CN114694779A - Method and system for improving nursing satisfaction degree of ICU patient - Google Patents
Method and system for improving nursing satisfaction degree of ICU patient Download PDFInfo
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Abstract
The invention provides a method and a system for improving ICU patient nursing satisfaction, wherein the method comprises the following steps: extracting the features of the first user basic information to obtain first user feature information which comprises a first disease feature and a first physiological feature; matching the first set of care metrics with the first care plan according to the first condition characteristics; performing relevance analysis on the first disease type through the first physiological characteristics to obtain a first relevance analysis result; inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result; and obtaining a first preset attribute and a second preset attribute, constructing a nursing satisfaction evaluation model, inputting the first nursing scheme and the first weight distribution result into the obtained first evaluation result, adjusting the first nursing scheme, and obtaining a second nursing scheme. The technical problem that the individual degree of the obtained care scheme is low in the prior art is solved.
Description
Technical Field
The invention relates to the technical field related to artificial intelligence, in particular to a method and a system for improving the nursing satisfaction degree of an ICU patient.
Background
ICU is critical medicine's abbreviation, and the patient that will be serious to a certain degree when the state of an illness will be sent to the ICU ward and guardianship, carries out comprehensive body and mind nursing control to the ICU patient, is favorable to patient's health recovery and treatment, in order to improve the nursing standard, generally need carry out continuous perfect to the nursing scheme according to actual nursing conditions, and then reach the purpose that improves the nursing satisfaction.
The determination of care regimens is currently typically made based on physician experience based on the type of condition of the patient, but the determination of care regimens based on physician experience is inefficient and the resulting care regimens may not be adaptable to the patient themselves due to lack of quantitative consideration of the patient's own condition.
In the prior art, the nursing scheme is determined mainly by manual experience, and quantitative consideration on the self condition of a patient is lacked, so that the technical problem that the obtained nursing scheme is low in degree of individuation exists.
Disclosure of Invention
The embodiment of the application provides a method and a system for improving the nursing satisfaction degree of an ICU patient, and solves the technical problem that in the prior art, due to the fact that the nursing scheme is mainly determined by means of manual experience and quantitative consideration on the self condition of the patient is lacked, the obtained nursing scheme is low in degree of individuation.
In view of the foregoing, embodiments of the present application provide a method and system for improving ICU patient care satisfaction.
In a first aspect, the present application provides a method for improving ICU patient care satisfaction, wherein the method comprises: performing feature extraction on first user basic information to obtain first user feature information, wherein the first user feature information comprises a first disease feature and a first physiological feature; matching a first set of care metrics with a first care plan based on the first condition characteristics; performing relevance analysis on the first disease type through the first physiological characteristics to obtain a first relevance analysis result; inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result; obtaining a first preset attribute and a second preset attribute, and constructing a nursing satisfaction evaluation model, wherein the first preset attribute represents the nursing index satisfaction rate, and the second preset attribute represents the nursing dissatisfaction degree; inputting the first care plan and the first weight assignment result into the care satisfaction evaluation model to obtain a first evaluation result; and adjusting the first nursing plan according to the first evaluation result to obtain a second nursing plan.
In another aspect, the present application provides a system for improving ICU patient care satisfaction, wherein the system comprises: the first obtaining unit is used for performing feature extraction on first user basic information to obtain first user feature information, wherein the first user feature information comprises a first disease feature and a first physiological feature; a first matching unit for matching a first set of care metrics with a first care plan according to the first condition characteristics; the second obtaining unit is used for carrying out association degree analysis on the first disease type through the first physiological characteristics to obtain a first association degree analysis result; the first generation unit is used for inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result; a third obtaining unit, configured to obtain a first preset attribute and a second preset attribute, and construct a nursing satisfaction evaluation model, where the first preset attribute represents a nursing index satisfaction rate, and the second preset attribute represents a nursing dissatisfaction degree; a fourth obtaining unit, configured to input the first care plan and the first weight assignment result into the care satisfaction evaluation model, and obtain a first evaluation result; a fifth obtaining unit, configured to adjust the first care plan according to the first evaluation result, so as to obtain a second care plan.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to any one of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method of any one of the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining physiological characteristics and disease characteristics by extracting characteristic information of a user to be nursed, and matching nursing indexes and historical nursing schemes according to the disease characteristics; analyzing the association degree of the physiological characteristics based on the type of the disease, determining the degree of influence of the physiological characteristics on the type of the disease, and recording as an analysis result of the association degree; carrying out weight distribution according to the correlation analysis result and the nursing index geometric nursing index; training an intelligent model based on historical data of nursing satisfaction evaluation, and determining satisfaction evaluation attributes including dissatisfaction indexes and dissatisfaction degrees; the method comprises the steps of inputting a historical care scheme and a weight distribution result, performing satisfaction evaluation on care indexes in the historical care scheme to obtain an evaluation result, further adjusting the historical care scheme according to the evaluation result to obtain a final care scheme, determining the association degree of physiological characteristics and diseases by using association degree analysis, performing weight distribution on a care index set according to the association degree analysis result, comprehensively analyzing physical characteristics of a patient, performing satisfaction evaluation on the historical care scheme and adjusting the historical care scheme based on the comprehensive analysis result, and achieving the technical effect of obtaining a care scheme with a high degree of individuation.
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.
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FIG. 1 is a schematic flow chart of a method for improving ICU patient care satisfaction according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating correlation analysis in a method for improving patient care satisfaction with an ICU according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a system for improving patient care satisfaction with an ICU according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first obtaining unit 11, a first matching unit 12, a second obtaining unit 13, a first generating unit 14, a third obtaining unit 15, a fourth obtaining unit 16, a fifth obtaining unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides a method and a system for improving the nursing satisfaction degree of an ICU (intensive care unit) patient, solves the technical problems that in the prior art, the nursing scheme is determined mainly by artificial experience, the condition of the patient is lack of quantitative consideration, the obtained individual degree of the nursing scheme is low, the association degree of physiological characteristics and diseases is determined by utilizing association degree analysis, then weight distribution is carried out on a nursing index set according to the association degree analysis result, the physical characteristics of the patient are comprehensively analyzed, the satisfaction degree of a historical nursing scheme is evaluated and adjusted based on the evaluation, and the technical effect of obtaining the nursing scheme with the high individual degree is achieved.
Summary of the application
The determination of the care plan of the ICU patient has important significance for the rehabilitation and treatment of the patient, the current technology relies on manual determination of the care plan to provide support for doctors, but the method is low in efficiency due to manual determination, and the obtained care plan has the technical problem of low patient adaptability due to the fact that quantitative analysis is not performed on the state of the patient.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a method and a system for improving the nursing satisfaction degree of an ICU patient, wherein the method comprises the steps of extracting characteristic information of a user to be nursed to obtain physiological characteristics and disease characteristics, and matching nursing indexes and historical nursing schemes according to the disease characteristics; analyzing the association degree of the physiological characteristics based on the type of the disease, determining the degree of influence of the physiological characteristics on the type of the disease, and recording as an analysis result of the association degree; carrying out weight distribution according to the correlation analysis result and the nursing index geometric nursing index; training an intelligent model based on historical data of nursing satisfaction evaluation, and determining satisfaction evaluation attributes including dissatisfaction indexes and dissatisfaction degrees; the method comprises the steps of inputting a historical nursing scheme and a weight distribution result, performing satisfaction evaluation on nursing indexes in the historical nursing scheme to obtain an evaluation result, further adjusting the historical nursing scheme according to the evaluation result to obtain a final nursing scheme, determining the association degree of physiological features and diseases by utilizing association degree analysis, performing weight distribution on a nursing index set according to the association degree analysis result, comprehensively analyzing the physical features of a patient, performing satisfaction evaluation and adjustment on the historical nursing scheme based on the comprehensive analysis result, and achieving the technical effect of obtaining a nursing scheme with a high individuation degree.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a method for improving ICU patient care satisfaction, wherein the method comprises:
s100: performing feature extraction on first user basic information to obtain first user feature information, wherein the first user feature information comprises a first disease feature and a first physiological feature;
specifically, the first user refers to a patient in an ICU ward that needs care; the first user basic information refers to the hospitalization basic information of the first user, including but not limited to: the basic information such as name, age, height, weight, disease type, medical history, complications, physiological monitoring data and the like.
The first user characteristic information refers to characteristic information representing the physical health state of the first user, preferably, the disease characteristics representing the physical state of the first user and complications thereof are extracted and recorded as first disease characteristics; and extracting and recording the physiological monitoring data representing the physical state of the first user, and recording the physiological monitoring data as a first physiological characteristic. The traditional nursing scheme is determined according to the types of the diseases through the experience of doctors, and the nursing scheme constructed based on the intelligent model has high individuation degree by collecting physiological characteristics and quantitatively analyzing different influence degrees among various types of physiological characteristics and the diseases, so that a solid information feedback basis can be provided for the doctors to carry out nursing work.
S200: matching a first set of care metrics with a first care plan based on the first condition characteristics;
specifically, the first care index set refers to a dimension set of indexes which can be customized according to the disease types and need to be cared, and exemplarily: if the patient is a cardiovascular disease person: the system comprises information such as nursing time, nursing action and patient reaction of various monitoring indexes of a cardiovascular system: including but not limited to: nursing time, nursing action and patient response of indexes such as heart rate, rhythm, cardiac output, heart stroke amount, left ventricular work index, right ventricular work index, central venous pressure, pulmonary artery pressure, pulmonary capillary incarceration pressure, systemic vascular resistance index, pulmonary vascular resistance index and the like;
if the patient is a respiratory disease person, the patient includes information such as nursing time, nursing actions, patient reactions and the like of monitoring indexes of the respiratory system, which is exemplarily shown as follows: tidal volume, minute ventilation, respiratory rate, respiratory amplitude, mean airway pressure, mean air resistance, inspiratory force, expiratory force, peak airway pressure, dynamic or static lung compliance, and the like.
Furthermore, the nursing time and nursing action of each monitoring index of the urinary system are included but not limited; nursing time, nursing actions, patient reactions and the like of all monitoring indexes of the central nervous system; the nursing time, nursing action, patient reaction and the like of various monitoring indexes of water, electrolyte and acid-base balance; nursing time, nursing action and the like of various monitoring indexes of the blood system; nursing time, nursing actions, patient reactions and the like of various monitoring indexes of the digestive system; nursing time, nursing actions, patient reactions and the like of various monitoring indexes of metabolism in the Newcastle. It should be noted that the care indexes determined for the disease types in the present application include the contents corresponding to the corresponding system disorders, and the indexes of other dimensions may also be added as secondary care indexes, and are determined according to actual care scenes, which is not limited herein.
The first care plan refers to care execution data of patients with the same type of symptoms in historical data, and includes but is not limited to multiple indexes to be monitored, care time nodes of the multiple monitoring indexes, normal intervals and abnormal intervals of the multiple monitoring data, care actions, care time lengths of the multiple monitoring indexes and the like.
Through setting up nursing time, nursing action and the nursing level that each item monitoring index of patient response concrete representation, further, be convenient for according to the timeliness of nursing time, the standardization of nursing action, the state of patient's reaction, the different nursing frequency quantification assessment nursing quality of multinomial nursing index, and then be convenient for carry out the adjustment of nursing scheme according to the assessment result, reach the technological effect that improves the nursing satisfaction.
S300: performing relevance analysis on the first disease type through the first physiological characteristics to obtain a first relevance analysis result;
specifically, the first relevancy analysis result refers to an analysis result of a set of characterization relevancy analysis performed on a plurality of first physiological characteristics and a first disease type to determine a gray relevancy analysis. The grey correlation degree analysis can evaluate the influence degree of other multi-dimensional factors on the reference element by setting the reference element, so that the importance degree of the multi-dimensional factors on the reference element can be determined, and differential analysis is facilitated, in the embodiment of the application, through a plurality of physiological monitoring indexes represented by the first physiological characteristics: including but not limited to: in the step S200, the vital sign indicators such as the monitoring indicators and the body temperature are respectively associated with the first disease type to determine the association of the physiological monitoring indicators to the first disease type, so that a differentiated physiological monitoring indicator care scheme can be set conveniently, and the technical effect of improving the individual degree of the care scheme is achieved.
S400: inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result;
specifically, the first weight distribution channel refers to a channel which is constructed based on a medical big data platform and used for distributing weights of multiple care indexes; the first weight distribution result refers to the analysis result which is determined by the first weight distribution channel and is used for representing the importance degree of the nursing index set.
The determination process is illustratively as follows: if the person is a cardiovascular disease person, the set care index dimensions comprise: cardiovascular system, respiratory system, water electrolyte and acid-base balance, central nervous system, blood system etc. dimension nursing time, nursing action, patient's reaction, every index dimension includes the monitoring index of a plurality of first physiological characteristics characterization. Performing primary weight distribution according to the relevance ranking results of the multiple physiological monitoring indexes in the relevance analysis results, wherein the total weight is 1, and the weights are the same if the relevance is the same; further, different weights of the nursing index dimensionality are determined according to the first weight distribution channel, and secondary weight distribution is carried out according to the primary weight distribution result and the different weights of the nursing index dimensionality to obtain a first weight distribution result. The method has the advantages that the weight distribution is carried out on the dimension with large nursing indexes according to the large data platform, the accuracy of the weight distribution result is improved, the weight distribution is carried out on the physiological monitoring indexes according to the association degree analysis result determined according to the physical state of the patient, the accuracy and the adaptability to the first user are further improved, and the technical effect of improving the individualization degree of the nursing scheme is achieved.
S500: obtaining a first preset attribute and a second preset attribute, and constructing a nursing satisfaction evaluation model, wherein the first preset attribute represents the nursing index satisfaction rate, and the second preset attribute represents the nursing dissatisfaction;
s600: inputting the first care plan and the first weight assignment result into the care satisfaction evaluation model to obtain a first evaluation result;
specifically, the first preset attribute refers to data representing nursing time, nursing actions, satisfaction rate of patient reactions, weight of multiple nursing indexes and matching degree of nursing time of the nursing indexes; the second preset attribute refers to the deviation degree when the satisfaction rates of the nursing time, the nursing action and the patient reaction which represent the nursing indexes do not reach the standard.
The nursing satisfaction evaluation model is an intelligent model constructed according to a neural network model, and the training process of the nursing satisfaction evaluation model is not limited: collecting historical care data according to a care plan of a first care plan, comprising: nursing time nodes of multiple nursing indexes, nursing actions, patient reactions, nursing duration of multiple nursing indexes and the like.
The satisfaction degree of the nursing time node is evaluated through determining an error preset interval of the nursing time, the satisfaction degree of the nursing action is evaluated through determining a nursing standard action, the satisfaction degree of the patient reaction is evaluated through determining an abnormal state (each index abnormal interval and the like) of the patient reaction, and the matching satisfaction degrees of the nursing time lengths and the weights of the multiple nursing indexes are evaluated through a first weight distribution result.
Collecting historical data in a plurality of groups of first care schemes as input data, setting information such as error preset intervals, care standard actions, abnormal intervals of various indexes, first weight distribution results and the like of care time as evaluation references, setting first preset attributes and second preset attributes as output constraint attributes, carrying out unsupervised training, and outputting more stable and accurate specific values of the first preset attributes and the second preset attributes which correspond to care indexes one by one and recording the specific values as first evaluation results after a care satisfaction evaluation model converges.
S700: and adjusting the first nursing plan according to the first evaluation result to obtain a second nursing plan.
Specifically, the second care scheme refers to a care scheme generated by determining satisfaction evaluation results of various care indexes through the first evaluation result and adjusting the indexes of which the satisfaction rates do not meet preset satisfaction rates (user-defined satisfaction rates) based on deviation degrees, and the fitness of the second care scheme and the first user is guaranteed due to the fact that the second care scheme is fitted with the first weight distribution result, and the provided care reference scheme achieves the technical effect of improving the individuation degree of the first user.
Further, as shown in fig. 2, based on the analyzing the association degree of the first disease type through the first physiological characteristic, a first association degree analysis result is obtained, and the step S300 includes:
s310: generating a first reference series of numbers according to the first type of condition;
s320: generating a first comparison sequence according to the first physiological characteristic;
s330: dimensionless processing is carried out on the first reference number sequence and the first comparison number sequence to obtain a second reference number sequence and a second comparison number sequence;
s340: traversing the second reference sequence and the second comparison sequence, and calculating a first association coefficient set;
s350: and obtaining a first association analysis result according to the first association coefficient set.
Specifically, the calculation of the degree of association is detailed as follows: the first baseline sequence refers to the result of characterizing the first type of condition for ease of calculation, preferably in the form of:
wherein
A first series of reference numbers is represented,
is indicative of a first type of condition,
indicates the type of complication, k indicates the number of complications, and there are r complications in total.
The first comparison series refers to the result of characterizing a plurality of first physiological characteristics for ease of calculation, preferably in the form of:
wherein
A first set of physiological characteristics representing a first comparison series and a first condition type corresponding to the ith group,
a first set of physiological characteristic features representing group i, and a first type of condition
In response to this, the mobile terminal is able to,
representing the total number of the ith group of first physiological characteristic feature sets, and assuming that the total number is L; the first disease type and the first physiological characteristic can be converted into data which can be identified by a computer through the serialized representation, and efficient and quick calling processing is facilitated.
Further, the second reference sequence and the second comparison sequence refer to results obtained after dimensionless adjustment is performed on the second reference sequence and the second comparison sequence respectively in order to unify dimensions of the first reference sequence and the first comparison sequence, and an adjustment manner is an example without limitation:
the dimension adjustment mode of the first reference sequence is as follows:
wherein, in the step (A),
a dimension adjustment result showing the kth care index in 1 to r in the first reference number series,
to represent
One value of any of the above-mentioned (b),
is firstThe standard deviation of the series of reference numbers,
a sample mean of the first reference series;
dimension adjustment mode of the first comparative series:
wherein, in the step (A),
a dimension adjustment result showing the k-th physiological characteristic in 1 to L in the first reference number series,
to represent
One value of any of the above-mentioned (b),
is the standard deviation of the first comparison series,
a sample mean of the first reference series;
dimension adjustment is completed by traversing the first reference number sequence and the first comparison number sequence, so that the uniformity of dimensions is ensured, and the error probability is reduced.
Further, the first correlation coefficient set refers to a coefficient that characterizes a degree of correlation between each second reference number sequence and its corresponding second comparison number sequence, and is preferably determined as follows:
and (3) difference sequence:
，
calculating the two pole differences:
，
calculating a correlation coefficient:
wherein the content of the first and second substances,
k-th of second contrast series representing i-th group1A sequence proximity between the physiological characteristic and a kth disorder type in a second baseline sequence corresponding to the ith group of second comparison sequences,
the resolution factor is a preset fixed value and is set by the working personnel,
k-th of second contrast series representing i-th group1A correlation coefficient between the physiological characteristic and a kth disorder type in a second baseline sequence corresponding to the ith second comparative sequence,
and
is to calculate
The set parameter values are two extreme values and are set by the staff. And traversing the first physiological characteristics corresponding to each disease type in the second reference sequence through the formula to obtain a plurality of correlation coefficients, thereby laying a data foundation for calculating the degree of correlation in the next step.
Further, the first correlation analysis result refers to a calculation result for calculating the correlation between each disease type and its corresponding physiological characteristic one by one based on the set of correlation coefficients, and preferably, the calculation result is calculated as follows: by the following formula:
，
refer to the kth of the ith group of second comparison series1A degree of association between the physiological characteristic and the kth disorder type in the second baseline sequence corresponding to the ith second comparative sequence,
to adjust for
In a form convenient for a computer to calculate the parameters. And traversing all the second reference series to correspondingly store the association degrees between the multiple groups of association degree set characterization disease types and the corresponding physiological characteristics thereof, and providing an information feedback basis for the subsequent process.
Further, based on the inputting of the first set of care metrics and the first relevancy analysis result into a first weight distribution channel for weight analysis, a first weight distribution result is generated, and step S400 includes:
s410: performing primary weight distribution on the first physiological characteristics according to the first association degree analysis result to obtain a primary weight distribution result;
s420: inputting the primary weight assignment result and the first set of care metrics into the first weight assignment channel, generating the first weight assignment result.
Specifically, the primary weight distribution result refers to a result of traversing the first disease condition type and the complication type thereof and performing weight distribution on the first physiological characteristic according to the first association analysis result. The allocation process is by way of example and not limitation:
if any one of the first disease type and the complication type thereof is traversed, extracting a corresponding association degree set of the physiological characteristics; further, calculating the sum of the relevance degrees, and using the ratio of the relevance degree of each physiological characteristic to the sum of the relevance degrees to represent the influence degree of each physiological characteristic on the type of the disease; furthermore, the physiological characteristics are adjusted in a serialized mode according to the weight from large to small, and a primary weight distribution result is generated and stored, so that the subsequent quick calling is facilitated.
Furthermore, the corresponding primary weight distribution result and the first nursing index set are input into the first weight distribution channel by traversing the disease types and the complication types of the disease types, the weight distribution is carried out on the first nursing index set based on the big data medical platform, the primary weight distribution result is fitted, the weight distribution result with higher adaptability to the first user is obtained, and a data feedback basis is provided for obtaining a nursing reference scheme with higher individuation degree.
Further, based on the inputting the primary weight assignment result and the first set of care metrics into the first weight assignment channel, generating the first weight assignment result, step S420 includes:
s421: obtaining a first scoring channel, a second scoring channel and an Nth scoring channel according to the first weight distribution channel, wherein the two scoring channels are in an information interaction isolation state, N is more than or equal to 15 and less than or equal to 25 and is a positive integer;
s422: inputting the first set of care metrics into the first scoring channel, the second scoring channel, and up to the Nth scoring channel, respectively, in parallel;
s423: obtaining a first scoring set, a second scoring set up to an Nth scoring set, wherein the scoring set is data that evaluates the importance of the first set of care metrics;
s424: adding all the scores to obtain a first total evaluation score;
s425: sequentially adding and calculating the grades of the nursing indexes to obtain a first nursing index evaluation total score, a second nursing index evaluation total score till an Mth nursing index evaluation total score, wherein M is the category of the nursing indexes;
s426: and constructing a first weight calculation formula by combining the primary weight distribution result, calculating the first evaluation total score and the first nursing index evaluation total score, and calculating the second nursing index evaluation total score until the Mth nursing index evaluation total score to obtain the first weight distribution result.
Specifically, the first scoring channel, the second scoring channel and the nth scoring channel refer to expert channels which are constructed based on a multi-party big data medical platform and score importance degrees of a nursing index set according to disease types and complication types of the disease types, information interaction isolation states are arranged among the N scoring channels, and N is more than or equal to 15 and less than or equal to 25 and is a positive integer. The first scoring set, the second scoring set and the nth scoring set are used for inputting the first nursing index set into the first scoring channel in parallel, and the second scoring channel and the nth scoring channel obtain scoring results representing the importance degree of the nursing indexes.
The first evaluation total score refers to the result of carrying out score addition calculation on the first score set, the second score set and the Nth score set; the first nursing index evaluation total score refers to a result of traversing the first score set, the second score set and the nth score set and performing summation calculation on the first nursing indexes in the first nursing index set, and if the first nursing index set has M nursing indexes, the second nursing index evaluation total score is further included until the mth nursing index evaluation total score.
Further, a first weight calculation formula is constructed:
wherein the content of the first and second substances,
m nursing index for characterizing the first disease type and its complication
A first weight assignment of the physiological characteristic,
the m-th overall index of care is characterized,
the first-assessment total score is characterized,
the mth nursing index for characterizing the first disease type and the corresponding complications
The physiological characteristics are based on the weight distribution result of the correlation degree; according to the first weight distribution formula, a first-level weight distribution result and a grading result can be fitted, the first total evaluation score and the first total nursing index evaluation score are traversed, the second total nursing index evaluation score is up to the Mth total nursing index evaluation score, the first weight distribution result is obtained, the obtained physiological characteristic monitoring index weight value and the first user have high fitness, and the accuracy of a nursing reference scheme obtained through the later analysis is guaranteed.
Further, the method step S421 includes:
s4211: obtaining an nth participating platform according to the first weight distribution channel, wherein N belongs to N and is a positive integer;
s4212: matching the first nursing index set on the nth participation platform to obtain nth family data;
s4213: and taking the nth family data as an input training data set, taking identification information of the data for identifying the importance degree of the first nursing index set as an output training data set, and constructing the nth scoring channel.
Specifically, the process of constructing the nth scoring channel is an example without limitation: the nth scoring channel is a channel used to score the importance of the first set of care metrics.
The nth participation platform refers to a data supply medical platform for constructing the nth scoring channel; the nth family data refers to a matched, care history data set searched at the nth participation platform according to the disease type and the first set of care metrics.
And when the data volume of the nth family data does not meet the preset data volume, preferably, evaluating the index importance degree through the expert opinions in the nth participation platform, wherein the preset data volume represents the lowest training data volume for constructing the intelligent model.
When the data volume of the nth family data meets the preset data volume, preferably, an intelligent model is constructed based on an expert system to evaluate the importance of the indexes, wherein the training mode is as follows: and training and constructing an expert system by taking the nth family data as an input training data set and taking identification information for identifying the data of the importance degree of the first care index set as an output training data set, wherein the expert system refers to an intelligent system for solving complex problems which can only be solved by experts by applying knowledge and reasoning steps, and the grading data of the importance degree of the experts in the nth participation platform to the first care index set can be fitted through training and constructing a large amount of knowledge data of the nth participation platform. Based on the same mode, scoring channels of N platforms are constructed, and the N platforms process information isolation states, so that the objectivity and the accuracy of the obtained scoring result are improved.
Further, the method step S420 further includes:
s427: returning the first weight distribution result to the first scoring channel, and obtaining first feedback information from the second scoring channel to the Nth scoring channel;
s428: and when the first feedback information does not meet a first preset requirement, correcting the first weight distribution result through the first feedback information to obtain a second weight distribution result.
Specifically, the first feedback information refers to feedback information obtained by parallelly feeding back the first weight distribution result to the first scoring channel and feeding back the first weight distribution result to the nth scoring channel when the calculation of the first weight distribution result is completed, namely, data representing the recognition degree of the N scoring channels to the first weight distribution result; the first preset requirement refers to that when the modification directions of the feedback information of the N scoring channels to a certain index weight are consistent and exceed a preset number, and the default value is 0.5N, the first weight distribution result is recorded as a second weight distribution result according to the first feedback information. By returning the first weight distribution result to each scoring channel for re-evaluation, the modified weight distribution result has stronger objectivity and higher accuracy.
Further, a nursing satisfaction evaluation model is constructed based on the obtained first preset attribute and the second preset attribute, and step S600 includes:
s610: constructing a first nursing satisfaction evaluation function according to the first preset attribute;
s620: constructing a second nursing satisfaction evaluation function according to the second preset attribute;
s630: generating a first nursing satisfaction evaluation submodel based on the first nursing satisfaction evaluation function and a first preset satisfaction;
s640: generating a second nursing satisfaction evaluation submodel based on the second nursing satisfaction evaluation function and a first preset deviation degree;
s650: and fully connecting the first nursing satisfaction evaluation submodel and the second nursing satisfaction evaluation submodel to generate the nursing satisfaction evaluation model.
Specifically, the first care satisfaction evaluation function refers to a function for evaluating a satisfaction rate of a care index, and
the detection result in the evaluation process is the ith physiological characteristic nursing scheme of the mth nursing index of the first disease type in the first nursing index set
The upper limit of the deviation of the physiological characteristics in the mth care index in the first care index set is set to
The lower limit of the degree of deviation is
(ii) a Then can be combined with
Or
As the degree of the deviation, it is preferable that,
has a value range of (0, 1), then:
If the ith physiological characteristic care plan of the mth care index of the first disease type is qualified, the method comprises the steps of
And = 0. If the ith physiological characteristic nursing scheme of the mth nursing index of the first disease type is unqualified, the following steps are carried out:
wherein the content of the first and second substances,
number of first disorder type at mth care index for first disorder type: the total number of major + complications.
Large indicates that the first disorder type is more dissatisfied at the nth point of care indicator. Of all care-indicators of the first condition typeThe dissatisfaction function is:
wherein the content of the first and second substances,
and the ith physiological characteristic of the m-th detection index is under the first disease type, the primary weight distribution result and the first nursing index set, and the corresponding weight is distributed in the first weight distribution result, so that the construction of the first nursing satisfaction evaluation function and the second nursing satisfaction evaluation function is completed.
The staff can set a first preset satisfaction and a first preset deviation according to the actual nursing scene, the first preset satisfaction has two values including the integral preset satisfaction and the local preset satisfaction, and when the first preset satisfaction is higher than the local preset satisfaction
If the overall preset satisfaction is not met, the nursing scheme corresponding to the corresponding disease type is represented to be not met, and if the overall preset satisfaction is not met, the nursing scheme corresponding to the corresponding disease type is represented to be not met
The nursing index which does not meet the local preset satisfaction degree needs to be adjusted, and further, the nursing index correspondingly has
The nursing scheme of the physiological characteristic monitoring information which does not meet the first preset deviation degree needs to be adjusted, a first nursing satisfaction degree evaluation sub-model and a second nursing satisfaction degree evaluation sub-model are constructed by further combining a first nursing satisfaction degree evaluation function and a second nursing satisfaction degree evaluation function and are combined to obtain a nursing satisfaction degree evaluation model, the evaluation on the original nursing scheme of the first disease can be completed, the unsatisfied indexes and the deviation degree of the physiological characteristic nursing subordinate to the unsatisfied indexes can be analyzed, the nursing scheme based on the adjustment is higher in adaptability to patients, the nursing scheme set by workers has more important reference significance, and the nursing satisfaction degree of the nursing can be effectively improvedThe intention and the efficiency of the formulation of the care plan are improved.
In summary, the method and the system for improving the care satisfaction of the ICU patient provided by the embodiments of the present application have the following technical effects:
1. the method comprises the steps of extracting characteristic information of a user to be nursed to obtain physiological characteristics and disease characteristics, and matching nursing indexes and historical nursing schemes according to the disease characteristics; analyzing the association degree of the physiological characteristics based on the type of the disease, determining the degree of influence of the physiological characteristics on the type of the disease, and recording as an analysis result of the association degree; carrying out weight distribution according to the correlation analysis result and the nursing index geometric nursing index; training an intelligent model based on historical data of nursing satisfaction evaluation, and determining satisfaction evaluation attributes including dissatisfaction indexes and dissatisfaction degrees; the method comprises the steps of inputting a historical care scheme and a weight distribution result, performing satisfaction evaluation on care indexes in the historical care scheme to obtain an evaluation result, further adjusting the historical care scheme according to the evaluation result to obtain a final care scheme, determining the association degree of physiological characteristics and diseases by using association degree analysis, performing weight distribution on a care index set according to the association degree analysis result, comprehensively analyzing physical characteristics of a patient, performing satisfaction evaluation on the historical care scheme and adjusting the historical care scheme based on the comprehensive analysis result, and achieving the technical effect of obtaining a care scheme with a high degree of individuation.
Example two
Based on the same inventive concept as the method for improving the ICU patient care satisfaction in the previous embodiment, as shown in FIG. 3, the present embodiment provides a system for improving the ICU patient care satisfaction, wherein the system comprises:
a first obtaining unit 11, configured to perform feature extraction on first user basic information to obtain first user feature information, where the first user feature information includes a first disease feature and a first physiological feature;
a first matching unit 12 for matching a first set of care metrics with a first care plan according to the first condition characteristics;
a second obtaining unit 13, configured to perform relevance analysis on the first disease type through the first physiological characteristic, so as to obtain a first relevance analysis result;
a first generating unit 14, configured to input the first set of care metrics and the first association analysis result into a first weight distribution channel for weight analysis, so as to generate a first weight distribution result;
a third obtaining unit 15, configured to obtain a first preset attribute and a second preset attribute, and construct a nursing satisfaction evaluation model, where the first preset attribute represents a nursing index satisfaction rate, and the second preset attribute represents a nursing dissatisfaction;
a fourth obtaining unit 16, configured to input the first care plan and the first weight assignment result into the care satisfaction evaluation model, and obtain a first evaluation result;
a fifth obtaining unit 17, configured to adjust the first care plan according to the first evaluation result, and obtain a second care plan.
Further, the system further comprises:
a second generation unit configured to generate a first reference number sequence according to the first disease type;
the third generating unit is used for generating a first contrast sequence according to the first physiological characteristic;
a sixth obtaining unit, configured to perform dimensionless processing on the first reference number sequence and the first comparison number sequence to obtain a second reference number sequence and a second comparison number sequence;
the first calculation unit is used for traversing the second reference sequence and the second comparison sequence and calculating a first association coefficient set;
and a seventh obtaining unit, configured to obtain a first association analysis result according to the first association coefficient set.
Further, the system further comprises:
an eighth obtaining unit, configured to perform primary weight distribution on the first physiological characteristic according to the first association analysis result, so as to obtain a primary weight distribution result;
a fourth generating unit, configured to input the primary weight distribution result and the first set of care metrics into the first weight distribution channel, and generate the first weight distribution result.
Further, the system further comprises:
a ninth obtaining unit, configured to obtain a first scoring channel, a second scoring channel, and an nth scoring channel according to the first weight distribution channel, where each two scoring channels are in an information interaction isolation state, N is greater than or equal to 15 and less than or equal to 25, and is a positive integer;
a first input unit, configured to input the primary weight assignment result and the first set of care metrics into the first scoring channel, the second scoring channel, and up to the nth scoring channel, respectively, in parallel;
a tenth obtaining unit, configured to obtain a first scoring set, a second scoring set, and up to an nth scoring set, where the scoring set is data for evaluating importance of the first care index set;
an eleventh obtaining unit, configured to sum all the scores to obtain a first total evaluation score;
a twelfth obtaining unit, configured to sequentially sum the scores of the care indexes to obtain a first total score of the care index evaluations, where the second total score of the care index evaluations is up to an mth total score of the care index evaluations, and M is a category of the care index;
a thirteenth obtaining unit, configured to construct a first weight calculation formula according to the primary weight distribution result, calculate the first total evaluation score and the first total care indicator evaluation score, and calculate the second total care indicator evaluation score until the mth total care indicator evaluation score, so as to obtain the first weight distribution result.
Further, the system further comprises:
a fourteenth obtaining unit, configured to obtain an nth participating platform according to the first weight distribution channel, where N belongs to N and is a positive integer;
a fifteenth obtaining unit, configured to perform matching on the nth participation platform according to the primary weight distribution result and the first care index set, so as to obtain nth family data;
the first construction unit is used for taking the nth family data as an input training data set, taking identification information of the data for identifying the importance degree of the first nursing index set as an output training data set, and constructing the nth scoring channel.
Further, the system further comprises:
a sixteenth obtaining unit, configured to return the first weight assignment result to the first scoring channel, where the second scoring channel reaches the nth scoring channel, and obtain first feedback information;
a seventeenth obtaining unit, configured to, when the first feedback information does not meet a first preset requirement, correct the first weight distribution result through the first feedback information, and obtain a second weight distribution result.
Further, the system further comprises:
the second construction unit is used for constructing a first nursing satisfaction evaluation function according to the first preset attribute;
the third construction unit is used for constructing a second nursing satisfaction evaluation function according to the second preset attribute;
the fifth generating unit is used for generating a first nursing satisfaction evaluation submodel based on the first nursing satisfaction evaluation function and the first preset satisfaction;
a sixth generating unit, configured to generate a second care satisfaction evaluation submodel based on the second care satisfaction evaluation function and a first preset deviation;
and the seventh generating unit is used for fully connecting the first nursing satisfaction evaluation submodel and the second nursing satisfaction evaluation submodel to generate the nursing satisfaction evaluation model.
EXAMPLE III
Based on the same inventive concept as the method for improving the patient care satisfaction of the ICU in the previous embodiment, the present application also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements the method of any one of the embodiments.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 4.
Based on the same inventive concept as the method for improving the ICU patient care satisfaction in the previous embodiment, the present application also provides an electronic device, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact disc read-only memory (compact disc)
read-only memory, CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute computer-executable instructions stored in the memory 301 to implement a method for improving patient care satisfaction with an ICU as provided by the above-described embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a method and a system for improving ICU patient nursing satisfaction, which are characterized in that the method comprises the steps of extracting characteristic information of a user to be nursed to obtain physiological characteristics and disease characteristics, and matching nursing indexes and historical nursing schemes according to the disease characteristics; analyzing the association degree of the physiological characteristics based on the type of the disease, determining the degree of influence of the physiological characteristics on the type of the disease, and recording as an analysis result of the association degree; carrying out weight distribution according to the correlation analysis result and the nursing index geometric nursing index; training an intelligent model based on historical data of nursing satisfaction evaluation, and determining satisfaction evaluation attributes including dissatisfaction indexes and dissatisfaction degrees; the method comprises the steps of inputting a historical care scheme and a weight distribution result, performing satisfaction evaluation on care indexes in the historical care scheme to obtain an evaluation result, further adjusting the historical care scheme according to the evaluation result to obtain a final care scheme, determining the association degree of physiological characteristics and diseases by using association degree analysis, performing weight distribution on a care index set according to the association degree analysis result, comprehensively analyzing physical characteristics of a patient, performing satisfaction evaluation on the historical care scheme and adjusting the historical care scheme based on the comprehensive analysis result, and achieving the technical effect of obtaining a care scheme with a high degree of individuation.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer finger
The instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, where the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by general purpose processors, digital signal processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic systems, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside as discrete components in a terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (10)
1. A method of increasing ICU patient care satisfaction, the method comprising:
performing feature extraction on first user basic information to obtain first user feature information, wherein the first user feature information comprises a first disease feature and a first physiological feature;
matching a first set of care metrics with a first care plan based on the first condition characteristics;
performing relevance analysis on the first disease type through the first physiological characteristics to obtain a first relevance analysis result;
inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result;
obtaining a first preset attribute and a second preset attribute, and constructing a nursing satisfaction evaluation model, wherein the first preset attribute represents the nursing index satisfaction rate, and the second preset attribute represents the nursing dissatisfaction;
inputting the first care plan and the first weight assignment result into the care satisfaction evaluation model to obtain a first evaluation result;
and adjusting the first nursing plan according to the first evaluation result to obtain a second nursing plan.
2. The method of claim 1, wherein performing a relevancy analysis on the first condition type through the first physiological characteristic to obtain a first relevancy analysis result comprises:
generating a first reference series of numbers according to the first type of condition;
generating a first comparison sequence according to the first physiological characteristic;
dimensionless processing is carried out on the first reference number sequence and the first comparison number sequence to obtain a second reference number sequence and a second comparison number sequence;
traversing the second reference sequence and the second comparison sequence, and calculating a first association coefficient set;
and obtaining a first association analysis result according to the first association coefficient set.
3. The method of claim 1, wherein the inputting the first set of care metrics and the first relevancy analysis result into a first weight distribution channel for weight analysis to generate a first weight distribution result comprises:
performing primary weight distribution on the first physiological characteristics according to the first association degree analysis result to obtain a primary weight distribution result;
inputting the primary weight assignment result and the first set of care metrics into the first weight assignment channel, generating the first weight assignment result.
4. The method of claim 3, wherein said inputting the primary weight assignment result and the first set of care metrics into the first weight assignment channel, generating the first weight assignment result, comprises:
obtaining a first scoring channel, a second scoring channel and an Nth scoring channel according to the first weight distribution channel, wherein the two scoring channels are in an information interaction isolation state, N is more than or equal to 15 and less than or equal to 25 and is a positive integer;
inputting the first set of care metrics into the first scoring channel, the second scoring channel, and up to the Nth scoring channel, respectively, in parallel;
obtaining a first scoring set, a second scoring set up to an Nth scoring set, wherein the scoring set is data that evaluates the importance of the first set of care metrics;
adding all the scores to obtain a first total evaluation score;
sequentially adding and calculating the grades of the nursing indexes to obtain a first nursing index evaluation total score, a second nursing index evaluation total score till an Mth nursing index evaluation total score, wherein M is the category of the nursing indexes;
and constructing a first weight calculation formula by combining the primary weight distribution result, calculating the first evaluation total score and the first nursing index evaluation total score, and calculating the second nursing index evaluation total score until the Mth nursing index evaluation total score to obtain the first weight distribution result.
5. The method of claim 4, wherein the method comprises:
obtaining an nth participating platform according to the first weight distribution channel, wherein N belongs to N and is a positive integer;
matching the first nursing index set on the nth participation platform to obtain nth family data;
and taking the nth family data as an input training data set, taking identification information of the data for identifying the importance degree of the first nursing index set as an output training data set, and constructing the nth scoring channel.
6. The method of claim 4, wherein the method further comprises:
returning the first weight distribution result to the first scoring channel, and obtaining first feedback information from the second scoring channel to the Nth scoring channel;
and when the first feedback information does not meet a first preset requirement, correcting the first weight distribution result through the first feedback information to obtain a second weight distribution result.
7. The method of claim 1, wherein obtaining the first preset attribute and the second preset attribute, constructing a care satisfaction assessment model, comprises:
constructing a first nursing satisfaction evaluation function according to the first preset attribute;
constructing a second nursing satisfaction evaluation function according to the second preset attribute;
generating a first nursing satisfaction evaluation submodel based on the first nursing satisfaction evaluation function and a first preset satisfaction;
generating a second nursing satisfaction evaluation submodel based on the second nursing satisfaction evaluation function and a first preset deviation degree;
and fully connecting the first nursing satisfaction evaluation submodel and the second nursing satisfaction evaluation submodel to generate the nursing satisfaction evaluation model.
8. A system for improving ICU patient care satisfaction, said system comprising:
the first obtaining unit is used for performing feature extraction on first user basic information to obtain first user feature information, wherein the first user feature information comprises a first disease feature and a first physiological feature;
a first matching unit for matching a first set of care metrics with a first care plan according to the first condition characteristics;
the second obtaining unit is used for carrying out association degree analysis on the first disease type through the first physiological characteristics to obtain a first association degree analysis result;
the first generation unit is used for inputting the first nursing index set and the first association degree analysis result into a first weight distribution channel for weight analysis, and generating a first weight distribution result;
a third obtaining unit, configured to obtain a first preset attribute and a second preset attribute, and construct a nursing satisfaction evaluation model, where the first preset attribute represents a nursing index satisfaction rate, and the second preset attribute represents a nursing dissatisfaction;
a fourth obtaining unit, configured to input the first care plan and the first weight assignment result into the care satisfaction evaluation model, and obtain a first evaluation result;
a fifth obtaining unit, configured to adjust the first care plan according to the first evaluation result, so as to obtain a second care plan.
9. An electronic device, comprising: a processor coupled to a memory for storing a program, wherein the program, when executed by the processor, causes a system to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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| CN116665871A (en) * | 2023-08-02 | 2023-08-29 | 上海迎智正能文化发展有限公司 | Monitoring scheme optimization method and system based on big data |
| CN116665871B (en) * | 2023-08-02 | 2023-11-03 | 上海迎智正能文化发展有限公司 | Monitoring scheme optimization method and system based on big data |
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