CN113470799A - Intelligent editor of hospital comprehensive quality supervision platform - Google Patents
Intelligent editor of hospital comprehensive quality supervision platform Download PDFInfo
- Publication number
- CN113470799A CN113470799A CN202110778043.2A CN202110778043A CN113470799A CN 113470799 A CN113470799 A CN 113470799A CN 202110778043 A CN202110778043 A CN 202110778043A CN 113470799 A CN113470799 A CN 113470799A
- Authority
- CN
- China
- Prior art keywords
- target
- quality
- data
- hospital
- center
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computer Security & Cryptography (AREA)
- General Business, Economics & Management (AREA)
- Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Computing Systems (AREA)
- Epidemiology (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Molecular Biology (AREA)
- Computer Hardware Design (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The embodiment of the application discloses intelligent editor of hospital's comprehensive quality supervision platform, the editor includes: the system comprises a triggering module, a data collecting module, a processing module and a monitoring module, wherein the triggering module is used for triggering a comprehensive quality monitoring process based on a target triggering instruction, and the target triggering instruction comprises a target hospital and a first time; the data collection module is used for acquiring a first data set from a database, wherein the first data set is a set of data generated by a target hospital in the first time; the processing module is used for calculating k quality indexes of the target hospital according to the first data set; and the supervision module is used for supervising the target hospital according to the k quality indexes. According to the quality monitoring method and the quality monitoring system, when a user triggers the comprehensive quality supervision process, the quality index for measuring the medical quality of the target hospital can be automatically calculated, and the medical quality monitoring of the target hospital is realized according to the quality index, so that the medical quality construction and management are continuously improved.
Description
Technical Field
The application relates to the technical field of medical information, in particular to an intelligent editor of a hospital comprehensive quality supervision platform.
Background
The medical quality is the centralized embodiment of various works and comprehensive strength of the hospital and is an important index for evaluating the overall level of the hospital. With the continuous deepening of the innovation of the health system, the medical market competition faced by the hospital is increasingly fierce, and as the science of the medical market competition, the medical quality directly influences the sustainable development capability of the hospital, so the supervision of the comprehensive quality of the hospital is increasingly important.
Disclosure of Invention
The embodiment of the application provides an intelligent editor of a hospital comprehensive quality supervision platform, which can realize hospital medical quality monitoring and promote the continuous improvement of medical quality construction and management.
In a first aspect, an embodiment of the present application provides an intelligent editor for a hospital comprehensive quality supervision platform, where the editor includes: a triggering module, a data collecting module, a processing module and a supervising module, wherein,
the triggering module is used for triggering a comprehensive quality supervision process based on a target triggering instruction, and the target triggering instruction comprises a target hospital and a first time;
the data collection module is used for acquiring a first data set from a database, wherein the first data set is a set of data generated by the target hospital in the first time;
the processing module is used for calculating k quality indexes of the target hospital according to the first data set, wherein m is a positive integer;
and the supervision module is used for supervising the target hospital according to the m quality indexes.
According to the technical scheme, a trigger module triggers a comprehensive quality supervision process based on a target trigger instruction, wherein the target trigger instruction comprises a target hospital and a first time; the data collection module acquires a first data set from a database, wherein the first data set is a set of data generated by a target hospital in the first time; the processing module is used for calculating k quality indexes of the target hospital according to the first data set; and the supervision module supervises the target hospital according to the k quality indexes. According to the quality monitoring method and the quality monitoring system, when a user triggers the comprehensive quality supervision process, the quality index for measuring the medical quality of the target hospital can be automatically calculated, and the medical quality monitoring of the target hospital is realized according to the quality index, so that the medical quality construction and management are continuously improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of an intelligent editor of a hospital comprehensive quality supervision platform provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a process for calculating k quality indicators according to an embodiment of the present application;
fig. 3 is a schematic diagram of another hospital comprehensive quality supervision platform intelligent editor provided in the embodiment of the present application.
Detailed Description
In order to better understand the technical solutions of the present application, the following description is given for clarity and completeness in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step on the basis of the description of the embodiments of the present application belong to the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, software, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic diagram of an intelligent editor of a hospital comprehensive quality supervision platform according to an embodiment of the present application. As shown in fig. 1, the editor includes: a triggering module 110, a data collection module 120, a processing module 130, and a supervision module 140.
Wherein the triggering module 110 is configured to trigger a comprehensive quality supervision flow based on a target triggering instruction, the target triggering instruction including a target hospital and a first time; the data collection 120 module is configured to obtain a first data set from a database, where the first data set is a set of data generated by the target hospital within the first time; the processing module 130 is configured to calculate k quality indicators of the target hospital according to the first data set, where k is a positive integer; the supervision module 140 is configured to supervise the target hospital according to the k quality indicators.
When the editor is started, a user can start a related comprehensive quality supervision process through any preset operation in a display interface of the editor, and specifically, a trigger instruction is generated according to the preset operation to trigger the comprehensive quality supervision process. The preset arbitrary operation may be set by a user or by an editor system, which is not limited in this embodiment of the application. For example, on a mobile phone, a comprehensive quality supervision process can be invoked by touching a current button in a display interface; on the computer, the comprehensive quality supervision process can be called by clicking the current button key in the display interface. Further, when triggering the comprehensive quality supervision process, the user can select the hospital and time for comprehensive quality supervision in the display interface, so that the medical quality service of the hospital can be supervised purposefully, and further the continuous improvement of medical quality construction and management is promoted.
Specifically, after the trigger module 110 receives the target trigger instruction to trigger the integrated quality supervision process, the editor data collection 120 obtains all data uploaded by the target hospital within the first time from the database. The processing module 130 then processes the obtained data to calculate a plurality of quality indicators for the target hospital. Finally, the supervision module 140 compares the calculated quality indexes to judge whether the medical quality of the target hospital is qualified or not, so as to realize the medical quality supervision of the target hospital.
Optionally, the calculating m quality indicators of the target hospital according to the first data set includes:
extracting the first data set to obtain a second data set; classifying the second data set according to data types to obtain n third data sets, wherein each third data set corresponds to one data type; structuring data in the n third data sets to obtain n fourth data sets; calculating the m quality indicators based on the n fourth data sets.
The first data set obtained from the database includes all data uploaded by the hospital, but not all of the data can be used for measuring the medical quality service of the hospital, so after the first data set is obtained from the database, the first data set needs to be extracted to extract the data capable of measuring the medical quality of the hospital from the first database. The second data set may include patient diagnostic and treatment data, hospital equipment usage data, cost data, hospital supplies data, and the like. The second data set also comprises various types of data, such as inspection data, image data, expense data, treatment data, diagnosis data and the like, different data types have different data formats, and the second data set is classified according to the data types, so that the processing of different data types can be facilitated.
In practical application, data uploaded by a hospital mainly includes data generated by a patient in a hospitalizing process, and mainly includes patient basic information, disease chief complaints, inspection data, image data, diagnosis data, treatment data and the like, and the data is basically unstructured data formed by free texts, and includes not only large-segment text descriptions but also form fields containing non-uniform texts. It is therefore necessary to convert unstructured medical data into a structured form suitable for computer analysis before calculating the quality index.
Further, since the data in the second data set includes a large amount of unstructured data, when the second data set is classified, advanced feature extraction, such as image edge information extraction, text embedding, fourier transform and wavelet transform on a time sequence, is performed on the data in the second data set. Then, the extracted features are subjected to feature selection, dimension reduction and other processing, and finally classification is carried out.
Optionally, the screening the data in the n third data sets to obtain n fourth data sets includes:
performing cluster analysis on a third data set i to obtain at least one cluster, wherein the third data set i is any one of the n first data subsets; determining a first center of each of the at least one cluster to obtain at least one first center; determining the center of the at least one first center to obtain a second center; determining the distance between each center of the at least one first center and the second center to obtain at least one distance; constructing at least one vector according to the at least one first center, the second center and the at least one distance, wherein each vector corresponds to one first center, the direction of each vector is the direction in which the second center points to the first center, and the size of each vector is in direct proportion to the distance corresponding to the vector; determining a resultant vector of the at least one vector; and taking the data in the preset range of the direction indicated by the resultant vector as a fourth data set.
In this case, the data in the third data set of the same data type may have different sources, so that the data may have different roles, and not every data may be used to calculate the quality index. Therefore, the data used for calculating the quality index are screened from each third data set through cluster analysis.
Specifically, data for calculating one or more quality indicators may be included in one third data set, each third data set is subjected to cluster analysis to obtain at least one cluster corresponding to at least one quality indicator, and a first center of each cluster, that is, a cluster center point, is calculated. Then, a cluster center point of a third data set is selected from the cluster center points, and data in a range from the origin point to the cluster center point of each cluster type can be determined as data which can be used for calculating the quality index calculation by taking the cluster center point of the third data set as the origin point, so that data in a range from a second center to at least one first center and data in a preset range of the at least one first center are used as fourth data for calculating k quality indexes.
Optionally, as shown in fig. 2, the calculating k quality indicators based on the n fourth data sets includes:
s210, determining dimensions m of the n quality indexes, and constructing a judgment matrix A of the n fourth data sets according to the dimensions;
s220, acquiring k preset weights corresponding to the k quality indexes, and calculating the degree of association between any two quality indexes;
s230, adjusting the k preset weights according to the relevance to obtain k target weights;
s240, calculating a weighted judgment matrix Z based on the k target weights and the judgment matrix A;
s250, calculating target matrixes of the n fourth data sets according to the weighting judgment matrix;
s260, calculating a target mean square error and a target expectation of each row in the target matrix according to the dimensionality, determining a target adjusting factor corresponding to the target mean square error according to a preset mapping relation between the mean square error and the adjusting factor, and adjusting the target expectation according to the target adjusting factor to obtain the k quality indexes.
Wherein the quality index may include an efficiency index, a benefit index, and a quality index.
Specifically, the medical quality index can be divided into an efficiency index, a benefit index and a quality index in a horizontal direction in consideration of the comprehensiveness of the medical service quality connotation and the commonality of different medical units. The efficiency index can comprise average hospitalization day, sickbed utilization rate and turnover rate, daily outpatient times, number of people discharged from each bed and the like; the benefit index can include average outpatient medical cost, average hospital bed daily cost, medical cost ratio, average operation cost and the like; the quality index can be divided into diagnosis quality index and treatment quality index, the diagnosis quality index can comprise outpatient service and discharge diagnosis coincidence rate, admission and discharge diagnosis coincidence rate and the like, and the treatment quality index can comprise cure rate, remission rate, fatality rate and the like.
The quality index needs to be subdivided according to dimensions, for example, according to a time dimension: hour, day, week, month, season, and year. According to different time dimensions, the same quality index can have multiple representation modes. Such as daily outpatient times, monthly outpatient times, annual outpatient times, etc. Constructing a judgment matrix A of k rows and m columns according to dimension m of the quality indexnmThe judgment matrix AnmCan be expressed as:
for example, since the quality indexes are many and the units are not uniform, the normalization processing may be performed on the k quality indexes by using formula (1), so as to obtain a normalization judgment matrix B.
Wherein, bijIs an index aijThe quantized value, c, is the quantized coefficient.
The k preset weights may be ranked according to the importance of experts on the quality index, or may be weights set according to experience. For k quality indicators X1，X2，…，Xk-1，XkAnd sorting the quality indexes from high to low according to the importance degree by experts with abundant experience in the industry field, and recording the sorting result as:
then calculating the relevance y between two adjacent quality indexesiThe association may be expressed as:
wherein q isiIs the weight of the ith quality indicator, said qi-1Is the weight of the (i-1) th quality indicator adjacent to the (i) th quality indicator. The k target weights are then calculated according to equation (2).
Multiplying the k target weights by a normalized judgment matrix B to obtain a weighted judgment matrix Z, wherein Z isij＝yij*wi。
Optionally, each fourth data set is cut to obtain at least one fourth data subset with the length m, the at least one fourth data subset with the length m is constructed into a combined matrix to obtain n combined matrices, and the weighted judgment matrix Z is multiplied by the transpose of each combined matrix to obtain the target matrix of each data type. And then calculating the target mean square error and the target expectation of each row in the target matrix, determining a target adjusting factor corresponding to the target mean square error according to a preset mapping relation between the mean square error and the adjusting factor, and adjusting the target expectation according to the target adjusting factor to obtain the k quality indexes.
Optionally, the monitoring the target hospital according to the m quality indexes includes: acquiring the hospital grade of the target hospital; determining m preset quality indexes of the target hospital according to the hospital grade; and comparing the m quality indexes with the m preset quality indexes respectively, and if a first quality index does not meet the corresponding preset quality index, alarming the first quality index of the target hospital, wherein the first quality index is any one of the m quality indexes.
The hospitals are divided into three levels after being reviewed, wherein each level is divided into a first level, a second level, a third level and the like according to hospital grades, and the third level is additionally provided with a special grade, so that the hospitals are divided into three levels, ten levels and the like, namely thirty-medium levels, such as three-grade, two-grade and two-grade. The method comprises the steps that the medical service quality required by each grade is different, calculated m quality indexes of a target hospital are compared with m preset quality indexes corresponding to the hospital grade, when the quality index of the target hospital is smaller than or equal to the corresponding preset quality index, the target hospital is indicated to be not up to the standard on the index, an editor can give an alarm to a user for the quality index not up to the standard and the target hospital, specifically, a first quality index and a display interface of a first editor of the target hospital are highlighted, and the target hospital is prompted to need to be strengthened in the aspect of the quality index not up to the standard.
In a possible example, as shown in fig. 3, the editor further comprises a login authentication module 150 for authenticating whether the login user is legitimate.
Optionally, when the user logs in, the login verification module 150 obtains a face image of the login user, verifies whether the login user is legal by performing face recognition on the face image, and allows the login user to log in the editor when the login user is legal.
Illustratively, verifying whether the login user is legal by performing face recognition on the face image may specifically include: extracting features of a face picture to obtain input data (which can be an input matrix H x W) of a convolutional neural network, performing convolutional operation on the input data and a convolutional kernel of the convolutional neural network to obtain a convolutional operation result (which can be n times of convolutional operation, n is greater than or equal to 1), performing full-connection operation on the convolutional operation result to obtain a full-connection result, performing difference operation on the full-connection result and a preset template result (which can be a right connection result corresponding to a preset identity) to obtain a difference value, and if the difference value is smaller than a first threshold value, determining that a login user is legal, otherwise, determining that the login user is illegal.
The difference value being smaller than the first threshold may specifically be determined to be smaller than the first threshold when the difference value is a matrix or a vector and an average value of all elements of the matrix or the vector is smaller than the first threshold.
Specifically, the input matrix [ H ] [ W ] of the input data is stored in the memory in the first row and the second row, the operation unit performs the operation step on the first three rows of element values to obtain a plurality of convolution values of the convolution operation result, and the operation step may specifically include: extracting 3 x 6 element values from the first three rows of element values of an input matrix [ H ] [ W ] to obtain a matrix [3] [6]11, storing the matrix [3] [6]11 in a cache according to the first row and the second row by an operation unit, extracting a convolution kernel [3] [3] by the operation unit, sliding the matrix [3] [6] 4 times in the row direction to obtain 4 matrices [3] [3]11, performing convolution operation on the 4 matrices [3] [ 1 and the convolution kernel [3] to obtain 4 convolution values, extracting the matrix [3] [4] after the matrix [3] [6] from the input matrix [ H ] [ W ] (extracting the [3] [4] every time in subsequent extraction, then forming a new matrix [3] [6] with the last 2 columns of the previous [3] [6], deleting the first 4 rows of the matrix [3] [6]11, and then forming a matrix [3] [6]21 by the second 2 columns of element values and the matrix [3] [4 ]; storing the [3] [6]21 in a cache according to the first column and the second row, sliding the matrix [3] [6]21 for 4 times according to the row direction to obtain 4 matrixes [3] [3]21, performing convolution operation on the 4 matrixes [3] [3]21 and the convolution kernel [3] to obtain 4 convolution values, traversing the input matrix 3W element values (namely the first 3 rows of element values) to obtain a plurality of convolution values of a convolution operation result, moving a convolution sliding window along the rows, performing operation steps on the subsequent row element values of the first 3 rows by an operation unit to obtain a plurality of convolution values of the convolution operation result, and combining the plurality of convolution results of the first 3 rows and the plurality of convolution kernels of the subsequent rows to obtain the convolution operation result.
For the following row, the element value of the basic unit of the row 3 is used, and the specific operation mode can be referred to the mode of the previous row 3.
Wherein, the subscript value in [3] [6]11 represents the number of times of extraction of three-row element values, and the superscript value represents the minimum row value of the three-row element values.
The intelligent editor comprises a triggering module, a data collecting module, a processing module and a monitoring module, wherein the triggering module is used for triggering the comprehensive quality monitoring process based on a target triggering instruction, and the target triggering instruction comprises a target hospital and first time; the data collection module is used for acquiring a first data set from a database, wherein the first data set is a set of data generated by a target hospital in the first time; the processing module is used for calculating k quality indexes of the target hospital according to the first data set; and the supervision module is used for supervising the target hospital according to the k quality indexes. According to the quality monitoring method and the quality monitoring system, when a user triggers the comprehensive quality supervision process, the quality index for measuring the medical quality of the target hospital can be automatically calculated, and the medical quality monitoring of the target hospital is realized according to the quality index, so that the medical quality construction and management are continuously improved.
It is to be understood that reference to "at least one" in the embodiments of the present application means one or more, and "a plurality" means two or more. "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, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the 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 implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software elements in a processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in a memory, and a processor executes instructions in the memory, in combination with hardware thereof, to perform the steps of the above-described method. To avoid repetition, it is not described in detail here.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (5)
1. An intelligent editor of a hospital comprehensive quality supervision platform, characterized in that the editor comprises: a triggering module, a data collecting module, a processing module and a supervising module, wherein,
the triggering module is used for triggering a comprehensive quality supervision process based on a target triggering instruction, and the target triggering instruction comprises a target hospital and a first time;
the data collection module is used for acquiring a first data set from a database, wherein the first data set is a set of data generated by the target hospital in the first time;
the processing module is used for calculating k quality indexes of the target hospital according to the first data set, wherein k is a positive integer;
and the supervision module is used for supervising the target hospital according to the k quality indexes.
2. The editor of claim 1, wherein said calculating m quality indicators for said target hospital from said first data set comprises:
extracting the first data set to obtain a second data set;
classifying the second data set according to data types to obtain n third data sets, wherein each third data set corresponds to one data type;
screening the data in the n third data sets to obtain n fourth data sets;
calculating the m quality indicators based on the n fourth data sets.
3. The editor of claim 1, wherein said computing said k quality indicators based on said n fourth data sets comprises:
determining dimensions m of the n quality indexes, and constructing judgment matrixes A of the n fourth data sets according to the dimensions;
acquiring k preset weights corresponding to the k quality indexes, and calculating the degree of association between any two quality indexes;
adjusting the k preset weights according to the relevance to obtain k target weights;
calculating a weighted decision matrix Z based on the k target weights and the decision matrix A;
calculating target matrixes of the n fourth data sets according to the weighted judgment matrix;
calculating a target mean square error and a target expectation of each row in the target matrix, determining a target adjusting factor corresponding to the target mean square error according to a preset mapping relation between the mean square error and the adjusting factor, and adjusting the target expectation according to the target adjusting factor to obtain the k quality indexes.
4. The editor of claim 2, wherein the filtering the data in the n third data sets to obtain n fourth data sets comprises:
performing cluster analysis on a third data set i to obtain at least one cluster, wherein the third data set i is any one of the n first data subsets;
determining a first center of each of the at least one cluster to obtain at least one first center;
determining the center of the at least one first center to obtain a second center;
determining the distance between each center of the at least one first center and the second center to obtain at least one distance;
constructing at least one vector according to the at least one first center, the second center and the at least one distance, wherein each vector corresponds to one first center, the direction of each vector is the direction in which the second center points to the first center, and the size of each vector is in direct proportion to the distance corresponding to the vector;
determining a sum vector of the at least one vector;
and taking the data in the preset range of the direction indicated by the sum vector as a fourth data set.
5. The editor of claim 1, wherein said supervising the target hospital according to the m quality indicators comprises:
acquiring the hospital grade of the target hospital;
determining m preset quality indexes of the target hospital according to the hospital grade;
and comparing the m quality indexes with the m preset quality indexes respectively, and if a first quality index does not meet the corresponding preset quality index, alarming the first quality index of the target hospital, wherein the first quality index is any one of the m quality indexes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110778043.2A CN113470799B (en) | 2021-07-09 | 2021-07-09 | Intelligent editor of comprehensive quality supervision platform for hospitals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110778043.2A CN113470799B (en) | 2021-07-09 | 2021-07-09 | Intelligent editor of comprehensive quality supervision platform for hospitals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113470799A true CN113470799A (en) | 2021-10-01 |
| CN113470799B CN113470799B (en) | 2023-08-08 |
Family
ID=77879481
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110778043.2A Active CN113470799B (en) | 2021-07-09 | 2021-07-09 | Intelligent editor of comprehensive quality supervision platform for hospitals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113470799B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113641825A (en) * | 2021-10-15 | 2021-11-12 | 人民法院信息技术服务中心 | Smart court system big data processing method and device based on objective information theory |
| CN114741752A (en) * | 2022-03-23 | 2022-07-12 | 广东省国际工程咨询有限公司 | Computing system for determining comprehensive hospital construction scale |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5544044A (en) * | 1991-08-02 | 1996-08-06 | United Healthcare Corporation | Method for evaluation of health care quality |
| CN101178804A (en) * | 2007-12-05 | 2008-05-14 | 卫生部医院管理研究所 | Hospital information system construction method taking medical quality as core |
| CN109411071A (en) * | 2018-09-29 | 2019-03-01 | 广州火龙果信息科技有限公司 | A method of based on TOPSIS and composite index algorithm evaluation analysis quality of medical care |
| CN109767831A (en) * | 2018-12-13 | 2019-05-17 | 平安医疗健康管理股份有限公司 | A kind of medical institutions' evaluation method, evaluating apparatus and computer readable storage medium |
| CN109785944A (en) * | 2018-12-13 | 2019-05-21 | 平安医疗健康管理股份有限公司 | Hospital evaluation method and Related product based on data analysis |
| CN112820416A (en) * | 2021-02-26 | 2021-05-18 | 重庆市公共卫生医疗救治中心 | Major infectious disease queue data typing method, typing model and electronic equipment |
-
2021
- 2021-07-09 CN CN202110778043.2A patent/CN113470799B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5544044A (en) * | 1991-08-02 | 1996-08-06 | United Healthcare Corporation | Method for evaluation of health care quality |
| CN101178804A (en) * | 2007-12-05 | 2008-05-14 | 卫生部医院管理研究所 | Hospital information system construction method taking medical quality as core |
| CN109411071A (en) * | 2018-09-29 | 2019-03-01 | 广州火龙果信息科技有限公司 | A method of based on TOPSIS and composite index algorithm evaluation analysis quality of medical care |
| CN109767831A (en) * | 2018-12-13 | 2019-05-17 | 平安医疗健康管理股份有限公司 | A kind of medical institutions' evaluation method, evaluating apparatus and computer readable storage medium |
| CN109785944A (en) * | 2018-12-13 | 2019-05-21 | 平安医疗健康管理股份有限公司 | Hospital evaluation method and Related product based on data analysis |
| CN112820416A (en) * | 2021-02-26 | 2021-05-18 | 重庆市公共卫生医疗救治中心 | Major infectious disease queue data typing method, typing model and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 许建强等: "《数学建模及其应用》", 31 August 2018, 上海交通大学出版社, pages: 97 - 99 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113641825A (en) * | 2021-10-15 | 2021-11-12 | 人民法院信息技术服务中心 | Smart court system big data processing method and device based on objective information theory |
| CN114741752A (en) * | 2022-03-23 | 2022-07-12 | 广东省国际工程咨询有限公司 | Computing system for determining comprehensive hospital construction scale |
| CN114741752B (en) * | 2022-03-23 | 2024-04-05 | 广东省国际工程咨询有限公司 | Computing system for determining construction scale of comprehensive hospital |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113470799B (en) | 2023-08-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111614690B (en) | Abnormal behavior detection method and device | |
| CN110781298A (en) | Medicine classification method and device, computer equipment and storage medium | |
| Ding et al. | The effectiveness of multitask learning for phenotyping with electronic health records data | |
| CN112951443A (en) | Syndrome monitoring and early warning method and device, computer equipment and storage medium | |
| CN113470799A (en) | Intelligent editor of hospital comprehensive quality supervision platform | |
| CN111314173A (en) | Monitoring information abnormity positioning method and device, computer equipment and storage medium | |
| CN111612038A (en) | Abnormal user detection method and device, storage medium and electronic equipment | |
| CN112131322B (en) | Time sequence classification method and device | |
| Castro et al. | Significant motifs in time series | |
| CN112686521A (en) | Wind control rule tuning method and system | |
| CN111159481B (en) | Edge prediction method and device for graph data and terminal equipment | |
| CN112348685A (en) | Credit scoring method, device, equipment and storage medium | |
| CN107451249B (en) | Event development trend prediction method and device | |
| CN107480426B (en) | Self-iteration medical record file clustering analysis system | |
| Cheng et al. | Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system | |
| CN117094184A (en) | Modeling method, system and medium of risk prediction model based on intranet platform | |
| CN113345564B (en) | Early prediction method and device for patient hospitalization duration based on graph neural network | |
| CN113886821A (en) | Malicious process identification method and device based on twin network, electronic equipment and storage medium | |
| CN115146890A (en) | Enterprise operation risk warning method and device, computer equipment and storage medium | |
| CN112632000A (en) | Log file clustering method and device, electronic equipment and readable storage medium | |
| CN114912109B (en) | Abnormal behavior sequence identification method and system based on graph embedding | |
| CN114817518B (en) | License handling method, system and medium based on big data archive identification | |
| CN114330987A (en) | Operation and maintenance behavior analysis method and device of power monitoring system and computer equipment | |
| CN114971110A (en) | Method for determining root combination, related device, equipment and storage medium | |
| CN116631641B (en) | Disease prediction device integrating self-adaptive similar patient diagrams |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |