CN114141349A - Intelligent allocation method and system for ICU nursing personnel - Google Patents
Intelligent allocation method and system for ICU nursing personnel Download PDFInfo
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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
- G16H40/20—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 management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
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- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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Abstract
The invention discloses an intelligent allocation method and system for ICU nursing personnel, wherein the method comprises the following steps: performing attribute classification on the first physiological characteristic according to the emergency category characteristic decision tree to obtain first emergency characteristic information; analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result; inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result; based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability; and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard. The technical problems of low manual allocation accuracy, labor cost waste and low efficiency in the prior art are solved.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an intelligent allocation method and system for ICU nursing personnel.
Background
The ICU, also known as intensive care unit, is the clinical base of critical care medicine, is the place where critically ill patients gather, and utilizes advanced medical equipment to perform continuous vital sign monitoring to capture the most significant, transient and earliest transient changes, immediately give feedback and more timely treatment and care. The ICU nurses have large workload, frequent shift and variable patient conditions, thereby simultaneously providing higher requirements for the management of ICU medical care personnel engaged in special tasks.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the manual allocation in the prior art has the problems of low accuracy, labor cost waste and low efficiency.
Disclosure of Invention
The embodiment of the application provides the intelligent allocation method and system for the ICU nursing staff, solves the technical problems of low accuracy, labor cost waste and low efficiency of manual allocation in the prior art, achieves bidirectional intelligent selection allocation by combining the physiological condition of a patient and the nursing capacity of the nursing staff, reduces labor cost, and further improves the technical effects of allocation accuracy and allocation efficiency.
In view of the above, the present invention has been developed to provide a method that overcomes, or at least partially solves, the above-mentioned problems.
In a first aspect, an embodiment of the present application provides an intelligent deployment method for an ICU caregiver, where the method includes: obtaining a first physiological characteristic of a first user; performing attribute classification on the first physiological characteristic according to an emergency category characteristic decision tree to obtain first emergency characteristic information; analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result, wherein the first nursing condition analysis result comprises a nursing grade and a nursing type; a nursing staff management file is constructed through a hospital staff management platform; inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result; based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability; and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard.
In another aspect, the present application further provides an intelligent deployment system for an ICU caregiver, the system comprising: a first obtaining unit, configured to obtain a first physiological characteristic of a first user; the second obtaining unit is used for carrying out attribute classification on the first physiological characteristic according to an emergency treatment category characteristic decision tree to obtain first emergency treatment characteristic information; a third obtaining unit, configured to perform nursing condition analysis on the first emergency characteristic information to obtain a first nursing condition analysis result, where the first nursing condition analysis result includes a nursing grade and a nursing type; the system comprises a first construction unit, a second construction unit and a third construction unit, wherein the first construction unit is used for constructing a nursing staff management file through a hospital staff management platform; a fourth obtaining unit, configured to input the first nursing condition analysis result and the caregiver management file into a nursing matching analysis model to obtain a first nursing matching result; a fifth obtaining unit, configured to perform bidirectional matching on the caregiver management profile and the first mapping matching result based on a user care time point to obtain a set of dispatchable caregivers, where the set of dispatchable caregivers is sorted according to a care suitability; a first management unit, configured to perform deployment management on the deployable caregiver collection according to a predetermined ICU management standard.
In a third aspect, an embodiment of the present invention provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the method for controlling output data includes any one of the steps described above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for controlling output data according to any one of the above.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the first physiological characteristics are subjected to attribute classification according to the emergency category characteristic decision tree, so that first emergency characteristic information is obtained; analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result, wherein the first nursing condition analysis result comprises a nursing grade and a nursing type; a nursing staff management file is constructed through a hospital staff management platform; inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result; based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability; and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard. And then reach and carry out two-way intelligent selection allotment through combining patient's physiological conditions and nursing staff's nursing ability, reduce the cost of labor, and then improve allotment accuracy and allotment efficient technological effect.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating an intelligent scheduling method for ICU caregivers according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating the construction of a caregiver management file in an intelligent ICU caregiver deployment method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a first nursing quality factor obtained in an intelligent nursing method for an ICU caregiver according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating a first care analysis result obtained in an intelligent nursing method for an ICU caregiver according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart illustrating a first care plan obtained in an intelligent scheduling method for an ICU caregiver according to an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of an intelligent scheduling system for ICU caregivers according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device for executing a method of controlling output data according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first constructing unit 14, a fourth obtaining unit 15, a fifth obtaining unit 16, a first managing unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the embodiments of the present invention, it should be apparent to those skilled in the art that the embodiments of the present invention can be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, embodiments of the invention may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, embodiments of the invention may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied in the medium.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In embodiments of the invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or apparatus.
Summary of the application
The method, the device and the electronic equipment are described through the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The embodiments of the present invention will be described below with reference to the drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides an intelligent deployment method for an ICU caregiver, where the method includes:
step S100: obtaining a first physiological characteristic of a first user;
specifically, the ICU is also called an intensive care unit, is a clinical base of critical illness medicine, is a place for gathering critical patients, and brings higher requirements for the management of ICU medical care personnel engaged in special tasks due to the large workload of ICU nursing personnel, frequent shift and frequent illness changes of patients. The first user is an ICU patient, namely a patient needing intensive care, and comprises various critical acute reversible diseases, such as a patient needing monitoring after major surgery, anesthesia accident, severe compound trauma, acute circulatory failure, acute respiratory failure, resuscitation after heartbeat and respiratory arrest, electric shock, resuscitation after drowning patients, various poisoning patients, various shock patients, septicemia, amniotic fluid embolism, severe toxemia of pregnancy and the like. The first physiological characteristics of the first user are clinical physiological characteristics of an ICU patient, including electrocardio, electroencephalogram, blood pressure, body temperature, respiratory frequency, blood oxygen saturation and the like, and are used for judging the state of illness of the patient and providing a physiological characteristic basis for follow-up accurate matching of nursing staff.
Step S200: performing attribute classification on the first physiological characteristic according to an emergency category characteristic decision tree to obtain first emergency characteristic information;
specifically, the first physiological feature is subjected to attribute classification according to an emergency category feature Decision Tree (Decision Tree), the Decision Tree (Decision Tree) is a Decision analysis method for evaluating the project risk and judging the feasibility of the project by constructing the Decision Tree on the basis of the known occurrence probability of various situations to obtain the probability that the expected value of the net present value is greater than or equal to zero, the Decision analysis method is a graphical method for intuitively applying probability analysis, and the classifier can give correct classification to newly-appeared objects and consists of a root node, an internal node and a leaf node. The physiological characteristics of the patient can be used as internal nodes of the emergency treatment category characteristic decision tree, the characteristics with the minimum entropy value can be classified preferentially by calculating the information entropy of the physiological characteristics, the emergency treatment category characteristic decision tree is constructed recursively by the method until the final characteristic leaf node cannot be subdivided, and the classification is finished, so that the emergency treatment category characteristic decision tree is formed, and the corresponding first emergency treatment characteristic information after the decision tree is classified is obtained, including the type, the severity, the emergency treatment medication and the like of the emergency treatment, so that the emergency treatment characteristic classification of the patient is more accurate, and the allocation accuracy of subsequent nursing staff is improved.
Step S300: analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result, wherein the first nursing condition analysis result comprises a nursing grade and a nursing type;
as shown in fig. 4, further, in which the performing of the care condition analysis on the first emergency characteristic information to obtain a first care condition analysis result further includes:
step S310: constructing a care plan coordinate system, wherein the care plan coordinate system is a multi-dimensional coordinate system;
step S320: performing regional labeling classification on the nursing scheme coordinate system to obtain a first label classification result;
step S330: inputting the first emergency characteristic information into the nursing scheme coordinate system to obtain a nursing scheme vector;
step S340: performing mapping matching according to the first label classification result and the nursing scheme vector to obtain a first nursing scheme;
step S350: determining a first care analysis result according to the first care plan.
Specifically, a nursing scheme coordinate system is established, the nursing scheme coordinate system is a multi-dimensional coordinate system, the coordinate axis is each feature information of emergency treatment, the nursing scheme comprises a nursing mode, nursing time, nursing medicine and the like, and analysis and classification are carried out according to the first emergency treatment feature information. And performing area labeling classification on the nursing scheme coordinate system, wherein different areas correspond to different label classification results, for example, different areas correspond to nursing schemes. Inputting the first emergency characteristic information into the nursing scheme coordinate system to obtain a nursing scheme vector corresponding to the patient, and performing mapping matching on the first label classification result according to the nursing scheme vector to obtain the matched first nursing scheme. According to the first care scheme, a first care condition analysis result is determined, the first care condition analysis result comprises care grades and care types, and graded care suitable for personal conditions of patients is achieved so as to perform professional matching care on the patients. The method for carrying out vector mapping by constructing the coordinate system of the nursing scheme is achieved, so that the classification result of the nursing scheme is more accurate, and the technical effect that the nursing effect of a patient is more accurate and effective is ensured.
Step S400: a nursing staff management file is constructed through a hospital staff management platform;
as shown in fig. 2, further, wherein the constructing a caregiver management profile, step S400 in this embodiment of the present application further includes:
step S410: obtaining a first care quality coefficient of a first caregiver;
step S420: performing workload assessment on the first nursing staff according to a preset assessment standard to obtain first nursing workload information;
step S430: evaluating the first nursing staff according to a nursing scoring index set to obtain first nursing satisfaction information;
step S440: performing weighted calculation on the first nursing quality coefficient, the first nursing workload information and the first nursing satisfaction degree information according to a preset weight distribution proportion to obtain first nursing staff capability score information;
step S450: and by analogy, acquiring the ability scoring information of a second nursing staff till the ability scoring information of the Nth nursing staff, and constructing a nursing staff management file according to the ability scoring information of the first nursing staff, the ability scoring information of the second nursing staff till the ability scoring information of the Nth nursing staff.
Particularly, a nursing staff management file is constructed through a hospital staff management platform, the nursing staff management file comprises basic information of nursing staff, field of excellence, nursing capacity and the like, the first nursing quality coefficient is the nursing quality of the first nursing staff to the patient, the nursing effect of the patient is judged, the workload of the first nursing staff is assessed according to a preset assessment standard, the preset assessment standard is a workload completion degree assessment standard specified in hospital nursing, and the more workload is completed, the more the work experience of the nursing staff is shown. And evaluating the first nursing staff according to a nursing scoring index set, wherein the nursing scoring index set is a scoring index for evaluating the satisfaction degree of the patient and the family members of the patient on the nursing staff, and comprises nursing timeliness, nursing attitude and the like, and the higher the first nursing satisfaction information is, the higher the satisfaction degree of the nursing staff is.
And performing weighted calculation on the first nursing quality coefficient, the first nursing workload information and the first nursing satisfaction degree information according to a preset weight distribution proportion, wherein the preset weight distribution proportion is a ratio of the nursing factors to the total caregiver capacity score, the calculated first caregiver capacity score information is obtained, and the higher the score is, the stronger the nursing capacity of the caregiver is. And by analogy, second caregiver ability scoring information is obtained till the Nth caregiver ability scoring information, a caregiver management file is constructed according to the first caregiver ability scoring information, the second caregiver ability scoring information till the Nth caregiver ability scoring information, and the caregiver management file is updated in real time so as to be more accurately matched with the physiological condition of the patient, so that the nursing effect is guaranteed.
Step S500: inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result;
step S600: based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability;
specifically, the first nursing condition analysis result and the caregiver management file are input into a nursing matching analysis model, the nursing matching analysis model is a neural network model and is used for matching the physiological state of the patient with the caregivers to obtain a first nursing matching result which is a training output result of the model, and the first nursing matching result is a set of caregivers matched with the patient. The nursing time point of the user is the time for ICU nursing of the patient, the nursing staff management file and the first mapping matching result are subjected to bidirectional matching based on the nursing time point of the user, namely, the nursing staff who can be allocated at the time point obtain an allocable nursing staff set, wherein the allocable nursing staff set is sorted according to nursing fitness, the nursing fitness is the matching degree between the patient and the nursing staff, and allocation is preferentially performed according to the nursing fitness, so that the nursing fitness is more accurate.
Step S700: and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard.
Specifically, the allotable caregiver set is subjected to allotment management according to a preset ICU management standard, wherein the preset ICU management standard is an ICU ward management standard and comprises patient caregiver proportion, equipment management, nursing shift and the like. The patient physiological condition and nursing ability of nursing staff are combined to carry out bidirectional intelligent selection and allocation, so that the labor cost is reduced, and the technical effects of allocation accuracy and allocation efficiency are improved.
As shown in fig. 3, further, wherein, in obtaining the first quality of care coefficient, step S410 of the embodiment of the present application further includes:
step S411: obtaining a value threshold of the nursing quality coefficient of the first nursing staff;
step S412: randomly obtaining N nursing quality coefficients from the value threshold of the nursing quality coefficient of the first nursing staff;
step S413: calculating the N nursing quality coefficients according to a genetic algorithm to obtain N predicted nursing effect curves, wherein the N predicted nursing effect curves correspond to the N nursing quality coefficients one to one;
step S414: obtaining an actual care effect curve of the first user;
step S415: and comparing the N predicted nursing effect curves with the actual nursing effect curve to obtain a first nursing quality coefficient, wherein the similarity between the predicted nursing effect curve corresponding to the first nursing quality coefficient and the actual nursing effect curve is the largest.
Specifically, the first nursing quality coefficient is the nursing quality of the first nursing staff to the patient, the nursing effect of the patient is used for judgment, the larger the nursing quality coefficient is, the stronger the nursing ability of the nursing staff is, and the value threshold is the fluctuation range of the nursing quality of the nursing staff. The essence of the genetic algorithm is that random search is continuously carried out in a solution space, new solutions are continuously generated in the search process, and a more optimal solution algorithm is reserved, so that the realization difficulty is low, and a satisfactory result can be obtained in a short time. The genetic algorithm directly operates the structural object when in use, has no limitation of derivation and function continuity, has inherent implicit parallelism and better global optimization capability, adopts a probabilistic optimization method, can automatically acquire and guide an optimized search space without determining rules, and adaptively adjusts the search direction, so the genetic algorithm is widely applied to various fields.
And randomly obtaining N nursing quality coefficients from the value threshold of the nursing quality coefficient of the first nursing staff, and calculating the N nursing quality coefficients according to a genetic algorithm to obtain N corresponding predicted nursing effect curves, wherein the N predicted nursing effect curves correspond to the N nursing quality coefficients one to one. The actual nursing effect curve of the first user is actual nursing effect record data of nursing staff nursing patients, the prediction curve with the closest similarity is obtained by comparing the N prediction nursing effect curves with the actual nursing effect curve, and the nursing quality coefficient corresponding to the prediction curve is the first nursing quality coefficient of the first nursing staff, so that the corresponding nursing quality coefficient obtained by calculation through a genetic algorithm is more accurate, the nursing ability of subsequent nursing staff is scored more reasonably and accurately, and the allocation accuracy and the allocation efficiency are further improved.
As shown in fig. 5, further, in which the performing mapping matching according to the first label classification result and the care plan vector to obtain a first care plan, step S340 in this embodiment of the present application further includes:
step S341: performing distance calculation on the nursing scheme vector to obtain an Euclidean distance data set;
step S342: obtaining a care scheme classification data set according to the Euclidean distance data set, wherein the care scheme classification data set is the shortest k distances in the Euclidean distance data set;
step S343: performing mapping matching according to the nursing scheme cleaning classification data set and the first label classification result to obtain a first classification result;
step S344: obtaining a first care plan according to the first classification result.
Specifically, the care plan vector is subjected to distance calculation to obtain a euclidean distance data set, which is a euclidean metric distance data set, i.e., a straight-line distance between two points in a coordinate system. The nursing plan classification data set is the shortest k distances in the Euclidean distance data set, and the k value is a part of the Euclidean distance data set and can be set by self. And performing mapping matching according to the nursing scheme cleaning classification data set and the first label classification result to obtain a classification label result corresponding to the vector, and determining a nursing scheme corresponding to the vector according to the classification result. The technical effects that the nursing scheme is classified and determined by a classification method for calculating the vector distance, the nursing effect on the patient is ensured, and the allocation accuracy of nursing staff is further ensured are achieved.
Further, step S450 in the embodiment of the present application further includes:
step S451: obtaining each index influence factor in the care scoring index set;
step S452: constructing a nursing satisfaction evaluation function according to the index influence factors, wherein the nursing satisfaction evaluation function is a multivariate linear function;
step S453: and inputting the index scoring information of the first nursing staff into the nursing satisfaction evaluation function to obtain first nursing satisfaction information.
Specifically, the nursing scoring index set is a scoring index for evaluating the satisfaction degree of patients and family members of the patients on nursing staff, and comprises nursing timeliness, nursing attitude and the like, wherein the influence factor of each index in the nursing scoring index set is the influence degree of each index on the satisfaction degree of the nursing staff, and the higher the influence factor is, the higher the importance degree of the index is. And constructing a nursing satisfaction evaluation function according to the influence factors of the indexes, wherein the nursing satisfaction evaluation function is a multivariate linear function, the dependent variable result is predicted or estimated by the optimal combination of a plurality of independent variables, and the satisfaction score of the nursing staff is obtained according to the sum of the products of the indexes and the influence factors corresponding to the indexes. And inputting the grading information of each index of the first nursing staff into the nursing satisfaction evaluation function to obtain an output result of the function, namely the first nursing satisfaction information, and predicting the result more accurately and effectively through the multivariate linear function, so that the method accords with the practical application and ensures the technical effect of more reasonable and accurate grading of the satisfaction of the nursing staff.
Further, step S453 of the embodiment of the present application further includes:
step S4531: obtaining a first correlation degree between the index influence factors;
step S4532: if the first association degree is within a preset association degree threshold value, taking the first association degree as a first influence factor;
step S4533: obtaining the nursing defect rate of the first nursing staff, and taking the nursing defect rate as a second influence factor;
step S4534: and calculating the average value of the first influence factor and the second influence factor to obtain a third influence factor and obtain second nursing satisfaction information.
Specifically, the first relevance between the influence factors of the indexes refers to a relevance between the indexes, such as a relevance between a nursing timeliness index and a nursing attitude index, and if the first relevance is within a predetermined relevance threshold, it indicates that the relevance between the two indexes is large, and needs to be considered in satisfaction evaluation, and the first relevance is taken as a first influence factor. And taking the nursing defect rate as a second influencing factor, wherein the nursing defect rate is the proportion of adverse events in nursing work of nursing staff, and the adverse events comprise medication hidden dangers, patient accidents, improper operation and other nursing errors. And calculating the average value of the first influence factor and the second influence factor to obtain a third influence factor, correcting the first nursing satisfaction information according to the third influence factor to obtain corrected second nursing satisfaction information, and considering the influence of multiple factors on nursing satisfaction, so that the ability of nursing staff is scored more reasonably and accurately, and the allocation accuracy and the allocation efficiency are improved.
To sum up, the intelligent dispatching method and system for the ICU nursing staff provided by the embodiment of the application have the following technical effects:
the first physiological characteristics are subjected to attribute classification according to the emergency category characteristic decision tree, so that first emergency characteristic information is obtained; analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result, wherein the first nursing condition analysis result comprises a nursing grade and a nursing type; a nursing staff management file is constructed through a hospital staff management platform; inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result; based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability; and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard. And then reach and carry out two-way intelligent selection allotment through combining patient's physiological conditions and nursing staff's nursing ability, reduce the cost of labor, and then improve allotment accuracy and allotment efficient technological effect.
Example two
Based on the same inventive concept as the intelligent dispatching method for the ICU nursing personnel in the previous embodiment, the invention also provides an intelligent dispatching system for the ICU nursing personnel, as shown in FIG. 6, the system comprises:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain a first physiological characteristic of a first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform attribute classification on the first physiological characteristic according to an emergency category characteristic decision tree, so as to obtain first emergency characteristic information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform nursing condition analysis on the first emergency characteristic information to obtain a first nursing condition analysis result, where the first nursing condition analysis result includes a nursing grade and a nursing type;
a first construction unit 14, the first construction unit 14 is used for constructing nursing staff management files through a hospital staff management platform;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to input the first nursing condition analysis result and the caregiver management file into a nursing matching analysis model to obtain a first nursing matching result;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to perform bidirectional matching on the caregiver management profile and the first mapping matching result based on a user care time point to obtain a set of dispatchable caregivers, where the set of dispatchable caregivers is sorted according to a care suitability;
a first management unit 17, said first management unit 17 being configured to perform a deployment management on said set of deployable caregivers according to a predetermined ICU management standard.
Further, the system further comprises:
a sixth obtaining unit for obtaining a first care quality coefficient for the first caregiver;
a seventh obtaining unit, configured to perform workload assessment on the first caregiver according to a predetermined assessment criterion, and obtain first nursing workload information;
an eighth obtaining unit, configured to evaluate the first caregiver according to a nursing scoring index set, and obtain first nursing satisfaction information;
a ninth obtaining unit, configured to perform weighted calculation on the first care quality coefficient, the first care workload information, and the first care satisfaction information according to a predetermined weight distribution ratio, and obtain first caregiver competence score information;
and the second construction unit is used for obtaining the ability scoring information of a second nursing staff till the Nth nursing staff ability scoring information by analogy, and constructing a nursing staff management file according to the first nursing staff ability scoring information, the second nursing staff ability scoring information till the Nth nursing staff ability scoring information.
Further, the system further comprises:
a tenth obtaining unit, configured to obtain a dereferencing threshold of the nursing quality coefficient of the first caregiver;
an eleventh obtaining unit, configured to randomly obtain N care quality coefficients from a value threshold of the care quality coefficient of the first caregiver;
a twelfth obtaining unit, configured to calculate the N care quality coefficients according to a genetic algorithm, and obtain N predicted care effect curves, where the N predicted care effect curves are in one-to-one correspondence with the N care quality coefficients;
a thirteenth obtaining unit for obtaining an actual care effect curve of the first user;
a fourteenth obtaining unit, configured to compare the N predicted care effect curves with the actual care effect curve to obtain a first care quality coefficient, where a similarity between a predicted care effect curve corresponding to the first care quality coefficient and the actual care effect curve is the largest.
Further, the system further comprises:
a third construction unit for constructing a care plan coordinate system, the care plan coordinate system being a multi-dimensional coordinate system;
a fifteenth obtaining unit, configured to perform area labeling classification on the care plan coordinate system to obtain a first label classification result;
a sixteenth obtaining unit, configured to input the first emergency characteristic information into the care plan coordinate system, and obtain a care plan vector;
a seventeenth obtaining unit, configured to perform mapping matching according to the first label classification result and the care plan vector to obtain a first care plan;
a first determination unit for determining a first care condition analysis result according to the first care plan.
Further, the system further comprises:
an eighteenth obtaining unit, configured to perform distance calculation on the care plan vector to obtain an euclidean distance dataset;
a nineteenth obtaining unit, configured to obtain a care plan classification dataset according to the euclidean distance dataset, where the care plan classification dataset is shortest k distances in the euclidean distance dataset;
a twentieth obtaining unit, configured to perform mapping matching according to the care plan washing classification dataset and the first label classification result, to obtain a first classification result;
a twenty-first obtaining unit for obtaining a first care plan according to the first classification result.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain each index impact factor in the care scoring index set;
a fourth construction unit, configured to construct a care satisfaction evaluation function according to the index influence factors, where the care satisfaction evaluation function is a multivariate linear function;
a twenty-third obtaining unit, configured to input the index score information of the first caregiver into the nursing satisfaction evaluation function, and obtain first nursing satisfaction information.
Further, the system further comprises:
a twenty-fourth obtaining unit configured to obtain a first degree of association between the index impact factors;
a first associating unit configured to take the first degree of association as a first influence factor if the first degree of association is within a predetermined degree of association threshold;
a twenty-fifth obtaining unit, configured to obtain a nursing defect rate of the first caregiver, and use the nursing defect rate as a second influence factor;
a twenty-sixth obtaining unit, configured to perform average calculation on the first influence factor and the second influence factor to obtain a third influence factor, and obtain second care satisfaction information.
Various changes and specific examples of the intelligent allocating method for the ICU caregiver in the first embodiment of fig. 1 are also applicable to the intelligent allocating system for the ICU caregiver in the present embodiment, and those skilled in the art can clearly understand the implementation method of the intelligent allocating system for the ICU caregiver in the present embodiment through the foregoing detailed description of the intelligent allocating method for the ICU caregiver, so for the brevity of the description, detailed descriptions are omitted here.
In addition, an embodiment of the present invention further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
Specifically, referring to fig. 7, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be global mobile communications devices, code division multiple access devices, global microwave interconnect access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, long term evolution advanced devices, universal mobile communications devices, enhanced mobile broadband devices, mass machine type communications devices, ultra-reliable low-latency communications devices, and the like.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer device-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the above method for controlling output data, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The above description is only a specific implementation of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the embodiments of the present invention, and all such changes or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent deployment method for ICU caregivers, wherein the method comprises the following steps:
obtaining a first physiological characteristic of a first user;
performing attribute classification on the first physiological characteristic according to an emergency category characteristic decision tree to obtain first emergency characteristic information;
analyzing the nursing condition of the first emergency characteristic information to obtain a first nursing condition analysis result, wherein the first nursing condition analysis result comprises a nursing grade and a nursing type;
a nursing staff management file is constructed through a hospital staff management platform;
inputting the first nursing condition analysis result and the nursing staff management file into a nursing matching analysis model to obtain a first nursing matching result;
based on a user nursing time point, performing bidirectional matching on the nursing staff management file and the first mapping matching result to obtain a set of allottable nursing staff, wherein the set of allottable nursing staff is sorted according to nursing suitability;
and performing allocation management on the set of allocable nursing staff according to a preset ICU management standard.
2. The method of claim 1, wherein the building a caregiver management profile comprises:
obtaining a first care quality coefficient of a first caregiver;
performing workload assessment on the first nursing staff according to a preset assessment standard to obtain first nursing workload information;
evaluating the first nursing staff according to a nursing scoring index set to obtain first nursing satisfaction information;
performing weighted calculation on the first nursing quality coefficient, the first nursing workload information and the first nursing satisfaction degree information according to a preset weight distribution proportion to obtain first nursing staff capability score information;
and by analogy, acquiring the ability scoring information of a second nursing staff till the ability scoring information of the Nth nursing staff, and constructing a nursing staff management file according to the ability scoring information of the first nursing staff, the ability scoring information of the second nursing staff till the ability scoring information of the Nth nursing staff.
3. The method of claim 2, wherein the obtaining a first quality of care coefficient comprises:
obtaining a value threshold of the nursing quality coefficient of the first nursing staff;
randomly obtaining N nursing quality coefficients from the value threshold of the nursing quality coefficient of the first nursing staff;
calculating the N nursing quality coefficients according to a genetic algorithm to obtain N predicted nursing effect curves, wherein the N predicted nursing effect curves correspond to the N nursing quality coefficients one to one;
obtaining an actual care effect curve of the first user;
and comparing the N predicted nursing effect curves with the actual nursing effect curve to obtain a first nursing quality coefficient, wherein the similarity between the predicted nursing effect curve corresponding to the first nursing quality coefficient and the actual nursing effect curve is the largest.
4. The method of claim 1, wherein performing a condition analysis on the first emergency characteristic information to obtain a first condition analysis result, the method comprising:
constructing a care plan coordinate system, wherein the care plan coordinate system is a multi-dimensional coordinate system;
performing regional labeling classification on the nursing scheme coordinate system to obtain a first label classification result;
inputting the first emergency characteristic information into the nursing scheme coordinate system to obtain a nursing scheme vector;
performing mapping matching according to the first label classification result and the nursing scheme vector to obtain a first nursing scheme;
determining a first care analysis result according to the first care plan.
5. The method of claim 4, wherein the mapping the first care plan according to the first label classification result and the care plan vector to obtain a first care plan comprises:
performing distance calculation on the nursing scheme vector to obtain an Euclidean distance data set;
obtaining a care scheme classification data set according to the Euclidean distance data set, wherein the care scheme classification data set is the shortest k distances in the Euclidean distance data set;
performing mapping matching according to the nursing scheme cleaning classification data set and the first label classification result to obtain a first classification result;
obtaining a first care plan according to the first classification result.
6. The method of claim 2, wherein the method comprises:
obtaining each index influence factor in the care scoring index set;
constructing a nursing satisfaction evaluation function according to the index influence factors, wherein the nursing satisfaction evaluation function is a multivariate linear function;
and inputting the index scoring information of the first nursing staff into the nursing satisfaction evaluation function to obtain first nursing satisfaction information.
7. The method of claim 6, wherein the method comprises:
obtaining a first correlation degree between the index influence factors;
if the first association degree is within a preset association degree threshold value, taking the first association degree as a first influence factor;
obtaining the nursing defect rate of the first nursing staff, and taking the nursing defect rate as a second influence factor;
and calculating the average value of the first influence factor and the second influence factor to obtain a third influence factor and obtain second nursing satisfaction information.
8. An intelligent compounding system for an ICU caregiver, the system comprising:
a first obtaining unit, configured to obtain a first physiological characteristic of a first user;
the second obtaining unit is used for carrying out attribute classification on the first physiological characteristic according to an emergency treatment category characteristic decision tree to obtain first emergency treatment characteristic information;
a third obtaining unit, configured to perform nursing condition analysis on the first emergency characteristic information to obtain a first nursing condition analysis result, where the first nursing condition analysis result includes a nursing grade and a nursing type;
the system comprises a first construction unit, a second construction unit and a third construction unit, wherein the first construction unit is used for constructing a nursing staff management file through a hospital staff management platform;
a fourth obtaining unit, configured to input the first nursing condition analysis result and the caregiver management file into a nursing matching analysis model to obtain a first nursing matching result;
a fifth obtaining unit, configured to perform bidirectional matching on the caregiver management profile and the first mapping matching result based on a user care time point to obtain a set of dispatchable caregivers, where the set of dispatchable caregivers is sorted according to a care suitability;
a first management unit, configured to perform deployment management on the deployable caregiver collection according to a predetermined ICU management standard.
9. An intelligent deployment electronics for ICU caregivers comprising a bus, a transceiver, a memory, a processor, and a computer program stored on and executable on said memory, said transceiver, said memory, and said processor being connected via said bus, wherein said computer program when executed by said processor implements the steps of the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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| CN202111476629.XA CN114141349A (en) | 2021-12-06 | 2021-12-06 | Intelligent allocation method and system for ICU nursing personnel |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN115775620A (en) * | 2023-02-13 | 2023-03-10 | 浙江妙智康健康科技有限公司 | Medical information management method and system based on artificial intelligence |
-
2021
- 2021-12-06 CN CN202111476629.XA patent/CN114141349A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN115775620A (en) * | 2023-02-13 | 2023-03-10 | 浙江妙智康健康科技有限公司 | Medical information management method and system based on artificial intelligence |
| CN115775620B (en) * | 2023-02-13 | 2023-04-21 | 浙江妙智康健康科技有限公司 | Medical information management method and system based on artificial intelligence |
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