Comprehensive management system for hearing tests and hearing aid fitting evaluation

Abstract

To provide a comprehensive management system for hearing tests and hearing aid fitting effect evaluation, which significantly reduces the workload of fitting technicians, improves usability, makes it easy to grasp the number of times problems have occurred within a period, and improves fitting effects.SOLUTION: In a comprehensive management system, a fitting technician builds a database of suggested answers using acquired hearing test data, hearing aid fitting data, hearing aid model data, and patient identification data, and a central processing module acquires problem feedback sent by a patient during daily use, determines suggested answers for push notification within a memory, and sends them together with the problem feedback to the fitting technician. The fitting technician uses the suggested answers for push notification, the problem feedback, and the patient's medical records to provide an answer, and the central processing module feeds the suggested answers back to the patient and builds the database of suggested answers in real time using the interaction data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present application relates to the technical field of comprehensive management of hearing aid fitting, specifically to a comprehensive management system for hearing tests and hearing aid fitting effect evaluation. [Background technology]

[0002] Hearing aid fitting requires professional hearing tests, fitting, and evaluation of hearing aid effectiveness. All of these procedures must be performed by a professional fitting technician. A patient can only wear a hearing aid after professional testing and fitting by a fitting technician. To facilitate the management of patients' medical records and the exchange of feedback and responses regarding hearing aid problems between patients and fitting technicians, hearing aid fitting companies often build their own fitting management platforms. For example, in a previous publication, publication number CN112289397A, a comprehensive management system for hearing tests and hearing aid fitting effectiveness evaluations was disclosed. Patients log in to the fitting management platform to send feedback about their problems to the fitting technician. The fitting technician then retrieves the patient's data through the management platform and responds to the problems based on the patient's feedback.

[0003] However, the conventional technology of a comprehensive management platform for fitting technicians and patient data has the following technical problems.

[0004] In actual use, different hearing aid models have their own common problems, such as not being able to hear clearly in certain situations, sounds being too loud, or sounds being too quiet. Although a single fitting company handles a relatively large number of users, the problems reported by many users tend to be focused on problems specific to each hearing aid model. However, traditional comprehensive management platforms for fitting technicians and patient data lack the functionality to store, select, and push notifications for solutions to different hearing aid problems. As a result, fitting technicians must re-edit their solutions every time a similar problem occurs, significantly increasing their workload.

[0005] Therefore, in order to solve the above technical problems, it is necessary to provide an integrated management system for hearing tests and hearing aid fitting effect evaluation. Summary of the Invention [Problem to be solved by the invention]

[0006] To solve the above technical problems, the present application provides a comprehensive management system for hearing tests and hearing aid fitting effect evaluations. [Means for solving the problem]

[0007] In order to achieve the above objectives, the technical solution adopted in this application is a comprehensive management system for hearing tests and hearing aid fitting effect evaluation, which comprises a fitting technician terminal, a patient terminal, a memory unit, and a central processing module; After the fitting technician performs the hearing test and fitting of the hearing aid on the patient, the fitting technician compiles the acquired hearing test data, hearing aid fitting data, hearing aid model data, and patient identification information data on the fitting technician terminal to obtain patient data, and stores the patient data in a memory unit via a central processing module; After fitting, the patient sends problem feedback via the patient terminal during daily use, the central processing module acquires the problem feedback output from the patient terminal, determines a proposed answer for push notification in the memory unit, extracts the patient's medical records in the memory unit, and sends them together with the determined proposed answer and problem feedback to the fitting technician terminal, the fitting technician uses the proposed answer for push notification, the problem feedback, and the patient's medical records to provide an answer using the fitting technician terminal, the central processing module feeds back the output proposed answer to the patient terminal, and uses the interaction data to build a proposed answer database in the memory unit in real time.

[0008] Preferably, the storage unit includes a first storage module for storing patient data, a second storage module for storing answer proposals, and a third storage module for storing problem description keyword groups.

[0009] Preferably, the real-time construction of the answer plan database specifically includes: The fitting technician responds to the problem feedback from the patient using the fitting technician terminal and displays a proposed answer E on the patient terminal. 出 After transmitting the information, the central processing module acquires the interaction data between the patient terminal and the fitting technician terminal to identify the patient's identity, extracts the patient data from the storage unit, and identifies the model of the hearing aid used by the patient in step A1; The central processing module uses character recognition technology to compare the problem feedback from the patient in the interaction data with the problem description keyword group, and determines whether the problem feedback contains content that matches the problem description keywords in the problem description keyword group. If not, the central processing module terminates the construction of the current answer proposal database and sends an instruction to update the problem description keyword group and the problem feedback to the fitting technician terminal. If present, the central processing module retrieves the problem description keywords W that match the problem feedback from the problem description keyword group. 抽The central processing module then extracts the answer plan E sent from the fitting technician terminal from the interaction data. 出 Step A2 of extracting The extracted problem description keywords W 抽 and Answer Plan E 出 and the contents of the answer plan database stored in the second storage module, and an answer plan E 出 and explanatory keyword W 抽 and a step A3 of determining whether to store the answer and the storage location in the answer plan database, The answer database expression is: JPEG0007825371000001.jpg54164, X represents the answer plan database, and R n represents the feedback proposal sub-database for the nth model of hearing aid, JPEG0007825371000002.jpg12167 represents the vth problem subdatabase for the nth model of hearing aid, and W n-v represents the problem description keyword for the vth problem of the nth model of hearing aid, JPEG0007825371000003.jpg12165 represents the mth proposed answer to problem v of model n of hearing aid.

[0010] Preferably, step A3 specifically includes the steps of: The central processing module extracts problem description keywords W from the interaction data. 抽 and Answer Plan E 出 After completing the extraction work of (1), a search is performed on the contents stored in the second storage module to determine whether a hearing aid feedback proposal sub-database matching the hearing aid model used by the patient exists in the second storage module. If a corresponding hearing aid feedback proposal sub-database does not exist, a new hearing aid feedback proposal sub-database is directly created in the second storage module, a new problem sub-database is created in the newly created hearing aid feedback proposal sub-database, and the problem description keywords W extracted from the interaction data are used. 抽 and Answer Plan E 出 in a newly created problem sub-database in the newly created hearing aid feedback proposal sub-database, and if it is determined that the problem sub-database exists, continue to perform the following steps: The central processing module identifies a hearing aid feedback suggestion sub-database in the second storage module that matches the model of hearing aid used by the patient, and then stores the problem description keyword W 抽 Matching problem description keywords W 記 Determine whether the problem description keyword W exists in the identified hearing aid feedback proposal sub-database, and if not, create a new problem sub-database in the identified hearing aid feedback proposal sub-database, and 抽 and Answer Plan E 出 in a newly created problem sub-database within the identified hearing aid feedback proposal sub-database, and if present, continue with step A32 of: In the identified hearing aid feedback proposal subdatabase, the problem description keyword W 抽 Matching problem description keywords W 記 The problem sub-database having the answer plan E is the identified problem sub-database, and the central processing module uses character recognition technology to identify the answer plan E. 出 Each solution plan in the problem subdatabase identified as E 記 Identify the overlap rate with Answer E 出 Answer plan E, whose overlap rate with the set value H is greater than 記If it exists, the operation of storing the answer plan is terminated. If it does not exist, the answer plan E extracted from the interaction data is 出 in the identified problems sub-database.

[0011] Preferably, the step of determining the proposed answer for push notification is specifically as follows.

[0012] In step B1, after identifying the hearing aid feedback suggestion sub-database and the patient terminal sends the problem feedback, the central processing module uses the interaction data to identify the hearing aid model used by the patient, and then searches the answer suggestion database X based on the identified hearing aid model to determine whether a hearing aid feedback suggestion sub-database matching the identified hearing aid model exists in the answer suggestion database X. If not, it is determined that there is no answer suggestion for push notification, and the central processing module sends the problem feedback directly to the fitting technician terminal for the fitting technician to answer. If there is, continue to perform the following steps.

[0013] In step B2, the problem sub-database is identified, and the central processing module uses character recognition technology to determine whether there is a content in the problem feedback that matches the content in the problem description keyword group. If there is not, the central processing module sends the problem feedback directly to the fitting technician terminal for response by the fitting technician. If there is, the central processing module selects a problem description keyword W that matches the problem feedback from the problem description keyword group. 抽 Extract the problem description keywords W 抽 Matching problem description keywords W 記Determine whether or not the proposed answer for push notification exists in the hearing aid feedback proposal sub-database identified in step B1. If not, it is determined that there is no proposed answer for push notification, and the problem feedback is sent directly to the fitting technician terminal for answering by the fitting technician. If it exists, continue to execute the following steps.

[0014] In step B3, a push notification answer plan is determined, and the problem description keyword W is identified in step B2. 抽 Matching problem description keywords W 記 The problem sub-database having the answer plan E is the identified problem sub-database, and the central processing module 記 The results are sorted in descending order according to the extraction frequency, and the first answer in the descending order is E. 記 The answer is extracted and sent to the fitting technician's device as a push notification answer proposal.

[0015] Preferably, the extraction frequency is calculated by the formula JPEG0007825371000004.jpg19162, where F represents the extraction frequency and f 1 represents the number of times during the Z days prior to step B3 that a proposed answer was determined by the central processing module as a proposed answer for push notification and used by the fitting technician as is, and f 2 represents the number of times during the Z days before step B3 that a proposed answer was decided by the fitting technician by logging in voluntarily and checking the proposed answer database in an interaction between the patient and the fitting technician, and G represents the total number of interactions between the fitting technician terminal and the patient terminal during the Z days before step B3.

[0016] Preferably, a collection period C is set, and at the end of the collection period C, the central processing module calculates the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X during the collection period C. At the end of the collection period C, based on the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X, the central processing module extracts each hearing aid model name in all hearing aid feedback suggestion sub-databases, sorts all hearing aid model names in descending order according to the total acquisition frequency of the hearing aid feedback suggestion sub-database corresponding to each hearing aid model name to obtain a hearing aid problem report, and sends the hearing aid problem report to the fitting technician terminal at the end of the collection period C.

[0017] Preferably, the total acquisition frequency of each hearing aid feedback proposal sub-database in the answer proposal database X during the collection period C is calculated using the following formula: JPEG0007825371000005.jpg19162

[0018] where ZP represents the total acquisition frequency, K represents the total number of times in the collection period C that the hearing aid feedback proposal sub-database was identified in step B1 by determining the answer proposal for the push notification, and J represents the total number of times in the collection period C that the patient provided feedback on the problem.

[0019] Preferably, the central processing module calculates the acquisition individual frequency of each problem sub-database in the hearing aid feedback proposal sub-database within the aggregation period C at the end of the aggregation period C, and the calculation formula for the acquisition individual frequency is: JPEG0007825371000006.jpg17165, where FP represents the individual acquisition frequency, U represents the total number of times in the aggregation period C that a problem sub-database was identified in steps B2 and B3 by determining a proposed answer for push notification, and Q represents the total number of times in the aggregation period C that all problem sub-databases in the hearing aid feedback proposal sub-database to which the problem sub-database belongs were identified in steps B2 and B3 by determining a proposed answer for push notification.

[0020] Problem description keywords from each problem sub-database within the hearing aid feedback proposal sub-database are extracted and compiled according to the individual frequency of acquisition to create an individual hearing aid problem distribution report, which is then sent to the fitting technician terminal. [Effects of the Invention]

[0021] The present invention provides a comprehensive management system for hearing tests and hearing aid fitting effect evaluation, which has the following beneficial effects compared with the prior art:

[0022] 1. A database of suggested answers is built using interaction data between fitting technicians and patients. When patients log in to the system on a daily basis and submit problem feedback, the central processing module automatically selects and retrieves suggested answers for push notifications from the database and sends them to the fitting technician's terminal for use by the fitting technician. Meanwhile, the fitting technician can directly modify the suggested answers for push notifications or send the suggested answers for push notifications as the final suggested answers. This eliminates the need for fitting technicians to re-edit the suggested answers every time a recurring problem occurs, significantly reducing their workload and improving usability.

[0023] 2. By monitoring the number of times each hearing aid feedback suggestion sub-database is identified in the process of determining the response suggestions for push notifications, the total frequency of retrieval for each feedback suggestion sub-database can be calculated, and a hearing aid problem report can be generated based on the total frequency of retrieval, which makes it easier for the fitting technician to compare each hearing aid model overall and understand the number of times a problem occurred with all the different models of hearing aids over a period of time.

[0024] 3. When determining the answer suggestions for push notifications, the number of times each problem sub-database is identified within the hearing aid feedback suggestion sub-database can be monitored to output an individual hearing aid problem distribution report, i.e., the probability of different problems occurring in each model of hearing aid. This makes it much easier for fitting technicians to understand the problem distribution situation for different models of hearing aids, thereby providing fitting technicians with more accurate data during fitting and improving the effectiveness of fitting. [Brief explanation of the drawings]

[0025] [Figure 1] 1 is a system schematic diagram of the present application. [Figure 2] FIG. 2 illustrates steps for building a real-time answer plan database according to the present invention. [Figure 3] FIG. 2 illustrates steps for determining a proposed answer for a push notification according to the present application. [Figure 4] 1 is a flowchart illustrating real-time construction of a response plan database according to the present invention. [Figure 5] 10 is a flowchart illustrating the determination of a proposed answer for push notification according to the present application. DETAILED DESCRIPTION OF THE INVENTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present application in combination with the accompanying drawings in the embodiments of the present application. It is clear that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments that a person skilled in the art can obtain without creative work fall within the scope of protection of the present application.

[0027] Referring to Figures 1 to 5, In Example 1, the comprehensive management system for hearing test and hearing aid fitting effect evaluation according to the technical solution provided in this example includes a fitting technician terminal, a patient terminal, a memory unit, and a central processing module; After the fitting technician performs the hearing test and fitting of the hearing aid on the patient, the fitting technician compiles the acquired hearing test data, hearing aid fitting data, hearing aid model data, and patient identification information data on the fitting technician terminal to obtain patient data, and stores the patient data in a memory unit via a central processing module; After fitting, the patient sends problem feedback via the patient terminal during daily use, the central processing module obtains the problem feedback output from the patient terminal, determines a proposed answer for push notification in the memory unit, extracts the patient's medical records in the memory unit and sends it to the fitting technician terminal together with the determined proposed answer and problem feedback, the fitting technician uses the fitting technician terminal to answer using the proposed answer for push notification, the problem feedback and the patient's medical records, the central processing module feeds back the output proposed answer to the patient terminal and uses the interaction data to build a proposed answer database in the memory unit in real time, the memory unit includes a first memory module for storing patient data, a second memory module for storing the proposed answer, and a third memory module for storing problem description keyword groups.

[0028] The problem description keyword group is manually set by the fitting technician using their work experience to combine common problems with different models of hearing aids with a summary of experiences described in daily problem feedback from patients. The specific contents of the problem description keyword group can be seen in Table 1 below.

[0029] [Table 1]

[0030] The real-time construction of the answer plan database specifically includes the following steps.

[0031] In step A1, the fitting technician uses the fitting technician terminal to respond to the problem feedback from the patient and displays a proposed answer E on the patient terminal.出 After transmitting the request, the central processing module acquires the interaction data between the patient terminal and the fitting technician terminal to identify the patient, extracts the patient data from the memory, and identifies the model of the hearing aid used by the patient.

[0032] To further explain the patient identification process, in the prior art, each fitting company needs to establish an internal management platform, i.e., the system provided by the present application, and when fitting technicians and patients log in to the system, they need to use their accounts to log in, and the identification process is completed during the login process. In this way, the central processing module completes the patient identification process. Since this is prior art, a description thereof will be omitted here.

[0033] In step A2, the central processing module uses character recognition technology to compare the problem feedback from the patient in the interaction data with the problem description keyword group, and determines whether there is content in the problem feedback that matches the problem description keywords in the problem description keyword group.

[0034] How to compare the content in the problem feedback with the problem description keyword group based on the context and determine whether the problem feedback contains the same description as the keyword in the problem description keyword group is an existing mature technology, and specifically can be achieved by several algorithms and technologies listed below.

[0035] Exact Matching Algorithm: The exact matching algorithm is the simplest algorithm, which directly searches whether the text contains the exact string of keywords. This method is very effective for exact keyword matching, but may not be able to deal with keyword variations, synonyms, or related expressions.

[0036] Fuzzy matching-based algorithms: Fuzzy matching algorithms can deal with issues such as misspellings, abbreviations, and synonyms in text. Common fuzzy matching algorithms include algorithms based on edit distance (e.g., Levenshtein distance), algorithms based on phonetic similarity, etc.

[0037] Semantic Matching: Semantic matching algorithms focus on the meaning of text rather than the surface similarity of characters. Common semantic matching techniques include word vectors (e.g., Word2Vec, GloVe) and deep learning models such as BERT, which convert text and keywords into points in a high-dimensional vector space and calculate the distance or similarity between these points to determine the semantic relationship between them.

[0038] If there is no content in the problem feedback that matches the problem description keyword in the problem description keyword group, the construction of the current answer plan database is terminated, and an instruction to update the problem description keyword group and the problem feedback are sent to the fitting technician terminal.If there is no content in the problem feedback that matches the problem description keyword in the problem description keyword group, the problem description keyword W that matches the problem feedback is selected from the problem description keyword group. 抽 The central processing module then extracts the answer plan E sent from the fitting technician terminal from the interaction data. 出 Extract.

[0039] After logging in to the fitting technician terminal, the fitting technician can receive instructions for updating the problem description keyword group and problem feedback, and the fitting technician further summarizes the problem feedback and performs additional operations on the content of the problem description keyword group in the third storage module through the fitting technician terminal to expand the content of the problem description keyword group.

[0040] In step A3, the extracted problem description keywords W are used according to the model of the hearing aid used by the patient. 抽 and Answer Plan E 出 and the contents of the answer plan database stored in the second storage module, and an answer plan E 出 and explanatory keyword W 抽 The answer plan database is expressed as follows: JPEG0007825371000008.jpg54164, X represents the answer plan database, and R n represents the feedback proposal sub-database for the nth model of hearing aid, JPEG0007825371000009.jpg12167 represents the vth problem subdatabase for the nth model of hearing aid, and W n-v represents the problem description keyword for the vth problem of the nth model of hearing aid, JPEG0007825371000010.jpg12165 represents the mth proposed answer to problem v of model n of hearing aid.

[0041] Specifically, step A3 includes the following steps.

[0042] In step A31, the central processing module selects the problem description keywords W from the interaction data. 抽 and Answer Plan E 出 After completing the extraction work of (1), a search is performed on the contents stored in the second storage module to determine whether a hearing aid feedback proposal sub-database matching the hearing aid model used by the patient exists in the second storage module. If a corresponding hearing aid feedback proposal sub-database does not exist, a new hearing aid feedback proposal sub-database is directly created in the second storage module, a new problem sub-database is created in the newly created hearing aid feedback proposal sub-database, and the problem description keywords W extracted from the interaction data are used. 抽 and Answer Plan E 出in the newly created problem sub-database within the newly created hearing aid feedback proposal sub-database, and if it is determined that it exists, continue by performing the following steps.

[0043] At A32, the central processing module identifies a hearing aid feedback suggestion sub-database in the second storage module that matches the model of hearing aid used by the patient, and then stores the problem description keyword W 抽 Matching problem description keywords W 記 Determine whether the problem description keyword W exists in the identified hearing aid feedback proposal sub-database, and if not, create a new problem sub-database in the identified hearing aid feedback proposal sub-database, and 抽 and Answer Plan E 出 in the newly created problem sub-database within the identified hearing aid feedback proposal sub-database, and if present, continue with the following steps.

[0044] In A33, the identified hearing aid feedback proposal subdatabase contained the problem description keyword W 抽 Matching problem description keywords W 記 The problem sub-database having the answer plan E is the identified problem sub-database, and the central processing module uses character recognition technology to identify the answer plan E. 出 Each solution plan in the problem subdatabase identified as E 記 Identify the overlap rate with Answer E 出 Answer plan E, whose overlap rate with the set value H is greater than 記 If it exists, the operation of storing the answer plan is terminated. If it does not exist, the answer plan E extracted from the interaction data is 出 in the identified problems sub-database.

[0045] The set value H is set by the fitting technician according to the actual working demands.

[0046] The overlap rate of the two text contents, i.e., the "Answer Plan E" mentioned above 出 Each solution plan in the problem subdatabase identified as E 記 Determining the "overlap rate with" is a conventional technique, and can be achieved by various algorithms in the conventional technique. The following are commonly used algorithms and techniques:

[0047] Token-based overlap detection: Divide text into tokens, which are usually words, phrases, or symbols. Compare sets of tokens in two texts and calculate the intersection and union between them. Overlap rate = (number of tokens in intersection / number of tokens in union) x 100%. This method is simple and intuitive, but it can be affected by the text's segmentation, punctuation, and case.

[0048] N-gram based overlap detection: An n-gram is a continuous string of characters, where n represents the length of the string. For example, the word "hello" has five trigrams: "he," "ell," "llo," "hell," and "hello." This is similar to token-based methods, but using n-grams makes it possible to capture overlapping patterns with finer granularity, especially when the order of words in a text changes. The overlap rate is calculated by calculating the intersection and union of n-grams in two texts.

[0049] Cosine similarity based overlap detection: Convert text into a vector representation (e.g., TF-IDF vector or word embedding vector). Use cosine similarity to evaluate the similarity between two vectors. The cosine similarity value is between 0 and 1, and the closer it is to 1, the more similar the two texts are. The cosine similarity value can be converted into an overlap rate, but a threshold must be defined to determine at what level two texts are considered overlapping.

[0050] The step of determining the answer plan for push notification is specifically as follows.

[0051] In step B1, after identifying the hearing aid feedback suggestion sub-database and the patient terminal sends the problem feedback, the central processing module uses the interaction data to identify the hearing aid model used by the patient, and then searches the answer suggestion database X based on the identified hearing aid model to determine whether a hearing aid feedback suggestion sub-database matching the identified hearing aid model exists in the answer suggestion database X. If not, it is determined that there is no answer suggestion for push notification, and the central processing module sends the problem feedback directly to the fitting technician terminal for the fitting technician to answer. If there is, continue to perform the following steps.

[0052] In step B2, the problem sub-database is identified, and the central processing module uses character recognition technology to determine whether there is a content in the problem feedback that matches the content in the problem description keyword group. If there is not, the central processing module sends the problem feedback directly to the fitting technician terminal for response by the fitting technician. If there is, the central processing module selects a problem description keyword W that matches the problem feedback from the problem description keyword group. 抽 Extract the problem description keywords W 抽 Matching problem description keywords W 記 Determine whether or not the proposed answer for push notification exists in the hearing aid feedback proposal sub-database identified in step B1. If not, it is determined that there is no proposed answer for push notification, and the problem feedback is sent directly to the fitting technician terminal for answering by the fitting technician. If it exists, continue to execute the following steps.

[0053] In step B3, a push notification answer plan is determined, and the problem description keyword W is identified in step B2. 抽 Matching problem description keywords W 記 The problem sub-database having the answer scheme is the identified problem sub-database, and the central processing module E 記 The results are sorted in descending order according to the extraction frequency, and the first answer in the descending order is E. 記 The answer is extracted and sent to the fitting technician's device as a push notification answer proposal.

[0054] After the push notification answer proposal, problem feedback, and patient medical records are sent to the fitting technician terminal, the fitting technician can determine whether the push notification answer proposal can be used as the answer this time based on viewing the patient's medical record data and the problem feedback from the patient. If it is determined that it can be used, the fitting technician can send the push notification answer proposal as is to the patient terminal as a method, or can make certain modifications or edits to the push notification answer proposal according to the actual situation before sending it to the patient terminal.

[0055] The extraction frequency is calculated by the formula JPEG0007825371000011.jpg19162, where F represents the extraction frequency and f 1 represents the number of times during the Z days prior to step B3 that a proposed answer was determined by the central processing module as a proposed answer for push notification and used by the fitting technician as is, and f 2 represents the number of times during the Z days before step B3 that a proposed answer was decided by the fitting technician by logging in voluntarily and checking the proposed answer database in an interaction between the patient and the fitting technician, and G represents the total number of interactions between the fitting technician terminal and the patient terminal during the Z days before step B3.

[0056] f 1 This is explained as follows:

[0057] The phrase "a proposed answer is determined by the central processing module as a proposed answer for push notification and is determined to be used as is by the fitting technician" applies to cases where the fitting technician uses the proposed answer for push notification determined through steps B1 to B3 on the fitting technician terminal as the final proposal and sends it directly to the patient terminal, or where the fitting technician uses the proposed answer for push notification determined through steps B1 to B3 on the fitting technician terminal, makes certain edits to it, and then sends it directly to the patient terminal as the final proposal.

[0058] f 2 This is explained as follows:

[0059] The push notification answer plan sent in steps B1 to B3 may not be decided by the fitting technician. In this case, the fitting technician may choose not to use it on the fitting technician terminal, and may access and view the memory unit and decide on answer plan E that they deem appropriate as an answer plan for the current problem feedback. 記 In this case, the fitting technician spontaneously selected the answer E 記 falls under the category of "answer suggestions are determined by the fitting technician voluntarily logging in and checking the answer suggestion database."

[0060] In addition, in one process in which a patient logs in to a patient terminal and submits problem feedback, in the problem feedback, the central processing module selects problem description keywords W that match the problem feedback from the problem description keyword group according to step B2. 抽 However, multiple problem description keywords W 抽 When each problem description keyword W is extracted, 抽 The process is performed according to step B3 for each of the above, and finally, multiple push notification answer plans are obtained and sent to the fitting technician terminal simultaneously. The fitting technician can select and decide from the multiple push notification answer plans by viewing the problem feedback.

[0061] As an example, the calculation of extraction frequency will be described below.

[0062] The specific value of Z is set manually based on experience.

[0063] If Z is set to 90, an answer E in the answer database 記 f 1 The value of 10, f 2 If you set the value of 2 and the value of G to 50, and then substitute the data into the extraction frequency calculation formula, the answer proposal E for the 90 days prior to step B3 of the push notification answer proposal determination process will be 記 The frequency of acquisition is

number

[0064] In Example 2, this example is a technical solution further provided based on Example 1, which sets a collection period C. At the end of the collection period C, the central processing module calculates the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X during the collection period C. At the end of the collection period C, based on the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X, the central processing module extracts each hearing aid model name in all hearing aid feedback suggestion sub-databases, sorts all hearing aid model names in descending order according to the total acquisition frequency of the hearing aid feedback suggestion sub-database corresponding to each hearing aid model name to obtain a hearing aid problem report, and sends the hearing aid problem report to the fitting technician terminal at the end of the collection period C.

[0065] The total acquisition frequency of each hearing aid feedback proposal sub-database in answer proposal database X during collection period C is calculated using the following formula: JPEG0007825371000013.jpg19162

[0066] where ZP represents the total acquisition frequency, K represents the total number of times in the collection period C that the hearing aid feedback proposal sub-database was identified in step B1 by determining the answer proposal for the push notification, and J represents the total number of times in the collection period C that the patient provided feedback on the problem.

[0067] As an example, the calculation of the total acquisition frequency will be described below.

[0068] The specific length of the aggregation period C is manually set, and can be set to three months or six months.

[0069] If the collection period C is set to 3 months, and the total number of times a certain hearing aid feedback proposal sub-database was identified in step B1 during collection period C is 15, and the total number of times that patients provided feedback on problems during collection period C is 60, then these are substituted into the formula for calculating the total acquisition frequency, and the total acquisition frequency of the hearing aid feedback proposal sub-database is calculated as follows:

number

[0070] A list of total acquisition frequency values ​​for all hearing aid feedback suggestion sub-databases for collection period C is shown in Table 2 below. [Table 2]

[0071] Based on the total acquisition frequency output in Table 2, the hearing aid model names were sorted in descending order to obtain Table 3 below. [Table 3]

[0072] Thus, Table 3 is the final output hearing aid problem report that the central processing module sends to the fitting technician terminal for analysis by the fitting technician.

[0073] In Example 3, this example further provides a technical solution based on Example 2. When the collection period C expires, the central processing module calculates the individual acquisition frequency of each problem sub-database in the hearing aid feedback proposal sub-database within the collection period C. The calculation formula for the individual acquisition frequency is: JPEG0007825371000017.jpg17165, where FP represents the individual acquisition frequency, U represents the total number of times in the aggregation period C that a problem sub-database was identified in steps B2 and B3 by determining a proposed answer for push notification, and Q represents the total number of times in the aggregation period C that all problem sub-databases in the hearing aid feedback proposal sub-database to which the problem sub-database belongs were identified in steps B2 and B3 by determining a proposed answer for push notification, Problem description keywords from each problem sub-database within the hearing aid feedback proposal sub-database are extracted and compiled according to the individual frequency of acquisition to create an individual hearing aid problem distribution report, which is then sent to the fitting technician terminal.

[0074] An example of individual hearing aid problem distribution reporting is provided below.

[0075] The hearing aid feedback proposal subdatabase R1 of the first model hearing aid is divided into three problem subdatabases: JPEG0007825371000018.jpg12160. The individual acquisition frequency values ​​for all problem sub-databases in hearing aid feedback proposal sub-database R1 during collection period C are as shown in Table 4 below. [Table 4]

[0076] The problem description keywords corresponding to the problem sub-database are obtained, and the individual frequencies of acquisition are compiled to obtain the individual hearing aid problem distribution report shown in Table 5 below. [Table 5]

[0077] By sending Table 5 to the fitting technician's terminal, the fitting technician can understand the distribution of problems with each model of hearing aid, making it easier for the fitting technician to provide detailed explanations to patients when helping them select a hearing aid, thereby improving the fitting effect.

[0078] In Examples 1, 2, and 3, Answer Plan E 出 represents a response method in which the fitting technician responds to problem feedback from the patient through the fitting technician terminal and finally sends the response to the patient terminal.

[0079] Problem description keyword W 抽 represents the problem description keywords that match the problem feedback, extracted from the problem description keyword group after determining that the problem feedback contains content that matches the problem description keywords in the problem description keyword group.

[0080] Problem description keyword W 記 represents the question explanation keywords stored in the answer plan database.

[0081] Draft answer E 記 represents the answer plan stored in the answer plan database.

[0082] At the same time, anything not described in detail herein is prior art well known to those skilled in the art.

[0083] It should be noted that, in this specification, relational terms such as first and second are used only to distinguish one entity or operation from another and do not necessarily require or imply that such an actual relationship or order exists between those entities or operations. Furthermore, the terms "comprise," "include," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or device of a series of elements includes not only those elements but also other elements not expressly listed or inherent in such process, method, article, or device.

[0084] Although the embodiments of the present application have been illustrated and described, those skilled in the art will understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is limited by the appended claims and their equivalents.

Claims

1. The device comprises a fitting technician terminal, a patient terminal, a memory unit, and a central processing module; The storage unit includes a first storage module for storing patient data, a second storage module for storing answer proposals, and a third storage module for storing problem description keyword groups; Including, the fitting technician terminal is configured to receive and edit hearing test data, hearing aid fitting data, hearing aid model data, and patient identification information data to obtain patient data; the central processing module is configured to store patient data obtained from the fitting technician terminal in the memory unit; the patient terminal is configured to transmit problem feedback from the patient after fitting and during daily use; The central processing module further comprises: obtaining the problem feedback output from the patient terminal; determining a proposed answer for push notification stored in the storage unit; configured to extract the patient's medical records from the storage unit and transmit them to a fitting technician terminal together with the determined solution plan and problem feedback; the fitting technician terminal is further configured to display the proposed solutions for push notification, the problem feedback, and the patient's medical record, and to receive and output proposed solutions determined by the fitting technician to the central processing module; the central processing module is configured to feed back the output answer plan to the patient terminal, and to build a database of answer plans in the storage unit in real time using exchange data; Specifically, the real-time construction of the answer plan database is as follows: Step A1, in which the central processing module acquires communication data between the patient terminal and the fitting technician terminal to identify the patient's identity, extracts the patient data from the storage unit, and identifies the model of the hearing aid used by the patient; The central processing module uses character recognition technology to compare the problem feedback from the patient in the exchange data with the problem description keyword group, and determines whether the problem feedback contains content that matches the problem description keywords in the problem description keyword group. If not, the central processing module terminates the current construction of the answer plan database and sends an instruction to update the problem description keyword group and the problem feedback to the fitting technician terminal. If present, the central processing module searches for the problem description keyword group that matches the problem feedback. 抽 The central processing module then extracts from the interaction data the answer plan E sent from the fitting technician terminal. 出 Step A2 of extracting The extracted problem description keyword W is based on the model of the hearing aid used by the patient. 抽 and Answer E 出 and the contents of the answer plan database stored in the second storage module, and 出 and the explanatory keyword W 抽 and a step A3 of determining whether to store the answer and the storage location in the answer plan database; The answer database expression is: and X represents the answer plan database, and R ｎ represents the feedback proposal sub-database for the n-th model of hearing aid; represents the vth problem sub-database for the nth model of hearing aid, and W n-v represents the problem description keyword for the vth problem of the nth model of hearing aid, represents the mth proposed answer to the vth problem of the nth model of hearing aid, Specifically, step A3 is The central processing module extracts the problem description keywords W from the interaction data. 抽 and Answer E 出 After completing the extraction work of the problem description keyword W, the content stored in the second storage module is searched to determine whether a hearing aid feedback suggestion sub-database matching the hearing aid model used by the patient exists in the second storage module. If a corresponding hearing aid feedback suggestion sub-database does not exist, a new hearing aid feedback suggestion sub-database is directly created in the second storage module, a new problem sub-database is created in the newly created hearing aid feedback suggestion sub-database, and the problem description keyword W extracted from the interaction data is stored in the second storage module. 抽 and Answer E 出 in a newly created problem sub-database within the newly created hearing aid feedback proposal sub-database, and if it is determined that it exists, continue to perform the following steps in step A31; After identifying a hearing aid feedback suggestion sub-database in the second storage module that matches the model of hearing aid used by the patient, the central processing module 抽 Matching problem description keywords W 記 Determine whether the problem description keyword W exists in the identified hearing aid feedback suggestion sub-database, and if not, create a new problem sub-database in the identified hearing aid feedback suggestion sub-database, and 抽 and Answer E 出 in a newly created problem sub-database within the identified hearing aid feedback proposal sub-database, and if present, subsequently executes the following steps: In the identified hearing aid feedback proposal sub-database, the problem description keyword W 抽 Matching problem description keywords W 記 The problem sub-database having the answer plan E is the identified problem sub-database, and the central processing module uses character recognition technology to identify the answer plan E. 出 Each answer plan E in the problem subdatabase identified as 記 Identify the overlap rate with the answer E 出 Answer plan E, where the overlap rate with 記 If it exists, the operation of storing the answer plan is terminated. If it does not exist, the answer plan E extracted from the exchange data is 出 in the identified problem sub-database; Specifically, the step of determining the answer plan for push notification includes: In step B1, after identifying a hearing aid feedback suggestion sub-database and the patient terminal sends problem feedback, the central processing module uses the interaction data to identify the hearing aid model used by the patient, and then searches an answer suggestion database X based on the identified hearing aid model to determine whether a hearing aid feedback suggestion sub-database matching the identified hearing aid model exists in the answer suggestion database X; if not, it is determined that there is no answer suggestion for push notification, and the central processing module sends the problem feedback directly to the fitting technician terminal for answering by the fitting technician; if there is, continue to perform the following steps: In step B2, a problem sub-database is identified, and the central processing module utilizes character recognition technology to determine whether there is a content in the problem feedback that matches with a problem description keyword group. If there is not, the central processing module sends the problem feedback directly to the fitting technician terminal for response by the fitting technician. If there is, the central processing module selects a problem description keyword W that matches with the problem feedback from the problem description keyword group. 抽 and extract the problem description keyword W 抽 Matching problem description keywords W 記 Determine whether the proposed hearing aid feedback sub-database identified in step B1 exists; if not, it is determined that there is no proposed answer for push notification, and the problem feedback is sent directly to the fitting technician terminal for answering by the fitting technician; if present, continue to perform the following steps: In step B3, a push notification answer plan is determined, and the problem description keyword W is identified in step B2. 抽 Matching problem description keywords W 記 is the identified problem sub-database, and the central processing module is 記 The results are sorted in descending order according to the extraction frequency, and the first answer in descending order is E. 記 and send it to the fitting technician's device as a push notification answer proposal. A comprehensive management system for hearing tests and hearing aid fitting effect evaluations.

2. The extraction frequency is calculated by the formula where F represents the extraction frequency and f １ represents the number of times that a proposed answer was determined as a proposed answer for push notification by the central processing module and was determined to be used as is by the fitting technician during the Z days prior to performing step B3, in the interaction between the patient terminal and the fitting technician terminal; and f ２ represents the number of times that the fitting technician voluntarily logged in and checked the answer plan database to determine an answer plan during the Z days before performing step B3 in the interaction between the patient terminal and the fitting technician terminal, and G represents the total number of interactions between the fitting technician terminal and the patient terminal during the Z days before performing step B3.

2. The comprehensive management system for hearing tests and hearing aid fitting effect evaluations according to claim 1.

3. The central processing module is configured to calculate, at the expiration of a set collection period C, the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X during the collection period C; at the end of the collection period C, based on the total acquisition frequency of each hearing aid feedback suggestion sub-database in the answer suggestion database X, extract each hearing aid model name in all hearing aid feedback suggestion sub-databases, sort all hearing aid model names in descending order according to the total acquisition frequency of the hearing aid feedback suggestion sub-database corresponding to each hearing aid model name to obtain a hearing aid problem report; and transmit the hearing aid problem report to the fitting technician terminal at the end of the collection period C.

3. The comprehensive management system for hearing tests and hearing aid fitting effect evaluations according to claim 2.

4. The total acquisition frequency of each hearing aid feedback proposal sub-database in the answer proposal database X during the aggregation period C is where ZP represents the total acquisition frequency, K represents the total number of times in the collection period C that the hearing aid feedback proposal sub-database was identified in step B1 by determining the answer proposal for push notification, and J represents the total number of times that the patient provided feedback on the problem in the collection period C.

4. The comprehensive management system for hearing tests and hearing aid fitting effect evaluations according to claim 3.

5. The central processing module calculates, at the expiration of the aggregation period C, the acquisition individual frequency of each problem sub-database in the hearing aid feedback proposal sub-database within the aggregation period C, and the calculation formula for the acquisition individual frequency is: where FP represents the individual acquisition frequency, U represents the total number of times in the aggregation period C that a problem sub-database was identified in steps B2 and B3 by determining a proposed answer for push notification, and Q represents the total number of times in the aggregation period C that all problem sub-databases in the hearing aid feedback proposal sub-database to which the problem sub-database belongs were identified in steps B2 and B3 by determining a proposed answer for push notification, The problem description keywords of each problem sub-database in the hearing aid feedback proposal sub-database are extracted, and then compiled according to the individual frequency of acquisition to create an individual hearing aid problem distribution report, which is then transmitted to the fitting technician terminal.

5. The comprehensive management system for hearing tests and hearing aid fitting effect evaluations according to claim 4.

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