Full-scene intelligent ward linkage nursing system
By integrating distributed module architecture and edge computing nodes in the ward management system, the problem of information silos and data not sharing in traditional ward management systems is solved, and the integration and real-time processing of medical equipment data is realized, which improves the accuracy of patient information and the efficiency of ward resource use.
Patent Information
- Application Number
- CN202510110350.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-23
- Publication Date
- 2025-05-16
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The traditional ward management system has the problem of information silos and data not sharing, which makes it difficult for medical staff to obtain comprehensive and accurate patient information and lacks effective ward safety monitoring and behavioral analysis mechanisms.
The full-scene smart ward Lianxun nursing system is adopted, and the distributed module architecture integrates the ward management module, patient monitoring module, intelligent decision-making module and big data analysis module to realize the integration and real-time processing of medical equipment data, and deploy edge computing nodes in the ward for preliminary data analysis and processing.
It realizes the integration and real-time processing of various medical equipment data in the ward, breaks the information island, provides medical staff with comprehensive and accurate patient information, improves the efficiency of ward resource use, and effectively prevents and responds to patient health problems in a timely manner.
Smart Images
Figure CN120015263A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of ward management, and in particular to a full-scenario smart ward communication nursing system. Background Art
[0002] With the rapid development of medical technology, ward management is gradually transforming towards digitalization and intelligence. Traditional ward management systems have the following problems:
[0003] Information island problem: the data of various medical devices in the ward are scattered, and there is a lack of effective data integration and real-time processing mechanisms, which makes it difficult for medical staff to obtain comprehensive and accurate patient information; ward safety problem: the lack of effective ward safety monitoring and behavior analysis mechanisms makes it difficult to detect and handle abnormal situations in the ward in a timely manner. In order to solve the above problems, there have been some attempts to improve ward management by introducing information technology. For example, some hospitals use electronic medical record systems (EHR) and hospital information systems (HIS) to manage patient information, but these systems are often limited to information recording and query, and lack the ability to process and analyze medical equipment data in real time. In addition, some studies have proposed the use of Internet of Things (IoT) technology to collect patient physiological data.
[0004] At present, traditional ward nursing systems generally face information silos and data non-sharing. The data generated by various medical devices in the ward are often stored in a scattered manner, lacking a unified management and analysis platform, making it difficult for medical staff to obtain comprehensive information about patients in a timely manner.
[0005] Therefore, we proposed a full-scenario smart ward interconnected nursing system to solve the above problems.
[0006] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention
[0007] The purpose of the present invention is to provide a full-scenario smart ward communication nursing system to solve the problem of data non-sharing caused by information islands between various devices in the prior art proposed in the above background technology.
[0008] To achieve the above-mentioned objectives, the present invention provides a full-scenario smart ward interconnected nursing system, including a distributed module architecture, wherein the distributed module architecture includes a ward management module, a patient monitoring module, an intelligent decision-making module and a big data analysis module. The ward management module is used to automatically schedule ward resources, and the patient monitoring module is used to obtain the patient's routine physical data to determine whether the patient's physical condition meets expectations. The intelligent decision-making module uses the patient's diagnosis and treatment data and the patient's routine physical data to predict the patient's disease progression and generate a nursing plan. The big data analysis module is used to summarize the above data, establish a database and analyze the rules; a number of edge computing nodes are deployed in the ward, which are connected to the distributed module architecture data for real-time processing and preliminary analysis of patient data, and uploading the processed data to a cloud server.
[0009] Preferably, the ward management module integrates the electronic medical record system and the hospital information system, obtains the patient's admission information, diagnosis information, and medical orders in real time, classifies the ward medical equipment according to importance and urgency, classifies the patients admitted to the ward according to importance and urgency, automatically allocates medical equipment according to the patient's diagnosis and treatment data, monitors and alarms the data of the medical equipment, automatically establishes a patient database, integrates the diagnosis and treatment data, patient admission and discharge management, and the formulation of ward facility maintenance plans.
[0010] Preferably, the patient monitoring module monitors the patient's physical condition based on the patient's routine physical data, wherein the routine physical data includes at least one of heart rate, blood pressure, blood oxygen saturation, and body temperature. When the patient's routine physical data is abnormal, a corresponding alarm signal is automatically issued, and the patient's behavioral data is recognized using images, wherein the patient's behavioral data includes at least one of activity trajectory, sleep quality, expression, and sound.
[0011] Preferably, the intelligent decision-making module receives data from the ward management module and the patient monitoring module, including diagnostic information, medical advice, routine physical data and behavioral data, and uses a rule engine combined with a reinforcement learning algorithm to automatically generate corresponding nursing plan tasks and assign them to corresponding caregivers.
[0012] Preferably, the big data analysis module records the data of the ward management module, the patient monitoring module and the intelligent decision-making module, adopts a big data processing framework, mines and predicts trends in massive data, uses a machine learning algorithm to discover potential medical laws and trends, and reversely updates the intelligent decision-making module.
[0013] Preferably, the distributed module architecture further includes a remote consultation module, which is used for remote real-time video calls and data sharing, and realizes online communication of data.
[0014] Preferably, the distributed module architecture also includes a rehabilitation guidance module, which determines the patient's condition and rehabilitation stage based on the patient's examination data, and automatically provides a rehabilitation plan and video tutorial for the corresponding stage.
[0015] Preferably, it also includes edge computing nodes, several of which are deployed in the ward, which are deployed in Docker containers and are connected to the distributed module architecture data to collect and process environmental data, and upload the processed environmental data to the cloud server.
[0016] Preferably, the edge computing node is connected to other IoT devices and big data analysis modules in the ward to collect data from various IoT devices. The patient monitoring module transmits the patient's physiological data to the edge computing node in real time via Bluetooth or Wi-Fi for preliminary processing.
[0017] Preferably, the edge computing node cleans, denoises and converts the format of the collected data, uses a real-time data stream processing framework to perform real-time analysis on the received data, and makes real-time decisions based on the analyzed data.
[0018] Compared with the prior art, the present invention has the following beneficial effects:
[0019] The present invention integrates the ward management module, patient monitoring module, intelligent decision-making module and big data analysis module through a distributed module architecture, realizes data integration and real-time processing of various medical equipment in the ward, breaks the information island, and provides medical staff with comprehensive and accurate patient information.
[0020] The ward management module integrates the electronic medical record system and the hospital information system to achieve automatic hierarchical management of ward medical equipment and patients, as well as automatic allocation and monitoring of medical equipment, thus improving the utilization efficiency of ward resources.
[0021] The patient monitoring module can monitor the patient's routine physical and behavioral data in real time, and automatically send out an alarm signal when the data is abnormal, effectively preventing and promptly responding to possible health problems that may occur in patients.
[0022] The intelligent decision-making module uses a rule engine combined with a reinforcement learning algorithm to automatically generate nursing plan tasks and assign them to corresponding nursing staff, greatly reducing the workload of medical staff and improving nursing efficiency.
[0023] The big data analysis module uses a big data processing framework and machine learning algorithms to mine and predict trends in massive data, discover potential medical laws and trends, and provide a scientific basis for medical decision-making.
[0024] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 is a schematic diagram of a first embodiment of the present invention;
[0026] Figure 2 is a schematic diagram of a second embodiment of the present invention;
[0027] Figure 3 is a schematic diagram of a third embodiment of the present invention. DETAILED DESCRIPTION
[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. It should be pointed out that the drawings are schematic and not drawn to scale. For the sake of clarity and convenience in the figures, the relative sizes and proportions of the parts shown in the figures are exaggerated or reduced in size, and any size is only exemplary and not restrictive.
[0029] Embodiment 1
[0030] See also Figure 1 , a full-scenario smart ward communication nursing system, including:
[0031] Distributed module architecture;
[0032] The distributed module architecture includes ward management module, patient monitoring module, intelligent decision-making module and big data analysis module;
[0033] The ward management module is used to automatically dispatch ward resources. It combines the hospital's electronic medical record system and hospital information system to automatically allocate beds to patients for hospitalization and automatically fill in the corresponding medical record information. According to the patient's condition, it automatically allocates medical equipment in the ward, records the ward bed usage data, arranges hospitalization and discharge by itself, and efficiently turns over bed resources without the need for medical staff to manually allocate beds and fill in data;
[0034] The patient monitoring module is used to obtain the patient's routine physical data and determine whether the patient's physical condition meets expectations. The patient monitoring module monitors the patient's routine physical data in real time through commonly used wearable devices, such as bracelets with multiple sensors, which monitor heart rate, blood oxygen, blood pressure and other data in real time. Other medical devices with IoT functions can also be used to monitor other data. The monitored data is transmitted through a wireless network;
[0035] The intelligent decision-making module uses the patient's diagnosis and treatment data and the patient's routine physical data to predict the patient's disease progression and generate a nursing plan. The patient's diagnosis and treatment data is obtained through other examination facilities outside the ward, such as MRI examinations, blood tests, etc. The intelligent decision-making module judges the patient's condition by integrating the doctor's orders, diagnosis and treatment data and routine physical data. According to the patient's different conditions, it selects different nursing plans in the database and integrates them to generate a complete nursing plan. At the same time, during the execution of the nursing plan, the intelligent decision-making module adjusts the nursing plan in real time and dynamically according to the data updated by the patient monitoring module;
[0036] The big data analysis module is used to summarize the above data, establish a database and analyze the patterns. All data from the ward management module, patient monitoring module and intelligent decision-making module are uploaded to the cloud. The stored data of the entire treatment process is analyzed, and the patterns are derived to predict the patient's data in the future. The nursing plan generated by the intelligent decision-making module is updated. As the data increases, the nursing plan can become more and more accurate.
[0037] Embodiment 2
[0038] See also Figure 2 The ward management module connects the electronic medical record system and the hospital information system to obtain the patient's admission information, diagnosis information, and doctor's orders in real time. It classifies the ward medical equipment according to the importance and urgency, and classifies the importance and urgency of the patients admitted to the ward. It automatically allocates medical equipment according to the patient's diagnosis and treatment data, monitors and alarms the data of medical equipment, automatically establishes a patient database, integrates diagnosis and treatment data, manages patient admission and discharge, and formulates ward facility maintenance plans. Acquisition of patient admission information, diagnosis information, and doctor's orders: The hospital information system (HIS) interface can be used to obtain patient information in real time through API calls. Medical equipment can be classified according to importance and urgency using a weighted scoring method to calculate the priority of each medical device for each patient, and different coefficients are set for each medical device. The formula is established:
[0039] (P i ,E i ,M j ) = argmax j (w P ×P i +w E ×E i )M j
[0040] Let P i is the importance of patient i, E i is the urgency of patient i, M j is the available status of medical device j (1 means available, 0 means unavailable), wP and w E They are the weights of importance and urgency, which need to be adjusted according to the actual situation, and the equipment is automatically allocated to each patient according to the priority;
[0041] The patient monitoring module monitors the patient's physical condition based on the patient's routine physical data, which includes at least one of heart rate, blood pressure, blood oxygen saturation, and body temperature. When the patient's routine physical data is abnormal, a corresponding alarm signal is automatically issued, and the patient's behavioral data is recognized using images, which includes at least one of activity trajectory, sleep quality, expression, and voice.
[0042] Assume D actual is the patient’s actual physical data (such as heart rate, blood pressure, etc.), D threshold is the corresponding alarm threshold, the alarm condition can be expressed as:
[0043]
[0044] Among them, data below the threshold is needed, such as blood pressure, and data above the threshold is needed, such as blood oxygen saturation.
[0045] The intelligent decision-making module receives data from the ward management module and the patient monitoring module, including diagnostic information, doctor's orders, routine physical data and behavioral data, and uses the rule engine combined with the reinforcement learning algorithm to automatically generate the corresponding nursing plan tasks and assign them to the corresponding caregivers; the algorithmic process of generating nursing plans by the rule engine and the reinforcement learning algorithm can be simplified into a decision model based on input data (diagnostic information, doctor's orders, routine physical data and behavioral data) and a rule base. Let R be the rule base and I be the input data set, then the nursing plan P can be expressed as:
[0046] P=RuleEngine(R,I)
[0047] Among them, RuleEngine is a function that generates a nursing plan based on the rule base R and input data I.
[0048] See also Figure 2 The distributed module architecture also includes a remote consultation module, which is used for remote real-time video calls and data sharing, to achieve online data communication, to seek expert support through remote diagnosis and treatment, and to provide online experts with a large amount of data for reference during remote diagnosis and treatment.
[0049] See also Figure 2,The distributed module architecture also includes a rehabilitation guidance module, which ,judges the patient’s condition and rehabilitation stage based on the patient’s examination data, and ,automatically provides rehabilitation plans and video tutorials for the corresponding stage. ,Since the intelligent decision making module has generated the ,corresponding nursing plan, the nursing staff will carry out the nursing ,plan, which is then generated based on the nursing plan, and the rehabilitation plan will ,be carried out by the patient.
[0050] Embodiment 3
[0051] See also Figure 3 ,The big data analysis module records the data of the ward management module, the patient monitoring module and the intelligent decision making module, adopts a big data processing framework, mines and predicts the trend of the massive data, uses machine learning algorithms to discover potential medical rules and trends, and reversely updates the intelligent decision making module;
[0052] The algorithm of the big data analysis module is simplified into a prediction formula based on historical data and prediction model. Let H be the historical data set and M be the prediction model. The prediction result F can be expressed as:
[0053] F=M(H)
[0054] Among them, M is a machine learning model that generates prediction results based on historical data H.
[0055] See also Figure 3 ,It also includes edge computing nodes, several of which are deployed in the ward, using Docker containerization deployment, connected to the distributed module architecture data, used to collect and process environmental data, and upload the processed environmental data to the cloud server.
[0056] See also Figure 3 The edge computing node is connected to other IoT devices and big data analysis modules in the ward. The patient monitoring module transmits the patient's physiological data to the edge computing node in real time via Bluetooth or Wi-Fi for preliminary processing. By setting up edge computing nodes in the ward, the data can be pre-processed locally and some commonly used data can be stored locally, saving bandwidth and calling data faster. The data uploaded to the cloud is pre-processed, which is more streamlined and saves cloud computing time.
[0057] See also Figure 3The edge computing node collects data from various IoT devices, cleans, denoises and converts the collected data, uses a real-time data stream processing framework to analyze the received data in real time, and makes real-time decisions based on the analyzed data. Since the edge computing node can receive patient data from various monitoring devices, the edge computing node can trigger different conditions based on patient data, weather, and other conditions to control the environmental equipment in the ward. For example, if the patient's body temperature drops or the outside weather cools down, the edge computing node can automatically start the air conditioner and other equipment in the ward to maintain the temperature in the ward, such as automatically controlling the lights on and off according to the local sunrise and sunset times.
[0058] The data processing process of edge computing nodes is simplified into a data processing and real-time analysis model.
[0059] Assume Draw is the original data, Dcleaned is the cleaned data, and A is the real-time analysis function. The real-time decision Ddecision can be expressed as:
[0060] Dcleaned=CleanAndFormat(Draw)
[0061] Ddecision=A(Dcleaned).
[0062] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.
[0063] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.
[0064] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
[0065] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.
[0066] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.
[0067] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.
[0068] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A full-scenario smart ward communication nursing system, characterized in that: include: Distributed module architecture; The distributed module architecture includes a ward management module, a patient monitoring module, an intelligent decision-making module and a big data analysis module; The ward management module is used to automatically schedule ward resources; The patient monitoring module is used to obtain the patient's routine physical data and determine whether the patient's physical condition meets expectations; The intelligent decision-making module uses the patient's diagnosis and treatment data and the patient's conventional physical data to predict the patient's disease progression and generate a nursing plan; The big data analysis module is used to summarize the above data, establish a database and analyze the rules.
2. According to claim 1, the full-scenario intelligent ward communication nursing system is characterized by: The ward management module is connected to the electronic medical record system and the hospital information system, obtains the patient's admission information, diagnosis information, and medical orders in real time, classifies the ward medical equipment according to importance and urgency, and classifies the importance and urgency of the patients admitted to the ward, automatically allocates medical equipment according to the patient's diagnosis and treatment data, monitors and alarms the data of the medical equipment, automatically establishes a patient database, integrates the diagnosis and treatment data, patient admission and discharge management, and the formulation of ward facility maintenance plans.
3. According to claim 2, the full-scenario intelligent ward communication nursing system is characterized by: The patient monitoring module monitors the patient's physical condition based on the patient's routine physical data, where the routine physical data includes at least one of heart rate, blood pressure, blood oxygen saturation, and body temperature. When the patient's routine physical data is abnormal, a corresponding alarm signal is automatically issued, and the patient's behavioral data is recognized using images, where the patient's behavioral data includes at least one of activity trajectory, sleep quality, expression, and sound.
4. According to claim 3, the full-scenario intelligent ward communication nursing system is characterized by: The intelligent decision-making module receives data from the ward management module and the patient monitoring module, including diagnostic information, doctor's orders, routine physical data and behavioral data, and uses a rule engine combined with a reinforcement learning algorithm to automatically generate corresponding nursing plan tasks and assign them to corresponding caregivers.
5. According to claim 4, the full-scenario intelligent ward communication nursing system is characterized by: The big data analysis module records the data of the ward management module, the patient monitoring module and the intelligent decision-making module, adopts a big data processing framework to mine and predict trends in massive data, uses a machine learning algorithm to discover potential medical laws and trends, and reversely updates the intelligent decision-making module.
6. The full-scenario intelligent ward communication nursing system according to claim 5 is characterized by: The distributed module architecture also includes a remote consultation module, which is used for remote real-time video calls and data sharing, and realizes online communication of data.
7. The full-scenario intelligent ward communication nursing system according to claim 6 is characterized by: The distributed module architecture also includes a rehabilitation guidance module, which determines the patient's condition and rehabilitation stage based on the patient's examination data and automatically provides a rehabilitation plan and video tutorial for the corresponding stage.
8. The full-scenario intelligent ward communication nursing system according to claim 7 is characterized by: It also includes edge computing nodes, several of which are deployed in the ward and are deployed in Docker containers. They are connected to the distributed module architecture data and are used to collect and process environmental data, and upload the processed environmental data to the cloud server.
9. The full-scenario intelligent ward communication nursing system according to claim 8 is characterized by: The edge computing node is connected to other IoT devices and big data analysis modules in the ward to collect data from various IoT devices. The patient monitoring module transmits the patient's physiological data to the edge computing node in real time via Bluetooth or Wi-Fi for preliminary processing.
10. The full-scenario intelligent ward communication nursing system according to claim 9 is characterized by: The edge computing node cleans, denoises and converts the format of the collected data, uses a real-time data stream processing framework to perform real-time analysis on the received data, and makes real-time decisions based on the analyzed data.