; weight measuring means for acquiring weight information of each instrument, comparing with information in the RFID tag to confirm the type and number of instruments; an anomaly detection and reporting module for detecting and reporting any possible anomalies, such as the absence or confusion of instruments, by analyzing the information of the RFID tags, the image recognition results and the weight measurement results, and recording the information of these anomalies in a database, while notifying the relevant personnel by email or other notification means; and the system learning and improvement module is used for continuously learning and improving according to the new data and feedback so as to update and optimize the system.
Further, the system comprises a data acquisition module, wherein the module deploys an RFID reader in a disinfection equipment area, a storage area or other places needing to track disinfection products of a hospital; the reader is configured to automatically read the RFID tag on the product and transmit the read data, including the name, lot number, date of manufacture, and expiration date, to the central database; by interfacing with existing systems or using standardized data transfer protocols, IT is ensured that the RFID reader is seamlessly integrated with the hospital's existing IT system.
Further, the system comprises a data preprocessing module, wherein the module cleans the acquired data by using a proper data cleaning algorithm to remove noise and abnormal values; when the acquired data have missing values, filling the missing values by using a statistical method or an artificial intelligence algorithm; the data is then transformed and normalized, which includes smoothing, aggregating, discretizing the data, and normalizing the data to scale the data to a similar range.
Further, the AI model identifies the number and type of instruments in the surgical kit by identifying the RFID tag, shape, and weight of each instrument; the AI model utilizes the data of the RFID tag and the data obtained by the image recognition and weighing system to carry out deep learning and pattern recognition so as to accurately recognize and count the instruments in the surgical instrument package; in addition, the system is capable of automatically detecting and reporting any anomalies, such as the absence or confusion of surgical instruments, and recording information of those anomalies in a database.
Further, the method comprises the steps of:
(a) An identifier allocation module:
selecting an RFID tag which is suitable for the size, the material and the application environment of the product: selecting a proper RFID tag according to the size, the material and the application environment of the product; different types or sizes of labels are selected for different products;
product name, lot number, manufacturing date, and expiration date are encoded in the RFID tag: encoding information related to the product into an RFID tag;
determining and attaching an RFID tag to a specific location on a product: determining the specific position of the RFID tag on the product and ensuring that the tag is properly attached to the product;
testing and verifying the RFID tag in a practical environment: testing and verifying RFID tags attached to products in an actual environment; the method comprises the steps of reading information of the tag, verifying the reliability and accuracy of the tag, and ensuring that the tag is not damaged in the sterilization process;
(b) And a data acquisition module:
deploying RFID readers in critical areas of a hospital: deploying the RFID reader in a disinfection equipment area, a storage area or other places needing to track disinfection products of a hospital;
the RFID reader is configured to automatically read the RFID tag of the product and to transfer the read data to the central database: configuring an RFID reader to automatically read an RFID tag on a product, wherein the read data comprises product information encoded in the tag, such as a name, a batch number, a manufacturing date and an expiration date, and transmitting the read data to a central database;
Ensure seamless integration of the RFID reader with existing IT systems: the RFID reader is ensured to be seamlessly integrated with the existing IT system of the hospital by interface docking with the existing system or using a standardized data transmission protocol, and after integration, the data of the RFID reader can be interacted and shared with other systems of the hospital to carry out comprehensive data management and utilization;
alarm and notification functions are set to monitor reader status: when the reader fails or cannot read the tag, sending an alarm to inform related personnel to process;
(c) And a data preprocessing module:
data cleaning using software algorithms to remove noise and outliers: cleaning the acquired data by using a proper data cleaning algorithm to remove noise and abnormal values in the acquired data;
filling up missing values using statistical methods or AI algorithms: missing values exist in the acquired data, and the missing values are filled by using a statistical method or an artificial intelligence algorithm; the statistical method can infer and fill up according to the statistical characteristics of other related data, and the AI algorithm can predict and fill up the missing value by learning the mode and the relevance of the data;
data conversion and normalization are carried out: converting and normalizing the data, wherein the data conversion comprises smoothing, aggregating and discretizing the data, and the data normalization scales the data to a similar range;
Storing and backing up the processed data: the processed data may be stored in a database or other data storage system and backed up periodically for access and backtracking as needed.
Another object of the present invention is to provide a surgical instrument identification management method fusing AI technology, including:
s1, identifier allocation: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
s2, data acquisition: collecting RFID-based UDI unique identification information by using a sensor;
s3, data preprocessing: preprocessing the collected unique identification information of the UDI based on the RFID;
s4, data analysis: performing deep analysis and prediction on the collected RFID data by using an AI technology;
s5, data prediction: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
s6, feedback and decision support: providing real-time feedback and decision support according to the prediction result of the AI model;
s7, management flow optimization: and optimizing the management flow according to the obtained real-time feedback result and the decision.
Further, the RFID-based UDI unique identification allows the system to track each stage of sterilizing a supply item, including purchasing, storing, dispensing, and use; each item has a unique RFID tag identification that enables tracking of the entire life cycle.
Further, in the data prediction module, the AI learns and identifies patterns from the collected data, identifying which sterilization supply items would increase in demand and which would decrease; helps predict future demand, thereby making inventory management and purchasing decisions more accurate and efficient.
Further, in the feedback and support module, the AI technology is utilized to optimize inventory management, so that waste is reduced, and the operation efficiency is improved; the AI model can optimize inventory levels to avoid excessive inventory and inventory shortages; the AI model can predict the lifetime of each product, reducing waste of expired products.
Further, the feedback and support module further includes: when the AI model predicts that the demand for a certain sterile supply item will increase, the system will give a warning suggesting an advance purchase; the forecast demand will decrease and the system will recommend a decrease in purchases, avoiding stock backlog and wastage.
Further, the data analysis module can trace back the information of the disinfection supply flow, and the specific steps include:
step one: acquiring flow information of disinfection supply;
step two: classifying the flow information according to the preset disinfection supply flow information category, and generating classified flow information;
step three: performing flow information data cleaning processing on the classified flow information to generate cleaning flow information;
Step four: performing data word segmentation processing on the cleaning flow information by using a naive Bayes algorithm to generate word segmentation flow information;
step five: and carrying out data denoising processing on the word segmentation flow information by using a flow information denoising formula to generate standard flow information.
Another object of the present invention is to provide a surgical instrument identification management system incorporating AI technology, including:
an identifier allocation module: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
and a data acquisition module: the RFID-based UDI unique identification information is collected with a sensor,
and a data preprocessing module: preprocessing the collected unique identification information of the UDI based on the RFID;
and a data analysis module: performing deep analysis and prediction on the collected RFID data by using an AI technology;
and a data prediction module: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
feedback and decision support module: providing real-time feedback and decision support according to the prediction result of the AI model;
and the management flow optimization module: and optimizing the management flow according to the obtained real-time feedback result and the decision.
Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps of the surgical instrument identification management method of the fusion AI technology.
Another object of the present invention is to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the steps of the surgical instrument identification management method of the fusion AI technology.
Another object of the present invention is to provide an information data processing terminal for implementing the surgical instrument identification management system of the fusion AI technology.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
first, the present invention uses AI technology to conduct in-depth analysis and prediction of RFID-collected data to optimize the management flow of disinfection supplies. AI technology can identify patterns, predict demand, provide real-time feedback, help administrators make data-driven decisions, and thereby improve the management efficiency and accuracy of disinfection supplies.
The invention relates to a novel method for optimizing disinfection supply management flow by using AI technology. The method firstly collects data by utilizing a Radio Frequency Identification (RFID) technology, and then uses an AI technology to carry out deep analysis and prediction on the data, thereby realizing the optimization of a disinfection supply management flow.
RFID technology is first used to track each stage of sterilizing a supply item, including purchasing, storing, dispensing, and use. Each item is identified by a unique RFID tag, making tracking of its entire life cycle a way.
The present invention utilizes AI technology to analyze and predict these RFID data. The AI may learn and identify patterns from the collected data, for example, it may identify which sterilization supply items would increase in demand and which would decrease. In this way, the AI can help predict future demands, thereby making inventory management and purchasing decisions more accurate and efficient.
AI technology can also provide real-time feedback to help administrators make data-driven decisions. For example, if the AI predicts that the need for a certain sterilization-supplied item will increase, the manager may purchase more such items ahead of time. Conversely, if the AI predicts that the demand for an item will decrease, the manager may reduce the purchase of such item, thereby avoiding wastage.
The invention realizes the optimization of the disinfection supply management flow by combining RFID and AI technologies. The invention can improve the management efficiency, reduce the waste and improve the management accuracy.
Second, this approach well blends RFID (radio frequency identification) technology and AI (artificial intelligence) technology for optimizing disinfection supply management. The following is a deeper explanation of each technology and how they function in the system.
Highly traceable: RFID tags allow each item to be tracked in real time, regardless of its link in the supply chain.
Information accuracy: RFID tags can provide more accurate and timely information such as the date of manufacture, expiration date, lot size, etc. of the product.
Low cost and high efficiency: although initial equipment is installed at a cost, RFID systems can reduce labor costs and increase efficiency.
Data analysis and pattern recognition: AI algorithms can analyze large amounts of complex data and identify useful patterns therefrom. For example, the frequency of usage and the pattern of demand of the disinfectant may be identified.
Prediction accuracy: through historical data and pattern recognition, AI models can more accurately predict future demands or potential problems, such as outages or backorders.
Automated decision support: the AI algorithm can automatically generate orders, adjust stock or priority, etc., reduce human errors and increase response speed.
Omnibearing optimization: through the combination of RFID and AI, not only can each product be tracked, but also deep analysis and prediction can be carried out on the data, so that the supply chain is comprehensively optimized.
Waste is reduced and efficiency is improved: accurate demand forecasting and inventory management can minimize expiration and waste while saving time and labor through automation.
Increase transparency and traceability: the system can provide real-time feedback and data visualization, enabling the manager to more easily monitor the entire supply chain and make timely decisions as needed.
Complexity and uncertainty should be addressed: the supply chain is essentially a complex system with many uncertainties. The combination of RFID and AI can better address these complexities and uncertainties, thereby improving the overall stability and risk resistance of the supply chain.
By the method, the efficiency and the accuracy of disinfection supply management can be improved, and the whole supply chain can be more flexible and sustainable.
Thirdly, this disinfection supply management method employing AI and RFID technology brings about the following significant technical advances:
1. predictive capability: AI models can predict future demands by learning historical data and patterns of disinfection supply usage. Such predictive capability may help institutions plan and prepare better, avoiding problems with supply shortages or excessive inventory.
2. Real-time monitoring and feedback: by combining RFID technology with AI models, the system can track the status of the disinfection supply in real time and alert to potential problems (e.g., product expiration, inventory shortage, etc.) in real time. The real-time feedback mechanism can help the mechanism to adjust the strategy in time and optimize management.
3. Automated decision support: the AI model may provide automated decision support, such as automatically sending a purchase order based on the forecast results, or automatically adjusting inventory levels. Such automation capability can greatly increase the efficiency of the disinfection supply management.
4. And (3) optimizing a management flow: by feeding back results and decisions in real time, the institution may further optimize the management process, such as optimizing the purchasing process, improving the storage and use of the product, etc. This optimization capability can help institutions to improve operating efficiency and reduce wastage.
5. Improving supply chain transparency: RFID technology can provide accurate knowledge of the real-time location and status of each product in the supply chain, thereby improving the transparency of the supply chain, helping institutions to better manage and control the supply chain.
The method has the advantages that the method combines the advantages of the RFID technology and the AI technology, and realizes the comprehensive optimization of the disinfection supply management. By the method, the demand can be effectively predicted, the inventory can be optimized, the waste can be reduced, the operation efficiency can be improved, and the competitiveness of a supply chain can be improved.
Fourth, the present invention creates a new disinfection supply management optimization method by integrating RFID tags, image recognition, weight measurement, anomaly detection and reporting, and system learning and improvement modules. This approach provides a comprehensive and accurate way to track and identify instruments in a surgical kit, improving medical safety and efficiency, and is a significant technological advance.
The present invention improves the efficiency and accuracy of data acquisition by deploying RFID readers in the hospital's disinfection facility area, storage area, or other location where disinfection products need to be tracked, and sending the read data to a central database. At the same time, the system also enhances the interoperability of the system by being seamlessly integrated with the existing system, which is also an important technical progress.
The data preprocessing module can ensure the quality and consistency of data, thereby improving the performance and accuracy of the AI model. The module remarkably improves the effect of data preprocessing by using an advanced data cleaning algorithm and filling the missing value by a statistical method and an AI algorithm, which is a remarkable technical progress.
The AI model of the present invention is able to accurately identify the number and type of instruments in a surgical kit by identifying the RFID tag, shape and weight of each instrument. In addition, the system is capable of automatically detecting and reporting any abnormal condition, such as the absence or confusion of instruments, and such automation and accuracy is a significant technological advance.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for managing surgical instrument identification of a fusion AI technique according to an embodiment of the invention;
FIG. 2 is a flow chart of a data analysis module provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a surgical instrument identification management system fusing AI technology according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides a surgical instrument identification management method and a surgical instrument identification management system fused with an AI technology, and the invention is described in detail below with reference to the accompanying drawings.
The surgical instrument identification management method integrating the AI technology is characterized by comprising the following steps:
(a) An identifier allocation module:
selecting an RFID tag which is suitable for the size, the material and the application environment of the product: selecting a proper RFID tag according to the size, the material and the application environment of the product; different types or sizes of labels are selected for different products;
product name, lot number, manufacturing date, and expiration date are encoded in the RFID tag: encoding information related to the product into an RFID tag;
determining and attaching an RFID tag to a specific location on a product: determining the specific position of the RFID tag on the product and ensuring that the tag is properly attached to the product;
testing and verifying the RFID tag in a practical environment: testing and verifying RFID tags attached to products in an actual environment; the method comprises the steps of reading information of the tag, verifying the reliability and accuracy of the tag, and ensuring that the tag is not damaged in the sterilization process;
(b) And a data acquisition module:
deploying RFID readers in critical areas of a hospital: deploying the RFID reader in a disinfection equipment area, a storage area or other places needing to track disinfection products of a hospital;
the RFID reader is configured to automatically read the RFID tag of the product and to transfer the read data to the central database: configuring an RFID reader to automatically read an RFID tag on a product, wherein the read data comprises product information encoded in the tag, such as a name, a batch number, a manufacturing date and an expiration date, and transmitting the read data to a central database;
Ensure seamless integration of the RFID reader with existing IT systems: the RFID reader is ensured to be seamlessly integrated with the existing IT system of the hospital by interface docking with the existing system or using a standardized data transmission protocol, and after integration, the data of the RFID reader can be interacted and shared with other systems of the hospital to carry out comprehensive data management and utilization;
alarm and notification functions are set to monitor reader status: when the reader fails or cannot read the tag, sending an alarm to inform related personnel to process;
(c) And a data preprocessing module:
data cleaning using software algorithms to remove noise and outliers: cleaning the acquired data by using a proper data cleaning algorithm to remove noise and abnormal values in the acquired data;
filling up missing values using statistical methods or AI algorithms: missing values exist in the acquired data, and the missing values are filled by using a statistical method or an artificial intelligence algorithm; the statistical method can infer and fill up according to the statistical characteristics of other related data, and the AI algorithm can predict and fill up the missing value by learning the mode and the relevance of the data;
data conversion and normalization are carried out: converting and normalizing the data, wherein the data conversion comprises smoothing, aggregating and discretizing the data, and the data normalization scales the data to a similar range;
Storing and backing up the processed data: the processed data may be stored in a database or other data storage system and backed up periodically for access and backtracking as needed.
As shown in fig. 1, the surgical instrument identification management method fusing AI technology provided by the embodiment of the invention includes:
s1, identifier allocation: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
s2, data acquisition: collecting RFID-based UDI unique identification information by using a sensor;
s3, data preprocessing: preprocessing the collected unique identification information of the UDI based on the RFID;
s1: identifier assignment
1. Selecting an appropriate RFID tag: the appropriate RFID tag is selected in consideration of the size, material and application environment of the product. For example, in the case of surgical instruments, a label capable of withstanding high temperatures is required.
2. Information coding: the necessary information such as product name, lot number, manufacturing date, expiration date, etc. is encoded in the RFID tag.
3. Label attachment: an optimal location and method for attaching an RFID tag to a product is determined. This involves special glues or straps etc.
4. Testing and verifying: the tag is tested in a practical environment, ensuring that the information is reliable and that the tag can operate under a variety of conditions as intended.
S2: data acquisition
1. Determining a reader position: depending on product mobility and importance, it is selected which critical areas of the hospital (e.g., warehouse, operating room, emergency room, etc.) to deploy the RFID reader.
2. And (3) data collection: the reader is configured to automatically read the RFID tag as the product passes by and to transfer the read data to the central database.
3. And (3) system integration: ensuring that the RFID reader can be seamlessly integrated with existing IT systems, such as hospital management systems.
4. And (3) continuously monitoring: alarms and notifications are set to alert immediately when problems (e.g., read failures, equipment failures, etc.) occur.
S3: data preprocessing
1. Data cleaning: noise and outliers, such as repeated reads, missed reads, etc., are removed using software algorithms.
2. Filling up missing values: if missing values are found in the data, statistical methods or AI algorithms are used to predict and populate these values.
3. Data conversion and normalization: the data is converted into a format and units suitable for analysis. For example, if the location data is provided in latitude and longitude form, it needs to be converted to a hospital floor or room number.
4. Data storage and backup: ensuring that all processed data is securely stored and backed up for later analysis and traceability.
By the implementation of each of the above steps, a robust, efficient and scalable disinfection supply management system can be established. Such a system not only increases operational efficiency, but also enhances visualization and control of the supply chain, thereby reducing errors and waste.
S4, data analysis: performing deep analysis and prediction on the collected RFID data by using an AI technology;
s5, data prediction: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
s6, feedback and decision support: providing real-time feedback and decision support according to the prediction result of the AI model;
s7, management flow optimization: and optimizing the management flow according to the obtained real-time feedback result and the decision.
The RFID-based UDI unique identification allows the system to track each stage of sterilizing a supply item, including purchasing, storing, dispensing, and use; each item has a unique RFID tag identification that enables tracking of the entire life cycle.
In the data prediction module, AI learns and identifies patterns from the collected data, identifying which sterilization supply items will increase in demand and which will decrease; helps predict future demand, thereby making inventory management and purchasing decisions more accurate and efficient.
In the feedback and support module, the AI technology is utilized to optimize inventory management, reduce waste and improve operation efficiency; the AI model can optimize inventory levels to avoid excessive inventory and inventory shortages; the AI model can predict the lifetime of each product, reducing waste of expired products.
The feedback and support module further comprises: when the AI model predicts that the demand for a certain sterile supply item will increase, the system will give a warning suggesting an advance purchase; the forecast demand will decrease and the system will recommend a decrease in purchases, avoiding stock backlog and wastage.
As shown in fig. 2, the data analysis module may trace back the disinfection supply flow information, and the specific steps include:
step one: acquiring flow information of disinfection supply;
step two: classifying the flow information according to the preset disinfection supply flow information category, and generating classified flow information;
step three: performing flow information data cleaning processing on the classified flow information to generate cleaning flow information;
step four: performing data word segmentation processing on the cleaning flow information by using a naive Bayes algorithm to generate word segmentation flow information;
step five: and carrying out data denoising processing on the word segmentation flow information by using a flow information denoising formula to generate standard flow information.
As shown in fig. 3, a surgical instrument identification management system provided with a fusion AI technology according to an embodiment of the present invention includes:
an identifier allocation module: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
and a data acquisition module: the RFID-based UDI unique identification information is collected with a sensor,
and a data preprocessing module: preprocessing the collected unique identification information of the UDI based on the RFID;
and a data analysis module: performing deep analysis and prediction on the collected RFID data by using an AI technology;
and a data prediction module: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
feedback and decision support module: providing real-time feedback and decision support according to the prediction result of the AI model;
and the management flow optimization module: and optimizing the management flow according to the obtained real-time feedback result and the decision.
The identifier allocation module works on the principle:
the product is processed by the identifier assignment module before entering the sterilization supply management system.
The identifier assignment module selects an RFID tag that is appropriate for the product and encodes information about the product in the tag. The RFID tag is attached to a specific location of the product using a robot or an automatic labeling apparatus. In the disinfection process of the product, the RFID tag is always attached to the product, and is tested and verified in the actual environment, and the information on the tag is read through an RFID reader-writer or other equipment, and the reliability and the accuracy of the tag are verified. During the supply management process, an RFID reader is used to scan RFID tags on products to obtain information about the products, which is used to track the lot, date of manufacture, and date of expiration of the products, and to perform inventory management and supply chain tracking operations.
By using an identifier assignment module, disinfection supply management may be more efficient and accurate. The use of RFID tags can provide real-time product information and tracking capabilities, reduce the risk of human error and data inconsistencies, and improve overall supply chain management efficiency.
The working principle of the data acquisition module is as follows:
RFID readers are deployed in critical areas of a hospital and are appropriately configured and connected to a network. The reader begins to automatically scan the surrounding product and reads the information of the RFID tag attached to the product. The read data is transferred to the central database either directly using a network connection or through an intermediate server. The central database receives and stores information of the RFID tags, including product names, lot numbers, manufacturing dates, expiration dates, and the like. The data acquisition module is integrated with the existing IT system, so that seamless transmission and sharing of data are ensured. For status monitoring of RFID readers, alarm and notification functions are provided to deal with any reader failures or problems in time.
By using the data acquisition module, hospitals can realize automatic data acquisition and management of the disinfection products. This provides real-time data updating and traceability, helps hospitals to better monitor the disinfection supply chain, improves work efficiency, and ensures quality and safety of disinfection products.
The working principle of the data preprocessing module is as follows:
and removing noise and abnormal values from the collected original data through a data cleaning algorithm, so as to ensure the accuracy and consistency of the data. If the missing value exists, the missing value is filled by using a statistical method or an AI algorithm, the statistical method can be deduced by utilizing the existing data characteristics, and the AI algorithm can be used for predicting and filling through learning the data mode. The data is converted and normalized, and operations such as smoothing, aggregation, discretization and the like are performed according to analysis requirements, and the data is scaled to a similar range. The processed data is stored in a database or other data storage system and is backed up periodically to ensure the safety and reliability of the data.
By using the data preprocessing module, the quality and accuracy of data can be improved, and high-quality input is provided for subsequent data analysis and modeling. Operations such as cleaning, shimming, converting, and normalizing may help the disinfection provisioning management system better understand and utilize the data, providing accurate decision support and insight.
The invention utilizes AI technology to conduct deep analysis and prediction on the data collected by RFID to optimize the management flow of disinfection supply. AI technology can identify patterns, predict demand, provide real-time feedback, help administrators make data-driven decisions, and thereby improve the management efficiency and accuracy of disinfection supplies.
The invention relates to a novel method for optimizing disinfection supply management flow by using AI technology. The method firstly collects data by utilizing a Radio Frequency Identification (RFID) technology, and then uses an AI technology to carry out deep analysis and prediction on the data, thereby realizing the optimization of a disinfection supply management flow.
In the present invention, RFID technology is first used to track each stage of sterilizing a supply item, including purchasing, storing, dispensing, and use. Each item is identified by a unique RFID tag, making tracking of its entire life cycle a way.
The present invention utilizes AI technology to analyze and predict these RFID data. The AI may learn and identify patterns from the collected data, for example, it may identify which sterilization supply items would increase in demand and which would decrease. In this way, the AI can help predict future demands, thereby making inventory management and purchasing decisions more accurate and efficient.
AI technology can also provide real-time feedback to help administrators make data-driven decisions. For example, if the AI predicts that the need for a certain sterilization-supplied item will increase, the manager may purchase more such items ahead of time. Conversely, if the AI predicts that the demand for an item will decrease, the manager may reduce the purchase of such item, thereby avoiding wastage.
The invention realizes the optimization of the disinfection supply management flow by combining RFID and AI technologies. The method can improve the management efficiency, reduce the waste and improve the management accuracy.
Data required for AI technology includes, but is not limited to: item type, rate of use, purchase cycle, provider delivery time, and other factors that affect the disinfection supply (e.g., seasonal effects or special events). These data may be collected by the RFID system and input into the AI model.
AI models employ different machine learning algorithms including, but not limited to, time series analysis, logistic regression, neural networks, etc. The model needs to be trained and tested to ensure its predictive accuracy and stability.
In practice, the present invention may be integrated with an existing inventory management system, either through a user interface or directly, to provide real-time predictions and feedback. It may generate various reports and warnings, such as warnings of predicted increased or decreased demand, and warnings of inventory levels below or above a standard level.
The technical effect of the present invention may be measured by several key indicators, including but not limited to inventory accuracy, inventory turnover speed, number of expired and remaining items, and variations in procurement and operating costs. In long-term operation, the effectiveness of the method can be assessed by continuously tracking these indicators.
The invention greatly improves the efficiency and accuracy of disinfection supply management. However, implementing this approach requires a deep understanding of the nature of the disinfection supply, selection and training of the appropriate AI model, and design and implementation of an integrated solution with existing systems.
An application embodiment of the present invention provides a computer device including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to execute the steps of a surgical instrument identification management method incorporating AI technology.
An application embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the steps of a surgical instrument identification management method incorporating AI technology.
The application embodiment of the invention provides an information data processing terminal which is used for realizing a surgical instrument identification management system integrating an AI technology.
Example 1: hospital disinfection supply management
In a large hospital setting, a sterile supply (e.g., surgical instruments, PPE, etc.) is critical. An AI-based disinfection supply management system may help hospitals to effectively track and manage these supplies.
S1: each item of sterile supply product (e.g., surgical instruments, PPE, etc.) is assigned an RFID tag.
S2: RFID readers are deployed in hospitals everywhere, and automatically read information whenever a product is close.
S3: the system automatically cleans and pre-processes the data in preparation for further analysis.
S4: the AI module analyzes the data to identify patterns and trends in the usage of the product.
S5: the AI module predicts future demands, for example, predicts how many disinfection instruments are needed for the operating room within a future week.
S6: based on the prediction, the system automatically provides feedback to the management layer, e.g., suggesting to purchase more PPE.
S7: the hospital adjusts purchasing and storing strategies to optimize disinfection supply management.
Example 2: public health facility disinfection supply management
In public health facilities (e.g., airports, shopping centers, etc.), it is necessary to maintain a sufficient supply of disinfection (e.g., disinfectant, paper towels, etc.). An AI-based disinfection supply management system may help facility managers to effectively manage these supplies.
S1: each sterilization supply (e.g., sterilizing fluid, tissue, etc.) is assigned an RFID tag.
S2: RFID readers are deployed in various places in the facility to automatically read information each time a product is removed or added.
S3: the system automatically cleans and pre-processes the data in preparation for further analysis.
S4: the AI module analyzes the data to identify patterns and trends in the usage of the product.
S5: the AI module predicts future demands, for example, predicts how much disinfectant is needed in the future week.
S6: based on the prediction, the system automatically provides feedback to the management layer, e.g., suggesting to purchase more disinfectant.
S7: the facility manager adjusts the procurement and storage policies to optimize the disinfection supply management.
In both embodiments, AI and RFID technology helps to achieve automated, real-time disinfection supply management, improving efficiency and reducing waste.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. The surgical instrument identification and management method integrating the AI technology is characterized by comprising the following steps:
RFID tag assembling and encoding means for assigning and encoding an RFID tag having unique information to each surgical instrument;
the image recognition module is used for recognizing the type and the number of the instruments by analyzing the images of the instruments by using the deep learning and pattern recognition technology;
weight measuring means for acquiring weight information of each instrument, comparing with information in the RFID tag to confirm the type and number of instruments;
an anomaly detection and reporting module for detecting and reporting any possible anomalies, such as the absence or confusion of instruments, by analyzing the information of the RFID tags, the image recognition results and the weight measurement results, and recording the information of these anomalies in a database, while notifying the relevant personnel by email or other notification means;
And the system learning and improvement module is used for continuously learning and improving according to the new data and feedback so as to update and optimize the system.
2. The method of claim 1, comprising a data acquisition module that deploys an RFID reader in a hospital sterilization facility area, storage area, or other location where tracking of sterilization products is desired; the reader is configured to automatically read the RFID tag on the product and transmit the read data, including the name, lot number, date of manufacture, and expiration date, to the central database; by interfacing with existing systems or using standardized data transfer protocols, IT is ensured that the RFID reader is seamlessly integrated with the hospital's existing IT system.
3. A method according to claim 1 or 2, comprising a data preprocessing module for cleaning the acquired data using a suitable data cleaning algorithm to remove noise and outliers therefrom; when the acquired data have missing values, filling the missing values by using a statistical method or an artificial intelligence algorithm; the data is then transformed and normalized, which includes smoothing, aggregating, discretizing the data, and normalizing the data to scale the data to a similar range.
4. The method of claim 1, 2 or 3, wherein the AI model identifies the number and type of instruments in the surgical kit by identifying the RFID tag, shape and weight of each instrument; the AI model utilizes the data of the RFID tag and the data obtained by the image recognition and weighing system to carry out deep learning and pattern recognition so as to accurately recognize and count the instruments in the surgical instrument package; in addition, the system is capable of automatically detecting and reporting any anomalies, such as the absence or confusion of surgical instruments, and recording information of those anomalies in a database.
5. The AI-technology-integrated surgical instrument identification management method according to claim 1, comprising:
s1, identifier allocation: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
s2, data acquisition: collecting RFID-based UDI unique identification information by using a sensor;
s3, data preprocessing: preprocessing the collected unique identification information of the UDI based on the RFID;
s4, data analysis: performing deep analysis and prediction on the collected RFID data by using an AI technology;
s5, data prediction: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
S6, feedback and decision support: providing real-time feedback and decision support according to the prediction result of the AI model;
s7, management flow optimization: and optimizing the management flow according to the obtained real-time feedback result and the decision.
6. The AI technology-converged surgical instrument identification management method of claim 1, wherein the RFID-based UDI unique identification allows the system to track each stage of sterilizing the supply item, including purchase, storage, distribution, and use; each article has a unique RFID tag identification, so that the whole life cycle can be tracked; in the data prediction module, AI learns and identifies patterns from the collected data, identifying which sterilization supply items will increase in demand and which will decrease; the future demand is forecast, so that inventory management and purchasing decisions are more accurate and efficient;
in the feedback and support module, the AI technology is utilized to optimize inventory management, reduce waste and improve operation efficiency; the AI model can optimize inventory levels to avoid excessive inventory and inventory shortages; the AI model can predict the lifetime of each product, reducing waste of expired products.
7. The AI-technology-converged surgical instrument recognition management method of claim 1, wherein the feedback and support module further comprises: when the AI model predicts that the demand for a certain sterile supply item will increase, the system will give a warning suggesting an advance purchase; the forecast demand will decrease and the system will recommend a decrease in purchases, avoiding stock backlog and wastage.
8. The AI-technology-integrated surgical instrument identification management method of claim 1, wherein the data analysis module is capable of tracing the sterilization supply flow information, and the specific steps include:
step one: acquiring flow information of disinfection supply;
step two: classifying the flow information according to the preset disinfection supply flow information category, and generating classified flow information;
step three: performing flow information data cleaning processing on the classified flow information to generate cleaning flow information;
step four: performing data word segmentation processing on the cleaning flow information by using a naive Bayes algorithm to generate word segmentation flow information;
step five: and carrying out data denoising processing on the word segmentation flow information by using a flow information denoising formula to generate standard flow information.
9. An AI technology-integrated surgical instrument identification management system, comprising:
an identifier allocation module: assigning a unique RFID-based UDI identification to each of the sterilization supply products;
and a data acquisition module: the RFID-based UDI unique identification information is collected with a sensor,
and a data preprocessing module: preprocessing the collected unique identification information of the UDI based on the RFID;
And a data analysis module: performing deep analysis and prediction on the collected RFID data by using an AI technology;
and a data prediction module: identifying and learning patterns in the data using AI models, predicting future demands and potential problems;
feedback and decision support module: providing real-time feedback and decision support according to the prediction result of the AI model;
and the management flow optimization module: and optimizing the management flow according to the obtained real-time feedback result and the decision.
10. An information data processing terminal for implementing the AI-technology-integrated surgical instrument identification management system according to claim 9.
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