CN111180066A - Health assessment method and device based on visit data - Google Patents
Health assessment method and device based on visit data Download PDFInfo
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Abstract
The invention provides a health assessment method and a health assessment device based on visit data, wherein the method comprises the following steps: acquiring current clinic data and historical clinic data of a patient, and evaluation indexes aiming at the current clinic data and the historical clinic data; establishing a multi-level analysis model according to historical clinic data and evaluation indexes; inputting the current clinic data of the patient into a multi-level analysis model, and outputting a total score value corresponding to the patient; and under the condition that the total score value is within the abnormal score value range, generating and displaying first early warning information. The method and the system can correlate the current clinic data of the patient with the historical clinic data, improve the health assessment accuracy, and adjust the time and the content of the next health assessment plan according to the current clinic data before the next health assessment is carried out on the patient, so that the health assessment is not limited to a fixed time, and the requirements of the patient can be met better.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a health assessment method and device based on visit data and a computer readable storage medium.
Background
In the medical field, it is common to perform a medical examination of a patient and determine a further treatment plan for the patient based on an analysis of current medical data.
In the prior art, an evaluator can configure several different sets of assessment scale templates according to the health assessment standard, such as: the body function assessment, the fall risk assessment, the comprehensive decline scale and the like are carried out, different assessment scales are used for carrying out assessment at a fixed time every year according to different conditions of current diagnosis data of the patient, the patient is scored to obtain the health score of the patient, and the subsequent treatment of the patient is carried out according to the numerical value of the health score.
However, in the current scheme, only a fixed template is adopted, and a health assessment method is performed based on current visit data of a patient, so that the accuracy is low, and due to the relatively fixed assessment time, personalized and accurate service for the patient cannot be realized.
Disclosure of Invention
In view of the above, the present invention provides a health assessment method and apparatus based on visit data, and a computer-readable storage medium, which solve the problem that the current scheme cannot realize personalized and accurate patient compliance to a certain extent.
According to a first aspect of the present invention, there is provided a method of health assessment based on visit data, the method may comprise:
acquiring current clinic data, historical clinic data of a patient and evaluation indexes aiming at the current clinic data and the historical clinic data;
establishing a multi-level analysis model according to the historical visit data and the evaluation index, wherein the multi-level analysis model comprises: a plurality of score value ranges, wherein the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes;
inputting the current clinic data of the patient into the multi-level analysis model, and outputting a total score value corresponding to the patient;
and under the condition that the total score value is within the range of the abnormal score value, generating and displaying first early warning information.
According to a second aspect of the present invention, there is provided a health assessment apparatus based on visit data, the apparatus may comprise:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring current clinic data and historical clinic data of a patient and evaluating indexes aiming at the current clinic data and the historical clinic data;
the establishing module is used for establishing a multi-level analysis model according to the historical clinic data and the evaluation index, and the multi-level analysis model comprises: a plurality of score value ranges, wherein the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes;
the analysis module is used for inputting the current clinic data of the patient into the multi-level analysis model and outputting a total score value corresponding to the patient;
and the generating module is used for generating and displaying first early warning information under the condition that the total score value is within the abnormal score value range.
Optionally, the analysis module includes:
the input sub-module is used for inputting the current visit data of the patient into the multi-level analysis model;
the first determining submodule is used for determining a target evaluation index corresponding to each current clinic data, and a target score value and a target weight value corresponding to the target evaluation index in the multi-level analysis model;
and the calculation submodule is used for performing weighted average calculation on the target score values and the target weight values of all the target evaluation indexes in the multi-level analysis model to obtain the total score value.
Optionally, the method further includes:
and the generation submodule is used for generating and displaying second early warning information aiming at the target evaluation index under the condition that the target score value is in the abnormal score value range and the current clinic data corresponding to the target score value is inconsistent with the historical clinic data.
Optionally, the method further includes:
the acquisition submodule is used for acquiring a standard evaluation scale which comprises a corresponding relation between an evaluation index and a solution;
the second determination submodule is used for matching the target evaluation index with the standard evaluation scale and determining a target solution corresponding to the target evaluation index;
and the display submodule is used for displaying the target solution.
Optionally, the standard assessment scale comprises: one or more of a care level assessment table, a fall risk level assessment table, and a nutrition risk detection table.
Optionally, the method further includes:
and the data cleaning module is used for performing data cleaning operation on the current clinic data and the historical clinic data through a preset noise data template to remove noise data in the current clinic data and the historical clinic data.
Optionally, the method further includes:
and the abnormal return module is used for adding the noise data into an abnormal data report and sending the abnormal data report to a source terminal corresponding to the noise data.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of the method for health assessment based on visit data according to the first aspect.
In a fourth aspect, an embodiment of the present invention provides a terminal, including:
processor, memory and computer program stored on and executable on said memory, said computer program when executed by said processor implementing a method of healthcare based assessment of health as described in the first aspect
Aiming at the prior art, the invention has the following advantages:
the invention provides a health assessment method based on visit data, which comprises the following steps: acquiring current clinic data and historical clinic data of a patient, and evaluation indexes aiming at the current clinic data and the historical clinic data; according to historical visit data and evaluation indexes, a multi-level analysis model is established, and comprises the following steps: the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes; inputting the current clinic data of the patient into a multi-level analysis model, and outputting a total score value corresponding to the patient; and under the condition that the total score value is within the abnormal score value range, generating and displaying first early warning information. The present invention can correlate the present clinic data of the patient with the historical clinic data by inputting the present clinic data of the patient into a multi-level analysis model to obtain the total score of the patient, and perform corresponding early warning measures according to the score value range of the total score, thereby improving the health evaluation precision.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart illustrating the steps of a method for health assessment based on visit data according to an embodiment of the present invention;
FIG. 2 is a block diagram of a multi-level analysis model according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a relationship between an evaluation index and a score value according to an embodiment of the present invention;
FIG. 4 is a flow chart illustrating the steps of another method for assessing health based on visit data according to an embodiment of the present invention;
fig. 5 is a block diagram of a health assessment apparatus based on visit data according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 is a flowchart of steps of a health assessment method based on visit data according to an embodiment of the present invention, which is applied to a terminal, and as shown in fig. 1, the method may include:
In the embodiment of the invention, for a patient with a long-term treatment plan, the patient often needs to be subjected to medical examination for multiple times, so that medical examination data generated by each medical examination is obtained, and the health of the patient is evaluated according to a preset evaluation index.
Specifically, the visit data generated by each physical examination and visit of the patient can be integrally transmitted to the health data center server through instant messaging software (MQ). The medical data can include physical examination data, medical data, examination data, inspection data, evaluation data, electronic medical record and the like. Different diagnosis systems, such as a hospital information system (His) or a physical examination system, send the diagnosis data to the MQ in real time when generating one piece of diagnosis data, create a relevant table in a database of the health data center server, and store the diagnosis data into the health data center server in a unified manner to form the original data of each system.
Furthermore, the evaluation indexes can be screened and generated from an evaluation scale template, the evaluation scale template comprises body function evaluation, falling risk evaluation, comprehensive decline scales and the like, the evaluation scales are provided with a set of evaluation problem library according to international standards, and different evaluation scales are formed according to different combinations, such as a nursing grade evaluation table, a falling risk grade evaluation table, a nutrition risk detection table and the like.
And 102, establishing a multi-level analysis model according to the historical clinic data and the evaluation index.
Wherein, multi-level analysis model includes: the score value range comprises a plurality of score value ranges, the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes.
In the embodiment of the invention, a multi-level analysis model can be constructed based on historical clinic data and evaluation indexes. After the examination of each patient, the historical examination data of the patient can be acquired according to the Identification (ID) of the patient, the patient is evaluated by a plurality of evaluation indexes through a multi-level analysis model, and the total score value of the health condition of the patient is output, so that the total score value of the patient can be further analyzed to draw a conclusion, and whether the patient needs to be evaluated or suggested again is judged.
Specifically, referring to fig. 2, the multi-level analysis model can be divided into 3 layers: the target layer, the criterion layer and the index layer can determine the influence factor indexes according to the current clinic data of the patient. For example, the body function assessment scale for the criterion layer, and the assessment indexes at the index layer may include indexes of blood pressure, blood sugar, blood fat, heart rate and the like.
In the process of establishing the multi-level analysis model, in order to determine the relative importance of each evaluation index, a fuzzy judgment scale for each evaluation index may be established according to table 1 below.
TABLE 1
For each evaluation index, a corresponding scale may be added based on table 1 above, and after the corresponding scale is added to each evaluation index, a fuzzy complementary matrix is obtained.
After obtaining the fuzzy complementary matrix, the fuzzy complementary matrix can be further passed as followsEquation 1 obtains a consistency matrix and calculates the relative weight w of each evaluation index according to equation 2i. Then, the comprehensive weight v is further calculated according to the formula 3ij。
Equation 1:
and
equation 2:
equation 3: v. ofij＝ωi·ωij，i＝1，2，…，n；j＝1，2，…，n
Further, after each evaluation index is comprehensively analyzed, a comprehensive index evaluation table 2 and each index evaluation table 3 can be established.
TABLE 2
TABLE 3
Tables 2 and 3 include a plurality of score value ranges, evaluation indexes included in the score value ranges, and score values corresponding to the evaluation indexes, and based on tables 2 and 3, a multi-level analysis model can be constructed.
According to the table 2 and the table 3, a code table corresponding to the evaluation index can be established in the database of the health data center server and stored in the multi-level analysis model. The code table may record scores corresponding to various indexes, such as fig. 3, which shows a code table corresponding to Body Mass Index (BMI). In the first row in fig. 3, when the work value of the body mass index evaluation index is greater than 40, it is scored as 90.
In the embodiment of the invention, the patient physical examination treatment and other manners can obtain the treatment data, the current treatment data of the patient is input into the multi-level analysis model, the score corresponding to the current treatment data can be found based on the tables 2 and 3, and the total score value corresponding to the patient can be obtained by carrying out weighted average on each evaluation index. The size of the total score value is used for reflecting the physical health condition of the patient, and based on the analysis of the total score value, the evaluation or the suggestion of the patient can be further realized.
And 104, generating and displaying first early warning information under the condition that the total score value is within the range of the abnormal score value.
In the embodiment of the invention, the multi-level analysis model comprises a plurality of score value ranges, one or more abnormal score value ranges exist in the plurality of score value ranges, and when the total score value of the patient is in the abnormal score value range, the abnormal condition of the body of the patient can be judged, and first early warning information needs to be generated and displayed.
In addition, under the condition that a plurality of abnormal score value ranges exist, the difference of the early warning priority and the importance degree exists between different abnormal score value ranges, and the difference display of the first early warning information can be realized based on the difference.
Referring to table 2, the multi-level analysis model may include 4 score value ranges, that is, when the score value > -80 score value range, the corresponding first warning information needs to be immediately evaluated; when the score value is in the score value range of 60-80, evaluating the corresponding first early warning information after important attention is needed; when the score value ranges from 40 to 60, the corresponding first early warning information is generally concerned and is not evaluated again; and when the score value is less than the score value range of 40, the corresponding first early warning information is normally executed. Referring to table 3, each evaluation index may also have the corresponding warning information in the range of 4 points.
For the abnormal evaluation index with the point value of more than 80 points, the abnormal evaluation index can be compared with the data corresponding to the abnormal evaluation index in the historical clinic data, and if the evaluation index and the data are consistent, first early warning information does not need to be sent to an evaluator.
If the evaluation indexes are not consistent or the evaluation conclusion does not exist, then:
1) and if the total score value is greater than or equal to 80, first early warning information is immediately generated and displayed, the first early warning information specifically comprises an evaluation number, an evaluation name and evaluation items with inconsistent results, and the evaluation items are recommended to be evaluated for the client again.
2) And if the total score value is between 60 and 80, generating and displaying first early warning information, wherein the first early warning information specifically comprises items needing to be focused on and having the score value of more than 80, and prompting that the items possibly need to be evaluated again.
3) And if the total score value is 40-60, generating and displaying first early warning information, wherein the first early warning information specifically comprises items needing the important attention score value of more than 80, but does not need to be evaluated again.
4) And if the total score value is less than 40, the first early warning information is not sent to the evaluators.
5) Since the old people fall down into a high risk event and can induce a lot of diseases or complications, if the keyword of 'fall down' appears in the evaluation index, the first early warning information can be directly generated and displayed without weight analysis.
After the first warning information is presented, the following steps may be performed:
1) and integrating a standard evaluation scale question library.
2) And when the evaluation schedule is generated, acquiring the latest score value result of each index of the patient, acquiring an abnormal evaluation index with the score of more than 80, and judging whether an evaluation item of the related abnormal evaluation index exists in the evaluation schedule.
3) And if the evaluation personnel exist, the evaluation personnel do not need to be reminded.
4) And if the abnormal evaluation index does not exist, inquiring the evaluation scale question bank according to the abnormal evaluation index item to find out the name of the problem related to the abnormal evaluation index.
5) And the abnormal evaluation index and the added evaluation item are suggested, and the patient information is sent to an evaluator to remind the evaluator that the patient has an abnormal index and needs to add a related evaluation item.
For example, an elderly person who entered the home-care community for post-stay general assessment of the elderly would have a good cardiovascular status in the assessment questionnaire. After the patient stays for a period of time, multiple assessment indexes of the patient are found through current diagnosis data, the scores of all the assessment indexes and the total score of the patient are obtained according to a multi-level analysis model, if the total score exceeds 80, first early warning information is generated and displayed to remind an evaluator that secondary assessment is needed, data corresponding to abnormal assessment indexes are sent to the evaluator together, if the total score is less than 80, the abnormal assessment indexes with the scores exceeding 80 are sent to remind the evaluator that important attention is needed, and if the total score is less than 40, the first early warning information is not generated.
If another old man carries out comprehensive assessment on the old man after entering the elderly-care community, the assessment result in the historical assessment questionnaire is that the cardiovascular condition is abnormal, the heart rate is irregular, and the coronary heart disease is suffered. If the score of the evaluation index is larger than 80 and the total score is smaller than 80 when the old people are in physical examination or in medical treatment, the system cannot send out early warning information, and nursing can be carried out according to the previous evaluation result.
To sum up, the method for health assessment based on visit data provided by the embodiment of the present invention includes: acquiring current clinic data and historical clinic data of a patient, and evaluation indexes aiming at the current clinic data and the historical clinic data; according to historical visit data and evaluation indexes, a multi-level analysis model is established, and comprises the following steps: the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes; inputting the current clinic data of the patient into a multi-level analysis model, and outputting a total score value corresponding to the patient; and under the condition that the total score value is within the abnormal score value range, generating and displaying first early warning information. The present invention can correlate the present clinic data of the patient with the historical clinic data by inputting the present clinic data of the patient into a multi-level analysis model to obtain the total score of the patient, and perform corresponding early warning measures according to the score value range of the total score, thereby improving the health evaluation precision.
Fig. 4 is a flowchart illustrating steps of another method for health assessment based on visit data according to an embodiment of the present invention, as shown in fig. 4, the method may include:
This step may specifically refer to step 101, which is not described herein again.
In the embodiment of the invention, the electronic medical records of different systems are usually different, so that the generated clinic data are different, wherein more noise data exist, and after the clinic data are analyzed according to the noise data template, the fields with the noise data removed can be stored in the database table to realize the cleaning of the data.
In addition, data with wrong format (identification numbers, mobile phone numbers and the like) and data with some empty fields can be put into the exception table firstly. And returning the abnormal data to each source system to inform the source system of the reason of the data error, and sending the data to a database table for storage after the data is completely investigated and supplemented by the service system.
And 203, establishing a multi-level analysis model according to the historical clinic data and the evaluation index.
This step may specifically refer to step 102, which is not described herein again.
And step 204, inputting the current clinic data of the patient into the multi-level analysis model.
Wherein, multi-level analysis model still includes: and the weight value corresponding to the score value.
In this step, referring to table 3, a target evaluation index corresponding to each current visit data, and a target score value and a target weight value corresponding to the target evaluation index may also be determined based on table 3 stored in the multi-level analysis model.
If a current visit data includes the Body Mass Index (BMI) in table 3, the target score value and the target weight value corresponding to the body mass index evaluation index may be further searched based on the code table shown in fig. 3. In addition, when other evaluation indexes are included in the current visit data, the corresponding target point value and the corresponding target weight value can be acquired in the same manner.
In this step, a total score value of the patient may be obtained by performing weighted average calculation on each target evaluation index, and whether to evaluate or suggest the patient again may be determined by analyzing the total score value based on table 2.
And step 207, generating and displaying first early warning information under the condition that the total score value is within the abnormal score value range.
Optionally, in this embodiment of the present invention, after step 205, the method may further include:
and A1, generating and displaying second early warning information aiming at the target evaluation index under the condition that the target score value is in the abnormal score value range and the current clinic data corresponding to the target score value is inconsistent with the historical clinic data.
In the embodiment of the present invention, a target score value corresponding to each evaluation index in the current visit data may be obtained based on a code table of the evaluation index in the multi-level analysis model, and further based on table 3 in the multi-level analysis model, a range of 4 score values may be included, that is, a range of score values where the score value > -80; a fractional value ranging from 60 to 80; a score value range between score values 40 and 60; score value range < 40. Different early warning information contents exist in different score value ranges of different evaluation indexes.
The abnormal score value range can be a score value range of 80; a fractional value ranging from 60 to 80; fractional values ranging between fractional values 40 to 60. And when the target score value is within the abnormal score value range and the current clinic data corresponding to the target score value is inconsistent with the historical clinic data, generating and displaying second early warning information aiming at the target evaluation index. If the current visit data corresponding to the target point value does not match the historical visit data, it is considered that the time at which the evaluation index of the patient deteriorates occurs between the historical visit data generation time and the current visit data generation time, and it is necessary to perform warning in an unexpected case.
For example, if the BMI index is 28 in the current visit data of the patient, the specific content of the second warning information can be determined to need to be focused and then evaluated based on table 3.
Optionally, after step a1, the method may further include:
and A2, obtaining a standard evaluation scale, wherein the standard evaluation scale comprises the corresponding relation between the evaluation index and the solution.
Optionally, the standard assessment scale comprises: one or more of a care level assessment table, a fall risk level assessment table, and a nutrition risk detection table.
In the embodiment of the present invention, a standard evaluation scale may be specified in advance to bind the evaluation index with the corresponding solution.
And A3, matching the target evaluation index with the standard evaluation scale, and determining a target solution corresponding to the target evaluation index.
And step A4, displaying the target solution.
In this step, the target assessment index with abnormality may be matched with the standard assessment scale to determine and display a target solution corresponding to the target assessment index to provide a treatment recommendation for the patient's assessment plan.
Optionally, after step 202, the method may further include:
and step B1, adding the noise data into an abnormal data report, and sending the abnormal data report to a source terminal corresponding to the noise data.
In this step, data with wrong format (identification number, mobile phone number, etc.) and data with some empty fields can be put into the exception table first. And returning the abnormal data to each source terminal to inform the source terminal of the reason of the data error, and sending the data to a database table for storage after the data is completely investigated and supplemented by the service system.
In summary, the health assessment method based on the visit data provided by the embodiment of the present invention includes: acquiring current clinic data and historical clinic data of a patient, and evaluation indexes aiming at the current clinic data and the historical clinic data; according to historical visit data and evaluation indexes, a multi-level analysis model is established, and comprises the following steps: the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes; inputting the current clinic data of the patient into a multi-level analysis model, and outputting a total score value corresponding to the patient; and under the condition that the total score value is within the abnormal score value range, generating and displaying first early warning information. The present invention can correlate the present clinic data of the patient with the historical clinic data by inputting the present clinic data of the patient into a multi-level analysis model to obtain the total score of the patient, and perform corresponding early warning measures according to the score value range of the total score, thereby improving the health evaluation precision.
Fig. 5 is a block diagram of a health assessment apparatus based on visit data according to an embodiment of the present invention, and as shown in fig. 5, the apparatus may include:
an obtaining module 301, configured to obtain current clinic data and historical clinic data of a patient, and an evaluation index for the current clinic data and the historical clinic data;
an establishing module 302, configured to establish a multi-level analysis model according to the historical visit data and the evaluation index, where the multi-level analysis model includes: a plurality of score value ranges, wherein the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes;
the analysis module 303 is configured to input the current visit data of the patient into the multi-level analysis model, and output a total score value corresponding to the patient;
optionally, the analysis module 303 includes:
the input sub-module is used for inputting the current visit data of the patient into the multi-level analysis model;
the first determining submodule is used for determining a target evaluation index corresponding to each current clinic data, and a target score value and a target weight value corresponding to the target evaluation index in the multi-level analysis model;
and the calculation submodule is used for performing weighted average calculation on the target score values and the target weight values of all the target evaluation indexes in the multi-level analysis model to obtain the total score value.
And the generation submodule is used for generating and displaying second early warning information aiming at the target evaluation index under the condition that the target score value is in the abnormal score value range and the current clinic data corresponding to the target score value is inconsistent with the historical clinic data.
The acquisition submodule is used for acquiring a standard evaluation scale which comprises a corresponding relation between an evaluation index and a solution;
the second determination submodule is used for matching the target evaluation index with the standard evaluation scale and determining a target solution corresponding to the target evaluation index;
and the display submodule is used for displaying the target solution.
And the generating module 304 is configured to generate and display the first early warning information when the total score value is within the abnormal score value range.
Optionally, the standard assessment scale comprises: one or more of a care level assessment table, a fall risk level assessment table, and a nutrition risk detection table.
Optionally, the method further includes:
and the data cleaning module is used for performing data cleaning operation on the current clinic data and the historical clinic data through a preset noise data template to remove noise data in the current clinic data and the historical clinic data.
Optionally, the method further includes:
and the abnormal return module is used for adding the noise data into an abnormal data report and sending the abnormal data report to a source terminal corresponding to the noise data.
In summary, the health assessment apparatus based on the visit data provided by the embodiment of the present invention includes: acquiring current clinic data and historical clinic data of a patient, and evaluation indexes aiming at the current clinic data and the historical clinic data; according to historical visit data and evaluation indexes, a multi-level analysis model is established, and comprises the following steps: the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes; inputting the current clinic data of the patient into a multi-level analysis model, and outputting a total score value corresponding to the patient; and under the condition that the total score value is within the abnormal score value range, generating and displaying first early warning information. The present invention can correlate the present clinic data of the patient with the historical clinic data by inputting the present clinic data of the patient into a multi-level analysis model to obtain the total score of the patient, and perform corresponding early warning measures according to the score value range of the total score, thereby improving the health evaluation precision.
For the above device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method embodiment.
Preferably, an embodiment of the present invention further provides a terminal, which includes a processor and a memory, where the memory stores a computer program that can be run on the processor, and when the computer program is executed by the processor, the computer program implements each process of the above health assessment method embodiment based on the visit data, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements each process of the above health assessment method embodiment based on the visit data, and can achieve the same technical effect, and is not described herein again to avoid repetition. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As is readily imaginable to the person skilled in the art: any combination of the above embodiments is possible, and thus any combination between the above embodiments is an embodiment of the present invention, but the present disclosure is not necessarily detailed herein for reasons of space.
The visit data based health assessment methods provided herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The structure required to construct a system incorporating aspects of the present invention will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the visit data based health assessment method according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (10)
1. A method of health assessment based on visit data, the method comprising:
acquiring current clinic data, historical clinic data of a patient and evaluation indexes aiming at the current clinic data and the historical clinic data;
establishing a multi-level analysis model according to the historical visit data and the evaluation index, wherein the multi-level analysis model comprises: a plurality of score value ranges, wherein the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes;
inputting the current clinic data of the patient into the multi-level analysis model, and outputting a total score value corresponding to the patient;
and under the condition that the total score value is within the range of the abnormal score value, generating and displaying first early warning information.
2. The method of claim 1, wherein the multi-level analysis model further comprises: the step of inputting the current visit data of the patient into the multi-level analysis model and outputting the total score value corresponding to the patient includes:
inputting current visit data of the patient into the multi-level analysis model;
in the multi-level analysis model, determining a target evaluation index corresponding to each current clinic data, and a target score value and a target weight value corresponding to the target evaluation index;
and in the multi-level analysis model, performing weighted average calculation on the target score values and the target weight values of all the target evaluation indexes to obtain the total score value.
3. The method of claim 2, wherein after determining a target assessment indicator corresponding to each current visit data, and a target score value and a target weight value corresponding to the target assessment indicator, the method further comprises:
and under the condition that the target point value is within the abnormal point value range and the current clinic data corresponding to the target point value is inconsistent with the historical clinic data, generating and displaying second early warning information aiming at the target evaluation index.
4. The method of claim 3, wherein after the generating and presenting of the second early warning information for the target assessment indicator, the method further comprises:
obtaining a standard evaluation scale, wherein the standard evaluation scale comprises a corresponding relation between an evaluation index and a solution;
matching the target evaluation index with the standard evaluation scale, and determining a target solution corresponding to the target evaluation index;
and displaying the target solution.
5. The method of claim 4, wherein the standard assessment scale comprises: one or more of a care level assessment table, a fall risk level assessment table, and a nutrition risk detection table.
6. The method of claim 1, wherein after acquiring current visit data, historical visit data for the patient, the method further comprises:
and performing data cleaning operation on the current clinic data and the historical clinic data through a preset noise data template, and removing noise data in the current clinic data and the historical clinic data.
7. The method of claim 6, wherein after said performing a data cleansing operation on said current visit data and said historical visit data through a preset noise data template to remove noise data in said current visit data and said historical visit data, the method further comprises:
and adding the noise data into an abnormal data report, and sending the abnormal data report to a source terminal corresponding to the noise data.
8. A health assessment device based on visit data, said device comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring current clinic data and historical clinic data of a patient and evaluating indexes aiming at the current clinic data and the historical clinic data;
the establishing module is used for establishing a multi-level analysis model according to the historical clinic data and the evaluation index, and the multi-level analysis model comprises: a plurality of score value ranges, wherein the score value ranges comprise evaluation indexes and score values corresponding to the evaluation indexes;
the analysis module is used for inputting the current clinic data of the patient into the multi-level analysis model and outputting a total score value corresponding to the patient;
and the generating module is used for generating and displaying first early warning information under the condition that the total score value is within the abnormal score value range.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the visit data based health assessment method according to any one of claims 1 to 7.
10. A terminal comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing a method of healthcare based assessment of health as claimed in any one of claims 1 to 7.
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