CN119069103B - Inventory intelligent management system for anesthesia consumable - Google Patents
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Abstract
本发明公开了一种用于麻醉耗材的库存智能管理系统，涉及麻醉耗材的库存智能管理技术领域，包括麻醉耗材数据收集模块、实时耗材富余量计算模块、耗材富余量分析与分类模块、手术复杂度评估模块以及库存更新策略调整模块；麻醉耗材数据收集模块，每台麻醉手术进行时，收集所有正在进行的麻醉手术的麻醉耗材信息，其中包括所有麻醉手术的预期使用量和实际拿取量。本发明通过实时计算耗材富余量和机器学习分析手术复杂度，优化库存管理，减少不必要补货，提升资源利用效率；基于复杂度评估，动态调整更新策略，尤其在高富余耗材场景下，下调更新频率，减少波动和浪费，增强系统灵活性与应急响应能力，确保耗材供应稳定性。
The present invention discloses an intelligent inventory management system for anesthesia consumables, which relates to the technical field of intelligent inventory management of anesthesia consumables, including an anesthesia consumables data collection module, a real-time consumables surplus calculation module, a consumables surplus analysis and classification module, a surgery complexity assessment module, and an inventory update strategy adjustment module; the anesthesia consumables data collection module collects anesthesia consumables information of all ongoing anesthesia surgeries during each anesthesia surgery, including the expected usage and actual take-up of all anesthesia surgeries. The present invention optimizes inventory management, reduces unnecessary replenishment, and improves resource utilization efficiency by calculating the consumables surplus in real time and analyzing the complexity of the surgery through machine learning; based on complexity assessment, the update strategy is dynamically adjusted, especially in scenarios with high surplus consumables, the update frequency is lowered to reduce fluctuations and waste, enhance system flexibility and emergency response capabilities, and ensure the stability of consumables supply.
Description
Technical Field
The invention relates to the technical field of inventory intelligent management of anesthesia consumables, in particular to an inventory intelligent management system for anesthesia consumables.
Background
The intelligent inventory management system for the anesthesia consumables is a comprehensive solution based on the Internet of things technology and data analysis, and aims to optimize inventory management of the anesthesia consumables in hospitals. The system predicts the future consumable demand by using an intelligent algorithm by monitoring the use condition of the consumable in real time, automatically recording inventory change and combining historical use data and operation scheduling information. Meanwhile, the system can automatically trigger the replenishment order, avoid the condition of insufficient stock or excessive stock, ensure the continuous supply of anesthesia consumables, improve the operation efficiency of hospitals and reduce the stock management cost.
Inventory updates for anesthesia consumables of the prior art are typically updated in real-time. Modern intelligent inventory management systems rely on internet of things technology, use and inventory states of all consumable parts are monitored in real time through RFID tags, sensors and the like, immediate updating of consumable parts after use can be achieved, and real-time accuracy of inventory data is guaranteed.
The prior art has the following defects:
In actual operation, consumable materials are needed to deal with emergency situations during anesthesia operation, and consumption of consumable materials can be misjudged due to real-time updating, so that unnecessary replenishment demands are triggered by the system, and confusion and resource waste of inventory management are caused.
For example:
in a complex cardiac procedure, an anesthesia team would prepare multiple sets of endotracheal tubes, anesthesia masks and other related consumables in order to address the possible occurrence of an emergency, even if only one set is expected to be used. In modern intelligent inventory management systems, these standby consumables may be identified by the system as consumed at the beginning of the procedure. However, after the operation is finished, the standby consumable is not actually used, so that the real-time update of the system misjudges the actual inventory consumption, and the consumable replenishment is possibly performed by a hospital when the system is not needed, thereby not only increasing the complexity of inventory management, but also possibly causing resource waste and affecting the accuracy of the whole inventory and the operation cost of the hospital.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide an inventory intelligent management system for anesthesia consumable materials, which is used for accurately identifying the difference between actual demands and standby consumable materials by calculating the consumable material surplus of anesthesia operation in real time, analyzing the operation complexity by a machine learning model, optimizing inventory management, reducing unnecessary replenishment demands, improving the resource utilization efficiency, and dynamically adjusting an inventory update strategy based on a complexity evaluation result, particularly, reducing the update frequency in a high surplus consumable material scene, reducing inventory fluctuation and resource waste, enhancing the flexibility and emergency response capability of the system, and ensuring the stability and sufficiency of consumable material supply so as to solve the problems in the background art.
In order to achieve the aim, the invention provides the technical scheme that the inventory intelligent management system for the anesthesia consumable comprises an anesthesia consumable data collection module, a real-time consumable surplus calculation module, a consumable surplus analysis and classification module, an operation complexity evaluation module and an inventory update strategy adjustment module;
the anesthesia consumable data collection module is used for collecting anesthesia consumable information of all ongoing anesthesia surgeries when each anesthesia surgery is carried out, wherein the anesthesia consumable information comprises expected usage amount and actual taking amount of all the anesthesia surgeries;
the real-time consumable surplus calculation module is used for carrying out real-time analysis on consumable conditions of all anesthesia operations and carrying out real-time consumable surplus calculation through the expected usage amount and the actual taking amount of the anesthesia consumable;
The consumable surplus analysis and classification module analyzes consumable surplus and divides the ongoing anesthesia operation into high surplus consumable stock and normal surplus consumable stock according to anesthesia consumable conditions;
the operation complexity evaluation module is used for comprehensively analyzing all the simultaneously performed anesthesia operations through a pre-trained machine learning model in a high-margin consumable inventory scene and evaluating the complexity of the anesthesia operations;
And the inventory updating strategy adjustment module is used for adjusting the actual inventory updating strategy based on the operation complexity evaluation result and carrying out down regulation on the actual inventory updating frequency through the real-time inventory updating frequency.
Preferably, as each anesthesia procedure is performed, the expected usage of all ongoing anesthesia procedures, i.e., the expected amount of anesthesia consumables, is collected as follows:
Acquiring scheduling information of all ongoing anesthesia operations through an operation scheduling system of a hospital;
according to the operation type information obtained from the scheduling system, matching each operation with a preset anesthesia consumable inventory template;
Personalized adjustment is carried out on the generated anesthesia consumable inventory through an anesthesia doctor;
after personalized adjustment is completed, the consumable list of each operation is summed up and calculated to obtain the expected usage amount of each anesthesia consumable;
And recording the calculated expected usage data of the anesthesia consumable in a central database, and binding the expected usage data with real-time data of the actual operation anesthesia consumable.
Preferably, the actual amount of access to all ongoing anesthesia procedures is collected by:
When the anaesthetist extracts anaesthetic consumables, the type and the number of the anaesthetic consumables are automatically recorded through the scanning equipment, recorded data are uploaded to the central inventory management system in real time and are associated with anaesthetic operation scheduling information, so that the accurate record of the actual anaesthetic consumable extraction amount of each anaesthetic operation is ensured, the actual extraction amount of each anaesthetic operation is collected, and the actual extraction amount of all the ongoing anaesthetic operations is collected.
Preferably, the consumable conditions of all anesthesia operations are analyzed in real time, the real-time analysis is performed through the expected usage amount and the actual taking amount of the anesthesia consumable, and the real-time consumable surplus is calculated, and the specific steps are as follows:
setting the expected consumption of consumable materials for each anesthesia operation as The actual taking amount isWherein i represents different operations, the type of consumable is represented by j, and the expected usage of each consumable isThe actual taking amount is;
Calculating the difference of each consumable in each operationThe calculated expression is: In which, in the process, Represents the difference amount of the jth consumable material of the ith operation,Representing a dynamic adjustment item, which is used for correcting the difference in real time according to the actual use condition;
based on the difference of each consumable Calculating the real-time margin of each operation, wherein the calculated expression is as follows: Wherein, the method comprises the steps of, wherein, Is the consumable importance coefficient of the j-th consumable of the i-th operation, represents the importance weight of each consumable to the operation,The use frequency of the jth consumable material of the ith operation is the use frequency of the jth consumable material of the ith operation, and n represents the total number of consumable material types;
Calculating the real-time consumable surplus of all anesthesia surgeries, wherein the calculated expression is as follows: In which, in the process, Represents the real-time consumable margin for all anesthesia procedures, and d represents the total number of all ongoing anesthesia procedures.
Preferably, the real-time consumable surplus generated by real-time analysis of consumable conditions of all anesthesia operations is compared with a preset real-time consumable surplus reference value, and the ongoing anesthesia operations are divided according to the anesthesia consumable conditions, wherein the specific dividing steps are as follows:
If the real-time consumable surplus is greater than or equal to the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into high surplus consumable stock;
and if the real-time consumable surplus is smaller than the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into normal surplus consumable stock.
Preferably, under the high surplus consumable inventory scene, the occurrence probability and the operation estimated time length of all the simultaneously performed anesthesia operations are obtained, the occurrence probability and the operation estimated time length are analyzed, then an intra-operation emergency expected index and an operation time length estimated uncertainty index are respectively generated, the intra-operation emergency expected index and the operation time length estimated uncertainty index are input into a pre-trained machine learning model, an anesthesia operation comprehensive complexity coefficient is generated, and the anesthesia operation complexity is estimated through the anesthesia operation comprehensive complexity coefficient.
Preferably, the specific steps for generating the intra-operative emergency expected index after analyzing the occurrence probability of all the simultaneously performed anesthesia operations are as follows:
collecting relevant data of all ongoing anesthesia operations, and setting the occurrence frequency of historical emergencies of each operation i as The pathological complexity parameter of the patient is thatThe operation type parameter isThe risk factor of the anesthetic is,For the historical emergency occurrence times of the ith operation,For the pathological complexity parameter of the ith surgical patient,As the operation type parameter of the ith operation,The risk coefficient of the anesthetic for the ith operation;
according to the collected parameters, the emergency occurrence probability of each operation is calculated, and the calculated expression is: In which, in the process, Representing the emergency occurrence probability of the ith operation;
according to the occurrence probability of the emergency of all operations Calculating an emergency expected index in arithmetic, wherein the calculated expression is as follows:, to reflect the contribution weight of surgery to the overall emergency risk for the surgery importance index, Representing the intraoperative emergency expectation index.
Preferably, the specific steps of generating the estimated uncertainty index of the operation duration after analyzing the operation estimated duration of all the simultaneously performed anesthesia operations are as follows:
Collecting the expected duration of each ongoing anesthesia operation, and setting the expected duration of each operation as Wherein, the method comprises the steps of, wherein,Representing the predicted duration of the ith procedure;
calculating a length uncertainty factor of each operation, wherein the calculated expression is as follows: In which, in the process, Representing the expected duration uncertainty factor for the ith procedure,Represents the p-th influencing factor related to the duration of the i-th surgery,For the weight of the p-th influence factor, the contribution degree of each influence factor to the uncertainty of the duration is represented,As the fluctuation coefficient of the p-th influence factor in the ith operation,The inherent complexity adjustment coefficient of the ith operation is used for balancing the complexity difference among the operations, and N represents the total number of the influence factors;
in calculating the duration uncertainty factor of all operations And then, calculating an overall operation duration estimated uncertainty index by the comprehensive duration uncertainty factor, wherein the calculated expression is as follows: In which, in the process, Represents the estimated uncertainty index of the duration of the operation,Representing the priority weight of the ith procedure, representing the importance of the procedure in the uncertainty assessment.
Preferably, based on the evaluation result of the complexity of the surgery, the actual inventory updating strategy is adjusted, and the specific steps are as follows:
Comparing the anesthesia operation comprehensive complexity coefficient with a preset anesthesia operation comprehensive complexity coefficient reference value, and calculating the complexity coefficient deviation degree, wherein the calculated expression is as follows: In which, in the process, The degree of deviation of the complexity coefficient is represented,Represents the comprehensive complexity coefficient of the anesthesia operation,Representing a comprehensive complexity coefficient reference value of the anesthesia operation;
According to the degree of deviation of the complexity coefficient Calculating a real-time inventory update frequency adjustment factor, the calculated expression being: In which, in the process, Representing a real-time inventory update frequency adjustment factor, determining an adjustment amplitude of the inventory update frequency, wherein e is the bottom of a natural index;
Updating frequency adjustment factors using real-time inventory Calculating a new actual inventory update frequency, wherein the calculated expression is: In which, in the process, Indicating the actual inventory update frequency,Representing the current real-time inventory update frequency.
In the technical scheme, the invention has the technical effects and advantages that:
According to the invention, the expected usage amount and the actual taking amount of each anesthesia operation are collected, and the consumable enrichment amount is calculated in real time, so that the difference between the actual consumable demand and the standby consumable can be accurately identified. Through dividing the operation consumable condition into high surplus consumable inventory and normal surplus consumable inventory, the possible inventory misjudgment condition can be accurately identified, and especially under the scene of high surplus consumable inventory, the comprehensive analysis is carried out on the operation complexity through a pre-trained machine learning model, so that the inventory management is not only dependent on simple consumable consumption data, but also the actual complexity of the operation is considered, unnecessary replenishment demands are greatly reduced, the inventory management confusion caused by real-time updating is avoided, and the accuracy of the inventory management and the utilization efficiency of hospital resources are improved.
According to the invention, based on the operation complexity evaluation result, the actual inventory updating strategy can be dynamically adjusted, particularly in the high surplus consumable inventory scene, the frequent inventory fluctuation is effectively reduced by the system through reducing the real-time inventory updating frequency, the resource waste is prevented, meanwhile, after the real-time data in the inventory and operation process are monitored, the inventory updating frequency is ensured to be matched with the actual operation complexity, and the flexible adjustment mechanism ensures that the system can better cope with unexpected emergency in the operation process, and ensures the stability and sufficiency of consumable supply, thereby improving the overall management capability and emergency response speed of hospitals in the complex operation environment.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a schematic block diagram of an inventory intelligent management system for anesthesia consumables according to the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments may be embodied in many different forms and should not be construed as limited to the examples set forth herein, but rather, the example embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides an inventory intelligent management system for anesthesia consumables, which is shown in fig. 1, and comprises an anesthesia consumable data collection module, a real-time consumable surplus calculation module, a consumable surplus analysis and classification module, an operation complexity evaluation module and an inventory update strategy adjustment module;
The anesthesia consumable data collection module is used for collecting anesthesia consumable information of all ongoing anesthesia surgeries when each anesthesia surgery is conducted, wherein the anesthesia consumable information comprises expected usage amount (namely, the consumption amount of the anesthesia consumable predicted according to surgery types and experience) and actual taking amount (namely, the quantity of the anesthesia consumable actually taken from stock by an anesthesiologist);
when each anesthesia operation is performed, the expected usage amount of all the ongoing anesthesia operations, namely the expected anesthesia consumable amount, is collected, and the specific steps are as follows:
Acquiring scheduling information of all ongoing anesthesia operations through an operation scheduling system of a hospital;
Such information typically includes the type of procedure, time, physician's primary knife, anesthesiologist, patient basic information, the expected duration of the procedure, etc. By seamless integration with the shift scheduling system, the system can acquire and update the surgical information in real time, ensuring that the base data for the expected usage calculation is up-to-date and accurate.
According to the operation type information obtained from the scheduling system, matching each operation with a preset anesthesia consumable inventory template;
These templates are predefined based on historical data, surgical guidelines, and physician experience, and contain all anesthesia consumables and corresponding amounts that are typically required for this type of procedure. For example, for cardiac surgery, the consumable inventory may include a particular type of endotracheal tube, anesthesia mask, monitoring electrode, etc. After the matching is completed, the system generates a preliminary consumable inventory for each operation as a basis for the subsequent calculation of the expected usage.
The specific requirements of each operation may vary depending on the condition of the patient and the complexity of the operation, so that the system may perform personalized adjustment on the generated anesthetic consumable list by the anesthesiologist;
the anesthesiologist can increase or decrease the type and the number of the consumables according to factors such as the body type, medical history, special requirements (such as allergy history or instrument contraindication) of the patient, and the like. For example, for a patient with respiratory problems, the physician may add a type of endotracheal tube to be spared. The adjusted inventory is submitted to the system and compared to the master template inventory to generate final expected usage data.
After personalized adjustment is completed, the consumable list of each operation is summed up and calculated to obtain the expected usage amount of each anesthesia consumable;
This process involves matching the consumables in the inventory with the corresponding items in the inventory system, confirming the number and specifications of consumables required. The system calculates an accurate expected usage data set based on the surgical type, inventory adjustment, and analysis of the historical data, and uses it as a basis for subsequent inventory management and monitoring. The key to this step is to ensure accuracy and comprehensiveness of the calculations in order to provide accurate consumable demand predictions.
Recording the calculated expected usage data of the anesthesia consumable in a central database, and binding the expected usage data with real-time data of the actual operation anesthesia consumable;
The expected usage of each procedure will be used as a benchmark against which any subsequent extraction, use or adjustment of consumables will be compared. The system can continuously update the database, ensure that the difference between the expected usage and the actual usage of all operations can be monitored and analyzed in time, and provide data support for updating and managing the post-operation inventory. The real-time data recording and updating of the step ensures the transparency and traceability of the whole system, so that the hospital management layer can know the consumable requirement and the use condition of each operation at any time.
The actual amount of access (i.e., the number of consumables that the anesthesiologist actually takes from inventory) for all ongoing anesthesia procedures is collected by:
When the anaesthetist extracts anaesthetic consumables, the type and the number of the anaesthetic consumables are automatically recorded through the scanning equipment, recorded data are uploaded to the central inventory management system in real time and are associated with anaesthetic operation scheduling information, so that the accurate record of the actual anaesthetic consumable extraction amount of each anaesthetic operation is ensured, the actual extraction amount of each anaesthetic operation is collected, and the actual extraction amount of all the ongoing anaesthetic operations is collected.
The real-time consumable surplus calculation module is used for carrying out real-time analysis on consumable conditions of all anesthesia operations and carrying out real-time consumable surplus calculation through the expected usage amount and the actual taking amount of the anesthesia consumable;
Real-time analysis is carried out on consumable conditions of all anesthesia operations, real-time analysis is carried out through the expected usage amount and the actual taking amount of the anesthesia consumable, and the real-time consumable surplus is calculated, and the specific steps are as follows:
setting the expected consumption of consumable materials for each anesthesia operation as The actual taking amount isWherein i represents different operations, the type of consumable is represented by j, and the expected usage of each consumable isThe actual taking amount is;
Calculating the difference of each consumable in each operationThe calculated expression is: In which, in the process, Represents the difference amount of the jth consumable material of the ith operation,Representing a dynamic adjustment item, which is used for correcting the difference in real time according to the actual use condition;
In particular, the method comprises the steps of, The real-time calculation can be performed according to the actual consumption speed of the consumable, the emergency conditions (such as the requirement of additional consumable or the use of accidentally reduced consumable) in the operation, and the like. For example, if during surgery it is found that the consumable usage deviates significantly from the expected usage (e.g., the usage suddenly increases or decreases), the system adjusts based on these changesTo more accurately reflect the actual needs of the consumable during surgery.The value of (2) can be updated in real time through a machine learning algorithm or a rule based on historical data so as to ensure the accuracy and flexibility of the difference calculation.
Based on the difference of each consumableCalculating the real-time margin of each operation, wherein the calculated expression is as follows: Wherein, the method comprises the steps of, wherein, Is the consumable importance coefficient of the j-th consumable of the i-th operation, represents the importance weight of each consumable to the operation,The use frequency of the jth consumable material of the ith operation is the use frequency of the jth consumable material of the ith operation, and n represents the total number of consumable material types;
importance coefficient of consumable The criticality and the degree of necessity of each consumable in the operation are expressed, namely, the influence weight of the consumable on the success of the operation and the safety of a patient. In particular, different anesthetic consumables play different roles in surgery, and some consumables such as an endotracheal tube, an anesthetic mask, etc. are of great importance to maintain vital signs of a patient and to perform surgery smoothly, while other consumables such as monitoring electrodes, sterilizing products, etc. are of relatively low importance.By giving different weight values to different consumables, the importance of priority in consumable management and inventory updating is reflected. For example, consumables with high importance weights may be prioritized in the margin calculation to ensure adequate supply and proper use of these critical consumables.
Calculating the real-time consumable surplus of all anesthesia surgeries, wherein the calculated expression is as follows: In which, in the process, Represents the real-time consumable margin for all anesthesia procedures, and d represents the total number of all ongoing anesthesia procedures.
The larger the real-time consumable surplus of all anesthesia operations, the risk that consumable consumption is misjudged possibly can be generated by means of real-time consumable updating. In particular, a larger margin means that the consumable actually taken is far more than expected, possibly due to the large number of spare consumables reserved during surgery. If the system performs real-time inventory update without considering such standby situation, the replenishment request may be triggered erroneously, resulting in confusion of inventory management and possibly causing resource waste. Therefore, when the real-time margin is large, the system needs to be carefully handled, avoiding frequent and unnecessary replenishment operations.
The consumable surplus analysis and classification module analyzes consumable surplus and divides the ongoing anesthesia operation into high surplus consumable stock and normal surplus consumable stock according to anesthesia consumable conditions;
Comparing and analyzing the real-time consumable surplus generated by real-time analysis of consumable conditions of all anesthesia operations with a preset real-time consumable surplus reference value, and dividing the ongoing anesthesia operations according to the anesthesia consumable conditions, wherein the specific dividing steps are as follows:
If the real-time consumable surplus is greater than or equal to the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into high surplus consumable stock;
and if the real-time consumable surplus is smaller than the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into normal surplus consumable stock.
The operation complexity evaluation module is used for comprehensively analyzing all the simultaneously performed anesthesia operations through a pre-trained machine learning model in a high-margin consumable inventory scene and evaluating the complexity of the anesthesia operations;
Under the high surplus consumable inventory scene, the emergency occurrence probability and the operation estimated time length of all the simultaneously performed anesthesia operations are obtained, the emergency occurrence probability and the operation estimated time length are analyzed, then an intraoperative emergency expected index and an operation time length estimated uncertainty index are respectively generated, the intraoperative emergency expected index and the operation time length estimated uncertainty index are input into a pre-trained machine learning model, an anesthesia operation comprehensive complexity coefficient is generated, and the anesthesia operation complexity is estimated through the anesthesia operation comprehensive complexity coefficient.
The pre-trained machine learning model refers to an algorithm model which is trained and optimized through a large amount of historical data, and can predict or calculate a target value (such as an anesthesia operation comprehensive complexity coefficient) according to input specific parameters (such as an expected index of an emergency in operation and an estimated uncertainty index of operation duration). In this scenario, the model may use a supervised learning approach to adjust the weights and parameters of the model by learning relationships between the incidents, duration uncertainty, and actual complexity of past surgery. After training, the model can quickly and accurately generate complexity evaluation of anesthesia operation after new operation data are input, and support is provided for clinical decision. Such models may be based on various algorithms, such as neural networks, support vector machines, random forests, etc., with the specific choice depending on the data characteristics and application requirements.
When the probability of occurrence of an emergency is high for all simultaneous anesthesia procedures, it is often shown that the overall situation of these procedures is complex. A higher probability of an emergency means that more unforeseen problems may occur during surgery, such as abnormal physiological response of the patient, occurrence of accidents during surgery or the need to temporarily adjust the anesthesia scheme, etc. These conditions increase the risk and difficulty of surgery, and therefore surgery with a higher probability of emergency generally means that the anesthesia team needs to deal with the complex surgical environment more carefully and flexibly, reflecting the higher complexity of the overall surgery.
The specific steps for generating the expected index of the emergency in the operation after analyzing the emergency occurrence probability of all the simultaneously performed anesthesia operations are as follows:
collecting relevant data of all ongoing anesthesia operations, and setting the occurrence frequency of historical emergencies of each operation i as The pathological complexity parameter of the patient is thatThe operation type parameter isThe risk factor of the anesthetic is,The occurrence frequency of the historical emergency of the ith operation is reflected to the frequency of the emergency in the similar operation in the past,The pathological complexity parameter of the ith operation patient is used for representing the influence of the health condition of the patient on the emergency,As a surgical type parameter for the ith surgery, the complexity of different types of surgery may lead to different incident probabilities,The risk coefficient of the anesthesia medicine for the ith operation represents the potential risk influence of the anesthesia medicine to the emergency;
Patient pathological complexity parameters Typically by comprehensive analysis of the patient's medical history, diagnostic records, and existing health conditions. These data can be obtained from the Electronic Health Record (EHR) system of the hospital, covering the past illness, complications, allergy history, organ function status, etc. of the patient. The medical team can use a pre-set scoring system (e.g., charlson Comorbidity Index, etc.) to quantify the patient's pathological complexity based on this information, ultimately yielding pathological complexity parameters suitable for use in surgery.
Surgical type parametersIs determined based on the specific nature and complexity of the procedure. The type information of the procedure may be obtained from a procedure scheduling system (OR Scheduling System) that may record in detail the classifications of each procedure, such as cardiac surgery, neurosurgery, or general surgery. The medical team sets a corresponding complexity parameter for each type of procedure, typically assessed by an expert based on multiple dimensions of the procedure's specifications, procedure time, risk factors, etc.
Risk factor of anestheticIs determined according to the type and dosage of anesthetic used in the operation. The anesthesiologist may choose different anesthetics, such as general anesthesia, local anesthesia, or sedatives, etc., based on the surgical needs, and this information may be obtained by an anesthesia recording system (ANESTHESIA INFORMATION MANAGEMENT SYSTEM, AIMS). Each drug has its specific risk factors, typically provided by pharmaceutical specialists or clinical guidelines, based on factors such as side effects of the drug, effects on the patient's physiology, interactions with other drugs, etc. By combining these factors, a corresponding anesthetic risk factor can be set for each procedure.
According to the collected parameters, the emergency occurrence probability of each operation is calculated, and the calculated expression is: In which, in the process, Representing the emergency occurrence probability of the ith operation;
Wherein, the times of the sudden events are historic Pathological complexity with patientTogether determine the basic risk of an emergency, and the type of surgeryAs denominator, the risk weights for different surgical types are adjusted. Risk factor of anestheticThe overall risk probability is further adjusted so that finallyThe occurrence probability of the surgery emergency can be reflected more accurately.
According to the occurrence probability of the emergency of all operationsCalculating an emergency expected index in arithmetic, wherein the calculated expression is as follows:, the operation importance index reflects the contribution weight of the operation to the overall emergency risk, and is higher The value means that the overall risk is greatly affected by the procedure,Representing the expected index of emergency in operation, reflecting the comprehensive emergency risks of all operations.
The larger the expression value of the expected index of the emergency in the operation generated after analyzing the occurrence probability of the emergency of all the simultaneously performed anesthesia operations, the more complex the comprehensive situation of all the simultaneously performed anesthesia operations is. A larger intraoperative emergency prediction index means a higher risk of emergency during surgery, possibly involving more unforeseen problems, complexity of patient condition, higher technical requirements, and flexible coping requirements of anesthesia scheme. Thus, the increase in the intraoperative emergency prediction index directly reflects the rise in the overall complexity of the procedure, requiring more rigorous monitoring and careful management by the anesthesia team during the procedure to ensure patient safety and success of the procedure.
When the estimated uncertainty of the operation duration of all the simultaneously performed anesthesia operations is large, the comprehensive situation of the operations is generally complex. The uncertainty of the surgical duration reflects various variables and unpredictable factors that may exist during the surgical procedure, such as high surgical difficulty, complex patient conditions, possible intraoperative emergencies, and the like. These factors all increase the difficult predictability of the procedure time, making anesthesia management and resource allocation more complex. Thus, a large uncertainty in the duration estimate is often an important feature of complex surgery.
The specific steps for generating the estimated operation duration uncertainty index after analyzing the operation estimated duration of all the simultaneously performed anesthesia operations are as follows:
Collecting the expected duration of each ongoing anesthesia operation, and setting the expected duration of each operation as Wherein, the method comprises the steps of, wherein,Representing the predicted duration of the ith procedure;
calculating a length uncertainty factor of each operation, wherein the calculated expression is as follows: In which, in the process, Representing the expected duration uncertainty factor for the ith procedure,Represents the p-th influencing factor (such as patient condition complexity, surgery type, historical data, etc.) related to the duration of the i-th surgery,For the weight of the p-th influence factor, the contribution degree of each influence factor to the uncertainty of the duration is represented,The fluctuation coefficient in the ith operation for the p-th influence factor is represented by the fluctuation degree of the factor,The inherent complexity adjustment coefficient of the ith operation is used for balancing the complexity difference among the operations, and N represents the total number of the influence factors;
the fluctuation coefficient Is typically based on analysis of historical data and real-time monitoring data. In particular, the variability of individual influencing factors in a number of similar procedures can be calculated by retrospectively analyzing the history of the factors, and thus the variability of the factors. For example, if the blood pressure of a patient fluctuates significantly in a certain type of surgery, the blood pressure-related factor of influence has a high fluctuation coefficient. In addition, real-time monitoring data (such as real-time vital signs during surgery, drug reactions, etc.) can also be used to dynamically adjust the volatility coefficient so that it more accurately reflects the actual complexity and uncertainty of surgery. By combining the history with real-time data, the volatility coefficient can accurately capture the fluctuation of various influencing factors in the operation.
In calculating the duration uncertainty factor of all operationsAnd then, calculating an overall operation duration estimated uncertainty index by the comprehensive duration uncertainty factor, wherein the calculated expression is as follows: In which, in the process, Represents the estimated uncertainty index of the duration of the operation,Representing the priority weight of the ith procedure, representing the importance of the procedure in the uncertainty assessment.
The larger the appearance value of the estimated operation duration uncertainty index generated after analyzing the operation estimated time durations of all the simultaneously performed anesthesia operations, the more complex the comprehensive situation of all the simultaneously performed anesthesia operations is generally indicated. The larger uncertainty index reflects the larger variability of the prediction of the operation duration, which means that various unpredictable factors, such as patient state fluctuation, operation difficulty increase, emergency response and the like, can occur in the operation process. These factors together increase the complexity of the procedure, making anesthesia management and resource allocation more difficult. Thus, a higher uncertainty index tends to mean a higher overall complexity of these procedures, requiring more careful management and decision making.
The machine learning model is not particularly limited herein, and can realize the expected index of the emergency in operationAnd an uncertainty index for the duration of the procedureComprehensive analysis is carried out to generate comprehensive complexity coefficient of anesthesia operationThe machine learning model of the invention can be realized, and in order to realize the technical scheme of the invention, the invention provides a specific implementation mode;
comprehensive complexity coefficient of anesthesia operation The generated calculation formula is as follows: In which, in the process, 、Respectively, the expected index of the emergency in the operationAnd an uncertainty index for the duration of the procedureIs a preset proportionality coefficient of (1), and、Are all greater than 0.
According to the comprehensive complexity coefficient of the anesthesia operation, the larger the expression value of the expected index of the in-operation emergency generated after analyzing the occurrence probability of all the simultaneous anesthesia operations, the larger the expression value of the estimated uncertainty index of the operation duration generated after analyzing the operation estimated duration of all the simultaneous anesthesia operations, namely the larger the expression value of the comprehensive complexity coefficient of the generated anesthesia operation, the more complex the comprehensive situation of all the simultaneous anesthesia operations is shown, and otherwise, the less complex the comprehensive situation of all the simultaneous anesthesia operations is shown.
The inventory updating strategy adjustment module is used for adjusting an actual inventory updating strategy based on the operation complexity evaluation result and performing down-regulation on the actual inventory updating frequency through the real-time inventory updating frequency;
Based on the operation complexity evaluation result, the actual inventory updating strategy is adjusted, and the specific steps are as follows:
Comparing the anesthesia operation comprehensive complexity coefficient with a preset anesthesia operation comprehensive complexity coefficient reference value, and calculating the complexity coefficient deviation degree, wherein the calculated expression is as follows: In which, in the process, The degree of deviation of the complexity coefficient is represented,Represents the comprehensive complexity coefficient of the anesthesia operation,Representing a comprehensive complexity coefficient reference value of the anesthesia operation;
According to the degree of deviation of the complexity coefficient Calculating a real-time inventory update frequency adjustment factor, the calculated expression being: In which, in the process, Representing a real-time inventory update frequency adjustment factor, determining an adjustment amplitude of the inventory update frequency, wherein e is the bottom of a natural index;
the formula adopts the form of Sigmoid function to adjust the factor The value of (2) varies smoothly between 0 and 1. Degree of deviation of complexity coefficientThe larger the size of the container,The closer to 1, the more significant adjustment of the inventory update frequency is indicated.
Updating frequency adjustment factors using real-time inventoryCalculating a new actual inventory update frequency, wherein the calculated expression is: In which, in the process, Indicating the actual inventory update frequency,Representing the current real-time inventory update frequency.
According to the invention, the expected usage amount and the actual taking amount of each anesthesia operation are collected, and the consumable enrichment amount is calculated in real time, so that the difference between the actual consumable demand and the standby consumable can be accurately identified. Through dividing the operation consumable condition into high surplus consumable inventory and normal surplus consumable inventory, the possible inventory misjudgment condition can be accurately identified, and especially under the scene of high surplus consumable inventory, the comprehensive analysis is carried out on the operation complexity through a pre-trained machine learning model, so that the inventory management is not only dependent on simple consumable consumption data, but also the actual complexity of the operation is considered, unnecessary replenishment demands are greatly reduced, the inventory management confusion caused by real-time updating is avoided, and the accuracy of the inventory management and the utilization efficiency of hospital resources are improved.
According to the invention, based on the operation complexity evaluation result, the actual inventory updating strategy can be dynamically adjusted, particularly in the high surplus consumable inventory scene, the frequent inventory fluctuation is effectively reduced by the system through reducing the real-time inventory updating frequency, the resource waste is prevented, meanwhile, after the real-time data in the inventory and operation process are monitored, the inventory updating frequency is ensured to be matched with the actual operation complexity, and the flexible adjustment mechanism ensures that the system can better cope with unexpected emergency in the operation process, and ensures the stability and sufficiency of consumable supply, thereby improving the overall management capability and emergency response speed of hospitals in the complex operation environment.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
Claims (5)
1. The inventory intelligent management system for the anesthesia consumable is characterized by comprising an anesthesia consumable data collection module, a real-time consumable surplus calculation module, a consumable surplus analysis and classification module, an operation complexity evaluation module and an inventory update strategy adjustment module;
the anesthesia consumable data collection module is used for collecting anesthesia consumable information of all ongoing anesthesia surgeries when each anesthesia surgery is carried out, wherein the anesthesia consumable information comprises expected usage amount and actual taking amount of all the anesthesia surgeries;
the real-time consumable surplus calculation module is used for carrying out real-time analysis on consumable conditions of all anesthesia operations and carrying out real-time consumable surplus calculation through the expected usage amount and the actual taking amount of the anesthesia consumable;
The consumable surplus analysis and classification module analyzes consumable surplus and divides the ongoing anesthesia operation into high surplus consumable stock and normal surplus consumable stock according to anesthesia consumable conditions;
the operation complexity evaluation module is used for comprehensively analyzing all the simultaneously performed anesthesia operations through a pre-trained machine learning model in a high-margin consumable inventory scene and evaluating the complexity of the anesthesia operations;
Under the high surplus consumable inventory scene, acquiring the occurrence probability and the operation estimated time length of all simultaneous anesthesia operations, respectively generating an intra-operation emergency expected index and an operation time length estimated uncertainty index after analyzing the occurrence probability and the operation estimated time length of all simultaneous anesthesia operations, inputting the intra-operation emergency expected index and the operation time length estimated uncertainty index into a pre-trained machine learning model, generating an anesthesia operation comprehensive complexity coefficient, and evaluating the complexity of the anesthesia operations through the anesthesia operation comprehensive complexity coefficient;
the specific steps for generating the expected index of the emergency in the operation after analyzing the emergency occurrence probability of all the simultaneously performed anesthesia operations are as follows:
collecting data related to all ongoing anesthesia procedures, and setting each procedure The number of times of the historical emergency isThe pathological complexity parameter of the patient is thatThe operation type parameter isThe risk factor of the anesthetic is,Is the firstThe number of times of occurrence of historical emergencies of the table operation,Is the firstThe pathological complexity parameter of the patient in the table operation,Is the firstThe operation type parameter of the table operation,Is the firstA table operation anesthetic risk coefficient;
according to the collected parameters, the emergency occurrence probability of each operation is calculated, and the calculated expression is: In which, in the process, Represent the firstThe emergency occurrence probability of the table operation;
according to the occurrence probability of the emergency of all operations Calculating an emergency expected index in arithmetic, wherein the calculated expression is as follows:, to reflect the contribution weight of surgery to the overall emergency risk for the surgery importance index, Representing an intraoperative emergency expectation index;
the specific steps for generating the estimated operation duration uncertainty index after analyzing the operation estimated duration of all the simultaneously performed anesthesia operations are as follows:
Collecting the expected duration of each ongoing anesthesia operation, and setting the expected duration of each operation as Wherein, the method comprises the steps of, wherein,Represent the firstThe expected duration of the table surgery;
calculating a length uncertainty factor of each operation, wherein the calculated expression is as follows:
;
In the formula, Represent the firstThe expected duration uncertainty factor for a table procedure,Representation and the firstDuration-dependent first of table surgeryThe number of influencing factors is one,Is the firstA weight of each influence factor, representing the degree of contribution of each influence factor to the duration uncertainty,Is the firstThe influencing factor is atThe fluctuation coefficient in the table operation,Is the firstThe inherent complexity adjustment coefficient of the table surgery is used for balancing the complexity difference among the surgeries, and N represents the total number of influence factors;
in calculating the duration uncertainty factor of all operations And then, calculating an overall operation duration estimated uncertainty index by the comprehensive duration uncertainty factor, wherein the calculated expression is as follows:
;
In the formula, Represents the estimated uncertainty index of the duration of the operation,Represent the firstSurgical priority weights for the table, representing the importance of surgery in the uncertainty assessment;
the inventory updating strategy adjustment module is used for adjusting an actual inventory updating strategy based on the operation complexity evaluation result and performing down-regulation on the actual inventory updating frequency through the real-time inventory updating frequency;
Based on the operation complexity evaluation result, the actual inventory updating strategy is adjusted, and the specific steps are as follows:
Comparing the anesthesia operation comprehensive complexity coefficient with a preset anesthesia operation comprehensive complexity coefficient reference value, and calculating the complexity coefficient deviation degree, wherein the calculated expression is as follows: In which, in the process, The degree of deviation of the complexity coefficient is represented,Represents the comprehensive complexity coefficient of the anesthesia operation,Representing a comprehensive complexity coefficient reference value of the anesthesia operation;
According to the degree of deviation of the complexity coefficient Calculating a real-time inventory update frequency adjustment factor, the calculated expression being:
;
In the formula, Representing the real-time inventory update frequency adjustment factor, determining the adjustment amplitude of the inventory update frequency,Is the bottom of the natural index;
Updating frequency adjustment factors using real-time inventory Calculating a new actual inventory update frequency, wherein the calculated expression is: In which, in the process, Indicating the actual inventory update frequency,Representing the current real-time inventory update frequency.
2. The intelligent inventory management system for anesthesia consumables according to claim 1 wherein each anesthesia operation is performed by collecting all the anticipated usage of ongoing anesthesia operations, i.e. the anticipated amount of anesthesia consumables, comprising the steps of:
Acquiring scheduling information of all ongoing anesthesia operations through an operation scheduling system of a hospital;
according to the operation type information obtained from the scheduling system, matching each operation with a preset anesthesia consumable inventory template;
Personalized adjustment is carried out on the generated anesthesia consumable inventory through an anesthesia doctor;
after personalized adjustment is completed, the consumable list of each operation is summed up and calculated to obtain the expected usage amount of each anesthesia consumable;
And recording the calculated expected usage data of the anesthesia consumable in a central database, and binding the expected usage data with real-time data of the actual operation anesthesia consumable.
3. The intelligent inventory management system for anesthesia consumables according to claim 1 wherein the actual pick-up of all ongoing anesthesia procedures is collected by:
When the anaesthetist extracts anaesthetic consumables, the type and the number of the anaesthetic consumables are automatically recorded through the scanning equipment, recorded data are uploaded to the central inventory management system in real time and are associated with anaesthetic operation scheduling information, so that the accurate record of the actual anaesthetic consumable extraction amount of each anaesthetic operation is ensured, the actual extraction amount of each anaesthetic operation is collected, and the actual extraction amount of all the ongoing anaesthetic operations is collected.
4. The inventory intelligent management system for anesthesia consumables according to claim 1, wherein real-time analysis is performed on consumable conditions of all anesthesia operations, real-time analysis is performed through expected usage and actual pickup of the anesthesia consumables, and real-time consumable enrichment is calculated, specifically comprising the following steps:
setting the expected consumption of consumable materials for each anesthesia operation as The actual taking amount isWhereinRepresenting different operations, the consumable material types are passed throughThe expected usage of each consumable is represented asThe actual taking amount is;
Calculating the difference of each consumable in each operationThe calculated expression is: In which, in the process, Represent the firstTable operation noThe difference amount of the seed consumables is calculated,Representing a dynamic adjustment item, which is used for correcting the difference in real time according to the actual use condition;
based on the difference of each consumable Calculating the real-time margin of each operation, wherein the calculated expression is as follows: Wherein, the method comprises the steps of, wherein, Is the firstTable operation noThe consumable importance coefficient of the various consumables represents the importance weight of each consumable to the operation,Is the firstTable operation noThe use frequency of the seed consumables is that,Representing the total number of consumable types;
Calculating the real-time consumable surplus of all anesthesia surgeries, wherein the calculated expression is as follows: In which, in the process, Represents the real-time consumable margin of all anesthesia procedures,Indicating the total number of all ongoing anesthesia procedures.
5. The inventory intelligent management system for anesthesia consumables according to claim 4, wherein the real-time consumable enrichment amount generated by real-time analysis of all anesthesia operation consumables is compared with a preset real-time consumable enrichment amount reference value for analysis, and the ongoing anesthesia operation is divided according to the anesthesia consumable amount conditions, and the specific dividing steps are as follows:
If the real-time consumable surplus is greater than or equal to the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into high surplus consumable stock;
and if the real-time consumable surplus is smaller than the real-time consumable surplus reference value, dividing the ongoing anesthesia operation into normal surplus consumable stock.
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| CN117059244A (en) * | 2023-08-16 | 2023-11-14 | 中国人民解放军陆军军医大学第一附属医院 | Anesthesia consumable management method and system |
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