CN111261242A - Method and device for determining product user satisfaction degree, storage medium and electronic equipment - Google Patents
Method and device for determining product user satisfaction degree, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to the technical field of medical big data, in particular to a method and a device for determining user satisfaction of a medical big data product, a storage medium and electronic equipment. The method comprises the following steps: acquiring prior perception satisfaction of a medical big data product; obtaining the product value satisfaction degree of a medical big data product; acquiring the service satisfaction degree of the medical big data product; and determining the satisfaction degree of the medical big data product based on the prior perception satisfaction degree, the product value satisfaction degree and the service satisfaction degree. By acquiring the satisfaction index, the product design and operation optimization effect for user satisfaction can be quantized, and qualitative analysis is switched to quantitative analysis, so that the design and operation optimization point of the product is clearer, the product is closer to user experience, and the user satisfaction is improved.
Description
Technical Field
The disclosure relates to the technical field of medical big data, in particular to a method and a device for determining user satisfaction of a medical big data product, a storage medium and electronic equipment.
Background
Like other products, the medical big data products and services are satisfied by users. User satisfaction is defined as a generalized metric of data quality improvement. User satisfaction depends on many aspects and is not limited to only narrowly understood production links. However, for 2B (To Business oriented) products that do not use a large number of users, guidance of standardized models is generally lacking, and effects are often not quantified, and user satisfaction is easily reduced To abstract concepts.
Two types of methods are common for modeling and measuring user satisfaction. The first is an explicit method, i.e. by a market survey, completed by the user filling out a questionnaire. Another category is implicit methods, where the user uses a log of products for analysis, estimating user satisfaction from a priori assumptions. The existing market products lack a modeling and mathematical quantification method for user satisfaction.
Through a user explicit feedback mode, namely a user investigation mode, the sampling rate is low, the cost is high, and specific reasons are difficult to find for systematic product optimization after feedback results are obtained. And the user implicit feedback mode depends on a large number of user use logs, so that the method is not suitable for products which are not mature or do not have a large number of users.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a user satisfaction determining method, apparatus, storage medium, and electronic device, thereby overcoming, at least to some extent, the problems due to the limitations and disadvantages of the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a user satisfaction determination method of a medical big data product, comprising:
obtaining prior perception satisfaction of the medical big data product;
obtaining the product value satisfaction degree of the medical big data product;
acquiring the service satisfaction degree of the medical big data product;
determining satisfaction of the medical big data product based on the prior perception satisfaction, product value satisfaction and service satisfaction.
In one embodiment, the formula for determining the satisfaction of the medical big data product is:
S＝S0·(w1·S1+w2·S2)；
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively, represent the weight coefficients.
In one embodiment, obtaining a priori perceptual satisfaction of the medical big data product comprises: and obtaining the prior perception satisfaction degree of the medical big data product through the prior trust score, the prior communication score and the prior experience score of the user.
In one embodiment, obtaining the prior perceptual satisfaction of the medical big data product through the prior trust score, the prior communication score and the prior experience score of the user comprises: determining the prior perception satisfaction of the medical big data product through the prior trust score, the prior communication score, the prior experience score and the prior relation score of the user.
In one embodiment, the prior perceptual satisfaction is determined by the formula:
S0＝w3·(T·C·I)+w4·R；
wherein S is0Representing a priori perceptual satisfaction, T representing a priori trust score, C representing a priori communication score, I representing a priori experience score, and R representing a priori relationship score.
In one embodiment, obtaining product value satisfaction for the medical big data product comprises: and determining the product value satisfaction degree of the medical big data product according to the task completion degree of the user, the product experience score, the data quality score and the user burden score.
In one embodiment, the formula for determining the product value satisfaction is:
wherein S is1Representing the satisfaction degree of the product value, y representing the completion degree of the task, U representing the experience score of the product, D representing the quality score of the data, B representing the burden score of the user, ExIndicating user expectation, w5、w6、w7Are weight coefficients.
In one embodiment, obtaining service satisfaction of the medical big data product comprises: and determining the service satisfaction degree of the medical big data product according to the service acquisition convenience score, the service feedback timeliness score and the service effectiveness score.
In one embodiment, the formula for determining the service satisfaction of the medical big data product is as follows:
S2＝w8·A+w9·F+w10·E；
wherein S is2Representing service satisfaction, A representing service acquisition convenience score, F representing service feedback timeliness score, E representing service effectiveness score, w8、w9、w10Are weight coefficients.
According to another aspect of the present invention, there is provided a user satisfaction determining apparatus of a medical big data product, including:
the prior perception satisfaction acquiring module is used for acquiring the prior perception satisfaction of the medical big data product;
the product value satisfaction acquiring module is used for acquiring the product value satisfaction of the medical big data product;
the service satisfaction acquiring module is used for acquiring the service satisfaction of the medical big data product;
and the total satisfaction determining module is used for determining the satisfaction of the medical big data product based on the prior perception satisfaction, the product value satisfaction and the service satisfaction.
In one embodiment, the overall satisfaction determination module determines the satisfaction of the medical big data product by the formula:
S＝S0·(w1·S1+w2·S2)；
S0＝w3·(T·C·I)+w4·R；
S2＝w8·A+w9·F+w10·E；
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively representing a weight coefficient, T representing a prior trust score, C representing a prior communication score, I representing a prior experience score, R representing a prior relation score, y representing a task completion degree, U representing a product experience score, D representing a data quality score, B representing a user burden score, and E representing a user burden scorexIndicating user expectation, w5、w6、w7For the weight coefficient, A represents a service acquisition convenience score, F represents a service feedback timeliness score, E represents a service validity score, w8、w9、w10Are weight coefficients.
According to yet another aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the user satisfaction determination method described above.
According to still another aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the user satisfaction determination method described above via execution of the executable instructions.
According to the user satisfaction determining method provided by the embodiment of the disclosure, by obtaining the satisfaction index, the product design operation optimization effect facing the user satisfaction can be quantified, and qualitative analysis is switched to quantitative analysis.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 illustrates a flow diagram of one embodiment of a medical big data product user satisfaction determination methodology, in accordance with the present invention;
FIG. 2 shows a schematic diagram relating to indicators in an embodiment of a medical big data product user satisfaction determination method according to the invention;
FIG. 3 illustrates a block diagram of one embodiment of a medical big data product user satisfaction determination apparatus in accordance with the present invention;
FIG. 4 shows a schematic diagram of an electronic device for use in the medical big data product user satisfaction determination method of the present invention; and
fig. 5 is a schematic view of a storage medium storing a program for executing the medical big data product user satisfaction determining method of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 shows a flowchart of an embodiment of a medical big data product user satisfaction determination method according to the present invention.
As shown in fig. 1, at step S102, a priori perceptual satisfaction of the medical big data product is obtained. In one embodiment, the a priori perceptual satisfaction is obtained by one or more of an a priori trust score, an a priori communication score, an a priori experience score, and an a priori relationship score.
In step S104, the product value satisfaction of the medical big data product is acquired. In one embodiment, product value satisfaction is obtained by one or more of user task completion, product experience score, data quality score, and user burden score.
In step S106, the service satisfaction of the medical big data product is acquired. In one embodiment, the service satisfaction is obtained by one or more of a service acquisition convenience score, a service feedback timeliness score, and a service effectiveness score.
In step S108, the satisfaction of the medical big data product is determined based on the prior perception satisfaction, the product value satisfaction and the service satisfaction.
In the embodiment, by obtaining the satisfaction index, the product design operation optimization effect for user satisfaction can be quantized, and qualitative analysis is switched to quantitative analysis, so that the design operation optimization point of the product is clearer, the product is closer to user experience, and the user satisfaction is improved. Through the disassembly and modeling of the detail indexes of the user satisfaction degree, the influence of some random accidental factors on the user satisfaction degree is eliminated.
Fig. 2 shows a schematic diagram relating to an index in an embodiment of the medical big data product user satisfaction determination method according to the present invention. As described in detail below in conjunction with fig. 2.
The user uses the products and services to complete certain tasks, such as scientific research tasks, management tasks and the like. Companies can provide users with the ability to complete their tasks, including the products delivered and the services offered. Therefore, the user satisfaction can be decomposed into the product value satisfaction and the service satisfaction. But the product service acceptance by the user is premised on its a priori perception. If the a priori perceived satisfaction is 0, the company will have no opportunity to provide products and services. User satisfaction can therefore be measured by the following formula:
S＝S0·(w1·S1+w2·S2) (1)
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively, represent the weight coefficients. The weighting coefficients can be adjusted according to specific situations or obtained by using historical data learning.
The satisfaction degree of the prior perception of the user mainly depends on the following aspects:
a priori confidence score t (trust):
the prior trust of users on company products mainly comes from market brand influence and user public praise effect of the companies. A company may promote the a priori trust score T by technical brand construction. The user's a priori trust score for a company product may be obtained by means of a survey or the like.
A priori communication score c (communication):
the communication with doctors depends on the operation of the front end of the company, and the effect is improved through the technical communication training of the front-end personnel. Can benefit from the front-end training of technology and communication skills, and aims to obtain the prior good feeling and trust of users. Once goodness and trust is anchored, the product challenges for later use by the user are more forgiving.
Prior experience score i (intuition):
the user's experience before actually using the company's product to solve a particular problem may determine the user's a priori ratings of the product. In one embodiment, the prior experience score may be boosted by the effect of the presentation platform.
It should be noted that if one of the three reaches the bottom line, i.e., the score is zero, it may result in the user completely abandoning a company's product. One possible retrieval approach is: if the company is a particularly well-associated entity or doctor, it is still possible to continue using the company's product after further communication. Therefore, under the condition that the prior relation score R (relationship) is high, the prior perception satisfaction degree S can still be ensured0Is not zero, thereby leaving the user satisfaction s non-zero. Namely:
S0＝w3·(T·C·I)+w4·R (2)
the score of the product value satisfaction degree depends on whether the user can solve the problem really or not and the task is completed. If the user is not helped to solve the problem, the user is not satisfied. To the extent that the user completes the task, there is a satisfactory likelihood. Any product cannot be as good as possible, and has the risk that a user cannot be helped to complete the task in a simple technology, so that a way for allowing the user to participate becomes more important, and the user can complete the task on the basis of the product and the service through the participation of the user. Such as a structured editor, interactive participation in retrieval is a good example. Another point is that user satisfaction with the product must be inversely proportional to user expectations for the product. The higher the customer expectations, the lower the likelihood of satisfaction, with a constant product level. Therefore, it is very important to control the user expectation reasonably. When the premise of completing the task is met, several important indexes seriously affect the satisfaction degree of the user:
product experience score U:
mainly depends on product design, interaction and usability. The impact of the product itself on the user satisfaction is also very great. On one hand, the iterative product needs to be updated continuously, and the migration cost of the use habit of the user needs to be improved continuously, and on the other hand, the migration cost of the use habit of the user needs to be controlled. This is a design and consideration that needs to be carefully done.
Data quality score D:
mainly depending on the data quality, a narrow data quality issue is of major concern here. Indexes are set up in each production link. The achievement of the index depends on the investment of cost besides the algorithm technology. Such as the cost of tagging data, and the cost of editing rules. This is a basic goal in the short term, with the greatest possible control over time and capital costs. In the process of processing medical data, the data quality problem is always the content which needs to be gradually improved and promoted in the data processing process. Because the data quality of the hospital may have problems, the problem in the data is actively discovered and is cooperatively solved with the hospital, otherwise, the risk that the user cannot complete the task exists. Therefore, the flexible rule knowledge base and the data governance are important work of the data quality part. One of the main aspects of the measurement of the data quality is that depending on the data verification result, the data verification process can be performed in each stage and process of data processing, and the adaptive definition of the verification rule also provides a flexible basis for the measurement of the data quality. And the mapping between the data verification process and the data quality measurement is based on the associated mapping of a series of key index items, so that the basis for comprehensively converging the data verification value to the data quality value is obtained according to the mapping relations. In one embodiment, medical service data information in different medical data systems is first obtained, and the medical service data information may include structured data and unstructured data; performing compliance verification on the acquired medical service data information content according to a predefined verification rule, a predefined verification index and a predefined weight; and evaluating different quality evaluation indexes of the acquired medical service data, and obtaining the comprehensive score of the quality evaluation indexes of the medical service data according to the weight of each quality evaluation index.
In one embodiment, the method is suitable for controlling the quality of brain wave medical data, and comprises the following steps: carrying out quality detection on the integrity of wave groups of electroencephalogram data extracted in the data acquisition stage, the integrity, accuracy, consistency and timeliness of data in the data storage and management stage, and judging whether the data quality meets a preset standard or not; when the data quality of the data acquisition stage and the data storage and management stage is not in accordance with the standard, giving corresponding negative scores; for those meeting the predetermined criteria, corresponding positive scores are given, and all the scores are combined to obtain a data quality score D.
In one embodiment, the data quality score is determined according to the following rules:
the initial score of the data quality score is 100 points of full score, and the score is deducted if the following conditions are met:
1. the hospital clinical information system to which the batch of data should be integrated comprises a hospital information management system (HIS), an electronic medical record system (EMR), an examination information system (LIS), a radiology information management system (RIS), a mobile nursing information management system, an operation anesthesia information management system, an intensive care information management system and a medical record management system. The above set integrates less than 10 deductions of 6 systems and less than 20 deductions of 4 systems (only one deduction is calculated for multiple manufacturers and multiple systems with the same function).
2. The field with the filling rate of less than 70% is limited in practicability, and the field rate with the filling rate of less than 70% in the field adopted by the batch of data is calculated. The ratio is less than 90% and less than 10 minutes and less than 80 and less than 20 minutes.
3. The original data often has some problems, so that the quality control indexes designed from the data applicability can not be satisfied ideally by 100%, and the number of the rules violating the quality control rules in the batch of data is calculated. And each rule which does not reach the quality control threshold deducts 1 point.
User burden score B:
the user also invests a lot of labor and time cost to operate in the process of using the product to complete the task. Therefore, the cost of reducing the burden of the user as much as possible by the artificial intelligence algorithm and the user interaction technology is another important factor for improving the satisfaction degree of the user. For example, the self-operation cost of the user can be saved by improving the accurate recall rate of the structuring as much as possible, and a large amount of repeated labor of the user can be reduced by the aid of the algorithm in the structuring editor.
Therefore, the temperature of the molten metal is controlled,
at present, unstructured information is a common mode of text recording in medical records, and when a doctor wants to perform medical research, the unstructured text needs to be structured, so that key information is extracted for data work of scientific research. Most scientific research processes also use manual content extraction and extraction.
Conventional structuring steps include: the physician looks up the medical records, manually extracts the required structured information from the medical records, and records it in a table. The operation is repeated for each scientific project. After a doctor newly makes a batch of extracted contents each time, the previous extraction mode cannot be reused, and re-extraction and statistics are needed.
In an improved scheme, a doctor labels through a small amount of text, and a software system analyzes the content of the doctor label and provides an extraction result of the unlabelled text. Each set-up extraction method can be reused in future projects. However, the disadvantages of this solution include: the text description of the physician's mark is limited and may in practice be only one of a number of descriptions. Such marks may affect the effectiveness of the extraction.
The further improvement scheme is that through the product design of the structured editor, the operation steps of a doctor when extracting the unstructured text are reduced, the existing extraction scheme is reused, and the extraction time of the doctor is reduced. The doctor selects the content to be extracted, the doctor extracts the content label, intelligently analyzes the characteristics of other texts and intelligently recommends related words according to the content labeled by the doctor, and the system finishes the extraction of the content to be extracted of other texts. The extracted content can be exported.
Specifically, the structured editor extraction method includes:
1) newly building a structured editor: the doctor creates a structured editor to be extracted, which includes the selection of which content to extract and the extraction of content features.
2) Selecting a structured source: the physician selects the structured source to extract and marks within this data range after selection.
3) Marking structured information: the doctor manually marks one or more pieces of data in the source, marks the content to be extracted, and marks the feature marks of the content to be extracted.
4) Obtaining other text extraction results: after some contents needing to be extracted are marked manually, the system provides results needing to be extracted in unmarked contents through analysis.
5) Exporting the extraction result to a file
Through the application design, the efficiency of extracting the structured text can be improved: when the method is used by a user, the large data content can be extracted in batch only by marking a small amount of texts; the degree of freedom of extracting the text by the doctor can be improved: the doctor can get the structured content to be extracted from a plurality of data ranges.
Regarding the service satisfaction, the following aspects need to be considered
Service acquisition convenience score a:
the user can conveniently feed back when encountering problems and needing to solve the problems, and a manual and tool explicit or implicit feedback channel is established. Such as contacts that can be found at any time or feedback paths in the system. This is taken into account in the product design. In addition, in the long term, the problems can be actively discovered and analyzed in the normal use of the user, and are actively solved in the background, so that the log recording and analysis are well done.
Service feedback timeliness score F:
when a user needs service assistance, it is very important for the user to be satisfied with feedback timely after receiving a demand. Feedback is not equivalent to the resolution of the problem, but allows the user to see that the company is trying to resolve, and continually updates the progress. Therefore, a standardized service management process and a progress tool need to be established, and a reasonable and efficient feedback process management system needs to be established. Help the company to manage themselves and is visible to the user.
Service effectiveness score E:
service availability means that the service request made by the user is eventually resolved, otherwise the feedback is not useful in time. Because the back-end needs to process data timely, a user request needs to be correctly and efficiently distributed to a back-end technical responsible person, and an efficient distribution mechanism and back-end process management need to be established. This requires constant iterative updating of standardized process management and techniques.
Therefore, the temperature of the molten metal is controlled,
S2＝w8·A+w9·F+w10·E (4)
in summary, user satisfaction is a broad data quality issue, and for data quality, user satisfaction should be targeted. The user satisfaction depends on many factors, and all front and back ends are required to make the relevant parts extremely.
Specifically, in one embodiment, the user satisfaction quantifiable model is defined as follows:
user satisfaction:
(S-prior perceptual satisfaction S)0·(w1Satisfaction degree of product quality S1+w2Service satisfaction S2)
Prior perceptual satisfaction:
S0＝w3- (Prior Trust score T. Prior communication score C. Prior experience score I)
+w4A priori relationship score R
Product quality satisfaction degree:
service satisfaction degree:
S2＝w8service acquisition convenience score A + w9Service feedback timeliness score F
+w10Service effectiveness score E
Symbolized definition:
S＝S0·(w1·S1+w2·S2)
S0＝w3·(T·C·I)+w4·R
S2＝w8·A+w9·F+w10·E
constraint conditions are as follows:
w1≥w2
w1+w2＝1
w3+w4＝1
w5+w6+w7＝1
w8+w9+w10＝1
0≤y≤1
y＝f(x,U,D,B)
Ex≥0
0≤T,C,I,U,D,B,A,F,E≤1。
fig. 3 shows a block diagram of an embodiment of a medical big data product user satisfaction determining apparatus according to the present invention. As shown in fig. 3, the apparatus includes: a priori perception satisfaction obtaining module 31, configured to obtain a priori perception satisfaction of the medical big data product; a product value satisfaction acquiring module 32, configured to acquire the product value satisfaction of the medical big data product; a service satisfaction acquiring module 33, configured to acquire the service satisfaction of the medical big data product; a total satisfaction determination module 34 for determining the satisfaction of the medical big data product based on the prior perception satisfaction, the product value satisfaction and the service satisfaction.
In one embodiment, the overall satisfaction determination module determines the satisfaction of the medical big data product by the formula:
S＝S0·(w1·S1+w2·S2)；
S0＝w3·(T·C·I)+w4·R；
S2＝w8·A+w9·F+w10·E；
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively representing a weight coefficient, T representing a prior trust score, C representing a prior communication score, I representing a prior experience score, R representing a prior relation score, y representing a task completion degree, U representing a product experience score, D representing a data quality score, B representing a user burden score, and E representing a user burden scorexIndicating user expectation, w5、w6、w7For the weight coefficient, A represents a service acquisition convenience score, F represents a service feedback timeliness score, E represents a service validity score, w8、w9、w10Are weight coefficients.
The specific details of each module in the user satisfaction determining apparatus have been described in detail in the corresponding user satisfaction determining method, and therefore are not described herein again.
In an exemplary embodiment of the present disclosure, there is also provided an electronic device capable of implementing the above-described user satisfaction determination method of a medical big data product.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 400 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 4, electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: the at least one processing unit 410, the at least one memory unit 420, and a bus 430 that couples various system components including the memory unit 420 and the processing unit 410.
Wherein the storage unit stores program code that is executable by the processing unit 410 to cause the processing unit 410 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 410 may execute S102 as shown in fig. 1, obtaining a priori perceptual satisfaction of the medical big data product; s104, obtaining the product value satisfaction degree of the medical big data product; s106, obtaining the service satisfaction degree of the medical big data product; and S108, determining the satisfaction degree of the medical big data product based on the prior perception satisfaction degree, the product value satisfaction degree and the service satisfaction degree.
The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 460. As shown, the network adapter 460 communicates with the other modules of the electronic device 400 over the bus 430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 5, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (13)
1. A method for determining user satisfaction of a medical big data product, comprising:
obtaining prior perception satisfaction of the medical big data product;
obtaining the product value satisfaction degree of the medical big data product;
acquiring the service satisfaction degree of the medical big data product;
determining satisfaction of the medical big data product based on the prior perception satisfaction, product value satisfaction and service satisfaction.
2. The method of claim 1, wherein the formula for determining the satisfaction of the medical big data product is:
S＝S0·(w1·S1+w2·S2)；
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively, represent the weight coefficients.
3. The method of claim 1 or 2, wherein obtaining a priori perceptual satisfaction of the medical big data product comprises:
and obtaining the prior perception satisfaction degree of the medical big data product through the prior trust score, the prior communication score and the prior experience score of the user.
4. The method of claim 4, wherein the obtaining a priori perceptual satisfaction of the medical big data product through a priori trust score, a priori communication score, and a priori experience score of a user comprises:
determining the prior perception satisfaction of the medical big data product through the prior trust score, the prior communication score, the prior experience score and the prior relation score of the user.
5. The method of claim 4, wherein the prior perceptual satisfaction is determined by the formula:
S0＝w3·(T·C·I)+w4·R；
wherein S is0Representing a priori perceptual satisfaction, T representing a priori trust score, C representing a priori communication score, I representing a priori experience score, R representing a priori relationship score, w3And w4Respectively, represent the weight coefficients.
6. The method of claim 1 or 2, wherein obtaining product value satisfaction of the medical big data product comprises:
and determining the product value satisfaction degree of the medical big data product according to the task completion degree of the user, the product experience score, the data quality score and the user burden score.
7. The method of claim 6, wherein the formula for determining the product value satisfaction is:
wherein S is1Representing the satisfaction degree of the product value, y representing the completion degree of the task, U representing the experience score of the product, D representing the quality score of the data, B representing the burden score of the user, ExIndicating user expectation, w5、w6、w7Are weight coefficients.
8. The method of claim 1 or 2, wherein obtaining service satisfaction of the medical big data product comprises:
and determining the service satisfaction degree of the medical big data product according to the service acquisition convenience score, the service feedback timeliness score and the service effectiveness score.
9. The method of claim 8, wherein the formula for determining the service satisfaction of the medical big data product is:
S2＝w8·A+w9·F+w10·E；
wherein S is2Representing service satisfaction, A representing service acquisition convenience score, F representing service feedback timeliness score, E representing service effectiveness score, w8、w9、w10Are weight coefficients.
10. A user satisfaction determination apparatus for a medical big data product, comprising:
the prior perception satisfaction acquiring module is used for acquiring the prior perception satisfaction of the medical big data product;
the product value satisfaction acquiring module is used for acquiring the product value satisfaction of the medical big data product;
the service satisfaction acquiring module is used for acquiring the service satisfaction of the medical big data product;
and the total satisfaction determining module is used for determining the satisfaction of the medical big data product based on the prior perception satisfaction, the product value satisfaction and the service satisfaction.
11. The apparatus of claim 10, wherein the overall satisfaction determination module determines the satisfaction of the medical big data product by the formula:
S＝S0·(w1·S1+w2·S2)；
S0＝w3·(T·C·I)+w4·R；
S2＝w8·A+w9·F+w10·E；
wherein S represents user satisfaction, S0Indicating a priori perceptual satisfaction, S1Represents the satisfaction of the product value, S2Indicates service satisfaction, w1And w2Respectively representing a weight coefficient, T representing a prior trust score, C representing a prior communication score, I representing a prior experience score, R representing a prior relationship score, w3And w4Respectively representing a weight coefficient, y representing a task completion degree, U representing a product experience score, D representing a data quality score, B representing a user burden score, and E representing a user burden scorexIndicating user expectation, w5、w6、w7For the weight coefficient, A represents a service acquisition convenience score, F represents a service feedback timeliness score, E represents a service validity score, w8、w9、w10Are weight coefficients.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a user satisfaction determination method according to any of claims 1-9.
13. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the user satisfaction determination method of any of claims 1-9 via execution of the executable instructions.
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