CN115473925A - Intelligent medical call management method and system based on cloud computing - Google Patents
Intelligent medical call management method and system based on cloud computing Download PDFInfo
- Publication number
- CN115473925A CN115473925A CN202211359058.6A CN202211359058A CN115473925A CN 115473925 A CN115473925 A CN 115473925A CN 202211359058 A CN202211359058 A CN 202211359058A CN 115473925 A CN115473925 A CN 115473925A
- Authority
- CN
- China
- Prior art keywords
- patient
- information
- nursing
- module
- treatment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H40/60—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 for the operation of medical equipment or devices
- G16H40/67—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 for the operation of medical equipment or devices for remote operation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M11/00—Telephonic communication systems specially adapted for combination with other electrical systems
- H04M11/02—Telephonic communication systems specially adapted for combination with other electrical systems with bell or annunciator systems
- H04M11/022—Paging systems
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Signal Processing (AREA)
- Medical Informatics (AREA)
- Business, Economics & Management (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Computing Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Business, Economics & Management (AREA)
- Accommodation For Nursing Or Treatment Tables (AREA)
Abstract
The invention discloses a medical call intelligent management method and system based on cloud computing, relating to the technical field of artificial intelligence, wherein the method comprises the following steps: acquiring state information of a patient user through a patient condition monitoring module and a patient demand acquisition module to acquire patient condition information; transmitting the patient condition information to a calling nursing layer through an information transmission layer, wherein the calling nursing layer comprises a patient condition evaluation module and a calling interaction module; acquiring a patient care level evaluation model through a patient condition evaluation module, inputting patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information; calling an intelligent terminal by a patient to acquire extension position information of a patient user; and performing call interaction management on the patient user based on the call interaction module, the nursing assessment level information and the extension set position information. The technical effects of intelligently and accurately evaluating the nursing level required by the patient and improving the rescue nursing efficiency and the nursing quality are achieved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a medical call intelligent management method and system based on cloud computing.
Background
The medical calling system becomes an indispensable basic device for improving the medical service quality, improving the working efficiency of medical staff and reducing medical accidents of hospitals, the existing medical calling systems for sickbeds are multiple, most of the existing medical calling systems for sickbeds are in wired communication, and the number of the bed for calling and asking for help is displayed and informed through sound-light alarm and an LED screen, so that the medical calling systems for the sickbeds are point-to-point calling and do not have the functions of storing, counting and managing calling information of patients.
However, the prior art has the technical problem that the medical calling mode is not high in intelligent degree, so that the rescue nursing efficiency is influenced.
Disclosure of Invention
According to the medical calling intelligent management method and system based on cloud computing, the technical problems that in the prior art, the rescue nursing efficiency is affected due to the fact that the degree of intellectualization of a medical calling mode is not high are solved, the medical calling nursing cloud platform is constructed to conduct real-time collection and analysis on the condition of a patient, the nursing level required by the patient is intelligently and accurately evaluated, then nursing interaction management is conducted on the patient, and the rescue nursing efficiency and the nursing quality are improved.
In view of the above problems, the present invention provides a cloud computing-based intelligent management method and system for medical calls.
In a first aspect, the present application provides a cloud computing-based intelligent management method for medical calls, including: the method comprises the steps of building a medical calling nursing cloud platform, wherein the medical calling nursing cloud platform comprises a data access layer, an information transmission layer and a calling nursing layer; the data access layer comprises a patient condition monitoring module and a patient demand acquisition module; acquiring the state information of a patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information; transmitting the patient condition information to the call care layer through the information transmission layer, wherein the call care layer comprises a patient condition evaluation module and a call interaction module; obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information; calling an intelligent terminal through a patient to acquire extension set position information of a patient user; performing call interaction management on the patient user based on the call interaction module, the care assessment tier information, and the extension location information.
On the other hand, the application also provides a medical call intelligent management system based on cloud computing, and the system comprises: the system comprises a platform building module, a medical calling nursing cloud platform and a service management module, wherein the platform building module is used for building the medical calling nursing cloud platform which comprises a data access layer, an information transmission layer and a calling nursing layer; the data access layer forming module is used for enabling the data access layer to comprise a patient condition monitoring module and a patient demand obtaining module; the information acquisition module is used for acquiring the state information of the patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information; the information transmission module is used for transmitting the patient condition information to the call nursing layer through the information transmission layer, and the call nursing layer comprises a patient condition evaluation module and a call interaction module; the model evaluation module is used for obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation and outputting care evaluation level information; the extension position acquisition module is used for acquiring the extension position information of the patient user by calling the intelligent terminal by the patient; and the call management module is used for performing call interaction management on the patient user based on the call interaction module, the nursing assessment level information and the extension position information.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the technical scheme is that a medical calling nursing cloud platform is built, state information of a patient user is collected through a patient condition monitoring module and a patient demand acquisition module of a data access layer of the medical calling nursing cloud platform, patient condition information is obtained, then the patient condition information is transmitted to a calling nursing layer through an information transmission layer, the calling nursing layer comprises a patient condition evaluation module and a calling interaction module, a patient nursing level evaluation model is obtained through the patient condition evaluation module, the patient condition information is input into the patient nursing level evaluation model for evaluation, nursing evaluation level information is output, extension position information of the patient user is obtained through a patient calling intelligent terminal, and finally calling interaction management is carried out on the patient user on the basis of the calling interaction module, the nursing evaluation level information and the extension position information. And then reached through constructing medical call nursing cloud platform and carry out real-time acquisition analysis to the patient condition, the required nursing level of intelligent accurate aassessment patient, and then nurse mutual management, improve the technical effect of rescue nursing efficiency and nursing quality to the patient.
Drawings
Fig. 1 is a schematic flowchart of a cloud computing-based intelligent management method for medical calls according to the present application;
fig. 2 is a schematic flowchart illustrating a process of obtaining patient condition information in the cloud computing-based medical call intelligent management method according to the present application;
fig. 3 is a schematic flowchart illustrating a process of invoking a patient monitoring analysis model in the cloud computing-based medical call intelligent management method according to the present application;
fig. 4 is a schematic structural diagram of a cloud computing-based medical call intelligent management system according to the present application;
description of the reference numerals: the system comprises a platform building module 11, a data access layer forming module 12, an information acquisition module 13, an information transmission module 14, a model evaluation module 15, an extension position obtaining module 16 and a calling management module 17.
Detailed Description
The medical calling intelligent management method and system based on cloud computing solve the technical problems that the medical calling mode is not high in intelligent degree and affects rescue nursing efficiency in the prior art, achieves the purpose of acquiring and analyzing the patient condition in real time by constructing the medical calling nursing cloud platform, intelligently and accurately evaluates the nursing level required by the patient, and further carries out nursing interactive management on the patient, and improves the rescue nursing efficiency and nursing quality.
Example one
As shown in fig. 1, the present application provides a cloud computing-based intelligent management method for medical calls, the method including:
step S100: the method comprises the steps of building a medical calling nursing cloud platform, wherein the medical calling nursing cloud platform comprises a data access layer, an information transmission layer and a calling nursing layer;
particularly, a medical calling system becomes essential basic equipment for hospitals to improve the quality of medical care services, improve the working efficiency of medical care personnel and reduce medical accidents, the existing medical calling systems for sickbeds are in wired communication, the sickbed numbers for calling and seeking help are informed through sound-light alarm and LED screen display, and the sickbed numbers are point-to-point calling and do not have the functions of storing, counting and managing the calling information of patients.
The medical calling nursing cloud platform is a multifunctional cloud computing platform for monitoring and managing the physical condition of a patient in real time and intelligently calling nursing, has the computing, network and storage capabilities for medical calling service, and comprises a data access layer, an information transmission layer and a calling nursing layer. Through interaction of all the platform functional layers and data sharing processing, intelligent service is provided for medical calling nursing, and the rescue nursing efficiency and the nursing quality of patients are improved.
Step S200: the data access layer comprises a patient condition monitoring module and a patient demand acquisition module;
specifically, the data access layer includes a patient condition monitoring module and a patient need acquisition module. The patient condition monitoring module is combined with hardware facilities, and real-time information acquisition is carried out on the physical condition of the patient through medical facilities. The patient demand acquisition module is an individual service function module for inputting patient demands, and the individual service function module comprises diet demands, drug delivery demands, turning-over and moving demands, metabolic demands and the like, and can comprehensively acquire the physical condition demands of the patients.
Step S300: acquiring state information of a patient user through the patient condition monitoring module and the patient demand acquisition module to acquire patient condition information;
as shown in fig. 2, further to obtain the patient condition information, step S300 of the present application further includes:
step S310: obtaining patient treatment attribute information by the patient condition monitoring module;
step S320: calling a calibrated patient monitoring analysis model based on the patient treatment attribute information;
step S330: acquiring real-time patient state monitoring information, and inputting the real-time patient state monitoring information into the calibrated patient monitoring analysis model to acquire a patient health state coefficient;
step S340: acquiring patient demand characteristic information through the patient demand acquisition module;
step S350: determining the patient condition information based on the patient health status coefficient and the patient need characteristic information.
As shown in fig. 3, further to invoke calibration of the patient monitoring analysis model based on the patient treatment attribute information, step S320 of the present application further includes:
step S321: obtaining a patient diagnosis and treatment information database through the medical call nursing cloud platform;
step S322: extracting labels from the patient diagnosis and treatment information database to obtain a patient diagnosis and treatment label database;
step S323: labeling the patient treatment attribute based on the patient treatment label library to obtain patient treatment label information;
step S324: performing label feature fusion on the patient treatment label information to obtain treatment feature fusion information;
step S325: and calling from a patient monitoring analysis model library to obtain the calibrated patient monitoring analysis model based on the treatment characteristic fusion information.
Further, the step S325 of obtaining the calibrated patient monitoring analysis model from the patient monitoring analysis model library based on the therapy characteristic fusion information further includes:
step S3251: clustering and dividing data information in the patient diagnosis and treatment information base based on the patient treatment label base to obtain a diagnosis and treatment information clustering result;
step S3252: carrying out feature labeling on the diagnosis and treatment information clustering result to obtain a diagnosis and treatment information training data set, wherein the diagnosis and treatment information training data set comprises patient diagnosis and treatment information of each diagnosis and treatment feature calibration parameter;
step S3253: respectively taking the diagnosis and treatment information training data set as training data according to the diagnosis and treatment characteristic calibration parameters to perform neural network model training, and constructing the patient monitoring analysis model base;
step S3254: and obtaining the calibrated diagnosis and treatment characteristic parameters of the treatment characteristic fusion information, and selectively calling the patient monitoring and analyzing model base based on the calibrated diagnosis and treatment characteristic parameters to obtain the calibrated patient monitoring and analyzing model.
Specifically, the state information of a patient user is acquired through the patient condition monitoring module and the patient demand acquisition module, specifically, the patient treatment attribute information is acquired through the patient condition monitoring module at first, and the patient treatment attribute information comprises related information such as the age, the sex, the height and the weight of a patient, the type of a patient suffering from a disease, the severity of the disease, complications and the like. Based on the patient treatment attribute information, a calibrated patient monitoring analysis model is invoked, the calibrated patient monitoring analysis model being a health condition monitoring model adapted to the physical attributes of the patient.
The model calling process is that a patient diagnosis and treatment information database is obtained through the medical call nursing cloud platform, the patient diagnosis and treatment information database is a historical patient diagnosis and treatment data set obtained through big data, and the historical patient diagnosis and treatment data set comprises treatment information of patients with different body attributes and corresponding body health condition information. And extracting labels of the patient diagnosis and treatment information database, namely labeling diagnosis and treatment information, wherein illustratively, diagnosis and treatment labels of patients are obesity, old age, light stomach illness and no complication, so that a patient diagnosis and treatment label database corresponding to various patients is formed. And labeling the patient treatment attribute based on the patient treatment label library, namely labeling the service patient to obtain corresponding patient treatment label information. And performing label characteristic fusion on the treatment label information of the patient to obtain treatment characteristic fusion information fused by each label, wherein the treatment characteristic fusion information is used for indicating the physical treatment characteristics of the patient.
Calling a calibrated patient monitoring analysis model suitable for the patient from a patient monitoring analysis model library based on the treatment characteristic fusion information, and firstly, clustering and dividing data information in the patient diagnosis and treatment information library based on the patient treatment label library, namely, dividing the patients with the same label in the patient diagnosis and treatment information library into the same class so as to obtain a diagnosis and treatment information clustering result. And then, carrying out feature marking on the diagnosis and treatment information clustering results, namely carrying out diagnosis and treatment feature marking on each type of division results, for example, classifying feature parameters of aged light pneumonia patients, carrying out feature marking on diagnosis and treatment data information corresponding to each type of feature parameters, and obtaining a diagnosis and treatment information training data set corresponding to each feature type, wherein the diagnosis and treatment information training data set comprises the diagnosis and treatment information of the patients with each diagnosis and treatment feature calibration parameter.
And respectively taking the diagnosis and treatment information training data set as training data to perform neural network model training according to the diagnosis and treatment characteristic calibration parameters, performing model training on patient diagnosis and treatment information training data corresponding to each type of diagnosis and treatment characteristic calibration parameters, and taking each patient monitoring and analyzing model set obtained by training as a model data basis for constructing the patient monitoring and analyzing model base. And extracting the calibrated diagnosis and treatment characteristic parameters of the treatment characteristic fusion information to obtain diagnosis and treatment characteristic attributes corresponding to the patient, and selectively calling the models with the same diagnosis and treatment characteristic parameters in the patient monitoring analysis model library based on the calibrated diagnosis and treatment characteristic parameters to obtain the calibrated patient monitoring analysis model corresponding to the models. And calling and obtaining the individualized calibration patient monitoring analysis model suitable for the patient from a patient monitoring analysis model library based on the treatment characteristic fusion information for health condition monitoring analysis.
The patient real-time state monitoring information including the respiratory frequency, the blood pressure, the body temperature, the appetite, the consciousness state, the sleep condition and the like of the patient is obtained through monitoring of medical hardware facilities, the patient real-time state monitoring information is input into the calibrated patient monitoring analysis model to be analyzed for the health state, a corresponding patient health state coefficient is obtained, and the larger the coefficient is, the better the health state of the patient is. And acquiring patient demand characteristic information, such as metabolic demand characteristics of the patient, by the patient demand acquisition module, and determining the patient condition information by combining the patient health state coefficient and the patient demand characteristic information. Through gathering patient health condition and patient self demand in real time, comprehensive accurate patient health characteristic demand that acquires, and then improve accuracy and nursing efficiency of calling out the nursing to patient's intelligence.
Step S400: transmitting the patient condition information to the call care layer through the information transmission layer, wherein the call care layer comprises a patient condition evaluation module and a call interaction module;
specifically, the patient condition information is transmitted to the calling nursing layer through the information transmission layer, the information transmission layer is used for transmitting the patient data and can adopt a wireless encryption transmission mode, the data transmission speed is high, and the safety is high. The calling nursing layer is used for accurately evaluating and calling interaction of a patient nursing level and comprises a patient condition evaluation module and a calling interaction module, so that the medical calling intellectualization and nursing efficiency of a patient are improved.
Step S500: obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information;
further, the step S500 of outputting the care assessment level information further includes:
step S510: the patient care level assessment model comprises an input layer, a feature analysis layer, a level assessment layer and an output layer;
step S520: inputting the patient condition information into the feature analysis layer through the input layer to obtain nursing feature information;
step S530: evaluating the nursing characteristic information based on the hierarchy evaluation layer to obtain nursing evaluation hierarchy information;
step S540: outputting, by the output layer, the care assessment level information as a model output result.
Specifically, a patient care level assessment model for performing care level assessment of a patient condition is obtained by the patient condition assessment module, and functional layers of the patient care level assessment model include an input layer, a feature analysis layer, a level assessment layer, and an output layer. The model evaluation process comprises the steps that the patient condition information is firstly input into the feature analysis layer through the input layer, the feature analysis layer is used for analyzing nursing features of the patient and can obtain the nursing feature information through historical training data, and the nursing feature information comprises nursing types, nursing grades, nursing requirements and the like.
The nursing characteristic information is evaluated based on the hierarchy evaluation layer, the hierarchy evaluation layer is used for analyzing nursing hierarchies, nursing hierarchies corresponding to different nursing characteristics are different, nursing evaluation hierarchy information is obtained through analysis, the nursing evaluation hierarchy information indicates the nursing requirement level of the patient, and the higher the hierarchy is, the higher the nursing quality level of the patient is. And finally, the nursing assessment level information is output as a model output result through the output layer, so that the nursing level of the patient is intelligently assessed through the patient nursing level assessment model, the assessment accuracy of the nursing level required by the patient is improved, and the rescue nursing efficiency and the nursing quality are improved.
Step S600: calling an intelligent terminal through a patient to acquire extension set position information of a patient user;
specifically, extension position information of a patient user is acquired through a patient calling intelligent terminal, the patient calling intelligent terminal is a user calling terminal of a medical calling nursing cloud platform, the terminal can display basic treatment information of the patient and comprises personal information, bed information, medical information of the patient and the like, the patient can interact with medical staff through the terminal, the extension position information is terminal number information, and the position information and the bed information of the patient can be acquired through the extension position information.
Step S700: performing call interaction management for the patient user based on the call interaction module, the care assessment hierarchy information, and the extension location information.
Further, for the call interaction management of the patient user, step S700 of the present application further includes:
step S710: obtaining nursing resource demand information based on the nursing assessment level information;
step S720: obtaining a medical care resource library through the medical call care cloud platform;
step S730: matching the nursing resource demand information with the medical nursing resource library to obtain nursing resource matching information, wherein the nursing resource matching information is arranged in a descending order according to resource adaptability;
step S740: and the call interaction module carries out call nursing on the patient user based on the nursing resource matching information and the extension set position information.
Further, step S740 of the present application further includes:
step S741: acquiring the work scheduling task information of each nursing resource in the nursing resource matching information;
step S742: monitoring the application state of the work scheduling task information to obtain nursing resource application state information;
step S743: and when the conflict factor occurs in the nursing resource application state information, carrying out nursing resource re-matching based on a resource adaptability rule to obtain nursing resource updating matching information.
Specifically, the medical care personnel performs call interaction management on the patient user through the call interaction module based on the nursing assessment level information and the extension position information. Specifically, nursing resource demand information of the nursing level is acquired based on the nursing assessment level information, wherein the nursing resource demand information comprises medical care personnel demands and demands of nursing instruments, medicines and the like. And obtaining a medical care resource library through the medical call care cloud platform, wherein the medical care resource library is medical human resources, medical instruments, medicine resources and the like of a hospital. And matching the nursing resource demand information with the medical nursing resource library to obtain nursing resource matching information meeting the nursing demands of the patients, wherein the nursing resource matching information is arranged in a descending order according to resource adaptability, namely the higher the adaptability is, the more the nursing resources are arranged in a forward order. And the medical care personnel carries out intelligent call interaction and medical care with the patient user through the call interaction module based on the nursing resource matching information and the extension position information.
In order to ensure the matching accuracy of the nursing resources, the work scheduling task information of each nursing resource in the nursing resource matching information, namely the application scheduling arrangement of each nursing resource, is obtained. And monitoring the application state of the work scheduling task information, and judging whether the work scheduling task information is in a use or damage state, so as to obtain the application state information of each nursing resource. And when the conflict factor occurs in the nursing resource application state information, namely the nursing resource is in a non-idle state and cannot be used, carrying out nursing resource re-matching based on the resource suitability rule, namely carrying out re-matching on the nursing resource according to the resource suitability sequence, and obtaining the nursing resource updating matching information after re-matching. The patient is nursed through the updated nursing resources, the timely accuracy of nursing resource matching is guaranteed, and then the rescue nursing efficiency and the nursing quality of the patient are improved.
In summary, the cloud computing-based medical call intelligent management method and system provided by the application have the following technical effects:
the technical scheme is that a medical calling nursing cloud platform is built, state information of a patient user is collected through a patient condition monitoring module and a patient demand acquisition module of a data access layer of the medical calling nursing cloud platform, patient condition information is obtained, then the patient condition information is transmitted to a calling nursing layer through an information transmission layer, the calling nursing layer comprises a patient condition evaluation module and a calling interaction module, a patient nursing level evaluation model is obtained through the patient condition evaluation module, the patient condition information is input into the patient nursing level evaluation model for evaluation, nursing evaluation level information is output, extension position information of the patient user is obtained through a patient calling intelligent terminal, and finally calling interaction management is carried out on the patient user on the basis of the calling interaction module, the nursing evaluation level information and the extension position information. And then reached and carried out real-time acquisition analysis to the patient condition through constructing medical call nursing cloud platform, the required nursing level of intelligent accurate aassessment patient, and then nurses the interactive management to the patient, improves rescue nursing efficiency and nursing quality's technical effect.
Example two
Based on the same inventive concept as the cloud computing-based medical call intelligent management method in the foregoing embodiment, the present invention further provides a cloud computing-based medical call intelligent management system, as shown in fig. 4, the system includes:
the platform building module 11 is used for building a medical calling nursing cloud platform, and the medical calling nursing cloud platform comprises a data access layer, an information transmission layer and a calling nursing layer;
a data access layer composition module 12, configured to enable the data access layer to include a patient condition monitoring module and a patient requirement acquisition module;
the information acquisition module 13 is used for acquiring the state information of the patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information;
an information transmission module 14, configured to transmit the patient condition information to the call care layer through the information transmission layer, where the call care layer includes a patient condition evaluation module and a call interaction module;
the model evaluation module 15 is used for obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information;
an extension position obtaining module 16, configured to obtain extension position information of the patient user by calling an intelligent terminal through a patient;
a call management module 17, configured to perform call interaction management on the patient user based on the call interaction module, the care assessment level information, and the extension location information.
Further, the information acquisition module further comprises:
the treatment attribute obtaining unit is used for calling and calibrating a patient monitoring analysis model based on the patient treatment attribute information;
the model calling unit is used for acquiring the real-time patient state monitoring information, inputting the real-time patient state monitoring information into the calibrated patient monitoring analysis model and acquiring the health state coefficient of the patient;
the model analysis unit is used for acquiring the characteristic information of the patient demand through the patient demand acquisition module;
the requirement characteristic obtaining unit is used for examining the student users based on the study and examination content to obtain study and examination characteristic information;
a patient condition determining unit for determining the patient condition information based on the patient health status coefficient and the patient need characteristic information.
Further, the model invoking unit further includes:
the database obtaining unit is used for obtaining a patient diagnosis and treatment information database through the medical call nursing cloud platform;
a tag library obtaining unit, configured to perform tag extraction on the patient diagnosis and treatment information database to obtain a patient diagnosis and treatment tag library;
the labeling unit is used for labeling the patient treatment attribute based on the patient treatment label library to obtain patient treatment label information;
the characteristic fusion unit is used for carrying out label characteristic fusion on the patient treatment label information to obtain treatment characteristic fusion information;
and the model obtaining unit is used for calling and obtaining the calibrated patient monitoring analysis model from a patient monitoring analysis model library based on the treatment characteristic fusion information.
Further, the model obtaining unit further includes:
the clustering and dividing unit is used for clustering and dividing the data information in the patient diagnosis and treatment information base based on the patient treatment label base to obtain a diagnosis and treatment information clustering result;
a training data set obtaining unit, configured to perform feature labeling on the diagnosis and treatment information clustering result to obtain a diagnosis and treatment information training data set, where the diagnosis and treatment information training data set includes patient diagnosis and treatment information of each diagnosis and treatment feature calibration parameter;
the model base building unit is used for respectively taking the diagnosis and treatment information training data set as training data according to the diagnosis and treatment characteristic calibration parameters to carry out neural network model training and build the patient monitoring analysis model base;
and the model selection calling unit is used for obtaining the calibrated diagnosis and treatment characteristic parameters of the treatment characteristic fusion information, and selectively calling the patient monitoring and analyzing model library based on the calibrated diagnosis and treatment characteristic parameters to obtain the calibrated patient monitoring and analyzing model.
Further, the model evaluation module further comprises:
a model construction unit for the patient care level evaluation model including an input layer, a feature analysis layer, a level evaluation layer and an output layer;
the characteristic analysis unit is used for inputting the patient condition information into the characteristic analysis layer through the input layer to obtain nursing characteristic information;
the hierarchy evaluation unit is used for evaluating the nursing characteristic information based on the hierarchy evaluation layer to obtain nursing evaluation hierarchy information;
and the model output unit is used for outputting the nursing assessment level information as a model output result through the output layer.
Further, the call management module further includes:
a resource demand obtaining unit for obtaining nursing resource demand information based on the nursing assessment level information;
a nursing resource library obtaining unit, configured to obtain a medical nursing resource library through the medical call nursing cloud platform;
the resource matching unit is used for matching the nursing resource demand information with the medical nursing resource library to obtain nursing resource matching information, wherein the nursing resource matching information is arranged in a descending order according to resource adaptability;
and the call management unit is used for performing call nursing on the patient user by the call interaction module based on the nursing resource matching information and the extension set position information.
Further, the system further comprises:
the work scheduling task obtaining unit is used for obtaining the work scheduling task information of each nursing resource in the nursing resource matching information;
the resource application state obtaining unit is used for monitoring the application state of the work scheduling task information to obtain nursing resource application state information;
and the nursing resource re-matching unit is used for performing nursing resource re-matching based on a resource adaptability rule when the nursing resource application state information has a conflict factor, and acquiring nursing resource updating matching information.
The application provides a medical call intelligent management method based on cloud computing, which comprises the following steps: the method comprises the steps of building a medical calling nursing cloud platform, wherein the medical calling nursing cloud platform comprises a data access layer, an information transmission layer and a calling nursing layer; the data access layer comprises a patient condition monitoring module and a patient demand acquisition module; acquiring the state information of a patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information; transmitting the patient condition information to the call care layer through the information transmission layer, wherein the call care layer comprises a patient condition evaluation module and a call interaction module; obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information; calling an intelligent terminal through a patient to acquire extension set position information of a patient user; performing call interaction management on the patient user based on the call interaction module, the care assessment tier information, and the extension location information. The technical problem that the medical calling mode in the prior art is not high in intelligent degree, so that rescue nursing efficiency is affected is solved. The technical effects that the patient condition is collected and analyzed in real time by constructing a medical call nursing cloud platform, the nursing level required by the patient is intelligently and accurately evaluated, then nursing interaction management is carried out on the patient, and the rescue nursing efficiency and the nursing quality are improved are achieved.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention covers the modifications and variations of this invention provided they come within the scope of the invention and its equivalents.
Claims (8)
1. A medical call intelligent management method based on cloud computing is characterized by comprising the following steps:
building a medical calling nursing cloud platform, wherein the medical calling nursing cloud platform comprises a data access layer, an information transmission layer and a calling nursing layer;
the data access layer comprises a patient condition monitoring module and a patient demand acquisition module;
acquiring the state information of a patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information;
transmitting the patient condition information to the call care layer through the information transmission layer, wherein the call care layer comprises a patient condition evaluation module and a call interaction module;
obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation, and outputting care evaluation level information;
calling an intelligent terminal through a patient to acquire extension set position information of a patient user;
performing call interaction management on the patient user based on the call interaction module, the care assessment tier information, and the extension location information.
2. The method of claim 1, wherein said obtaining patient condition information comprises:
obtaining patient treatment attribute information by the patient condition monitoring module;
calling a calibrated patient monitoring analysis model based on the patient treatment attribute information;
acquiring real-time patient state monitoring information, and inputting the real-time patient state monitoring information into the calibrated patient monitoring analysis model to acquire a patient health state coefficient;
acquiring patient demand characteristic information through the patient demand acquisition module;
determining the patient condition information based on the patient health status coefficient and the patient need characteristic information.
3. The method of claim 2, wherein invoking a calibration patient monitoring analysis model based on the patient treatment attribute information comprises:
obtaining a patient diagnosis and treatment information database through the medical call nursing cloud platform;
extracting labels from the patient diagnosis and treatment information database to obtain a patient diagnosis and treatment label database;
labeling the patient treatment attribute based on the patient treatment label library to obtain patient treatment label information;
performing label characteristic fusion on the patient treatment label information to obtain treatment characteristic fusion information;
and calling from a patient monitoring analysis model library based on the treatment characteristic fusion information to obtain the calibrated patient monitoring analysis model.
4. The method of claim 3, wherein said obtaining the calibrated patient monitoring analysis model from a patient monitoring analysis model library based on the therapy feature fusion information comprises:
clustering and dividing data information in the patient diagnosis and treatment information base based on the patient treatment label base to obtain a diagnosis and treatment information clustering result;
carrying out feature marking on the diagnosis and treatment information clustering result to obtain a diagnosis and treatment information training data set, wherein the diagnosis and treatment information training data set comprises patient diagnosis and treatment information of each diagnosis and treatment feature calibration parameter;
respectively taking the diagnosis and treatment information training data set as training data according to the diagnosis and treatment characteristic calibration parameters to perform neural network model training, and constructing the patient monitoring analysis model base;
and obtaining calibration diagnosis and treatment characteristic parameters of the treatment characteristic fusion information, and selectively calling the patient monitoring and analyzing model base based on the calibration diagnosis and treatment characteristic parameters to obtain the calibration patient monitoring and analyzing model.
5. The method of claim 1, wherein outputting care assessment tier information comprises:
the patient care level assessment model comprises an input layer, a feature analysis layer, a level assessment layer and an output layer;
inputting the patient condition information into the feature analysis layer through the input layer to obtain nursing feature information;
evaluating the nursing characteristic information based on the hierarchy evaluation layer to obtain nursing evaluation hierarchy information;
outputting, by the output layer, the care assessment level information as a model output result.
6. The method of claim 1, wherein said managing the patient user's call interactions comprises:
obtaining nursing resource demand information based on the nursing assessment level information;
obtaining a medical care resource library through the medical call care cloud platform;
matching the nursing resource demand information with the medical nursing resource library to obtain nursing resource matching information, wherein the nursing resource matching information is arranged in a descending order according to resource adaptability;
the call interaction module carries out call nursing on the patient user based on the nursing resource matching information and the extension position information.
7. The method of claim 6, wherein the method comprises:
acquiring the work scheduling task information of each nursing resource in the nursing resource matching information;
monitoring the application state of the work scheduling task information to obtain nursing resource application state information;
and when the conflict factor occurs in the nursing resource application state information, carrying out nursing resource re-matching based on a resource adaptability rule to obtain nursing resource updating matching information.
8. A medical call intelligent management system based on cloud computing is characterized in that the system comprises:
the system comprises a platform building module, a medical calling nursing cloud platform and a service management module, wherein the platform building module is used for building the medical calling nursing cloud platform which comprises a data access layer, an information transmission layer and a calling nursing layer;
the data access layer forming module is used for enabling the data access layer to comprise a patient condition monitoring module and a patient demand obtaining module;
the information acquisition module is used for acquiring the state information of the patient user through the patient condition monitoring module and the patient demand acquisition module to acquire the patient condition information;
the information transmission module is used for transmitting the patient condition information to the call nursing layer through the information transmission layer, and the call nursing layer comprises a patient condition evaluation module and a call interaction module;
the model evaluation module is used for obtaining a patient care level evaluation model through the patient condition evaluation module, inputting the patient condition information into the patient care level evaluation model for evaluation and outputting care evaluation level information;
the extension position acquisition module is used for acquiring the extension position information of the patient user by calling the intelligent terminal by the patient;
and the call management module is used for performing call interaction management on the patient user based on the call interaction module, the nursing assessment level information and the extension position information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211359058.6A CN115473925B (en) | 2022-11-02 | 2022-11-02 | Intelligent medical call management method and system based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211359058.6A CN115473925B (en) | 2022-11-02 | 2022-11-02 | Intelligent medical call management method and system based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115473925A true CN115473925A (en) | 2022-12-13 |
| CN115473925B CN115473925B (en) | 2023-02-03 |
Family
ID=84337658
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211359058.6A Active CN115473925B (en) | 2022-11-02 | 2022-11-02 | Intelligent medical call management method and system based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115473925B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116864099A (en) * | 2023-08-29 | 2023-10-10 | 济南鸿泰医疗管理集团有限公司 | Medical equipment remote supervision system and method based on big data |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023073A (en) * | 2014-04-16 | 2015-11-04 | 北京百和园科技有限公司 | Hospital intelligence assessment triage system based on artificial neural network |
| CN110890146A (en) * | 2019-11-04 | 2020-03-17 | 广东德澳智慧医疗科技有限公司 | Bedside intelligent interaction system for intelligent ward |
| CN112005315A (en) * | 2018-03-01 | 2020-11-27 | 费森尤斯医疗保健控股公司 | System and method for determining patient hospitalization risk and treating patient |
| CN112365978A (en) * | 2020-11-10 | 2021-02-12 | 北京航空航天大学 | Method and device for establishing early risk assessment model of tachycardia event |
| US20210225495A1 (en) * | 2018-05-15 | 2021-07-22 | Nunetz, Inc. | Systems and methods for adapting a ui based platform on patient medical data |
| CN113314201A (en) * | 2021-06-17 | 2021-08-27 | 南通市第一人民医院 | Neurology clinical nursing potential safety hazard analysis method and system |
| CN114792561A (en) * | 2022-06-24 | 2022-07-26 | 山东第一医科大学附属省立医院(山东省立医院) | One-stop clinical support and service method and system |
| CN114882983A (en) * | 2022-05-13 | 2022-08-09 | 浙江远图技术股份有限公司 | Smart ward interaction method, system and storage medium |
-
2022
- 2022-11-02 CN CN202211359058.6A patent/CN115473925B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105023073A (en) * | 2014-04-16 | 2015-11-04 | 北京百和园科技有限公司 | Hospital intelligence assessment triage system based on artificial neural network |
| CN112005315A (en) * | 2018-03-01 | 2020-11-27 | 费森尤斯医疗保健控股公司 | System and method for determining patient hospitalization risk and treating patient |
| US20210225495A1 (en) * | 2018-05-15 | 2021-07-22 | Nunetz, Inc. | Systems and methods for adapting a ui based platform on patient medical data |
| CN110890146A (en) * | 2019-11-04 | 2020-03-17 | 广东德澳智慧医疗科技有限公司 | Bedside intelligent interaction system for intelligent ward |
| CN112365978A (en) * | 2020-11-10 | 2021-02-12 | 北京航空航天大学 | Method and device for establishing early risk assessment model of tachycardia event |
| CN113314201A (en) * | 2021-06-17 | 2021-08-27 | 南通市第一人民医院 | Neurology clinical nursing potential safety hazard analysis method and system |
| CN114882983A (en) * | 2022-05-13 | 2022-08-09 | 浙江远图技术股份有限公司 | Smart ward interaction method, system and storage medium |
| CN114792561A (en) * | 2022-06-24 | 2022-07-26 | 山东第一医科大学附属省立医院(山东省立医院) | One-stop clinical support and service method and system |
Non-Patent Citations (2)
| Title |
|---|
| 《3GPP》: ""160922 NGMN - Perspectives on Vertical Industries and Implications for 5..."", 《3GPP INBOX\LSS_FROM_EXTERNAL_BODIES》 * |
| 徐日: "基于移动医疗的医养结合脑卒中延续性护理模式研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116864099A (en) * | 2023-08-29 | 2023-10-10 | 济南鸿泰医疗管理集团有限公司 | Medical equipment remote supervision system and method based on big data |
| CN116864099B (en) * | 2023-08-29 | 2023-11-28 | 济南鸿泰医疗管理集团有限公司 | Medical equipment remote supervision system and method based on big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115473925B (en) | 2023-02-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kalid et al. | Based real time remote health monitoring systems: A review on patients prioritization and related" big data" using body sensors information and communication technology | |
| CN105260588B (en) | A kind of health guards robot system and its data processing method | |
| CN107242858B (en) | Personalized pregnant infant monitoring method and system | |
| US9317662B2 (en) | Devices, systems, and methods for automated data collection | |
| CN105118010A (en) | Chronic disease management method with functions of real-time data processing and real-time information sharing and life style intervention information | |
| CN103690240A (en) | Medical system | |
| CN102299952A (en) | Remote medical system and intelligent household medical system | |
| CN101919684B (en) | Device for monitoring pain state of patient and method thereof | |
| CN111613291A (en) | Medicine management, classification and medical staff and patient association system | |
| CN110289062A (en) | Three high condominium three-level collaboration internet management systems and management method | |
| CN115473925B (en) | Intelligent medical call management method and system based on cloud computing | |
| CN202206423U (en) | Remote medical system and intelligent household medical system | |
| Efat et al. | IoT based smart health monitoring system for diabetes patients using neural network | |
| CN111581300A (en) | Label matrix construction and updating method based on health medical data | |
| CN116504373A (en) | Comprehensive management information platform for digital intelligent ward | |
| Ahmed | An intelligent healthcare service to monitor vital signs in daily life–a case study on health-iot | |
| Mittal et al. | 2 Applications of | |
| CN114868203A (en) | Information transmission device and information transmission method | |
| TWI702537B (en) | Smart text of nursing generating system based on lexical analysis and smart nursing information platform using the same | |
| CN111950234A (en) | Processing method based on intelligent medical text processing system | |
| CN116825337A (en) | Patient safety nursing early warning system | |
| Rani et al. | Prediction of the mortality rate and framework for remote monitoring of pregnant women based on IoT | |
| TW201019257A (en) | Patient pain-status monitoring device and method | |
| Nagarnaidu Rajaperumal et al. | Cloud-based intelligent internet of medical things applications for healthcare systems | |
| Dobre et al. | Parameters weighting in elderly monitoring based on multi-criteria methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |