CN116825337A - Patient safety nursing early warning system - Google Patents
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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Abstract
The application provides a patient safety nursing early warning system, which comprises: the system comprises physical sign acquisition equipment, an Internet of things platform, a data center, a database, an AI early warning rule engine and a medical client. The internet of things platform receives vital sign data sent by the vital sign acquisition equipment in real time and sends the vital sign data to the data center in real time. The data center is used for synchronizing vital sign data of a patient in real time and sending the vital sign data to the database. The AI early warning rule engine intelligently gathers and analyzes patient vital sign data acquired in real time according to an early warning rule data model constructed by clinical index rules, pushes early warning information containing risk classification assessment results to a medical client, and displays the early warning information in a visual form. Aiming at the comprehensive upgrade and intervention of medical workers on the patient nursing early warning, a patient nursing safety early warning database is comprehensively established, and the risk level of the patient is analyzed, screened and early warned in real time.
Description
Technical Field
The application relates to a medical intelligent management system, in particular to a patient safety nursing early warning system.
Background
The hospital information management system in the prior art has the following problems: (1) The existing medical data are distributed in all levels of systems, and related data are collected by an HIS system, an LIS system, an RIS system, a CDR clinical data center, mobile medical treatment and the like, and the current level and state of a current patient are manually judged through various forms. (2) The existing data center is too scattered, so that no judgment rule exists, and unified judgment standards for real data of patients are absent. The medical information is too scattered, no early warning is provided, the early warning standard is not uniform, and the medical staff can grasp the current state of the patient.
Disclosure of Invention
To overcome the technical drawbacks described above, a first aspect of the present application provides a patient safety care early warning system, comprising:
the vital sign acquisition equipment is used for acquiring vital sign data of a patient in real time;
the system comprises an Internet of things platform, a data center and a data center, wherein the Internet of things platform is used for receiving vital sign data sent by a vital sign acquisition device in real time and sending the vital sign data to the data center in real time;
the data center is used for synchronizing vital sign data of a patient in real time and sending the vital sign data to the database; the real-time synchronization is performed according to the early warning information change in the database;
the database is used for enabling the AI early warning rule engine to call vital sign data of a patient in real time; the method is also used for sending the received early warning information to a data center;
the AI early warning rule engine comprises a preset trained data standard identification model, an evaluation form system and an artificial intelligent control module, wherein the artificial intelligent control module carries out intelligent summarization and analysis on vital sign data of a patient acquired in real time according to an early warning rule data model constructed by clinical index rules and pushes early warning information containing risk grading evaluation results to a medical client; the early warning information is also used for sending the early warning information to a database;
and the medical client is used for receiving the early warning information pushed by the AI early warning rule engine and displaying the early warning information in a visual form.
Further, the internet of things platform is used for carrying out centralized management on all internet of things wearable devices established in the prior or future in hospitals.
Furthermore, the AI early warning rule engine is used for combining basic information, vital signs, inspection indexes, nursing evaluation results, operation conditions, inspection reports, current medical history, past medical history, allergy history and family history of the patient to form early warning rules.
Further, an evaluation form system is incorporated in advance and used for inputting data which cannot be provided by the current medical system of the hospital according to the requirements of the early warning rules, and various nursing evaluation and generation of a nursing record list are completed in a flexible and configurable mode; the risk classification refers to classifying the risk of the existing patient according to the early warning rule, and adjusting the priority of early warning message pushing according to the classification.
Further, the early warning rule establishes a linkage rule of vital signs and early warning evaluation, and when the vital signs reach a field value, the evaluation period is evaluated or adjusted, and a dynamic intelligent data center is formed along with the continuous establishment of a data model.
Further, the algorithm of the early warning rule data model comprises the following steps:
step S1: grouping the detection item data of the patients according to the unique identification information of the patients, and executing two early warning rules by each patient simultaneously: unidirectional detection early warning rules and multiple detection early warning rules;
step S2: comparing the name of the detection item in the detection data of the patient with the name of the detection item in the early warning rule corresponding to the detection item and the detection result respectively, and if abnormality exists, storing the abnormality information into an early warning information table;
step S3: pushing the generated early warning information to a medical client by using a timing task, and setting a pushing identification to be 1;
step S4: when the patient detects and reaches the early warning condition again, the early warning content in the early warning information is updated to be the latest early warning content, whether the pushing mark is set to be 0 or not, and the timing task is waited to execute pushing again.
Further, the data center further comprises a plurality of data interfaces, the data interfaces are used for collecting data from the HIS system, the LIS system, the RIS system, the CDR clinical data center and the mobile medical equipment, and the patient safety nursing early warning system adopts a layered structure design, so that the performance of the existing system is not influenced, and the data safety problem is not generated.
Further, the data center is used for intensively acquiring, analyzing and pushing early warning information and content to other systems of the subscribed hospitals by adopting a message queue technology in real time.
Further, the database comprises complication early warning related data, safety early warning related data and vital sign early warning related data.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
1. medical care intelligent early intervention based on real data: patient care early warning data pushed by an AI early warning rule engine are visually displayed through an intelligent screen of a medical client, and manual intervention and feedback are conveniently and timely carried out by medical workers through display forms such as reminding, list, charts and the like, so that advanced intervention management of patient compliance is realized, and an environment foundation for effective interaction between the patient and the medical workers is established.
2. Establishing a data center with a real medical care scene early warning model: the existing data center is too scattered, so that no judgment rule exists, and unified judgment standards for real data of patients are absent. The application comprehensively establishes a patient care safety early warning database aiming at the comprehensive upgrading and intervention of the nursing early warning of the patient by the medical staff, and analyzes, screens and early warns the risk level of the patient in real time. The platform collects rule templates of different pathologies in the field through each department, and brings in the judgment standard, so that automatic analysis and pushing of the AI early warning rule engine are realized, and the early warning intervention efficiency and accuracy are greatly improved. The rule engine is a core component of the whole platform, and mainly combines a plurality of medical related indexes such as basic information, vital signs, inspection indexes, nursing evaluation results, operation conditions, inspection reports, current medical history, past medical history, allergy history, family history and the like of a patient to form an early warning rule.
3. And (3) interconnection of everything: the system realizes the access and information collection of the intelligent sign acquisition equipment by means of an independently developed internet of things platform. The IOT platform is responsible for centralized management of the existing or future-established and used internet-of-things wearable equipment in a hospital, and the running states of the equipment are conveniently known through equipment registration, such as: electric quantity, on/off, data communication, positioning, real-time data acquisition and the like.
4. By the construction of the application, the problems of too scattered medical information, no early warning, non-uniform early warning standard and the like are solved, and medical staff can grasp the current state of a patient.
5. Aiming at the comprehensive upgrade and intervention of medical workers on the patient nursing early warning, a patient nursing safety early warning database is comprehensively established, and the risk level of the patient is analyzed, screened and early warned in real time.
Drawings
FIG. 1 is a block diagram of a patient safety care early warning system according to an embodiment of the present application;
FIG. 2 is a diagram illustrating an example engine rule structure according to an embodiment of the present application;
FIG. 3 is a flowchart of an AI early warning rule engine algorithm implemented in the present application;
FIG. 4 is an example of criteria for risk stratification and related clinical indicators of incision bleeding in complications;
FIG. 5 is an example of criteria for risk stratification of clinical indicators related to incision infection and pulmonary embolism among complications;
fig. 6 is a diagram of an example of a visual presentation of a smart large screen of a healthcare client.
Detailed Description
Advantages of the application are further illustrated in the following description, taken in conjunction with the accompanying drawings and detailed description. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and that this application is not limited to the details given herein.
As shown in fig. 1, the patient safety nursing early warning system of the embodiment includes a sign acquisition device, an internet of things platform, a data center, a database of an early warning platform, an AI early warning rule engine and a medical care client.
The patient safety nursing and early warning system is adopted for carrying out the flow of patient safety nursing and early warning as follows:
1. the vital sign acquisition equipment is used for acquiring vital sign data of a patient in real time.
Illustratively, the vital sign acquisition device includes, but is not limited to, an intelligent mattress, an intelligent continuous blood glucose monitoring device, an intelligent multi-vital monitoring device, an intelligent continuous body temperature monitoring device, an intelligent continuous electrocardiographic monitoring device, and the like.
And 2, the Internet of things platform is used for receiving vital sign data sent by the vital sign acquisition equipment in real time and sending the vital sign data to the data center in real time.
And the intelligent sign acquisition interface is associated with various compatible sign acquisition devices to acquire data, and the data is pushed to a database of a patient early warning platform. The internet of things platform is used for carrying out centralized management on all internet of things wearable devices established in the prior or future of a hospital. The operation state of each device can be conveniently known through device registration, such as: electric quantity, on/off, data communication, positioning, real-time data acquisition and the like.
And 3, the data center is used for synchronizing vital sign data of the patient in real time and sending the vital sign data to the database.
The data center comprises a plurality of data interfaces, and not only can data be directly acquired from the internet of things platform, but also can data be acquired from an HIS system, an LIS system, an RIS system, a CDR clinical data center, mobile medical equipment and the like. And the patient safety nursing early warning system adopts a layered structure design, so that the influence on the performance of the existing system is avoided, and the data safety problem is avoided.
Optionally, the internet of things platform may also be directly connected with the HIS system, and data collected from all the internet of things wearable devices is sent to the HIS system first.
The data center acquires, analyzes and adopts a message queue technology to push early warning information and content to other systems of the subscribed information in real time, so that the medical care worker can take effective measures for processing in the first time.
Information summarization, analysis and upgrading are realized through information bidirectional interaction among multiple hospital areas and multiple disease areas of the data center.
And 4, the database is used for enabling the AI early warning rule engine to call vital sign data of the patient in real time.
The database comprises complication early warning related data, safety early warning related data and vital sign early warning related data.
Illustratively, the present application first performs a test for an inpatient, with test data being generated in the HIS system. And the early warning platform periodically goes to the HIS system to acquire inpatient detection data and stores the inpatient detection data into the early warning platform. After the detection data of the inpatients are obtained by the early warning platform, an early warning rule analysis task is executed.
5. The AI early warning rule engine comprises a preset trained data standard identification model, an evaluation form system and an artificial intelligent control module, wherein the artificial intelligent control module carries out intelligent summarization and analysis on vital sign data of a patient acquired in real time according to an early warning rule data model constructed by clinical index rules and pushes early warning information containing risk grading evaluation results to a medical client.
The AI early warning rule engine comprises a preset trained data standard identification model (which is realized by adopting the conventional technology in the field), an evaluation form system (which is realized by adopting the conventional technology in the field) and an artificial intelligent control module, wherein the artificial intelligent control module carries out intelligent summarization and analysis on the vital sign data of the patient acquired in real time according to an early warning rule data model constructed by clinical index rules and pushes early warning information containing risk classification evaluation results to a medical client. The AI early warning rule engine models nursing data according to clinical associated indexes of complications of patients, incorporates a data identification system in advance, pushes data which can only be summarized and analyzed and result data according to real-time data, pushes the data to medical staff, and reminds the medical staff to perform manual intervention.
Optionally, the AI early warning rule engine may also push early warning information including the risk classification evaluation result to the patient client.
In addition, the AI early warning rule engine also sends early warning information to the database. And the database sends the received early warning information to the data center. The data center also performs real-time synchronization according to the early warning information change in the database, and can push the early warning information to other systems of the hospital in a message queue mode.
The AI early warning rule engine is mainly used for combining a plurality of medical related indexes such as basic information, vital signs, inspection indexes, nursing evaluation results, operation conditions, inspection reports, current medical history, past medical history, allergy history, family history and the like of patients to form early warning rules.
The system is incorporated into an evaluation form system in advance, and is a system for inputting data which cannot be provided by the current medical system of the hospital according to the requirement of the early warning rule. And completing various nursing evaluation and generation of nursing record sheets in a flexible and configurable mode. The risk classification refers to classifying the risk of the existing patient according to the early warning rule, and adjusting the priority of message pushing and early warning according to the classification.
The early warning rule establishes a linkage rule of vital signs and early warning evaluation, and when the vital signs reach abnormal values, the evaluation period is sent out or adjusted, and a dynamic intelligent data center is formed along with the continuous establishment of a data model, so that a key positive pre-action is played for medical intervention.
Illustratively, the present implementation first uses a message queuing technique to periodically obtain in-patient test data from the HIS system. Because the HIS system has huge detection data, the JAVA multithreading technology is used for improving the detection data synchronization efficiency. The detection data of inpatients are of a plurality of types, and the detection data of the inpatients are grouped by using a caching technology. After detecting the data packet, the patient performs data verification according to the detection item rule, and data comparison is performed on the single rule and the multiple rules simultaneously by using a cache+SQL technology. If the pre-warning rule condition is met, the abnormal data of the inpatient detection is stored in the patient pre-warning information pool. Mainly comprises the following steps: patient name, patient hospitalization number, patient room, patient bed, early warning detection item, early warning notification content, whether pushed or not. Message pushing is carried out on early warning patient information by using a message queue and WebService technology. And actively displaying early warning information after the large screen receives the message, and checking the history early warning pushing record.
The push message body is Json format data:
{
"DeptCode":"835",
"MessageCategory":2,
"MessagePriority":2,
"ContentMessageModel":{
"MessageTime":"2022-12-12 00:00:00.000",
"MessageContent" 85 bed: patient name patient incision bleeding detects abnormalities. ",
"MessageType":0,
"MessageFrom":0,
"FontSize":0,
"Alignment":””,
“BackGroundColor”:”#CC58C5”
}
}
in order to more clearly illustrate the technical solutions in the practice of the present application, the drawings that are used in the description of the practice will be briefly described below. The following describes the embodiments of the present application further with reference to the drawings, in which:
the first step of the application is to detect in-patient and the detection data is generated in the HIS system. And the early warning platform periodically goes to the HIS system to acquire inpatient detection data and stores the inpatient detection data into the early warning platform. After the detection data of the inpatients are obtained by the early warning platform, an early warning rule analysis task is executed.
Fig. 2 shows the structure of the warning rule. First, the detection item table defines the number CheckNo and the detection item name CheckName of the detection item. The detection item table defines the number ItemNo of the detection item, the detection item name ItemName and the detection item type ItemType. The detection item rule table defines the Condition of the detection item, the standard Result of the detection item, the AdviseResult of the detection item, and whether the unidirectional detection item is only. The detection item-detection item table defines detection items corresponding to the detection items.
FIG. 3 shows an algorithm flow of the artificial intelligence control module, which is described as follows:
first, the test item data of inpatients is grouped because there are a plurality of test items for each patient. Such as: heart rate, blood pressure, respiration, pulse, systolic pressure, diastolic pressure, etc. Based on patient unique information: patient hospitalization numbers are grouped and then 2 rules are simultaneously executed for each patient: unidirectional detection early warning rules and multiple detection early warning rules. Fig. 4-5 are exemplary criteria for clinical indicators and risk stratification associated with incision bleeding, incision infection and pulmonary embolism among complications.
Patient test data: the detection item names and the detection results are compared with the detection item names and the detection item rules in the detection item rules. If abnormal, the abnormal information of the inpatient examination is stored in the inpatient early warning information table.
And pushing the generated early warning information to a large screen system through a WebService interface by using a timing task, and completing the timely notification of the final early warning information. And setting a pushing identification to be 1 for the patient early warning information which is pushed.
When the patient detects and reaches the early warning condition again, the early warning content in the early warning information is updated to be the latest early warning content, whether the pushing mark is set to be 0 or not, and the timing task is waited to execute pushing again.
The internet of things platform is associated with various compatible physical sign acquisition devices through an intelligent physical sign acquisition interface to acquire data and pushes the data to a patient safety early warning database of the patient early warning platform;
the medical care intelligent large screen receives patient care early warning data pushed by the AI early warning rule engine, and is convenient for medical care workers to perform manual intervention and feedback in time through presentation forms such as reminding, list, charts and the like;
the intelligent ward nursing and early warning system data center of the lead hospital realizes information summarization, analysis and upgrading through information bidirectional interaction in multiple hospital areas and multiple ward areas.
Further, the artificial intelligent control module compares, identifies and analyzes the acquired image, image and audio information with a pre-trained standard identification model, judges whether the learning and training result of the patient meets the training standard, and feeds back the judging result to the intelligent large screen of the lead hospital in real time to inform and remind a nursing staff to follow up guidance. When the learning training effect of the patient does not meet the training standard or the compliance of the patient does not meet the standard, the nursing staff can realize the real-time communication with the patient through the information interaction between the intelligent screen of the lead hospital and the intelligent screen of the patient to perform manual intervention.
And 6, the medical care client is used for receiving the early warning information pushed by the AI early warning rule engine and displaying the early warning information in a visual form.
The medical care client comprises an intelligent large screen, the medical care intelligent large screen receives patient care early warning data pushed by the AI early warning rule engine, and the medical care client is convenient for medical workers to perform manual intervention and feedback in time through reminding, list, chart and other presentation forms, as shown in fig. 6.
It should be noted that the embodiments of the present application are preferred and not limited in any way, and any person skilled in the art may make use of the above-disclosed technical content to change or modify the same into equivalent effective embodiments without departing from the technical scope of the present application, and any modification or equivalent change and modification of the above-described embodiments according to the technical substance of the present application still falls within the scope of the technical scope of the present application.
Claims (9)
1. A patient safety care early warning system, comprising:
the vital sign acquisition equipment is used for acquiring vital sign data of a patient in real time;
the system comprises an Internet of things platform, a data center and a data center, wherein the Internet of things platform is used for receiving vital sign data sent by a vital sign acquisition device in real time and sending the vital sign data to the data center in real time;
the data center is used for synchronizing vital sign data of a patient in real time and sending the vital sign data to the database; the real-time synchronization is performed according to the early warning information change in the database;
the database is used for enabling the AI early warning rule engine to call vital sign data of a patient in real time; the method is also used for sending the received early warning information to a data center;
the AI early warning rule engine comprises a preset trained data standard identification model, an evaluation form system and an artificial intelligent control module, wherein the artificial intelligent control module carries out intelligent summarization and analysis on vital sign data of a patient acquired in real time according to an early warning rule data model constructed by clinical index rules and pushes early warning information containing risk grading evaluation results to a medical client; the early warning information is also used for sending the early warning information to a database;
and the medical client is used for receiving the early warning information pushed by the AI early warning rule engine and displaying the early warning information in a visual form.
2. The patient safety care pre-warning system of claim 1, wherein the internet of things platform is configured to centrally manage all internet of things wearable devices that are currently available or are configured for future use in a hospital.
3. The patient safety care alert system as claimed in claim 1, wherein the AI alert rule engine is configured to combine the patient's basic information, vital signs, test indicators, care assessment results, surgical conditions, exam reports, current medical history, past medical history, allergy history, family history to form alert rules.
4. The patient safety nursing early warning system as set forth in claim 1, wherein the pre-inclusion evaluation form system is used for inputting data which cannot be provided by the current medical system of the hospital according to the requirement of the early warning rule, and the generation of various nursing evaluation and nursing record forms is completed in a flexible and configurable manner; the risk classification refers to classifying the risk of the existing patient according to the early warning rule, and adjusting the priority of early warning message pushing according to the classification.
5. The patient safety care pre-warning system of claim 1, wherein the pre-warning rules establish a linkage rule of vital signs and pre-warning assessment, and when the vital signs reach abnormal values, the assessment is sent out or an assessment period is adjusted, and as a data model is continuously established, a dynamic intelligent data center is formed.
6. The patient safety care early warning system of claim 1, wherein the algorithm of the early warning rules data model comprises the steps of:
step S1: grouping the detection item data of the patients according to the unique identification information of the patients, and executing two early warning rules by each patient simultaneously: unidirectional detection early warning rules and multiple detection early warning rules;
step S2: comparing the name of the detection item in the detection data of the patient with the name of the detection item in the early warning rule corresponding to the detection item and the detection result respectively, and if abnormality exists, storing the abnormality information into an early warning information table;
step S3: pushing the generated early warning information to a medical client by using a timing task, and setting a pushing identification to be 1;
step S4: when the patient detects and reaches the early warning condition again, the early warning content in the early warning information is updated to be the latest early warning content, whether the pushing mark is set to be 0 or not, and the timing task is waited to execute pushing again.
7. The patient safety care and early warning system of claim 1, wherein the data center further comprises a plurality of data interfaces for collecting data from the HIS system, LIS system, RIS system, CDR clinical data center, ambulatory medical device, and wherein the patient safety care and early warning system is of a hierarchical design that does not impact performance on existing systems and does not create data security problems.
8. The patient safety care pre-warning system of claim 1, wherein the data center is configured to centrally acquire, analyze, and push pre-warning information and content to other systems of the hospital that have subscribed to the information in real time using a message queuing technique.
9. The patient safety care warning system of claim 1, wherein the database includes complication warning related data, safety warning related data, and vital sign warning related data.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117393155A (en) * | 2023-12-12 | 2024-01-12 | 智业软件股份有限公司 | Intelligent clinical care decision-making method and system based on vital signs of patient |
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2022
- 2022-12-28 CN CN202211693575.7A patent/CN116825337A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117393155A (en) * | 2023-12-12 | 2024-01-12 | 智业软件股份有限公司 | Intelligent clinical care decision-making method and system based on vital signs of patient |
| CN117393155B (en) * | 2023-12-12 | 2024-03-26 | 智业软件股份有限公司 | Intelligent clinical care decision-making method and system based on vital signs of patient |
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