Remote intelligent infusion management system
Through the multi-dimensional data collection, dynamic compensation and graded early warning of the remote intelligent infusion management system, the problems of insufficient prediction accuracy and disordered resource scheduling of the infusion detection system in the existing technology are solved, and accurate prediction and efficient management of the infusion process are achieved.
Patent Information
- Application Number
- CN202511029123.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-25
- Publication Date
- 2025-09-26
AI Technical Summary
The existing clinical infusion detection system relies on single parameter monitoring, with insufficient prediction accuracy, lack of multi-dimensional signal fusion in the early warning mechanism, disordered task allocation, and lack of full-link closed-loop management.
Build a remote intelligent infusion management system, obtain multi-dimensional data through the data acquisition module, perform accurate predictions through the dynamic compensation module, generate multi-level warning instructions through the graded warning module, and optimize the allocation of nursing resources through the resource scheduling module, forming a closed-loop management of accurate prediction-graded warning-intelligent scheduling.
It achieves accurate prediction of the remaining time of infusion and multi-level hierarchical early warning, optimizes the scheduling of nursing resources, and improves the safety and efficiency of the infusion process.
Smart Images
Figure CN120708859A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent infusion monitoring, and in particular to a remote intelligent infusion management system. Background Art
[0002] Current clinical infusion monitoring systems generally use basic sensing technologies to automate some functions. Existing systems primarily rely on single-parameter monitoring mechanisms, using load cells or flow meters to independently collect remaining infusion data and generate remaining time predictions (e.g., gravity-sensing time estimation systems). Fixed time thresholds are set to trigger audio and visual alerts (e.g., unified alarms at fixed points before the end of an infusion). Location-based responses are achieved through nurse call systems (e.g., ward call button-linked positioning technology). These systems represent an initial transition from purely manual monitoring to semi-automated monitoring, but the functional modules remain discrete and data collaboration capabilities are yet to be achieved.
[0003] However, existing technologies still have some technical issues. They rely solely on single physical parameters such as flow or weight, failing to factor in dynamic changes in drug viscosity and the patient's physiological state (e.g., heart rate affecting drug metabolism), resulting in insufficient prediction accuracy. Early warning mechanisms are triggered solely based on time thresholds, failing to integrate multi-dimensional signals such as abnormal venous pressure and bubble risk, and lack prioritized alerts at all levels. Task allocation relies on manual dispatch, failing to integrate the real-time location of nursing staff and ward access constraints (e.g., avoiding isolation areas). Resource allocation is disrupted when multiple tasks are concurrently performed, leading to delayed responses. Monitoring of drug solution characteristics, physiological parameter feedback, and equipment operating status operate in isolation, preventing functional modules like bubble detection and blockage warning from coordinating, resulting in a lack of a closed-loop "risk identification-decision-making-action" mechanism.
[0004] Therefore, there is an urgent need for a remote intelligent infusion management system to form a full-link closed-loop management of accurate prediction-graded alarm-intelligent scheduling. Summary of the Invention
[0005] In order to solve the above technical problems, the present invention provides a remote intelligent infusion management system, which solves the problems of inaccurate prediction, rigid response and inefficient scheduling by breaking data silos and building a collaborative decision-making mechanism.
[0006] The present invention provides a remote intelligent infusion management system, comprising: Data acquisition module: used to obtain real-time infusion container weight data, liquid flow data, drug characteristic parameters, patient physiological parameters and nursing staff real-time location data; Dynamic compensation module: used to calculate the remaining infusion time based on the real-time acquisition of the infusion container weight signal, flow data, drug characteristic parameters and patient physiological parameters; A hierarchical warning module is used to generate multi-level warning instructions based on the remaining infusion time and a set of preset thresholds, and simultaneously generate audio and visual prompts on the central console of the nurse station; Resource scheduling module: used to generate a scheduling path based on the early warning instructions and the real-time location of the nursing staff.
[0007] Furthermore, the weight data of the infusion container is collected in real time by an integrated weighing sensor; The liquid flow data is collected in real time by a flow sensor; The drug characteristic parameters include drug name, concentration and viscosity data; The patient's physiological parameters include heart rate, blood pressure and body temperature data; The real-time location data of the nursing staff is obtained through a positioning terminal worn by the nursing staff.
[0008] Furthermore, the dynamic compensation module calculates the remaining infusion time, specifically including: Calculating the initial remaining infusion time according to the liquid flow rate data and the viscosity data in the drug characteristic parameters; Dynamically calibrating the initial remaining infusion time based on the real-time change trend of the infusion container weight data to obtain a preliminary corrected remaining infusion time; According to the real-time changes in the patient's physiological parameters, the initially corrected remaining infusion time is subjected to secondary compensation correction to obtain the remaining infusion time.
[0009] Furthermore, the calculation formula for the initial remaining infusion time is: ; Wherein, T0 represents the initial remaining infusion time; W current Indicates the current infusion container weight; K indicates the weight-volume conversion coefficient; V t represents the liquid flow data measured in real time by the liquid flow sensor; f(η) represents the viscosity correction function; η represents the viscosity data in the drug characteristic parameters; Based on the real-time change trend of the infusion container weight data, the initial remaining infusion time is dynamically calibrated, specifically: ; ; Among them, V real Indicates the actual liquid flow rate. Indicates the weight change rate of the infusion container; T1 indicates the remaining infusion time after initial correction; Secondary compensation correction based on physiological parameters, including: Based on the real-time values of heart rate, blood pressure and body temperature, a physiological abnormality risk score is performed, specifically: ; Among them, R represents the physiological abnormality risk score; Indicates the absolute deviation between the real-time heart rate value and the patient's baseline heart rate value; Indicates the absolute deviation between the real-time blood pressure value and the patient's baseline blood pressure value; Indicates the absolute deviation between the real-time body temperature value and the patient's baseline body temperature value; HR0 indicates the patient's baseline heart rate value; BP0 indicates the patient's baseline blood pressure value; 、 、 represents the weight coefficient; The remaining infusion time is determined based on the physiological abnormality risk score, specifically: If the physiological abnormality risk score is less than the risk score threshold, T2 = T1, and T2 represents the remaining infusion time; If the physiological abnormality risk score is greater than or equal to the risk score threshold, then T2 = T1 × (1-g × R); g represents the safety acceleration factor, 0 <g<1。
[0010] Furthermore, the preset threshold set includes: a normal state threshold, a first-level warning threshold, a second-level emergency warning threshold, and a third-level alarm threshold; The method for generating the sound and light prompt information includes: When the remaining infusion time is less than the normal state threshold, no sound and light information prompt is generated; When the remaining infusion time reaches the first-level warning threshold, a visual prompt including an infusion point identifier is generated; When the remaining infusion time reaches the second-level emergency warning threshold, generating an audio-visual combination prompt; When the third-level alarm threshold is triggered, a continuous sound and light alarm instruction is generated.
[0011] Furthermore, the data acquisition module is also used to obtain venous pressure data; The hierarchical warning module is also used for: When the venous pressure data continuously exceeds the safety pressure threshold, generating a venous pressure abnormality alarm instruction; The priority of the abnormal venous pressure alarm instruction is higher than the multi-level warning instruction generated based on the remaining infusion time.
[0012] Furthermore, when the resource scheduling module generates a scheduling path, it preferentially points to the infusion point that triggers the third-level alarm threshold or the abnormal venous pressure alarm instruction; Secondly, it points to the infusion point that triggers the secondary emergency warning threshold and there are no other caregivers in the area.
[0013] Furthermore, a remote intelligent infusion management system further includes: The bubble detection module uses an ultrasonic sensor integrated in the infusion line to monitor bubble characteristics in real time; The hierarchical warning module is also used for: When the bubble characteristics exceed the preset safety standard, a bubble alarm instruction is generated; The bubble alarm instruction triggers the resource scheduling module to generate an emergency scheduling path.
[0014] Furthermore, the scheduling path generated by the resource scheduling module is executed as a path guide through a mobile terminal carried by the nursing staff or a digital identification system in the ward.
[0015] The present invention has the following technical effects: The dynamic compensation module integrates drug viscosity (which affects flow rate), physiological parameters (such as heart rate, which accelerates drug metabolism), and weight change trends, transcending the limitations of single flow / weight parameters. A secondary compensation mechanism, combining a viscosity correction function and a physiological abnormality risk score, eliminates prediction bias caused by differences in drug solution properties or changes in patient status. The actual infusion flow rate is calculated based on the weight change rate. Combined with the physiological parameter risk score, the remaining infusion time is dynamically adjusted to achieve accurate predictions of the remaining infusion time. Preset threshold sets (conventional / level 1 / level 2 / level 3) correspond to differentiated alarm modes, creating a multi-level hierarchical early warning system. Abnormal venous pressure alarms are forcibly overwritten by time threshold alarms, preventing high-risk events (such as leaks) from being overwhelmed by regular fluid change reminders. Bubble alarms trigger independent emergency pathways, ensuring that air embolism risks are addressed first. The resource scheduling module simultaneously calculates: the real-time location of the nursing staff (positioning terminal data), the urgency of the task (level 3 alarm > venous abnormality > level 2 warning), and regional access constraints (such as avoiding the infection isolation area), generates the shortest response path, and pushes the scheduling path directly to the mobile terminal / digital identification system, replacing manual paper dispatch and dynamically allocating resources when multiple tasks are concurrent. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0017] Figure 1 The figure is a schematic diagram of the system structure of a remote intelligent infusion management system provided by an embodiment of the present invention.
[0018] Figure 2 This is a flow chart of generating remaining infusion time in a remote intelligent infusion management system provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.
[0020] Figure 1 This is a schematic diagram of the system structure of a remote intelligent infusion management system provided by an embodiment of the present invention. Figure 1 , specifically including: Data acquisition module: used to obtain real-time infusion container weight data, liquid flow data, drug characteristic parameters, patient physiological parameters and nursing staff real-time location data.
[0021] In some embodiments, the infusion container weight data is collected in real time by an integrated weighing sensor; The liquid flow data is collected in real time by a flow sensor; The drug characteristic parameters include drug name, concentration and viscosity data; The patient's physiological parameters include heart rate, blood pressure and body temperature data; The real-time location data of the nursing staff is obtained through a positioning terminal worn by the nursing staff.
[0022] In some embodiments, a micro-weighing sensor is integrated into the bottom of the infusion container. This sensor uses strain gauges to sense weight changes in real time and converts the weight signal into an electrical signal for transmission to the central processing unit. This sensor features an anti-interference design, effectively isolating errors caused by bed shaking or external contact. Liquid flow monitoring utilizes a non-invasive ultrasonic flow sensor. Its transmitter and receiver terminals are clamped to the outside of the infusion line. By calculating the time difference between ultrasonic waves traveling through the liquid, it accurately captures instantaneous flow data without compromising the line's tightness. Drug characteristics, including the standard name, concentration ratio, and laboratory-determined viscosity coefficient, are automatically acquired through the hospital information system interface, forming a structured database for access by the dynamic compensation module. Patient physiological parameters are collected using medical-grade wearable devices. Heart rate and blood pressure data are updated in real time using photoelectric pulse waves and cuff oscillometric methods, while body temperature is continuously monitored using a chip thermistor. Nursing staff positioning is based on low-power Bluetooth beacon technology. Positioning base stations are deployed in wards to capture the signal strength of positioning terminals worn on nurse badges in real time. This, combined with a trilateration algorithm, enables meter-level accuracy in position tracking.
[0023] The core of this design concept is to break through the limitations of traditional single-parameter monitoring and build a digital twin model of the real infusion scenario through the multi-dimensional integration of physical quantities (weight, flow), biochemical properties (drug viscosity) and physiological status (heart rate and blood pressure).
[0024] Dynamic compensation module: used to calculate the remaining infusion time based on the real-time acquisition of the infusion container weight signal, flow data, drug characteristic parameters and patient physiological parameters.
[0025] Dynamic compensation is implemented to overcome the static limitations of traditional prediction methods. The system first calculates the initial remaining time using a built-in algorithm based on real-time infusion container weight signals, instantaneous flow rate values collected by flow sensors, and viscosity parameters from a drug property database. This calculation dynamically links the liquid flow rate data with a viscosity correction function. High-viscosity drugs automatically trigger a stronger flow rate attenuation compensation factor, thereby eliminating baseline errors caused by differences in the physical properties of the drug solution. Next, the system enters the weight calibration phase, where the container weight change rate continuously monitored by the load cell is converted into the actual drug flow rate. When disturbances such as compression deformation or positional displacement of the infusion line are detected, the system automatically replaces the raw flow rate data with the weight change trend as the primary weight, achieving self-correction of the data under environmental interference. Finally, the physiological compensation phase is implemented. Real-time heart rate, blood pressure, and temperature data transmitted by the wearable device are normalized to generate a physiological abnormality risk score. If the score exceeds a safety threshold, a time compression mechanism is activated, proportionally shortening the prediction time to match the patient's accelerated metabolic state.
[0026] See also Figure 2 In some embodiments, the dynamic compensation module calculates the remaining infusion time, specifically including: The initial remaining infusion time is calculated based on the real-time acquired infusion container weight signal, the liquid flow data and the viscosity data in the drug characteristic parameters.
[0027] Specifically, the calculation formula for the initial remaining infusion time is: ; Wherein, T0 represents the initial remaining infusion time; W current Indicates the current infusion container weight; K indicates the weight-volume conversion coefficient; V t represents the liquid flow data measured in real time by the liquid flow sensor; f(η) represents the viscosity correction function; η represents the viscosity data in the drug characteristic parameters.
[0028] K is obtained through experimental calibration; f(η) is constructed through viscosity-flow rate relationship experiments, and the optional typical form is .
[0029] Based on the real-time change trend of the infusion container weight data, the initial remaining infusion time is dynamically calibrated to obtain a preliminarily corrected remaining infusion time.
[0030] Based on the real-time change trend of the infusion container weight data, the initial remaining infusion time is dynamically calibrated, specifically including: ; ; Among them, V real Indicates the actual liquid flow rate. Indicates the weight change rate of the infusion container; T1 indicates the remaining infusion time after initial correction.
[0031] According to the real-time changes in the patient's physiological parameters, the initially corrected remaining infusion time is subjected to secondary compensation correction to obtain the remaining infusion time.
[0032] Secondary compensation correction based on physiological parameters, including: Based on the real-time values of heart rate, blood pressure and body temperature, a physiological abnormality risk score is performed, specifically: ; Among them, R represents the physiological abnormality risk score; Indicates the absolute deviation between the real-time heart rate value and the patient's baseline heart rate value; Indicates the absolute deviation between the real-time blood pressure value and the patient's baseline blood pressure value; Indicates the absolute deviation between the real-time body temperature value and the patient's baseline body temperature value; HR0 indicates the patient's baseline heart rate value; BP0 indicates the patient's baseline blood pressure value; 、 、 represents the weight coefficient; The weight coefficient is used to quantify the importance of physiological parameters on drug metabolism, and its value can be based on the characteristics of drug type and individual differences of patients; For example, if the weight coefficient is determined based on the characteristics of the drug type: Cardiotoxic drugs directly affect heart rate stability, so the heart rate weight α is increased (α=0.6) to ensure that heart rate fluctuations are responded to first; Vasoactive drugs significantly change blood pressure levels, so the blood pressure weight β is increased (β=0.7) and the monitoring weight of abnormal blood pressure is strengthened; If the weight coefficient is determined based on individual differences among patients: The system automatically retrieves medical history (such as coronary heart disease, renal insufficiency) from the electronic medical record and dynamically adjusts the weight configuration: Patients with coronary heart disease: automatically increase α (heart rate weight) to 0.65; Patients with renal failure: Increasing γ (body temperature weight) can easily lead to infection and fever due to decreased metabolic rate.
[0033] Furthermore, the remaining infusion time is determined based on the physiological abnormality risk score, specifically: If the physiological abnormality risk score is less than the risk score threshold, T2 = T1, and T2 represents the remaining infusion time; If the physiological abnormality risk score is greater than or equal to the risk score threshold, then T2 = T1 × (1-g × R); g represents the safety acceleration factor, 0 <g<1。
[0034] The safety acceleration factor g determines the compression intensity of physiological risk on the remaining time and is dynamically calculated through a pharmacological model.
[0035] Hierarchical warning module: used to generate multi-level warning instructions based on the remaining infusion time and the preset threshold set, and simultaneously generate sound and light prompts on the nurse station console.
[0036] In some embodiments, the preset threshold set includes: a normal state threshold, a level one warning threshold, a level two emergency warning threshold, and a level three alarm threshold.
[0037] The method for generating the sound and light prompt information includes: When the remaining infusion time is less than the normal state threshold, no sound and light information prompt is generated; When the remaining infusion time reaches the first-level warning threshold, a visual prompt including an infusion point identifier is generated; When the remaining infusion time reaches the second-level emergency warning threshold, generating an audio-visual combination prompt; When the third-level alarm threshold is triggered, a continuous sound and light alarm instruction is generated.
[0038] In some embodiments, the data acquisition module is further used to obtain venous pressure data; The hierarchical warning module is also used for: When the venous pressure data continuously exceeds the safety pressure threshold, generating a venous pressure abnormality alarm instruction; The priority of the abnormal venous pressure alarm instruction is higher than the multi-level warning instruction generated based on the remaining infusion time.
[0039] The graded warning module is a key component of the intelligent infusion monitoring system. Its primary function is to generate warning instructions based on real-time remaining infusion time and venous pressure data, combined with pre-set multi-level thresholds. These warnings are then output via the nurses' station console, enabling timely response and intervention by medical staff. In its implementation, the module features multiple warning levels, including normal status, level one warning, level two emergency warning, and level three alarm, each corresponding to different triggering conditions and notification methods. When the remaining infusion time reaches the level one warning threshold, the system generates a visual cue with the infusion point identifier, such as a flashing bed number or infusion number on the display, and a color (e.g., yellow) to alert the nursing staff. If the remaining time further decreases to the level two warning threshold, a combined visual and audio alert is activated, with a flashing light (e.g., red) and a beeping alarm, and the relevant information is highlighted on the screen. As the remaining time nears completion and the level three alarm threshold is triggered, the system issues a continuous, strong visual and audio alert, which pops up on the screen and automatically stays at the top, requiring manual confirmation to dismiss. At the same time, the module also has the ability to independently monitor abnormal venous pressure. Once it detects that the patient's venous pressure continues to exceed the safety threshold, the system will immediately generate a venous pressure abnormality alarm instruction and respond in priority to all time-based early warning mechanisms to ensure that when an abnormal condition occurs that may endanger the patient's safety, medical staff can be notified immediately to deal with it. This dual-dimensional early warning strategy not only improves the intelligence level of the system, but also significantly enhances the safety and efficiency of clinical infusion management. In order to achieve the above functions, the system needs to obtain accurate data input through sensors and infusion pumps, and at the same time control the sound and light devices of the central console for effective prompts, and can be linked with the hospital information system for remote alarm or recording. The entire process uses reasonable priority control logic to ensure that critical alarms are not ignored, thereby protecting the lives of patients.
[0040] Resource scheduling module: used to generate a scheduling path based on the early warning instructions and the real-time location of the nursing staff.
[0041] In some embodiments, when the resource scheduling module generates a scheduling path, it preferentially points to the infusion point that triggers the third-level alarm threshold or the abnormal venous pressure alarm instruction; Secondly, it points to the infusion point that triggers the secondary emergency warning threshold and there are no other caregivers in the area.
[0042] The resource scheduling module is an important component of the intelligent infusion monitoring system for realizing the dynamic allocation of nursing resources. Its core function is to intelligently generate the optimal scheduling path based on the various warning instructions generated by the hierarchical warning module and the real-time location information of the nursing staff, ensuring that medical staff can respond to emergencies in a timely manner and complete nursing tasks efficiently.
[0043] During the specific implementation process, the module comprehensively analyzes factors such as the alarm level, the location of the infusion point, and the distribution of nursing staff, and performs path planning according to the preset priority rules. When the system receives a third-level alarm or an abnormal venous pressure alarm instruction, it will immediately mark the corresponding infusion point as the highest priority, and give priority to sending task prompts to the nearest and dispatchable nursing staff. At the same time, it will generate the shortest path from the current location to the target infusion point on the electronic map; if multiple high-priority alarms occur at the same time, the scheduling order will be further optimized based on the time sequence and spatial distribution to reduce response delays. After processing the highest-level alarm, the system continues to evaluate the remaining warning status. For infusion points that trigger the second-level emergency warning threshold, if there are no other nursing staff covering the area where they are located, they will be included in the next stage of the scheduling plan, and combined with the movement direction and idle status of the nursing staff, the path planning will be dynamically adjusted to avoid idle resources or task accumulation.
[0044] In addition, the resource scheduling module also has the ability to communicate with the nurse station console, nursing staff's handheld terminals or wearable devices, and can update location data and task execution status in real time, thereby realizing scientific and efficient unified management of nursing resources throughout the entire infusion monitoring process, and improving the intelligence level of hospital nursing work and emergency response capabilities.
[0045] In some embodiments, a remote intelligent infusion management system further includes: The bubble detection module uses an ultrasonic sensor integrated in the infusion line to monitor bubble characteristics in real time; The hierarchical warning module is also used for: When the bubble characteristics exceed the preset safety standard, a bubble alarm instruction is generated; The bubble alarm instruction triggers the resource scheduling module to generate an emergency scheduling path.
[0046] Furthermore, the scheduling path generated by the resource scheduling module is executed as a path guide through a mobile terminal carried by the nursing staff or a digital identification system in the ward.
[0047] Specifically, a remote intelligent infusion management system further integrates a bubble detection module on the original basis. This module monitors the liquid flow status in real time through the ultrasonic sensor integrated in the infusion pipeline, and can identify and analyze the characteristics of possible bubbles, including key parameters such as bubble size, quantity, flow rate and continuity. Once the system detects that the bubble characteristics exceed the preset safety standards, such as the bubble diameter exceeds the safety threshold or the bubble group density reaches the potential risk level, the graded warning module will immediately generate a bubble alarm instruction to prevent air from entering the patient's vein and causing serious medical accidents. This bubble alarm instruction will not only trigger the corresponding sound and light prompts on the nurse station console, but will also be sent synchronously to the resource scheduling module to participate in the priority scheduling of nursing resources as an emergency event.
[0048] Upon receiving the bubble alarm command, the resource scheduling module prioritizes all current alarm types, places the infusion point in the highest-level response queue, and generates the optimal emergency dispatch route based on the nurse's real-time location information, ensuring that the nearest available nurse can arrive at the scene to handle the abnormal situation in the shortest possible time. To improve scheduling efficiency and execution accuracy, the generated dispatch route can be navigated and displayed via a mobile terminal (such as a smartphone) carried by the nurse, or visually guided by a digital signage system deployed within the ward (such as an electronic map, LED guide screen, or smart floor lights). This creates a closed-loop management process from anomaly detection and early warning triggering to resource scheduling and on-site disposal, comprehensively improving the safety and intelligent management of the infusion process.
[0049] The remote intelligent infusion management system proposed in this application utilizes multiple functional modules working together to achieve real-time monitoring and intelligent management of the infusion process. The system first uses a data acquisition module to acquire key data such as infusion container weight, liquid flow rate, drug characteristics, patient physiological parameters, and the real-time location of the caregiver. This data provides the foundation for subsequent analysis and decision-making.
[0050] Subsequently, the dynamic compensation module dynamically calculates the remaining time of the current infusion based on the collected weight signal and flow data, combined with the drug characteristics and the patient's physiological state, overcoming the errors caused by traditional fixed-rate estimation and improving the accuracy of time prediction.
[0051] Next, the remaining infusion time is compared with the preset threshold set through the graded warning module to generate warning instructions of different levels, including level one visual prompts, level two sound and light alarms, and level three continuous alarms. The corresponding sound and light prompts are triggered synchronously on the central console of the nurse station to remind medical staff to deal with it in time.
[0052] Ultimately, the resource scheduling module prioritizes responses to high-priority alarms based on the warning level and the real-time location information of nursing staff, generates the optimal scheduling path, and provides navigation guidance through mobile terminals or ward digital identification systems to ensure efficient allocation of nursing resources.
[0053] The entire system realizes closed-loop management from data collection, intelligent analysis, early warning prompts to resource scheduling, significantly improving the safety and efficiency of infusion care.
[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims
1. A remote intelligent infusion management system, characterized in that: The system comprises: Data acquisition module: used to obtain real-time infusion container weight data, liquid flow data, drug characteristic parameters, patient physiological parameters and nursing staff real-time location data; Dynamic compensation module: used to calculate the remaining infusion time based on the real-time acquisition of the infusion container weight signal, flow data, drug characteristic parameters and patient physiological parameters; A hierarchical warning module is used to generate multi-level warning instructions based on the remaining infusion time and a set of preset thresholds, and simultaneously generate audio and visual prompts on the central console of the nurse station; Resource scheduling module: used to generate a scheduling path based on the early warning instructions and the real-time location of the nursing staff.
2. A remote intelligent infusion management system according to claim 1, characterized in that: The weight data of the infusion container is collected in real time by an integrated weighing sensor; The liquid flow data is collected in real time by a flow sensor; The drug characteristic parameters include drug name, concentration and viscosity data; The patient's physiological parameters include heart rate, blood pressure and body temperature data; The real-time location data of the nursing staff is obtained through a positioning terminal worn by the nursing staff.
3. A remote intelligent infusion management system according to claim 2, characterized in that: The dynamic compensation module calculates the remaining infusion time, specifically including: Calculating the initial remaining infusion time based on the real-time acquired infusion container weight signal, liquid flow data, and viscosity data in the drug characteristic parameters; Dynamically calibrating the initial remaining infusion time based on the real-time change trend of the infusion container weight data to obtain a preliminary corrected remaining infusion time; According to the real-time changes in the patient's physiological parameters, the initially corrected remaining infusion time is subjected to secondary compensation correction to obtain the remaining infusion time.
4. A remote intelligent infusion management system according to claim 3, characterized in that: The calculation formula for the initial remaining infusion time is: ； Wherein, T0 represents the initial remaining infusion time; W current Indicates the current infusion container weight; K indicates the weight-volume conversion coefficient; V t represents the liquid flow data measured in real time by the liquid flow sensor; f(η) represents the viscosity correction function; η represents the viscosity data in the drug characteristic parameters; Based on the real-time change trend of the infusion container weight data, the initial remaining infusion time is dynamically calibrated, specifically: ； ； Among them, V real Indicates the actual liquid flow rate. Indicates the weight change rate of the infusion container; T1 indicates the remaining infusion time after initial correction; Secondary compensation correction based on physiological parameters, including: Based on the real-time values of heart rate, blood pressure and body temperature, a physiological abnormality risk score is performed, specifically: ； Among them, R represents the physiological abnormality risk score; Indicates the absolute deviation between the real-time heart rate value and the patient's baseline heart rate value; Indicates the absolute deviation between the real-time blood pressure value and the patient's baseline blood pressure value; Indicates the absolute deviation between the real-time body temperature value and the patient's baseline body temperature value; HR0 indicates the patient's baseline heart rate value; BP0 indicates the patient's baseline blood pressure value; represents the weight coefficient; The remaining infusion time is determined based on the physiological abnormality risk score, specifically: If the physiological abnormality risk score is less than the risk score threshold, T2 = T1, and T2 represents the remaining infusion time; If the physiological abnormality risk score is greater than or equal to the risk score threshold, then T2 = T1 × (1-g × R); g represents the safety acceleration factor, 0 <g<1。 5. A remote intelligent infusion management system according to claim 1, characterized in that: The preset threshold set includes: normal state threshold, first level warning threshold, second level emergency warning threshold and third level alarm threshold; The method for generating the sound and light prompt information includes: When the remaining infusion time is less than the normal state threshold, no sound and light information prompt is generated; When the remaining infusion time reaches the first-level warning threshold, a visual prompt including an infusion point identifier is generated; When the remaining infusion time reaches the second-level emergency warning threshold, generating an audio-visual combination prompt; When the third-level alarm threshold is triggered, a continuous sound and light alarm instruction is generated.
6. A remote intelligent infusion management system according to claim 5, characterized in that: The data acquisition module is also used to obtain venous pressure data; The hierarchical warning module is also used for: When the venous pressure data continuously exceeds the safety pressure threshold, generating a venous pressure abnormality alarm instruction; The priority of the abnormal venous pressure alarm instruction is higher than the multi-level warning instruction generated based on the remaining infusion time.
7. A remote intelligent infusion management system according to claim 6, characterized in that: When the resource scheduling module generates a scheduling path, it preferentially points to the infusion point that triggers the third-level alarm threshold or the abnormal venous pressure alarm instruction; Secondly, it points to the infusion point that triggers the secondary emergency warning threshold and there are no other caregivers in the area.
8. The remote intelligent infusion management system according to claim 1, characterized in that: Also includes: The bubble detection module uses an ultrasonic sensor integrated in the infusion line to monitor bubble characteristics in real time; The hierarchical warning module is also used for: When the bubble characteristics exceed the preset safety standard, a bubble alarm instruction is generated; The bubble alarm instruction triggers the resource scheduling module to generate an emergency scheduling path.
9. The remote intelligent infusion management system according to claim 8, characterized in that: The scheduling path generated by the resource scheduling module is executed through the mobile terminal carried by the nursing staff or the digital identification system in the ward to guide the path.