CN116919361A - Electric intelligent control method and system based on Internet of things for realizing negative pressure ward - Google Patents
Electric intelligent control method and system based on Internet of things for realizing negative pressure ward Download PDFInfo
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Abstract
The application is applicable to the technical field of the Internet of things, and provides an electric intelligent control method and system for realizing negative pressure ward based on the Internet of things, wherein the method comprises the following steps: acquiring current vital sign data of a patient; comparing the current vital sign data of the patient with historical diagnostic data; calculating the optimal medical staff closest to the emergency patient and the working field closest to the historical etiology of the emergency patient; an emergency notification is sent to the preferred healthcare worker. Compared with the existing electric intelligent control method, the electric intelligent control method based on the Internet of things for realizing the negative pressure ward has remarkable progress in the aspects of monitoring vital sign data of patients in real time, comprehensively analyzing the change condition of vital signs, optimizing the selection of medical staff, informing the medical staff in real time and the like, and can improve the rescue efficiency and the treatment quality of emergency patients.
Description
Technical Field
The application belongs to the technical field of the Internet of things, and particularly relates to an electric intelligent control method and system for realizing negative pressure ward based on the Internet of things.
Background
The ward electric intelligent control system based on the Internet of things is a system for realizing the intelligent control and management of ward electric equipment by utilizing the Internet of things technology. According to the system, the electrical equipment in the ward is connected to the Internet, and data acquisition, analysis and control are performed through components such as the sensor, the controller and the cloud platform, so that remote monitoring, automatic control and intelligent management of the ward electrical equipment are realized.
In conventional approaches, the selection of medical staff is typically based on a fixed schedule or manual selection, and the field of work of the medical staff and the historic etiology of the patient cannot be fully considered. The method calculates the most suitable medical staff by collecting the position and state information of the medical staff and combining the ward position and the historical etiology of the emergency patient, thereby improving the selection accuracy of the medical staff.
Disclosure of Invention
The embodiment of the application aims to provide an electric intelligent control method based on the Internet of things for realizing negative pressure ward, and aims to solve the technical problems in the prior art determined in the background art.
The embodiment of the application is realized by the method for realizing the electric intelligent control under the negative pressure ward based on the Internet of things, and the method comprises the following steps:
acquiring current vital sign data of a patient, and simultaneously acquiring historical diagnostic data of the patient;
comparing the current vital sign data of the patient with historical diagnostic data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data and the change fluctuation exceeds a safety threshold value, listing the patient as an urgent patient;
collecting the current position and state of the medical staff, simultaneously obtaining the ward position of an emergency patient, and calculating the optimal medical staff closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field;
an emergency notification is sent to a preferred healthcare worker and the healthcare worker is synchronized with the historic diagnostic data and the current vital sign data of the emergency patient.
As a further aspect of the present application, the acquiring current vital sign data of the patient and simultaneously acquiring historical diagnostic data of the patient specifically includes:
collecting vital sign data of a patient monitored by a sensor, wherein the vital sign data comprise, but are not limited to, heart rate, blood pressure and body temperature, and carrying out data transmission through an Internet of things platform;
reading historical diagnostic data bound to the patient, while reading recently updated body data of the patient;
and calculating the change threshold value of each vital sign data of the patient compared with the historical diagnosis data through the historical diagnosis data of the patient and the latest updated body data.
As a further aspect of the present application, the comparing the current vital sign data of the patient with the historical diagnostic data, analyzing the current vital sign change status of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data, the patient is classified as an emergency patient when the change fluctuation exceeds the safety threshold, specifically including:
comparing each item of data in the current vital sign data of the patient with corresponding items in the historical diagnosis data respectively to obtain the change values of each item of data;
comparing the change value of each item of data with the change threshold value of each item of vital sign data compared with the historical diagnosis data, and judging whether the change value exceeds the threshold value;
when the change value exceeds the threshold value, the patient is classified as an emergency patient.
As a further aspect of the present application, the method for acquiring the current position of an on-site medical staff and the state of the on-site medical staff, simultaneously acquiring the ward position of an emergency patient, and calculating the preferred medical staff closest to the emergency patient and having a working field closest to the historical etiology of the emergency patient, specifically includes:
acquiring the position information of each medical staff member in real time, and simultaneously acquiring the state information and the personal information of the medical staff member in work, wherein the state information comprises but is not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
the ward position and the emergency grade of the emergency patient are acquired, an emergency access model is established and used for calculating the on-site medical staff most suitable for the emergency patient through the distance between the on-site medical staff and the ward of the emergency patient, the state of the on-site medical staff and the personal information of the on-site medical staff, and the calculation formula is as follows:
S＝F 1 ×F 2 ×F 3 ×(F A ×ω a +F B ×ω b +…+F X ×ω X )
wherein F1 represents a score of whether idle, and the value is 0 or 1; f2 represents the score of whether to patrol, and the value is 0 or 1; f3 represents a score of whether to process the urgent transaction, and the score is 0 or 1;
F A 、F B 、…、F X the historical etiology correlation degree score of the personal information of the representative medical staff and the emergency patient is valued according to the degree of correlation, and the range is 0-100; omega a 、ω b 、…、ω X Representing the weight of each item of personal information of the medical staff at the time of the emergency patient visit.
As a further aspect of the present application, the sending an emergency notification to a preferred healthcare worker and synchronizing the historical diagnostic data and the current vital sign data of the emergency patient to the healthcare worker specifically includes:
sending an emergency notification to the preferred healthcare personnel and synchronizing the ward location, abnormal vital sign data, and historical diagnostic data of the emergency patient;
and receiving feedback information of the optimal medical staff, and changing the state information of the optimal medical staff into processing emergency matters if the feedback information is the received notification.
As a further aspect of the present application, the system includes:
the data acquisition module is used for acquiring current vital sign data of a patient and simultaneously acquiring historical diagnosis data of the patient;
the data analysis module is used for comparing the current vital sign data of the patient with the historical diagnosis data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnosis data and the change fluctuation exceeds a safety threshold value, listing the patient as an urgent patient;
the medical care recommendation module is used for collecting the current position of the medical staff and the state of the medical staff, simultaneously obtaining the ward position of the emergency patient, and calculating the optimal medical staff which is closest to the emergency patient and the working field closest to the historical etiology of the emergency patient;
an emergency notification module for sending an emergency notification to a preferred healthcare worker and synchronizing historical diagnostic data and current vital sign data of the emergency patient to the healthcare worker.
As a further aspect of the present application, the data acquisition module specifically includes:
the current data acquisition unit is used for acquiring vital sign data of a patient monitored by the sensor, wherein the vital sign data comprise, but are not limited to, heart rate, blood pressure and body temperature, and data transmission is carried out through the internet of things platform;
a history data reading unit for reading history diagnostic data bound to the patient, and simultaneously reading body data newly updated by the patient;
and the threshold value calculating unit is used for calculating the change threshold value of each vital sign data of the patient compared with the historical diagnosis data through the historical diagnosis data of the patient and the latest updated body data.
As a further aspect of the present application, the data analysis module specifically includes:
the change analysis unit is used for comparing each item of data in the current vital sign data of the patient with the corresponding item in the historical diagnosis data respectively to obtain a change value of each item of data;
the data judging unit is used for comparing the change value of each item of data with the change threshold value of each item of vital sign data compared with the historical diagnosis data and judging whether the change value exceeds the threshold value;
an emergency dividing unit for listing the patient as an emergency patient when the change value exceeds the threshold value.
As a further aspect of the present application, the medical care recommendation module specifically includes:
a medical information acquisition unit for acquiring the position information of each medical staff in real time and simultaneously acquiring the state information and personal information of the medical staff in process, wherein the state information comprises but is not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
and the medical care calculating unit is used for acquiring the ward position and the emergency grade of the emergency patient, and establishing an emergency access model for calculating the medical care personnel most suitable for the emergency patient through the distance between the medical care personnel and the ward of the emergency patient, the state of the medical care personnel and the personal information of the medical care personnel.
As a further aspect of the present application, the emergency notification module specifically includes:
an information notification unit for transmitting an emergency notification to a preferred medical staff and synchronizing a ward position, abnormal vital sign data, and history diagnosis data of an emergency patient;
and the state synchronization unit is used for receiving the feedback information of the optimal medical staff, and changing the state information of the optimal medical staff into the state information for processing emergency matters if the feedback information is the received notification.
The embodiment of the application has the beneficial effects that:
the method can acquire the current vital sign data of the patient in real time through the internet of things technology, wherein the current vital sign data comprises indexes such as blood pressure, heart rate, respiratory rate, body temperature and the like. Compared with the traditional manual measurement mode, the method can monitor vital sign data of the patient more accurately and timely;
the current vital sign data of the patient is compared with the historical diagnostic data and the patient's vital sign change condition is analyzed. By setting the safety threshold, a patient is classified as an emergency patient when the vital sign data of the patient changes beyond the safety threshold. The comprehensive analysis mode can evaluate the condition of the patient more comprehensively, and reduce the risks of misjudgment and missed diagnosis;
by acquiring the current position of the medical staff and the state of the medical staff, the ward position of the emergency patient is acquired at the same time, and the optimal medical staff closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field is calculated. Therefore, the emergency patient can be ensured to be concerned and treated by the most suitable medical staff, and the rescue efficiency and the treatment quality are improved.
Drawings
Fig. 1 is a flowchart of an electric intelligent control method based on the internet of things for realizing a negative pressure ward according to an embodiment of the present application;
FIG. 2 is a flow chart for acquiring current vital sign data of a patient and simultaneously acquiring historical diagnostic data of the patient according to an embodiment of the present application;
FIG. 3 is a flowchart for comparing current vital sign data of a patient with historical diagnostic data, analyzing a change condition of the current vital sign of the patient, and listing the patient as an emergency patient when the current vital sign data of the patient has a change fluctuation exceeding a safety threshold compared with the historical diagnostic data, according to the embodiment of the present application;
FIG. 4 is a flowchart of an embodiment of the present application for acquiring the current location of an on-site medical staff and the status of the on-site medical staff, simultaneously acquiring the ward location of an emergency patient, and calculating the preferred medical staff closest to the emergency patient and having a working area closest to the historic etiology of the emergency patient;
FIG. 5 is a flow chart providing an embodiment of the present application for sending an emergency notification to a preferred healthcare worker and synchronizing the historical diagnostic data and current vital sign data of the emergency patient to the healthcare worker;
fig. 6 is a structural block diagram of an electric intelligent control system based on the internet of things for realizing a negative pressure ward according to an embodiment of the present application;
FIG. 7 is a block diagram illustrating a data acquisition module according to an embodiment of the present application;
FIG. 8 is a block diagram of a data analysis module according to an embodiment of the present application;
FIG. 9 is a block diagram of a medical recommendation module according to an embodiment of the present application;
fig. 10 is a block diagram of an emergency notification module according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of this disclosure.
Fig. 1 is a flowchart of an electrical intelligent control method for implementing a negative pressure ward based on the internet of things, according to an embodiment of the present application, as shown in fig. 1, the method includes:
s100, acquiring current vital sign data of a patient, and simultaneously acquiring historical diagnosis data of the patient;
in this step, current vital sign data and historical diagnostic data of the patient are acquired by the device and recorded and archived, which may include heart rate, blood pressure, respiratory rate, body temperature, etc. The correct use of the device and the accuracy of the data need to be ensured during the acquisition process. The data can provide important reference information for medical staff, help the medical staff to make accurate diagnosis and treatment decisions, and improve the nursing quality and safety of patients. At the same time, the accuracy and integrity of the data is also very important, and it is necessary to ensure the accuracy and normalization in the acquisition and extraction process.
S200, comparing the current vital sign data of the patient with historical diagnostic data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data and the change fluctuation exceeds a safety threshold value, listing the patient as an urgent patient;
in the step, the abnormal condition can be found in time by comparing the current vital sign data of the patient with the historical diagnosis data, and the abnormal change of the vital sign of the patient can be found in time. These abnormal changes may be a sign of patient exacerbation or complications that require timely intervention and treatment. This will help to improve the rescue efficiency and the quality of treatment for emergency patients, to ensure the life safety and health of the patient, and to list the patient as an emergency patient when the current vital sign data of the patient fluctuates by more than a safety threshold compared to the historical diagnostic data. Therefore, medical staff can be helped to recognize emergency patients more quickly, rescue and treatment are carried out preferentially, and the rescue efficiency and treatment quality of the emergency patients are improved.
S300, acquiring the current position of the medical staff and the state of the medical staff, simultaneously acquiring the ward position of an emergency patient, and calculating the optimal medical staff closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field;
in this step, the current working state of the medical staff can be known by acquiring the state information of the medical staff, such as whether the staff is idle, patrol and process emergency transactions. This helps to determine which medical personnel can immediately respond to the emergency; by acquiring personal information of medical staff, such as medical treatment history, primary responsibility for departments, and adequacy, the professional background and ability of the staff can be known. This helps to match the medical personnel most suitable for treating emergency patients; and establishing an emergency access model, and selecting the optimal medical personnel closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field according to the calculation result of the emergency access model, so that the emergency patient rescue efficiency and the treatment quality are improved, the emergency patient can be subjected to symptomatic drug delivery, and the emergency problem treatment efficiency is improved.
S400, sending an emergency notification to a preferred medical staff and synchronizing historical diagnosis data and current vital sign data of the emergency patient to the medical staff.
In this step, a communication system (e.g., a cell phone message, a calling system, etc.) is used to send an emergency notification to the preferred healthcare worker informing that an emergency patient needs emergency treatment. And the historical diagnostic data of the emergency patient is extracted from the electronic medical record system or other related system and synchronized to the preferred healthcare worker. The historical diagnostic data may include information about the patient's medical history, past treatment regimens, surgical records, and the like. Current vital sign data, such as heart rate, blood pressure, respiratory rate, etc., of the emergency patient is obtained from a monitoring device or other vital sign monitoring system and then synchronized to a preferred healthcare worker. These data may help healthcare workers to better understand the current condition of the patient. Preferably, upon receipt of the emergency notification and patient data, the healthcare worker needs to confirm receipt and understand the condition of the patient. They can confirm the reception by replying to the notification, looking up the data, etc., and after the reception, the state of the medical staff is changed to process the emergency transaction, and the emergency transaction is excluded from other real objects.
Fig. 2 is a flowchart for acquiring current vital sign data of a patient and simultaneously acquiring historical diagnostic data of the patient according to an embodiment of the present application, as shown in fig. 2, where the acquiring current vital sign data of the patient and simultaneously acquiring the historical diagnostic data of the patient specifically includes:
s110, collecting vital sign data of a patient monitored by a sensor, wherein the vital sign data comprise, but are not limited to, heart rate, blood pressure and body temperature, and data transmission is carried out through an Internet of things platform;
in this step, vital sign data of the patient, such as heart rate, blood pressure, body temperature, etc., is acquired using a sensor or monitoring device. The devices can be directly connected with a patient or can be used for data acquisition through a wireless sensor network, and wireless communication technology such as Wi-Fi, bluetooth or Zigbee can be used for transmitting data to a designated data receiving end during data transmission.
S120, reading historical diagnosis data bound with the patient, and simultaneously reading recently updated body data of the patient;
s130, calculating a change threshold value of each vital sign data of the patient compared with the historical diagnosis data through the historical diagnosis data of the patient and the latest updated body data.
In the step, by comparing the latest body data with the historical diagnosis data, the change threshold value of each vital sign data can be calculated, and whether the current vital sign of the patient is abnormal or changed can be judged according to the calculated change threshold value. If a vital sign exceeds a threshold, it can be considered that an abnormal change occurs in the vital sign, which requires attention from medical personnel.
Fig. 3 is a flowchart of comparing current vital sign data of the patient with historical diagnostic data, analyzing a current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data and the change fluctuation exceeds a safety threshold, listing the patient as an emergency patient, as shown in fig. 3, wherein the comparing the current vital sign data of the patient with the historical diagnostic data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data and the change fluctuation exceeds the safety threshold, listing the patient as an emergency patient, specifically including:
s210, comparing each item of data in the current vital sign data of the patient with the corresponding item in the historical diagnosis data respectively to obtain the change value of each item of data;
s220, comparing the change value of each item of data with a change threshold value of each item of vital sign data compared with historical diagnosis data, and judging whether the change value exceeds the threshold value;
and S230, when the change value exceeds the threshold value, the patient is listed as an urgent patient.
Fig. 4 is a flowchart of acquiring the current position of an on-site medical staff and the state of an on-site medical staff, acquiring the ward position of an emergency patient, and calculating the preferred medical staff closest to the emergency patient and having the working field closest to the historical etiology of the emergency patient, according to the embodiment of the present application, as shown in fig. 4, the acquiring the current position of an on-site medical staff and the state of an on-site medical staff, acquiring the ward position of an emergency patient, and calculating the preferred medical staff closest to the emergency patient and having the working field closest to the historical etiology of the emergency patient, specifically including:
s310, acquiring the position information of each medical staff member in real time, and simultaneously acquiring the state information and the personal information of the medical staff member, wherein the state information comprises but is not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
s320, acquiring ward positions and emergency grades of emergency patients, and establishing an emergency access model, wherein the emergency access model is used for calculating the on-site medical staff most suitable for the emergency patients through the distance between the on-site medical staff and the ward of the emergency patients, the state of the on-site medical staff and the personal information of the on-site medical staff, and the calculation formula is as follows:
S＝F 1 ×F 2 ×F 3 ×(F A ×ω a +F B ×ω b +…+F X ×ω X )
wherein F1 represents a score of whether idle, and the value is 0 or 1; f2 represents the score of whether to patrol, and the value is 0 or 1; f3 represents a score of whether to process the urgent transaction, and the score is 0 or 1;
F A 、F B 、…、F X the historical etiology correlation degree score of the personal information of the representative medical staff and the emergency patient is valued according to the degree of correlation, and the range is 0-100; omega a 、ω b 、…、ω X Representing the weight of each item of personal information of the medical staff at the time of the emergency patient visit.
Fig. 5 is a flowchart of sending an emergency notification to a preferred healthcare worker and synchronizing the historical diagnostic data and the current vital sign data of the emergency patient to the healthcare worker according to an embodiment of the present application, as shown in fig. 5, where the sending the emergency notification to the preferred healthcare worker and synchronizing the historical diagnostic data and the current vital sign data of the emergency patient to the healthcare worker specifically includes:
s410, sending emergency notification to the preferable medical personnel and synchronizing ward position, abnormal vital sign data and historical diagnostic data of the emergency patient;
s420, receiving feedback information of the preferred medical staff, and changing the state information of the preferred medical staff into processing emergency matters if the feedback information is the received notification.
Fig. 6 is a block diagram of a structure of an electrical intelligent control system for implementing a negative pressure ward based on the internet of things, according to an embodiment of the present application, as shown in fig. 6, the system includes:
a data acquisition module 100 for acquiring current vital sign data of a patient, and simultaneously acquiring historical diagnostic data of the patient;
a data analysis module 200, configured to compare the current vital sign data of the patient with the historical diagnostic data, analyze the current vital sign change status of the patient, and list the patient as an emergency patient when the current vital sign data of the patient has a change fluctuation exceeding a safety threshold compared with the historical diagnostic data;
the medical care recommendation module 300 is used for collecting the current position of the medical staff and the state of the medical staff, simultaneously obtaining the ward position of the emergency patient, and calculating the optimal medical staff closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field;
an emergency notification module 400 for sending an emergency notification to a preferred healthcare worker and synchronizing the historical diagnostic data and current vital sign data of the emergency patient to the healthcare worker.
Fig. 7 is a block diagram of a data acquisition module according to an embodiment of the present application, as shown in fig. 7, where the data acquisition module specifically includes:
the current data acquisition unit 110 is configured to acquire vital sign data of the patient monitored by the sensor, where the vital sign data includes, but is not limited to, heart rate, blood pressure, and body temperature, and perform data transmission through the internet of things platform;
a history data reading unit 120 for reading history diagnostic data bound to the patient, and simultaneously reading body data newly updated by the patient;
a threshold calculating unit 130 for calculating a change threshold of each vital sign data of the patient compared to the historical diagnosis data from the historical diagnosis data of the patient and the latest updated body data.
Fig. 8 is a block diagram of a data analysis module according to an embodiment of the present application, as shown in fig. 8, where the data analysis module specifically includes:
the change analysis unit 210 is configured to compare each item of data in the current vital sign data of the patient with a corresponding item in the historical diagnostic data, respectively, to obtain a change value of each item of data;
the data judging unit 220 is configured to compare the change value of each item of data with a change threshold value of each item of vital sign data compared with the historical diagnostic data, and judge whether the change value exceeds the threshold value;
an emergency dividing unit 230 for listing the patient as an emergency patient when the change value exceeds the threshold value.
Fig. 9 is a block diagram of a medical care recommendation module according to an embodiment of the present application, as shown in fig. 9, where the medical care recommendation module specifically includes:
a medical information acquiring unit 310 for acquiring the position information of each medical staff member in real time, and simultaneously acquiring the status information and personal information of the medical staff member, the status information including but not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
a medical care calculating unit 320 for acquiring ward position and emergency level of the emergency patient and establishing an emergency access model for calculating an on-site medical care person most suitable for the emergency patient from the distance between the on-site medical care person and the ward of the emergency patient, the state of the on-site medical care person, and the personal information of the on-site medical care person.
Fig. 10 is a block diagram of an emergency notification module according to an embodiment of the present application, and as shown in fig. 10, the emergency notification module specifically includes:
an information notification unit 410 for transmitting an emergency notification to a preferred medical staff and synchronizing a ward position of an emergency patient, abnormal vital sign data, and history diagnostic data;
the status synchronization unit 420 is configured to receive feedback information of the preferred medical staff, and if the feedback information is a notification, change the status information of the preferred medical staff into processing an emergency transaction.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (10)
1. The method for realizing the electric intelligent control under the negative pressure ward based on the Internet of things is characterized by comprising the following steps:
acquiring current vital sign data of a patient, and simultaneously acquiring historical diagnostic data of the patient;
comparing the current vital sign data of the patient with historical diagnostic data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data and the change fluctuation exceeds a safety threshold value, listing the patient as an urgent patient;
collecting the current position and state of the medical staff, simultaneously obtaining the ward position of an emergency patient, and calculating the optimal medical staff closest to the emergency patient and closest to the historical etiology of the emergency patient in the working field;
an emergency notification is sent to a preferred healthcare worker and the healthcare worker is synchronized with the historic diagnostic data and the current vital sign data of the emergency patient.
2. The method according to claim 1, wherein the acquiring current vital sign data of the patient and simultaneously acquiring the patient historical diagnostic data comprises:
collecting vital sign data of a patient monitored by a sensor, wherein the vital sign data comprise, but are not limited to, heart rate, blood pressure and body temperature, and carrying out data transmission through an Internet of things platform;
reading historical diagnostic data bound to the patient, while reading recently updated body data of the patient;
and calculating the change threshold value of each vital sign data of the patient compared with the historical diagnosis data through the historical diagnosis data of the patient and the latest updated body data.
3. The method according to claim 2, wherein the comparing the current vital sign data of the patient with the historical diagnostic data, analyzing the current vital sign change status of the patient, and when the current vital sign data of the patient is compared with the historical diagnostic data, the patient is classified as an emergency patient when the change fluctuation exceeds a safety threshold, comprising in particular:
comparing each item of data in the current vital sign data of the patient with corresponding items in the historical diagnosis data respectively to obtain the change values of each item of data;
comparing the change value of each item of data with the change threshold value of each item of vital sign data compared with the historical diagnosis data, and judging whether the change value exceeds the threshold value;
when the change value exceeds the threshold value, the patient is classified as an emergency patient.
4. The method according to claim 1, wherein the step of acquiring the current medical staff position and the current medical staff state, and acquiring the ward position of the emergency patient, and calculating the preferred medical staff closest to the emergency patient and closest to the historic etiology of the emergency patient in the working area, specifically comprises:
acquiring the position information of each medical staff member in real time, and simultaneously acquiring the state information and the personal information of the medical staff member in work, wherein the state information comprises but is not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
the ward position and the emergency grade of the emergency patient are acquired, an emergency access model is established and used for calculating the on-site medical staff most suitable for the emergency patient through the distance between the on-site medical staff and the ward of the emergency patient, the state of the on-site medical staff and the personal information of the on-site medical staff, and the calculation formula is as follows:
S＝F 1 ×F 2 ×F 3 ×(F A ×ω a +F B ×ω b +...+F X ×ω X )
wherein F1 represents a score of whether idle, and the value is 0 or 1; f2 represents the score of whether to patrol, and the value is 0 or 1; f3 represents a score of whether to process the urgent transaction, and the score is 0 or 1;
F A 、F B 、...、F X the historical etiology correlation degree score of the personal information of the representative medical staff and the emergency patient is valued according to the degree of correlation, and the range is 0-100; omega a 、ω b 、...、ω X Representing the weight of each item of personal information of the medical staff at the time of the emergency patient visit.
5. The method according to claim 4, wherein said sending an emergency notification to a preferred healthcare worker and synchronizing the historical diagnostic data and current vital sign data of the emergency patient to the healthcare worker, in particular comprises:
sending an emergency notification to the preferred healthcare personnel and synchronizing the ward location, abnormal vital sign data, and historical diagnostic data of the emergency patient;
and receiving feedback information of the optimal medical staff, and changing the state information of the optimal medical staff into processing emergency matters if the feedback information is the received notification.
6. Realize electric intelligent control system under negative pressure ward based on thing networking, its characterized in that, the system includes:
the data acquisition module is used for acquiring current vital sign data of a patient and simultaneously acquiring historical diagnosis data of the patient;
the data analysis module is used for comparing the current vital sign data of the patient with the historical diagnosis data, analyzing the current vital sign change condition of the patient, and when the current vital sign data of the patient is compared with the historical diagnosis data and the change fluctuation exceeds a safety threshold value, listing the patient as an urgent patient;
the medical care recommendation module is used for collecting the current position of the medical staff and the state of the medical staff, simultaneously obtaining the ward position of the emergency patient, and calculating the optimal medical staff which is closest to the emergency patient and the working field closest to the historical etiology of the emergency patient;
an emergency notification module for sending an emergency notification to a preferred healthcare worker and synchronizing historical diagnostic data and current vital sign data of the emergency patient to the healthcare worker.
7. The system of claim 6, wherein the data acquisition module specifically comprises:
the current data acquisition unit is used for acquiring vital sign data of a patient monitored by the sensor, wherein the vital sign data comprise, but are not limited to, heart rate, blood pressure and body temperature, and data transmission is carried out through the internet of things platform;
a history data reading unit for reading history diagnostic data bound to the patient, and simultaneously reading body data newly updated by the patient;
and the threshold value calculating unit is used for calculating the change threshold value of each vital sign data of the patient compared with the historical diagnosis data through the historical diagnosis data of the patient and the latest updated body data.
8. The system according to claim 7, wherein the data analysis module specifically comprises:
the change analysis unit is used for comparing each item of data in the current vital sign data of the patient with the corresponding item in the historical diagnosis data respectively to obtain a change value of each item of data;
the data judging unit is used for comparing the change value of each item of data with the change threshold value of each item of vital sign data compared with the historical diagnosis data and judging whether the change value exceeds the threshold value;
an emergency dividing unit for listing the patient as an emergency patient when the change value exceeds the threshold value.
9. The system of claim 6, wherein the healthcare recommendation module specifically comprises:
a medical information acquisition unit for acquiring the position information of each medical staff in real time and simultaneously acquiring the state information and personal information of the medical staff in process, wherein the state information comprises but is not limited to: whether to idle, whether to patrol, whether to process emergency transactions; the personal information includes, but is not limited to: medical treatment experience, mainly responsible for medical departments, conditions of tampering;
and the medical care calculating unit is used for acquiring the ward position and the emergency grade of the emergency patient, and establishing an emergency access model for calculating the medical care personnel most suitable for the emergency patient through the distance between the medical care personnel and the ward of the emergency patient, the state of the medical care personnel and the personal information of the medical care personnel.
10. The system according to claim 9, characterized in that said emergency notification module comprises in particular:
an information notification unit for transmitting an emergency notification to a preferred medical staff and synchronizing a ward position, abnormal vital sign data, and history diagnosis data of an emergency patient;
and the state synchronization unit is used for receiving the feedback information of the optimal medical staff, and changing the state information of the optimal medical staff into the state information for processing emergency matters if the feedback information is the received notification.
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