CN110796382A - Assessment analysis method and system applied to nursing subject - Google Patents

Assessment analysis method and system applied to nursing subject Download PDF

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CN110796382A
CN110796382A CN201911058642.6A CN201911058642A CN110796382A CN 110796382 A CN110796382 A CN 110796382A CN 201911058642 A CN201911058642 A CN 201911058642A CN 110796382 A CN110796382 A CN 110796382A
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陈肖敏
过湘钗
裘文娟
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Zhejiang Provincial Peoples Hospital
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Abstract

The invention relates to the technical field of evaluation and analysis of nursing disciplines, and discloses an evaluation and analysis method applied to the nursing disciplines, which comprises the following steps: performing hierarchical analysis on the nursing disciplines, establishing a hierarchical evaluation model of the nursing disciplines, and calculating the weight of each evaluation submodel and the weight of each evaluation subentry of the evaluation submodel; the nursing science big data platform is in interactive communication with an evaluation user of a client through electronic nursing discipline display evaluation system software, and big data of index scores of all evaluation sub-items of the nursing discipline are acquired; calculating index scores of the nursing disciplines; the technical problems that the existing evaluation analysis method lacks objective evaluation standards for the construction conditions of the nursing disciplines, cannot scientifically evaluate the true level of the construction conditions of the nursing disciplines, and cannot ensure the evaluation analysis quality of the construction conditions of the nursing disciplines are solved. The invention also provides an evaluation and analysis system applied to the nursing subject.

Description

Assessment analysis method and system applied to nursing subject
Technical Field
The invention relates to the technical field of evaluation and analysis of nursing subjects, in particular to an evaluation and analysis method and system applied to the nursing subjects.
Background
Since the innovation, China initially establishes a medical service system which radiates nationwide, covers urban and rural areas and is complete in specialties. Among them, nursing work is an important component of medical service work, and plays an increasingly important role. With the continuous deepening of medical and health system innovation, it is very important to strengthen the nursing connotation construction, improve the overall strength of the nursing discipline and meet the increasing health requirements, which puts higher requirements on the construction of the nursing discipline.
At present, each hospital has a method for promoting the construction and development of the respective nursing subject, but the method generally lacks an objective evaluation standard for the construction condition of the nursing subject, cannot scientifically evaluate the true level of the construction condition of the nursing subject, and cannot ensure the evaluation and analysis quality of the construction condition of the nursing subject.
Therefore, how to objectively measure and scientifically evaluate the construction condition of the nursing subject and how to ensure the quality of evaluation and analysis of the construction condition of the nursing subject are very important and urgent problems to be faced at present.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an evaluation analysis method applied to nursing subjects, which aims to solve the technical problems that the existing evaluation analysis method lacks objective evaluation standards for the construction conditions of the nursing subjects, cannot scientifically evaluate the true level of the construction conditions of the nursing subjects and cannot ensure the evaluation analysis quality of the construction conditions of the nursing subjects;
meanwhile, the invention also provides an evaluation analysis system applied to the nursing subject, which realizes the technical purpose of lean management of the construction of the nursing subject by performing objective and scientific evaluation analysis on the construction condition of the nursing subject.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme:
an evaluation analysis method applied to a nursing subject, comprising the following steps:
the method comprises the following steps: the method comprises the following steps of performing hierarchical analysis on the nursing subject:
(1) determining hierarchical evaluation dimensions of the nursing discipline, wherein the hierarchical evaluation dimensions comprise precision evaluation, depth evaluation, breadth evaluation, thickness evaluation and temperature evaluation;
(2) establishing a hierarchical evaluation model of the nursing subject according to the hierarchical evaluation dimension, wherein the hierarchical evaluation model comprises a precision evaluation submodel, a depth evaluation submodel, an breadth evaluation submodel, a thickness evaluation submodel and a temperature evaluation submodel;
(3) constructing a first-layer contrast matrix A according to the importance level of each evaluation submodel in the hierarchical evaluation model, and calculating the weight of each evaluation submodel;
(4) further refining each evaluation submodel in the hierarchical evaluation model into specific evaluation submodels;
(5) constructing a second layer contrast matrix A corresponding to the precision evaluation submodel, the depth evaluation submodel, the breadth evaluation submodel, the thickness evaluation submodel and the temperature evaluation submodel respectively1、A2、A3、A4、A5(ii) a Then, the following calculations are performed in sequence: the weight of each evaluation sub-item of the precision evaluation sub-model, the weight of each evaluation sub-item of the depth evaluation sub-model, the weight of each evaluation sub-item of the breadth evaluation sub-model, the weight of each evaluation sub-item of the thickness evaluation sub-model and the weight of each evaluation sub-item of the temperature evaluation sub-model;
step two: manufacturing the construction condition of the nursing subject into a nursing subject electronic display evaluation system with an interactive communication function, and pushing client software of the nursing subject electronic display evaluation system to a client of an evaluation user through a network platform;
the nursing science big data platform is in interactive communication with an evaluation user of a client through electronic nursing discipline display evaluation system software, and big data of index scores of all evaluation sub-items of the nursing discipline are acquired;
step three: dividing the big data of the index scores of the evaluation sub-items collected in the step two into two types, specifically: evaluation data A belonging to the professional field of the nursing subject and evaluation data B not belonging to the professional field of the nursing subject;
calculating the mean value of the evaluation data A as UAThe mean value of the evaluation data B was UBAccording to the formula S ═ x% UA+(100-x)％UBCalculating index scores S of all the evaluation sub-items; wherein x is more than or equal to 60;
step four: calculating the index score of the corresponding evaluation sub-model according to the index score S of each evaluation sub-item and the weight of each evaluation sub-item of the evaluation sub-model corresponding to the index score S of each evaluation sub-item;
and calculating the index scores of the nursing disciplines according to the index scores of the evaluation submodels and the weights of the evaluation submodels corresponding to the index scores of the evaluation submodels.
Further, the accuracy evaluation submodel includes: one or more of a nursing management system evaluation sub-item, a regulation evaluation sub-item, a post responsibility evaluation sub-item, a quality monitoring evaluation sub-item and a security culture evaluation sub-item;
the depth evaluation submodel includes: one or more of an academic occupational evaluation sub-item, a professional commission evaluation sub-item, a specialist base evaluation sub-item, a specialist nurse evaluation sub-item, and a specialist characteristic evaluation sub-item;
the breadth evaluation submodel comprises: one or more of an academic exchange evaluation sub-item, a help guide evaluation sub-item, a progress study evaluation sub-item, a practice education evaluation sub-item and a undertaking relay class evaluation sub-item at home and abroad;
the thickness evaluation submodel includes: the talents culture and evaluation sub-items, talent constitution and evaluation sub-items, scientific research result evaluation sub-items and book publishing and evaluation sub-items;
the temperature evaluation submodel comprises: and the system comprises one or more of a human system evaluation sub-item, a human activity evaluation sub-item, a promotion incentive mechanism evaluation sub-item, a professional protection evaluation sub-item and an improvement service evaluation sub-item.
Further, the third step further includes: after the maximum value and the minimum value of the evaluation data A are removed, the average value of the rest evaluation data A is calculated to be UA(ii) a After the maximum value and the minimum value of the evaluation data B are removed, the average value of the rest evaluation data B is calculated to be UB(ii) a Then, 70% U is obtained according to the formula SA+30％UBCalculating the respective appraisersThe index score S of the item.
An assessment analysis system for use in a care discipline, comprising: the system comprises a local server A, a local server B, an intelligent terminal and/or a PC (personal computer) terminal and a local server C, wherein the local server A is provided with and runs nursing subject hierarchical analysis software, the local server B is provided with and runs nursing subject electronic display evaluation system software and belongs to a nursing subject big data platform, the intelligent terminal and/or the PC terminal is provided with and runs an evaluation user of the nursing subject electronic display evaluation system software, and the local server C is provided with and runs nursing subject big data analysis processing software;
the CPU of the local server A and the CPU of the local server C realize mutual communication connection through a serial port master-slave mode communication mechanism, the CPU of the local server B is a master device, and the CPU of the local server C is a slave device;
the local server B is in communication connection with the intelligent terminal and/or the PC terminal on the nursing subject electronic display evaluation system software through network communication equipment;
the CPU of the local server B of the nursing science and technology data platform is in communication connection with the CPU of the local server C through a serial port master-slave mode communication mechanism, the CPU of the local server B is a slave device, and the CPU of the local server C is a master device;
the nursing subject hierarchical analysis software is used for carrying out hierarchical analysis on the nursing subject, establishing a hierarchical evaluation model of the nursing subject, and calculating the weight of each evaluation submodel and the weight of each evaluation subentry of each evaluation submodel;
the electronic display evaluation system software of the nursing discipline is used for displaying the construction condition of the nursing discipline and evaluating each evaluation sub item of the construction condition of the nursing discipline in an interactive communication mode;
and the nursing science big data analysis and processing software is used for analyzing and processing the collected big data of each evaluation sub-item of the nursing discipline and calculating the index score of each evaluation sub-item.
(III) advantageous technical effects
Compared with the prior art, the invention has the following beneficial technical effects:
1. the invention adopts the technical means of carrying out hierarchical analysis on the nursing subject, establishing a hierarchical evaluation model of the nursing subject, calculating the weight of each evaluation submodel and the weight of each evaluation submodel of the evaluation submodel, and establishing an objective evaluation standard for the construction condition of the nursing subject;
the technical means that the nursing subject big data platform is in interactive communication with an evaluation user of a client through electronic nursing subject display evaluation system software, and big data of index scores of all evaluation sub-items of a nursing subject are acquired is adopted, so that the technical effect of scientifically evaluating the real level of the construction condition of the nursing subject is realized;
calculating the index score of the corresponding evaluation sub-model according to the index score S of each evaluation sub-item and the weight of each evaluation sub-item of the evaluation sub-model corresponding to the index score S of each evaluation sub-item; the technical means of calculating the index scores of the nursing disciplines according to the index scores of the evaluation submodels and the weights of the evaluation submodels corresponding to the index scores of the evaluation submodels ensures the technical effect of evaluating and analyzing the quality of the construction condition of the nursing disciplines;
therefore, the technical problems that the existing evaluation analysis method lacks objective evaluation standards for the construction conditions of the nursing disciplines, cannot scientifically evaluate the true level of the construction conditions of the nursing disciplines, and cannot ensure the evaluation analysis quality of the construction conditions of the nursing disciplines are solved.
2. The technical means that the nursing subject hierarchical analysis software is adopted to carry out hierarchical analysis on the nursing subject, the electronic nursing subject display evaluation system software is used to display the construction condition of the nursing subject, evaluate each evaluation sub-item of the construction condition of the nursing subject in an interactive communication mode and acquire the big data of the index value of each evaluation sub-item of the nursing subject is acquired, the technical purpose of objective and scientific evaluation analysis on the construction condition of the nursing subject is achieved, and the technical effect of lean management of the construction of the nursing subject is achieved.
Detailed Description
An evaluation analysis method applied to a nursing subject, comprising the following steps:
the method comprises the following steps: the method comprises the following steps of performing hierarchical analysis on the nursing subject:
determining hierarchy evaluation dimensions of a nursing subject, wherein the hierarchy evaluation dimensions comprise precision evaluation, depth evaluation, breadth evaluation, thickness evaluation and temperature evaluation;
(1) and (3) precision evaluation: the nursing system, the process and the emergency plan are perfected, the quality management scheme such as inspection and the like is standardized, the nursing quality is improved through monitoring and improving the structural index, the process index and the result index, and the safety of patients is guaranteed;
(2) depth evaluation: deep nursing special development, establishing a special nurse training base, culturing special nursing talents, and providing a more professional special nursing service for patients;
(3) evaluation of the breadth: by participating in academic communication, study and holding of successive classes of academic organizations, a culture mode combined with colleges and universities is adopted to develop the visual field of nurses, and the nursing awareness and reputation of the colleges and universities are improved;
(4) thickness evaluation: through reasonable scientific research training courses, the scientific research innovation ability of nurses is improved, and the ability of nurses for solving clinical practical problems from the scientific research angle is cultured. Through a perfect education training system, nursing talents are cultured, and the structure of the nursing talents is perfected;
(5) temperature evaluation: inherits the value view of 'patient as the center and employee as the core', and provides the warm heart service for the patient with the same reason; a perfect promotion and incentive mechanism is provided, a good employment environment is created for nurses, the attaching feeling of the nurses is enhanced, and the satisfaction degree of the nurses is improved;
(II) establishing a hierarchical evaluation model of the nursing subject according to the hierarchical evaluation dimension, wherein the hierarchical evaluation model comprises a precision evaluation submodel, a depth evaluation submodel, a breadth evaluation submodel, a thickness evaluation submodel and a temperature evaluation submodel;
(III) according to the importance level of each evaluation submodel in the hierarchical evaluation model, constructing a first-layer contrast matrix A, wherein the sequence of rows and columns is respectively as follows: precision evaluation, depth evaluation, breadth evaluation, thickness evaluation and temperature evaluation;
Figure BDA0002257249460000071
taking the factor i and the factor j as examples, the constructed contrast matrix is the matrix A, aijThe matrix A is an ith row and a jth column element, and the importance of a factor i and a factor j is referred to;
(1) if the factor i is as important as the factor j, then aij＝1；
(2) If the factor i is slightly more important than the factor j, then aij＝3；
(3) If the factor i is more important than the factor j, then aij＝5；
(4) If the factor i is much more important than the factor j, then aij＝7；
(5) If the factor i is extremely important compared to the factor j, then aij＝9；
(6)aij2n, n is 1, 2, 3, 4, when aijThe importance is between 2n-1 or 2n + 1;
(7)
Figure BDA0002257249460000072
namely, the matrix A is a symmetric matrix;
comparing the consistency check of the matrix, and if the consistency check requirement is met, performing the next weight calculation; if the consistency check requirement is not met, returning to the matrix construction of the previous step to correct the comparison matrix;
the definition formula of the consistency is as follows:
Figure BDA0002257249460000073
the consistency can ensure the transitivity of the weight, but the general contrast matrix can not ensure the complete consistency, but the consistency degree can be calculated by the following formula, if the calculated CR is within 0.1, the consistency is higher, otherwise, the consistency is lower;
Figure BDA0002257249460000081
wherein λmax(A) Is the maximum eigenvalue of matrix a, n is the rank of matrix a, RI is an empirical value, as shown in table 1;
TABLE 1
N 1 2 3 4 5 6 7 8 9
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45
Respectively calculating the consistency coefficients of the contrast matrixes, and finding that the consistency coefficients of the contrast matrixes are less than 0.1 through calculation, meet the consistency requirement and can be used for calculating the weight;
calculating the weight of each evaluation submodel, specifically:
calculating the maximum eigenvalue lambdamaxCorresponding feature vector vmaxTo v is to vmaxNormalizing to obtain the weight of each subentry, and evaluating the lambda of the subentry according to calculation and precisionmaxThe normalized feature vector is shown below as 5.04:
vA，max＝[0.52 0.16 0.07 0.07 0.17]
through calculation, the cr is 0.01, and the consistency requirement is met;
and (IV) further refining each evaluation submodel in the hierarchical evaluation model into specific evaluation submodels, specifically:
the accuracy evaluation submodel includes: one or more of a nursing management system evaluation sub-item, a regulation evaluation sub-item, a post responsibility evaluation sub-item, a quality monitoring evaluation sub-item and a security culture evaluation sub-item;
the depth evaluation submodel includes: one or more of an academic occupational evaluation sub-item, a professional commission evaluation sub-item, a specialist base evaluation sub-item, a specialist nurse evaluation sub-item, and a specialist characteristic evaluation sub-item;
the breadth evaluation submodel comprises: one or more of an academic exchange evaluation sub-item, a help guide evaluation sub-item, a progress study evaluation sub-item, a practice education evaluation sub-item and a undertaking relay class evaluation sub-item at home and abroad;
the thickness evaluation submodel includes: the talents culture and evaluation sub-items, talent constitution and evaluation sub-items, scientific research result evaluation sub-items and book publishing and evaluation sub-items;
the temperature evaluation submodel comprises: one or more of a human system evaluation sub-item, a human activity evaluation sub-item, a promotion incentive mechanism evaluation sub-item, a professional protection evaluation sub-item and an improvement service evaluation sub-item;
(V) constructing second layer contrast matrixes A corresponding to the precision evaluation submodel, the depth evaluation submodel, the breadth evaluation submodel, the thickness evaluation submodel and the temperature evaluation submodel respectively1、A2、A3、A4、A5The method specifically comprises the following steps:
5 types of evaluation sub-items of contrast precision evaluation are constructed one by one, and a contrast matrix A of precision evaluation is constructed1The order of rows and columns is: management system evaluation, regulation and regulation evaluation, post responsibility evaluation, quality monitoring evaluation, safety culture evaluation and the like;
Figure BDA0002257249460000091
constructing a contrast matrix A of the depth evaluation by contrast matrix A according to 5 types of evaluation sub-items of contrast depth evaluation2；
Figure BDA0002257249460000092
Constructing a contrast matrix A for deep evaluation by comparing 5 types of evaluation sub-items for breadth evaluation3；
Figure BDA0002257249460000093
4 types of evaluation sub-items for comparing the thickness evaluation one by one are constructed to form a contrast matrix A for depth evaluation4；
Gradually comparing 5 types of evaluation sub items of the temperature evaluation, and constructing a contrast matrix A of the depth evaluation5；
Then, the following calculations are performed in sequence: the weight of each evaluation sub-item of the precision evaluation sub-model, the weight of each evaluation sub-item of the depth evaluation sub-model, the weight of each evaluation sub-item of the breadth evaluation sub-model, the weight of each evaluation sub-item of the thickness evaluation sub-model and the weight of each evaluation sub-item of the temperature evaluation sub-model are as follows:
calculating the maximum eigenvalue lambdamaxCorresponding feature vector vmaxTo v is to vmaxCarrying out normalization to obtain the weight of each sub item; if the feature vector vmaxIf the negative number exists, revising the negative number by taking the reciprocal of the absolute value;
precision evaluation subentry weight calculation, according to the calculation, precision evaluation subentry lambdamaxThe normalized feature vector is shown below at 5.08:
vA1，max＝[0.46 0.19 0.16 0.07 0.12]
through calculation, the cr is 0.016, which meets the requirement of consistency;
the weight of the depth evaluation sub-item is calculated, and the lambda of the sub-item is evaluated according to the calculation and the precisionmaxThe normalized eigenvectors are shown below at 5.04:
vA2，max＝[0.54 0.19 0.11 0.06 0.1]
through calculation, the cr is 0.009, which meets the requirement of consistency;
carrying out weight calculation on the breadth evaluation subentries, and evaluating the lambda of the subentries according to the calculation and the precisionmaxThe normalized feature vector is shown below at 5.15:
vA3，max＝[0.05 0.15 0.38 0.33 0.09]
through calculation, the cr is 0.03, and the consistency requirement is met;
the weight of the thickness evaluation subentry is calculated, and the lambda of the subentry is evaluated according to the calculation and the precisionmaxThe normalized feature vector is shown below at 4.03:
vA4，max＝[0.31 0.5 0.12 0.07]
through calculation, the cr is 0.012, which meets the requirement of consistency;
the weight of the temperature evaluation subentry is calculated, and the lambda of the subentry is evaluated according to the calculation and the precisionmaxThe normalized feature vector is shown below at 5.06:
νA5,max＝[0.06 0.42 0.1 0.16 0.26]
through calculation, the cr is 0.015, which meets the requirement of consistency;
step two: manufacturing the construction condition of the nursing subject into a nursing subject electronic display evaluation system with an interactive communication function, and pushing client software of the nursing subject electronic display evaluation system to a client of an evaluation user through a network platform;
the nursing science big data platform is in interactive communication with an evaluation user of a client through electronic nursing discipline display evaluation system software, and big data of index scores of all evaluation sub-items of the nursing discipline are acquired;
step three: dividing the big data of the index scores of the evaluation sub-items collected in the step two into two types, specifically: evaluation data A belonging to the professional field of the nursing subject and evaluation data B not belonging to the professional field of the nursing subject;
calculating the mean value of the evaluation data A as UAThe mean value of the evaluation data B was UBAccording to the formula S ═ x% UA+(100-x)％UBCalculating index scores S of all the evaluation sub-items; wherein x is more than or equal to 60;
step four: calculating the index score of the corresponding evaluation sub-model according to the index score S of each evaluation sub-item and the weight of each evaluation sub-item of the evaluation sub-model corresponding to the index score S of each evaluation sub-item;
and calculating the index scores of the nursing disciplines according to the index scores of the evaluation submodels and the weights of the evaluation submodels corresponding to the index scores of the evaluation submodels.
Further, the third step further includes: after the maximum value and the minimum value of the evaluation data A are removed, the average value of the rest evaluation data A is calculated to be UA(ii) a After the maximum value and the minimum value of the evaluation data B are removed, the average value of the rest evaluation data B is calculated to be UB(ii) a Then, 70% U is obtained according to the formula SA+30％UBAnd calculating the index score S of each evaluation sub-item.
Experimental cases:
selecting a certain two three hospitals in Zhejiang province, respectively evaluating indexes of nursing specialties of the three hospitals based on the latitudes such as precision, depth, breadth, thickness and temperature, summarizing the indexes, and then evaluating the indexes for five degrees, wherein the final score of the X hospital is 8.49, the score of the Y hospital is 8.18, and the specific scoring process is shown in the following tables 2-7.
TABLE 2
Figure BDA0002257249460000121
TABLE 3
Figure BDA0002257249460000122
TABLE 4
Figure BDA0002257249460000123
Figure BDA0002257249460000131
TABLE 5
Figure BDA0002257249460000132
TABLE 6
Figure BDA0002257249460000133
TABLE 7
Figure BDA0002257249460000134
An assessment analysis system for use in a care discipline, comprising: the system comprises a local server A, a local server B, an intelligent terminal and/or a PC (personal computer) terminal and a local server C, wherein the local server A is provided with and runs nursing subject hierarchical analysis software, the local server B is provided with and runs nursing subject electronic display evaluation system software and belongs to a nursing subject big data platform, the intelligent terminal and/or the PC terminal is provided with and runs an evaluation user of the nursing subject electronic display evaluation system software, and the local server C is provided with and runs nursing subject big data analysis processing software;
the CPU of the local server A and the CPU of the local server C realize mutual communication connection through a serial port master-slave mode communication mechanism, the CPU of the local server B is a master device, and the CPU of the local server C is a slave device;
the local server B is in communication connection with the intelligent terminal and/or the PC terminal on the nursing subject electronic display evaluation system software through network communication equipment;
the CPU of the local server B of the nursing science and technology data platform is in communication connection with the CPU of the local server C through a serial port master-slave mode communication mechanism, the CPU of the local server B is a slave device, and the CPU of the local server C is a master device;
the nursing subject hierarchical analysis software is used for carrying out hierarchical analysis on the nursing subject, establishing a hierarchical evaluation model of the nursing subject, and calculating the weight of each evaluation submodel and the weight of each evaluation subentry of each evaluation submodel;
the electronic display evaluation system software of the nursing discipline is used for displaying the construction condition of the nursing discipline and evaluating each evaluation sub item of the construction condition of the nursing discipline in an interactive communication mode;
the nursing subject big data analysis and processing software is used for analyzing and processing the big data of each evaluation sub-item of the collected nursing subject and calculating the index score of each evaluation sub-item;
the technical purpose of lean management of the nursing subject construction is achieved by performing objective and scientific evaluation and analysis on the nursing subject construction condition.
The present invention is not limited to the above-mentioned preferred embodiments, and any other products in various forms can be obtained by anyone in the light of the present invention, but any changes in the shape or structure thereof, which have the same or similar technical solutions as those of the present application, fall within the protection scope of the present invention.

Claims (5)

1. An evaluation analysis method applied to a nursing subject, which is characterized by comprising the following steps:
the method comprises the following steps: the method comprises the following steps of performing hierarchical analysis on the nursing subject:
(1) determining hierarchical evaluation dimensions of the nursing discipline, wherein the hierarchical evaluation dimensions comprise precision evaluation, depth evaluation, breadth evaluation, thickness evaluation and temperature evaluation;
(2) establishing a hierarchical evaluation model of the nursing subject according to the hierarchical evaluation dimension, wherein the hierarchical evaluation model comprises a precision evaluation submodel, a depth evaluation submodel, an breadth evaluation submodel, a thickness evaluation submodel and a temperature evaluation submodel;
(3) constructing a first-layer contrast matrix A according to the importance level of each evaluation submodel in the hierarchical evaluation model, and calculating the weight of each evaluation submodel;
(4) further refining each evaluation submodel in the hierarchical evaluation model into specific evaluation submodels;
(5) constructing a second layer contrast matrix A corresponding to the precision evaluation submodel, the depth evaluation submodel, the breadth evaluation submodel, the thickness evaluation submodel and the temperature evaluation submodel respectively1、A2、A3、A4、A5(ii) a Then, the following calculations are performed in sequence: the weight of each evaluation sub-item of the precision evaluation sub-model, the weight of each evaluation sub-item of the depth evaluation sub-model, the weight of each evaluation sub-item of the breadth evaluation sub-model, the weight of each evaluation sub-item of the thickness evaluation sub-model and the weight of each evaluation sub-item of the temperature evaluation sub-model;
step two: manufacturing the construction condition of the nursing subject into a nursing subject electronic display evaluation system with an interactive communication function, and pushing client software of the nursing subject electronic display evaluation system to a client of an evaluation user through a network platform;
the nursing science big data platform is in interactive communication with an evaluation user of a client through electronic nursing discipline display evaluation system software, and big data of index scores of all evaluation sub-items of the nursing discipline are acquired;
step three: dividing the big data of the index scores of the evaluation sub-items collected in the step two into two types, specifically: evaluation data A belonging to the professional field of the nursing subject and evaluation data B not belonging to the professional field of the nursing subject;
calculating the mean value of the evaluation data A as UAThe mean value of the evaluation data B was UBAccording to the formula S ═ x% UA+(100-x)％UBCalculating index scores S of all the evaluation sub-items; wherein x is more than or equal to 60;
step four: calculating the index score of the corresponding evaluation sub-model according to the index score S of each evaluation sub-item and the weight of each evaluation sub-item of the evaluation sub-model corresponding to the index score S of each evaluation sub-item;
and calculating the index scores of the nursing disciplines according to the index scores of the evaluation submodels and the weights of the evaluation submodels corresponding to the index scores of the evaluation submodels.
2. The evaluation analysis method of claim 1, wherein the accuracy evaluation submodel comprises: one or more of a nursing management system evaluation sub-item, a regulation evaluation sub-item, a post responsibility evaluation sub-item, a quality monitoring evaluation sub-item and a security culture evaluation sub-item;
the depth evaluation submodel includes: one or more of an academic occupational evaluation sub-item, a professional commission evaluation sub-item, a specialist base evaluation sub-item, a specialist nurse evaluation sub-item, and a specialist characteristic evaluation sub-item;
the breadth evaluation submodel comprises: one or more of an academic exchange evaluation sub-item, a help guide evaluation sub-item, a progress study evaluation sub-item, a practice education evaluation sub-item and a undertaking relay class evaluation sub-item at home and abroad;
the thickness evaluation submodel includes: the talents culture and evaluation sub-items, talent constitution and evaluation sub-items, scientific research result evaluation sub-items and book publishing and evaluation sub-items;
the temperature evaluation submodel comprises: and the system comprises one or more of a human system evaluation sub-item, a human activity evaluation sub-item, a promotion incentive mechanism evaluation sub-item, a professional protection evaluation sub-item and an improvement service evaluation sub-item.
3. The assessment analysis method according to claim 1, wherein the third step further comprises: after the maximum value and the minimum value of the evaluation data A are removed, the average value of the rest evaluation data A is calculated to be UA(ii) a After the maximum value and the minimum value of the evaluation data B are removed, the average value of the rest evaluation data B is calculated to be UB(ii) a Then, 70% U is obtained according to the formula SA+30％UBAnd calculating the index score S of each evaluation sub-item.
4. An assessment analysis system for use in a care discipline, comprising: the system comprises a local server A, a local server B, an intelligent terminal and/or a PC (personal computer) terminal and a local server C, wherein the local server A is provided with and runs nursing subject hierarchical analysis software, the local server B is provided with and runs nursing subject electronic display evaluation system software and belongs to a nursing subject big data platform, the intelligent terminal and/or the PC terminal is provided with and runs an evaluation user of the nursing subject electronic display evaluation system software, and the local server C is provided with and runs nursing subject big data analysis processing software;
the CPU of the local server A and the CPU of the local server C realize mutual communication connection through a serial port master-slave mode communication mechanism, the CPU of the local server B is a master device, and the CPU of the local server C is a slave device;
the local server B is in communication connection with the intelligent terminal and/or the PC terminal on the nursing subject electronic display evaluation system software through network communication equipment;
the CPU of the local server B of the nursing science and technology data platform is in communication connection with the CPU of the local server C through a serial port master-slave mode communication mechanism, the CPU of the local server B is a slave device, and the CPU of the local server C is a master device.
5. The evaluation analysis system according to claim 4, wherein the nursing discipline hierarchical analysis software is used for performing hierarchical analysis on the nursing disciplines, establishing a hierarchical evaluation model of the nursing disciplines, and calculating the weight of each evaluation submodel and the weight of each evaluation subentry of the evaluation submodel;
the electronic display evaluation system software of the nursing discipline is used for displaying the construction condition of the nursing discipline and evaluating each evaluation sub item of the construction condition of the nursing discipline in an interactive communication mode;
the nursing science big data analysis and processing software is used for analyzing and processing the big data of each evaluation sub-item of the collected nursing science and calculating the index score of each evaluation sub-item.
CN201911058642.6A 2019-11-01 2019-11-01 Assessment analysis method and system applied to nursing subject Pending CN110796382A (en)

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