Intelligent Infusion Monitoring Method and System

Abstract

The present invention belongs to the technical field of infusion monitoring, and provides an intelligent infusion monitoring method and system, including: obtaining drug liquid information and infusion information, determining the input drug liquid according to the patient's drug liquid to be infused and in combination with an infusion sequence table, presetting a monitoring period, and performing a stability analysis on the infusion drip rate of the drug liquid input during the monitoring period to determine whether the infusion drip rate during the monitoring period is stable; if the infusion drip rate is stable, the infusion drip rate is averaged to determine the infusion end time; if the infusion drip rate is unstable, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous; if it is discontinuously abnormal, the abnormal infusion drip rate data is eliminated to determine the infusion end time. The present invention determines the infusion end time by judging whether the infusion drip rate is stable and adopts different processing methods respectively, thereby ensuring the accuracy of the determination of the infusion end time and helping medical staff to arrange their work reasonably.

Description

Technical Field

[0001] The present invention belongs to the technical field of infusion monitoring, and in particular relates to an intelligent infusion monitoring method and system. Background Art

[0002] During medical infusion, accurate control and real-time monitoring of the infusion drip rate are crucial to the patient's treatment effect and safety.

[0003] Traditional infusion methods mainly rely on manual observation and manual adjustment by nurses, which has many shortcomings. On the one hand, manual observation of the infusion drip rate not only consumes a lot of manpower, but is also prone to errors and negligence. It is difficult to achieve real-time and accurate monitoring, and it is impossible to detect the unstable drip rate during the infusion process in time. The treatment effect may be affected by infusion that is too fast or too slow, and even cause serious harm to the patient. On the other hand, when determining the end time of infusion, there is a lack of scientific and accurate methods, and usually only rely on empirical estimates, resulting in inaccurate judgment of the end time of infusion, and prone to problems such as untimely bottle replacement, which affects the normal progress of the infusion process. In addition, for the situation where multiple patients are infused at the same time, the existing technology cannot reasonably classify patients and optimize resource allocation. It is difficult for medical staff to reasonably arrange their work according to the actual needs of patients and the infusion process, resulting in waste of medical resources and low work efficiency.

[0004] To this end, the present invention provides an intelligent infusion monitoring method and system. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] In a first aspect, the present invention provides an intelligent infusion monitoring method, comprising the following steps:

[0007] Obtaining drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate;

[0008] According to the patient's infusion solution, combined with the infusion sequence table, the infusion solution is determined, and the monitoring period is preset. The stability of the infusion drip rate of the infusion solution during the monitoring period is analyzed to determine whether the infusion drip rate during the monitoring period is stable;

[0009] If the infusion drip rate is stable, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate is unstable, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is discontinuously abnormal, the abnormal infusion drip rate data is eliminated to determine the effective infusion drip rate, and the average is calculated to determine the end time of the infusion;

[0010] According to the end time of the current infusion of the drug solution, all patients are clustered and analyzed, and priority treatment clusters are determined in chronological order, and patients in the priority treatment clusters are given priority treatment.

[0011] As a further solution of the present invention: the process of performing stability analysis on the dripping rate of the infusion liquid during the monitoring period is as follows:

[0012] The monitoring period is divided into several monitoring time points, and the infusion drip rate of the current input drug solution at the monitoring time point is obtained and analyzed and processed to obtain the standard deviation and mean of the infusion drip rate of the current input drug solution during the monitoring period. The drip rate variation value is obtained using the coefficient of variation calculation formula; if the drip rate variation value ≥ the drip rate variation threshold, it indicates that the infusion drip rate of the current input drug solution during the monitoring period is unstable; otherwise, it indicates that the infusion drip rate of the current input drug solution during the monitoring period is stable.

[0013] As a further solution of the present invention: the method for obtaining the standard deviation and the mean of the infusion drip rate of the current infusion liquid during the monitoring period is:

[0014] Integrate the infusion drip rates at all monitoring time points within the monitoring period into an infusion drip rate sequence;

[0015] Extract all data in the infusion drip rate sequence and use the mean calculation formula to obtain the mean infusion drip rate of the current input drug solution during the monitoring period. Extract all data in the infusion drip rate sequence and use the standard deviation calculation formula to obtain the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period.

[0016] As a further solution of the present invention: the process of obtaining the end time of the infusion with a stable infusion dripping rate is as follows:

[0017] The duration of the monitoring period is multiplied by the average infusion drip rate to obtain the amount of liquid medicine that has been infused;

[0018] The remaining amount of the input liquid is compared with the average infusion drip rate of the current liquid to obtain the remaining infusion time of the current liquid. The end time of the remaining infusion time is recorded as the end time of the infusion of the current liquid.

[0019] As a further solution of the present invention: the infusion drip rate is unstable, and the specific process of abnormal continuity analysis of the infusion drip rate is as follows:

[0020] Analyze the infusion drip rate at the monitoring time point to determine the sudden change time point in the monitoring time point;

[0021] Traverse all sudden change time points on the time axis, count the number of consecutive occurrences of sudden change time points on the time axis, and compare them with the maximum value of the preset number of consecutive occurrences of sudden change time points to obtain the sudden change continuity value; if the sudden change continuity value is less than the sudden change continuity threshold, it means that the infusion drip rate shows discontinuous abnormality.

[0022] As a further solution of the present invention: the process of obtaining the sudden change time point in the monitoring time point is:

[0023] The infusion drip rate at the monitoring time point is subtracted from the preset infusion drip rate, and the absolute value of the difference is taken to obtain the abnormal drip rate deviation value;

[0024] If the abnormal dripping rate deviation value is not within the preset dripping rate deviation range, the corresponding monitoring time point is recorded as the sudden change time point.

[0025] As a further solution of the present invention: the process of obtaining the effective infusion drip rate is:

[0026] Calculate the proportion of sudden change time points in the monitoring time points. If the proportion of sudden change time points is less than the threshold of the proportion of sudden change time points, then remove the abnormal drip rate corresponding to the sudden change time points from the infusion drip rate sequence during the monitoring period, and retain the infusion drip rate at the normal time points. The retained infusion drip rate at the normal time points is recorded as the effective drip rate.

[0027] As a further solution of the present invention: the process of obtaining the end time of the infusion with unstable infusion dripping rate is as follows:

[0028] The effective drip rates at all normal time points are integrated into an effective drip rate sequence. All data in the effective drip rate sequence are extracted and averaged to obtain the average effective drip rate of the current drug solution. The remaining input drug volume is ratioed with the average effective drip rate of the current drug solution to obtain the remaining infusion time of the current input drug solution. The end time of the remaining infusion time is recorded as the end time of the infusion of the current input drug solution.

[0029] As a further solution of the present invention: the process of obtaining the priority processing cluster is:

[0030] The end time of the infusion of all patients' current medications is integrated into the infusion end time dataset, and the K-Means algorithm is used to cluster and determine the clusters of the infusion end time period;

[0031] All the infusion end period clusters are sorted in chronological order to obtain an infusion end period cluster sorting table, and the infusion end period cluster that is first in the infusion end period cluster sorting table is extracted as a priority processing cluster.

[0032] In a second aspect, the present invention provides an intelligent infusion monitoring system, the system comprising:

[0033] Data acquisition module: acquires drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate;

[0034] Drip rate stability analysis module: Determine the infusion rate based on the patient's infusion solution and the infusion sequence table, preset the monitoring period, and perform stability analysis on the drip rate of the infusion solution during the monitoring period to determine whether the drip rate is stable during the monitoring period;

[0035] Abnormal continuity judgment module: If the infusion drip rate is stable, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate is unstable, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is discontinuous, the abnormal infusion drip rate data is eliminated, the effective infusion drip rate is determined, and the average is calculated to determine the end time of the infusion;

[0036] Priority cluster generation module: Based on the end time of the current infusion of the drug solution, cluster analysis is performed on all patients, priority clusters are determined in chronological order, and patients in the priority clusters are given priority treatment.

[0037] The beneficial effects of the present invention are as follows:

[0038] 1. The present invention obtains drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and the infusion drip rate. According to the patient's drug solution to be infused, combined with the infusion sequence table, the drug solution to be infused is determined, and a monitoring period is preset. The stability of the infusion drip rate of the drug solution input during the monitoring period is analyzed to determine whether the infusion drip rate during the monitoring period is stable. By determining whether the infusion drip rate is stable, the present invention helps medical staff to promptly discover unstable drip rates during the infusion process, ensure infusion safety and treatment effects, and effectively monitor and adjust the infusion process according to the actual conditions of different patients and drug solutions.

[0039] 2. The present invention determines the end time of infusion by judging the stability of the infusion drip rate of the current infusion liquid during the monitoring period; according to the end time of the infusion of the current infusion liquid, cluster analysis is performed on all patients to determine the cluster of the end time of infusion, and the priority processing cluster is determined in chronological order, and the patients in the priority processing cluster are given priority. The present invention adopts different processing methods to determine the end time of infusion according to whether the infusion drip rate is stable, thereby ensuring the accuracy of the determination of the end time of infusion, helping medical staff to arrange their work reasonably and perform operations such as changing bottles in time. When the infusion drip rate is unstable, by judging whether the infusion drip rate is continuous or discontinuous, corresponding measures are taken for different types of abnormalities, thereby ensuring the safety of the infusion process, and timely discovering and handling potential risks such as equipment failure or changes in patient conditions. K-Means is used The algorithm performs cluster analysis on the infusion end time of all patients, determines the infusion end time cluster, and determines the priority treatment cluster in chronological order. It enables medical staff to arrange work reasonably according to the order in which patients' infusions end, give priority to patients in the priority treatment cluster, improve the efficiency of medical work, realize the optimal allocation of medical resources, and avoid waste of resources and disorderly work. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 This is a flowchart of the steps of the intelligent infusion monitoring method according to an embodiment of the present invention;

[0042] Figure 2 This is a system block diagram of the intelligent infusion monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0044] Example 1:

[0045] See also Figure 1 As shown, the intelligent infusion monitoring method according to the embodiment of the present invention includes the following steps:

[0046] Step 1: Obtaining drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate;

[0047] It is understandable that the drug solution information and infusion information are determined by relevant medical staff based on the actual situation of the patient. Different drugs to be infused have different suitable infusion drip rates, and different patients have different infusion drip rates for the same drug to be infused.

[0048] When preparing an infusion for a patient, the nurse uses a scanner to scan the electronic label on the infusion bottle. The scanner transmits the read medication information to the data processing terminal, which stores this information in the database. At the same time, the nurse determines the order of medication to be infused based on the patient's actual situation and the doctor's instructions on the data processing terminal. The system integrates this into an infusion sequence table. The infusion drip rate is obtained through the intelligent infusion pump.

[0049] Step 2: Determine the infusion solution based on the patient's infusion solution and the infusion sequence table, preset a monitoring period, and perform a stability analysis on the infusion drip rate of the infusion solution during the monitoring period to determine whether the infusion drip rate is stable during the monitoring period;

[0050] Extract the first infusion liquid in the infusion sequence table and record it as the infusion liquid;

[0051] Divide the monitoring period into several monitoring time points with equal time intervals, obtain the infusion drip rate of the current infusion liquid at the monitoring time point, and integrate the infusion drip rates of all monitoring time points within the monitoring period into an infusion drip rate sequence , where n represents the total number of monitoring time points;

[0052] Extract all data in the infusion drip rate sequence and use the mean calculation formula to obtain the mean infusion drip rate of the current input drug solution during the monitoring period; extract all data in the infusion drip rate sequence and use the standard deviation calculation formula to obtain the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period; use the coefficient of variation calculation formula to perform ratio processing on the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period and the mean infusion drip rate to obtain the drip rate variation value of the infusion drip rate of the current input drug solution during the monitoring period;

[0053] In some embodiments, the drip rate variation value is compared with the drip rate variation threshold, and the specific comparison process is:

[0054] If the drip rate variation value is greater than or equal to the drip rate variation threshold, it means that the drip rate of the current infusion liquid is unstable during the monitoring period;

[0055] If the drip rate variation value is less than the drip rate variation threshold, it means that the drip rate of the current infusion liquid is stable during the monitoring period;

[0056] For example, assuming the infusion drip rate sequence (unit: drops / minute) is (30, 32, 35, 28, 34, 36, 31, 29, 33, 30),

[0057] Calculate the mean: (30+32+35+28+34+36+31+29+33+30) / 10=32.8;

[0058] Calculate the standard deviation: Calculate the square of the difference between each infusion drip rate data and the mean in the infusion drip rate sequence, and sum the results, that is: 7.84+0.64+5.76+23.04+1.44+10.24+3.24+14.44+0.04+7.84=74.48. The standard deviation is: ;

[0059] The variation value of drip rate is: 2.729 / 32.8*100%=8.30%;

[0060] During clinical infusion, medical staff need to quickly determine the stability of the drip rate. Usually, the drip rate fluctuation range is controlled within 10% to be considered stable. The drip rate variation threshold is set at 10%. If the drip rate variation value is 8.30% < 10%, it means that the drip rate of the current infusion solution is stable during the monitoring period.

[0061] The functions of determining the drip rate variation value are as follows:

[0062] Function 1: By judging whether the drip rate of the infusion solution is stable during the monitoring period, it helps medical staff to promptly detect abnormal conditions that may occur during the infusion process, such as drip rate that is too fast or too slow, and excessive fluctuations in the drip rate, so that appropriate measures can be taken to adjust and ensure the safety and effectiveness of infusion therapy;

[0063] Function 2: Effective monitoring and control of infusion drip rate is an important part of medical quality management. Determining the drip rate variation value helps standardize infusion operations and improve the quality and safety of medical care. At the same time, it also provides data support for the evaluation and improvement of medical quality, helping hospitals to continuously improve the infusion management process and enhance the overall level of medical services.

[0064] The technical solution of this embodiment is: obtaining drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and the infusion drip rate. The drug solution to be infused is determined based on the patient's drug solution to be infused in combination with the infusion sequence table, and a monitoring period is preset. The stability of the infusion drip rate of the drug solution input during the monitoring period is analyzed to determine whether the infusion drip rate during the monitoring period is stable. By determining whether the infusion drip rate is stable, the present invention helps medical staff to promptly discover unstable drip rates during the infusion process, ensure infusion safety and treatment effects, and effectively monitor and adjust the infusion process according to the actual conditions of different patients and drug solutions.

[0065] Example 2:

[0066] See also Figure 2 As shown, the intelligent infusion monitoring method according to the embodiment of the present invention further includes the following steps:

[0067] Step 3: If the infusion drip rate of the current infusion liquid is stable during the monitoring period, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate of the current infusion liquid is unstable during the monitoring period, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is discontinuously abnormal, the abnormal infusion drip rate data is eliminated to determine the effective infusion drip rate, and the effective infusion drip rate is averaged to determine the end time of the infusion;

[0068] The remaining amount of the currently input liquid is collected through a weighing sensor. Based on the stability of the infusion drip rate of the currently input liquid during the monitoring period, the duration corresponding to the monitoring period is multiplied by the average infusion drip rate to obtain the amount of liquid that has been input;

[0069] The remaining amount of the infusion liquid is compared with the average infusion drip rate of the current infusion liquid to obtain the remaining infusion time of the current infusion liquid, and the end time of the remaining infusion time is recorded as the end time of the infusion of the current infusion liquid;

[0070] Based on the instability of the infusion drip rate of the current infusion liquid during the monitoring period, the infusion drip rate at the monitoring time point is subtracted from the preset infusion drip rate, and the absolute value of the difference is taken to obtain the abnormal drip rate deviation value;

[0071] If the abnormal drip rate deviation value is not within the preset drip rate deviation range, the corresponding monitoring time point is recorded as the sudden change time point;

[0072] If the abnormal drip rate deviation value is within the preset drip rate deviation range, the corresponding monitoring time point will be recorded as a normal time point;

[0073] Traverse all sudden change time points on the time axis, count the number of times the sudden change time points appear continuously on the time axis, and perform ratio processing with the maximum value of the number of consecutive appearances of the preset sudden change time points to obtain the sudden change continuous value;

[0074] It should be explained that if the number of times a sudden change time point appears on the time axis is greater than two, it means that the sudden change time point appears continuously on the time axis. The maximum number of consecutive occurrences of a sudden change time point is set by those skilled in the art based on historical experience.

[0075] If the sudden change continuous value is greater than or equal to the sudden change continuous threshold, it means that the infusion drip rate is abnormally continuous. Close the infusion clamp and suspend the infusion. The medical staff will be prompted to intervene manually and check the equipment or patient's condition.

[0076] If the sudden change continuous value is less than the sudden change continuous threshold, it means that the infusion drip rate is abnormally discontinuous;

[0077] If the infusion drip rate shows discontinuous abnormalities, the number of sudden change time points is counted and the percentage of sudden change time points is calculated. If the percentage of sudden change time points is greater than or equal to the percentage threshold, the infusion clamp is closed, the infusion is suspended, and medical staff are prompted to intervene manually to check for changes in the equipment or patient's condition.

[0078] If the proportion of the number of sudden change time points is less than the threshold of the number of sudden change time points, a rejection signal is generated;

[0079] Based on the elimination signal, the abnormal drip rate corresponding to the sudden change time point is eliminated from the infusion drip rate sequence during the monitoring period, and the infusion drip rate at the normal time point is retained. The infusion drip rate at the normal time point is recorded as the effective drip rate, and the effective drip rates of all normal time points are integrated into the effective drip rate sequence. , where m represents the total number of normal time points;

[0080] Extract all data in the effective drip rate sequence and calculate the mean to obtain the effective drip rate mean of the current drug solution. Ratio the remaining input drug volume with the effective drip rate mean of the current drug solution to obtain the remaining infusion time of the current drug solution. The end time of the remaining infusion time is recorded as the end time of the infusion of the current drug solution.

[0081] It is understandable that at the end of the infusion, the infusion clamp is closed to remind the medical staff to change the infusion bottle;

[0082] Step 4: Perform cluster analysis on all patients based on the end time of the current infusion of the drug solution, determine the cluster of the end time of the infusion, determine the priority treatment cluster in chronological order, and give priority treatment to the patients in the priority treatment cluster;

[0083] Integrate the infusion end time of all patients' current drug infusions into an infusion end time dataset;

[0084] Use K-Means algorithm to cluster and determine the number of clusters K;

[0085] Specifically, the number of clusters K can be determined by the elbow method, and the sum of squared errors SSE under different K values can be calculated. in, is the jth cluster, From data point x to cluster center The Euclidean distance of

[0086] Set the candidate range of K. For each K, use the K-Means clustering algorithm to cluster the data and calculate the corresponding SSE.

[0087] With K as the horizontal axis and SSE as the vertical axis, draw the K-SSE curve, calculate the rate of change of the SSE decrease corresponding to adjacent K values, and select the K value at which the rate of change tends to be flat after a sudden drop as the optimal number of clusters;

[0088] For example, the SSE corresponding to different K values is calculated for the infusion end time dataset. The candidate range of K is set to 1, 2, 3, 4, and 5, and the SEE corresponding to different K values is 1000, 600, 300, 200, and 150. After plotting the K-SSE curve, when K=3, the decline rate slows down significantly. K=3 is the elbow point and can be regarded as the optimal number of clusters.

[0089] The infusion end time is regarded as a point on the time axis, the time difference between the infusion end times of different patients is calculated as the distance, and the infusion end time of K patients is randomly selected as the initial cluster center;

[0090] For each patient, the time difference between the end of the infusion and the center of each cluster is calculated, and the patient is assigned to the cluster with the smallest time difference.

[0091] Recalculate the average of the infusion end time of all patients in each cluster and use it as the new cluster center;

[0092] Repeat the above steps of assigning patients to clusters and updating cluster centers until the cluster centers no longer change or the preset number of iterations is reached, and determine the clustering cluster for the end of the infusion period;

[0093] All the infusion end period clusters are sorted in chronological order to obtain an infusion end period cluster sorting table, and the infusion end period cluster that is ranked first in the infusion end period cluster sorting table is extracted as the priority treatment cluster, and the patients in the priority treatment cluster are given priority treatment;

[0094] For example, if the clustering result is 3 clusters, with Cluster 1 ending at 10:00 (5 patients), Cluster 2 at 10:15 (8 patients), and Cluster 3 at 10:30 (3 patients), the nurse can prioritize Cluster 1 by preparing 5 doses of the next stage of medication; at 10:15, focus on treating the 8 patients in Cluster 2, and so on, thus avoiding frequent trips back and forth between wards and optimizing time utilization.

[0095] In summary, this step uses the K-Means algorithm to cluster patients with the same infusion end time. The optimal number of clusters K is determined by the elbow method, and the initial cluster centers are optimized using the K-Means++ algorithm. The cluster centers are iteratively calculated until convergence. The priority clusters are determined based on the time sequence of the clusters at the end of the infusion period, and patients in the priority clusters are given priority treatment.

[0096] Function 1: Through cluster analysis, hospitals can understand the distribution of patients who have completed infusion in different time periods. For time periods with a relatively concentrated end time of infusion, they can rationally arrange nursing staff to perform patrols, needle removal, and other tasks, thereby improving the efficiency and quality of nursing work and avoiding waste of human resources.

[0097] Function 2: Clustering results can be used to analyze the infusion characteristics of different patient groups and optimize the infusion process. Patients in different clusters may have different characteristics and needs. Patients who finish infusion earlier may be in better physical condition and recover faster, while patients who finish infusion later may have more complicated conditions or be weaker. Based on the clustering results, medical staff can provide more targeted care for patients in different groups, improving patient satisfaction and treatment outcomes.

[0098] The technical solution of this embodiment is as follows: if the infusion drip rate of the current input drug solution is stable during the monitoring period, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate of the current input drug solution is unstable during the monitoring period, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is abnormally discontinuous, the abnormal infusion drip rate data is eliminated to determine the effective infusion drip rate, the effective infusion drip rate is averaged to determine the end time of the infusion, and cluster analysis is performed on all patients according to the end time of the infusion of the current input drug solution to determine the cluster of the infusion end period. The invention determines the priority processing cluster in time sequence and gives priority to the patients in the priority processing cluster. The invention adopts different processing methods to determine the infusion end time according to whether the infusion drip rate is stable, thereby ensuring the accuracy of the determination of the infusion end time, helping medical staff to arrange their work reasonably and perform operations such as changing bottles in time. When the infusion drip rate is unstable, the invention judges whether the infusion drip rate is continuous or discontinuous abnormal, and takes corresponding measures for different types of abnormalities, thereby ensuring the safety of the infusion process, and timely discovering and handling potential risks such as equipment failure or changes in patient conditions. The K-Means algorithm is used to perform cluster analysis on the infusion end time of all patients to determine the infusion end time cluster, and determine the priority processing cluster according to the time sequence. The invention enables medical staff to arrange their work reasonably according to the order in which the patients' infusions end, give priority to the patients in the priority processing cluster, improve the efficiency of medical work, realize the optimal allocation of medical resources, and avoid waste of resources and disorder of work.

[0099] Example 3:

[0100] See also Figure 2 As shown, the intelligent infusion monitoring system according to the embodiment of the present invention includes the following modules:

[0101] Data acquisition module: acquires drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate;

[0102] Based on the patient's actual situation, the nurse determines the order of infusion according to the doctor's instructions on the data processing terminal, and the system integrates it into an infusion sequence table;

[0103] Obtain the infusion drip rate through the smart infusion pump;

[0104] Drip rate stability analysis module: Determine the infusion rate based on the patient's infusion solution and the infusion sequence table, preset the monitoring period, and perform stability analysis on the drip rate of the infusion solution during the monitoring period to determine whether the drip rate is stable during the monitoring period;

[0105] Extract the first infusion liquid in the infusion sequence table and record it as the infusion liquid;

[0106] Preset the monitoring period, divide the monitoring period into several monitoring time points with equal time intervals, obtain the infusion drip rate of the current input drug solution at the monitoring time point, and integrate the infusion drip rates of all monitoring time points in the monitoring period into an infusion drip rate sequence , where n represents the total number of monitoring time points;

[0107] Extract all data in the infusion drip rate sequence and use the mean calculation formula to obtain the mean infusion drip rate of the current input drug solution during the monitoring period; extract all data in the infusion drip rate sequence and use the standard deviation calculation formula to obtain the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period; use the coefficient of variation calculation formula to perform ratio processing on the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period and the mean infusion drip rate to obtain the drip rate variation value of the infusion drip rate of the current input drug solution during the monitoring period;

[0108] If the drip rate variation value is greater than or equal to the drip rate variation threshold, it indicates that the drip rate of the current infusion of the drug solution is unstable during the monitoring period; otherwise, it indicates that the drip rate of the current infusion of the drug solution is stable during the monitoring period;

[0109] Abnormal continuity judgment module: If the infusion drip rate of the current infusion liquid is stable during the monitoring period, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate of the current infusion liquid is unstable during the monitoring period, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormal in continuity. If it is discontinuous, the abnormal infusion drip rate data is eliminated to determine the effective infusion drip rate, and the effective infusion drip rate is averaged to determine the end time of the infusion;

[0110] The remaining amount of the currently input liquid is collected through a weighing sensor. Based on the stability of the infusion drip rate of the currently input liquid during the monitoring period, the duration corresponding to the monitoring period is multiplied by the average infusion drip rate to obtain the amount of liquid that has been input;

[0111] The remaining amount of the infusion liquid is compared with the average infusion drip rate of the current infusion liquid to obtain the remaining infusion time of the current infusion liquid, and the end time of the remaining infusion time is recorded as the end time of the infusion of the current infusion liquid;

[0112] Based on the instability of the infusion drip rate of the current infusion liquid during the monitoring period, the infusion drip rate at the monitoring time point is subtracted from the preset infusion drip rate, and the absolute value of the difference is taken to obtain the abnormal drip rate deviation value;

[0113] If the abnormal drip rate deviation value is not within the preset drip rate deviation range, the corresponding monitoring time point is recorded as the sudden change time point;

[0114] If the abnormal drip rate deviation value is within the preset drip rate deviation range, the corresponding monitoring time point will be recorded as a normal time point;

[0115] Traverse all sudden change time points on the time axis, count the number of times the sudden change time points appear continuously on the time axis, and perform ratio processing with the maximum value of the number of consecutive appearances of the preset sudden change time points to obtain the sudden change continuous value;

[0116] If the sudden change continuous value is greater than or equal to the sudden change continuous threshold, it means that the infusion drip rate is abnormally continuous. Close the infusion clamp and suspend the infusion. The medical staff will be prompted to intervene manually and check the equipment or patient's condition.

[0117] If the sudden change continuous value is less than the sudden change continuous threshold, it means that the infusion drip rate is abnormally discontinuous;

[0118] If the infusion drip rate shows discontinuous abnormalities, the number of sudden change time points is counted and the percentage of sudden change time points is calculated. If the percentage of sudden change time points is greater than or equal to the percentage threshold, the infusion clamp is closed, the infusion is suspended, and medical staff are prompted to intervene manually to check for changes in the equipment or patient's condition.

[0119] If the proportion of the number of sudden change time points is less than the threshold of the number of sudden change time points, a rejection signal is generated;

[0120] Based on the elimination signal, the abnormal drip rate corresponding to the sudden change time point is eliminated from the infusion drip rate sequence during the monitoring period, and the infusion drip rate at the normal time point is retained. The infusion drip rate at the normal time point is recorded as the effective drip rate, and the effective drip rates of all normal time points are integrated into the effective drip rate sequence. , where m represents the total number of normal time points;

[0121] Extract all data in the effective drip rate sequence and calculate the mean to obtain the effective drip rate mean of the current drug solution. Ratio the remaining input drug volume with the effective drip rate mean of the current drug solution to obtain the remaining infusion time of the current drug solution. The end time of the remaining infusion time is recorded as the end time of the infusion of the current drug solution.

[0122] Priority cluster generation module: Based on the end time of the current infusion of the drug solution, cluster analysis is performed on all patients to determine the cluster of the end time of the infusion. The priority cluster is determined in chronological order, and the patients in the priority cluster are given priority treatment.

[0123] Integrate the infusion end time of all patients' current drug infusions into an infusion end time dataset;

[0124] Use the K-Means algorithm to cluster and determine the number of clusters K. Based on the number of clusters K, consider the end time of infusion as a point on the time axis, calculate the time difference between the end times of infusion of different patients as the distance, and randomly select the end time of infusion of K patients as the initial cluster center;

[0125] For each patient, the time difference between the end of the infusion and the center of each cluster is calculated, and the patient is assigned to the cluster with the smallest time difference.

[0126] Recalculate the average of the infusion end time of all patients in each cluster and use it as the new cluster center;

[0127] Repeat the above steps of assigning patients to clusters and updating cluster centers until the cluster centers no longer change or the preset number of iterations is reached, and determine the clustering cluster for the end of the infusion period;

[0128] All the infusion end period clusters are sorted in chronological order to obtain an infusion end period cluster sorting table, and the infusion end period cluster that is ranked first in the infusion end period cluster sorting table is extracted as the priority treatment cluster, and the patients in the priority treatment cluster are given priority treatment.

[0129] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. Intelligent infusion monitoring system, characterized by: Includes the following modules: Data acquisition module: acquires drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate; Drip rate stability analysis module: Determine the infusion rate based on the patient's infusion solution and the infusion sequence table, preset the monitoring period, and perform stability analysis on the drip rate of the infusion solution during the monitoring period to determine whether the drip rate is stable during the monitoring period; Abnormal continuity judgment module: If the infusion drip rate is stable, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate is unstable, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is discontinuous, the abnormal infusion drip rate data is eliminated, the effective infusion drip rate is determined, and the average is calculated to determine the end time of the infusion; Priority cluster generation module: Based on the end time of the current infusion of the drug solution, all patients are clustered and analyzed, priority clusters are determined in chronological order, and patients in the priority clusters are given priority treatment; The system is used to perform the following method, which includes the following steps: Obtaining drug solution information and infusion information, wherein the drug solution information includes the drug solution to be infused, and the infusion information includes the infusion start time and infusion drip rate; According to the patient's infusion solution, combined with the infusion sequence table, the infusion solution is determined, and the monitoring period is preset. The stability of the infusion drip rate of the infusion solution during the monitoring period is analyzed to determine whether the infusion drip rate during the monitoring period is stable; If the infusion drip rate is stable, the infusion drip rate is averaged to determine the end time of the infusion; if the infusion drip rate is unstable, the infusion drip rate is analyzed for abnormal continuity to determine whether the infusion drip rate is abnormally continuous. If it is discontinuously abnormal, the abnormal infusion drip rate data is eliminated to determine the effective infusion drip rate, and the average is calculated to determine the end time of the infusion; According to the end time of the current infusion of the drug solution, all patients are clustered and analyzed, and priority treatment clusters are determined in chronological order, and patients in the priority treatment clusters are given priority treatment.

2. The intelligent infusion monitoring system according to claim 1, characterized in that: The process of performing stability analysis on the drip rate of the infusion liquid during the monitoring period is as follows: The monitoring period is divided into several monitoring time points, and the infusion drip rate of the current input drug solution at the monitoring time point is obtained and analyzed and processed to obtain the standard deviation and mean of the infusion drip rate of the current input drug solution during the monitoring period. The drip rate variation value is obtained using the coefficient of variation calculation formula; if the drip rate variation value ≥ the drip rate variation threshold, it indicates that the infusion drip rate of the current input drug solution during the monitoring period is unstable; otherwise, it indicates that the infusion drip rate of the current input drug solution during the monitoring period is stable.

3. The intelligent infusion monitoring system according to claim 2, characterized in that: The method for obtaining the standard deviation and the mean of the infusion drip rate of the current infusion liquid during the monitoring period is as follows: Integrate the infusion drip rates at all monitoring time points within the monitoring period into an infusion drip rate sequence; Extract all data in the infusion drip rate sequence and use the mean calculation formula to obtain the mean infusion drip rate of the current input drug solution during the monitoring period. Extract all data in the infusion drip rate sequence and use the standard deviation calculation formula to obtain the standard deviation of the infusion drip rate of the current input drug solution during the monitoring period.

4. The intelligent infusion monitoring system according to claim 3, characterized in that: The process of obtaining the end time of the infusion with a stable infusion drip rate is as follows: The duration of the monitoring period is multiplied by the average infusion drip rate to obtain the amount of liquid medicine that has been infused; The remaining amount of the input liquid is compared with the average infusion drip rate of the current liquid to obtain the remaining infusion time of the current liquid. The end time of the remaining infusion time is recorded as the end time of the infusion of the current liquid.

5. The intelligent infusion monitoring system according to claim 4, characterized in that: The infusion drip rate is unstable, and the specific process of abnormal continuity analysis of the infusion drip rate is as follows: Analyze the infusion drip rate at the monitoring time point to determine the sudden change time point in the monitoring time point; Traverse all sudden change time points on the time axis, count the number of consecutive occurrences of sudden change time points on the time axis, and compare them with the maximum value of the preset number of consecutive occurrences of sudden change time points to obtain the sudden change continuity value; if the sudden change continuity value is less than the sudden change continuity threshold, it means that the infusion drip rate shows discontinuous abnormality.

6. The intelligent infusion monitoring system according to claim 5, characterized in that: The process of obtaining the sudden change time point in the monitoring time point is as follows: The infusion drip rate at the monitoring time point is subtracted from the preset infusion drip rate, and the absolute value of the difference is taken to obtain the abnormal drip rate deviation value; If the abnormal dripping rate deviation value is not within the preset dripping rate deviation range, the corresponding monitoring time point is recorded as the sudden change time point.

7. The intelligent infusion monitoring system according to claim 5, characterized in that: The process of obtaining the effective infusion drip rate is as follows: Calculate the proportion of sudden change time points in the monitoring time points. If the proportion of sudden change time points is less than the threshold of the proportion of sudden change time points, then remove the abnormal drip rate corresponding to the sudden change time points from the infusion drip rate sequence during the monitoring period, and retain the infusion drip rate at the normal time points. The retained infusion drip rate at the normal time points is recorded as the effective drip rate.

8. The intelligent infusion monitoring system according to claim 5, characterized in that: The process of obtaining the end time of the infusion with unstable dripping rate is as follows: The effective drip rates at all normal time points are integrated into an effective drip rate sequence. All data in the effective drip rate sequence are extracted and averaged to obtain the average effective drip rate of the current drug solution. The remaining input drug volume is ratioed with the average effective drip rate of the current drug solution to obtain the remaining infusion time of the current input drug solution. The end time of the remaining infusion time is recorded as the end time of the infusion of the current input drug solution.

9. The intelligent infusion monitoring system according to claim 5, characterized in that: The acquisition process of the priority processing cluster is as follows: The end time of the infusion of all patients' current medications is integrated into the infusion end time dataset, and the K-Means algorithm is used to cluster and determine the clusters of the infusion end time period; All the infusion end period clusters are sorted in chronological order to obtain an infusion end period cluster sorting table, and the infusion end period cluster that is first in the infusion end period cluster sorting table is extracted as a priority processing cluster.

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