CN112541831B - Medical insurance risk identification method, device, medium and electronic equipment - Google Patents
Medical insurance risk identification method, device, medium and electronic equipment Download PDFInfo
- Publication number
- CN112541831B CN112541831B CN202011490453.9A CN202011490453A CN112541831B CN 112541831 B CN112541831 B CN 112541831B CN 202011490453 A CN202011490453 A CN 202011490453A CN 112541831 B CN112541831 B CN 112541831B
- Authority
- CN
- China
- Prior art keywords
- risk
- information
- control model
- settlement
- wind control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000012545 processing Methods 0.000 claims abstract description 37
- 230000008569 process Effects 0.000 claims abstract description 25
- 201000010099 disease Diseases 0.000 claims description 27
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 27
- 238000012549 training Methods 0.000 claims description 26
- 230000002159 abnormal effect Effects 0.000 claims description 24
- 239000003814 drug Substances 0.000 claims description 23
- 230000005856 abnormality Effects 0.000 claims description 17
- 229940079593 drug Drugs 0.000 claims description 15
- 238000003745 diagnosis Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 238000007689 inspection Methods 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 5
- 238000012795 verification Methods 0.000 claims description 4
- 230000036541 health Effects 0.000 abstract description 14
- 239000002609 medium Substances 0.000 description 13
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000011835 investigation Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 206010010356 Congenital anomaly Diseases 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 206010012601 diabetes mellitus Diseases 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000001225 therapeutic effect Effects 0.000 description 2
- 206010004637 Bile duct stone Diseases 0.000 description 1
- 201000009331 Choledocholithiasis Diseases 0.000 description 1
- 206010010904 Convulsion Diseases 0.000 description 1
- 208000026350 Inborn Genetic disease Diseases 0.000 description 1
- 208000028017 Psychotic disease Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 208000016361 genetic disease Diseases 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- WIGIZIANZCJQQY-RUCARUNLSA-N glimepiride Chemical compound O=C1C(CC)=C(C)CN1C(=O)NCCC1=CC=C(S(=O)(=O)NC(=O)N[C@@H]2CC[C@@H](C)CC2)C=C1 WIGIZIANZCJQQY-RUCARUNLSA-N 0.000 description 1
- 229960004346 glimepiride Drugs 0.000 description 1
- 210000001872 metatarsal bone Anatomy 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 239000012120 mounting media Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000012954 risk control Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 201000009032 substance abuse Diseases 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Finance (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Accounting & Taxation (AREA)
- Evolutionary Biology (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Medical Treatment And Welfare Office Work (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The embodiment of the invention discloses a risk identification method, a risk identification device, a risk identification medium and electronic equipment for medical insurance. The method comprises the following steps: if a medical insurance claim settlement event conforming to the application range of the pre-configured identification method is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information; inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model; if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue; and carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue. According to the technical scheme, the intelligent identification of the high-risk cases can be realized, the operation flow of staff can be simplified, and the processing efficiency of health risk and claim cases is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data analysis, in particular to a risk identification method, a risk identification device, a risk identification medium and a risk identification electronic device for medical insurance.
Background
With the rapid development of the economic society, various business or other medical insurance patterns are increasing. At present, risk identification of health insurance claim cases is mainly realized by a manual off-line checking mode. When claim case processing is carried out, a claim settlement person needs to check information collected by clients in a plurality of links such as a plurality of links of underwriting, checking, claim settlement and investigation one by one, and the like, specifically comprises a insurance layer (such as dangerous seeds, responsibility types and the like), a client layer (such as birth date, sex and the like), a claim settlement event layer (such as hospital name, insurance passing, historical claim case information and the like), a bill layer (such as disease diagnosis, approval cost amount and the like) and an organization layer (such as branch office coding and the like) which are five-level near hundred item data, and image data such as medical record data, disease diagnosis, accident proof, physical examination report and the like, and has the problems of large manual workload, high cost and slow timeliness.
In the process of health insurance claim case decision, an insurance company firstly judges the medication rationality and the treatment track rationality on line by medical professionals, then checks customer underwriting information and check underwriting information off line by claim settlement personnel, finally carries out on-line operation, and completes the settlement of reasonable cost by inputting reasonable cost and unreasonable cost in terms.
The technology has obviously fallen behind under the conditions that health insurance claim cases are increased at a high speed year by year, medical cheating protection cases are frequently generated and customer demands are all-weather refined, and the claim settlement service experience is greatly reduced.
Disclosure of Invention
The embodiment of the invention provides a risk identification method, a device, a medium and electronic equipment for medical insurance, which can realize intelligent identification of high-risk cases, simplify the working flow of staff and improve the processing efficiency of health insurance claims.
In a first aspect, an embodiment of the present invention provides a risk identification method for medical insurance, where the method includes:
If the medical insurance claim settlement event is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information;
Inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model;
if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue;
And carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue.
Further, the training process of the claim intelligent wind control model comprises the following steps:
Acquiring a preset number of historical claim case data, and taking the historical claim case data as a training sample;
And inputting the training sample into an initial model, and training the initial model according to the output result of the initial model and the historical risk evaluation result of the training sample to obtain the intelligent air control model for claim settlement.
Further, the claim intelligent wind control model comprises at least five classifiers for outputting responsibility relief, whether a treatment subject is abnormal, whether a treatment track is abnormal, whether disease medication is abnormal and whether hospitalization is abnormal.
Further, after obtaining the claim information, the method further comprises:
performing data intelligent quality inspection on the claim information to determine whether the claim information has a data quality problem or not;
If the data exists, the prompt information of the data quality problem is returned so as to intelligently correct the data and timely find out the potential risk of claim settlement.
Further, after determining the risk level of the claim case information according to the result output by the claim intelligent wind control model, the method further includes:
And if the risk level of the claim case information accords with a preset condition, carrying out claim case processing and subsequent processes.
Further, after sending the claim information to the manual risk verification queue, the method further comprises:
judging whether the output result identification of the claim settlement intelligent wind control model is accurate or not according to the checking result of the manual risk checking queue;
if the output result of the claim intelligent wind control model is not accurately identified, updating the claim intelligent wind control model according to the checking result.
Further, updating the claim intelligent wind control model includes:
and if the error feedback times of the check result to the target rule corresponding to the output result reach a preset threshold value, rejecting and updating the target rule of the intelligent air control model of the claim.
In a second aspect, an embodiment of the present invention further provides a risk identification device for online medical insurance, where the device includes:
The claim case information acquisition module is used for acquiring claim case information if the medical insurance claim event is detected; wherein the claim information includes application information and claim settlement request information;
The risk level output module is used for inputting the claim case information into a pre-trained claim intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim intelligent wind control model;
the checking module is used for sending the claim case information to a manual risk checking queue if the risk level of the claim case information does not accord with a preset condition;
and the claim case processing module is used for carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue.
In a third aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a risk identification method for medical insurance according to embodiments of the present application.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of being executed by the processor, where the processor executes the computer program to implement a risk identification method for medical insurance according to the embodiment of the present application.
According to the technical scheme provided by the embodiment of the application, if the medical insurance claim settlement event which accords with the application range of the preconfigured identification method is detected, the claim case information is obtained; wherein the claim information includes application information and claim settlement request information; inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model; if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue; and carrying out claim case processing according to the checking result of the manual risk checking queue. The technical scheme provided by the application can realize intelligent identification of the high-risk cases, simplify the working flow of staff and improve the processing efficiency of health risk claim cases.
Drawings
FIG. 1 is a flow chart of a risk identification method for medical insurance according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a risk identification system for medical insurance according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a risk identification device for medical insurance according to a second embodiment of the present invention;
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Fig. 1 is a flowchart of a risk identification method for medical insurance according to an embodiment of the present invention, where the embodiment is applicable to a case of medical insurance claim settlement, and the method may be performed by a risk identification device for medical insurance according to the embodiment of the present invention, where the device may be implemented by software and/or hardware, and may be integrated in an electronic device.
As shown in fig. 1, the risk identification method of the medical insurance includes:
s110, if a medical insurance claim settlement event is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information.
The medical insurance claim settlement event may be an event that after the user makes an insurance, a corresponding medical accident occurs, and medical claim settlement needs to be performed by adopting the insurance. The claim settlement request sent by the user is received on the platform, or the telephone report event of the user is received, and the detection of the medical insurance claim settlement event is determined.
The claim information can be information input by a user according to the requirements of the platform, such as the event, place, reason, treatment mode, generated fee, payment certificate and the like of the accident, and can also be related information input by telephone wiring personnel according to the report telephone of the user. Here, it is understood that the case information may include insurance information, i.e., the dangerous seed and time limit for insurance purchased by oneself before an accident occurs, and the like. The application information may be retrieved from a database based on the user's phone number, identification card number, etc.
The claim case information also comprises claim settlement request information, wherein the claim settlement request information can be information such as cost generated in the accident rescue process, text information, picture certificates uploaded by users or payment codes in payment records. The payment code can be used for a worker to acquire detailed payment information, such as operation fees, medical fees, hospitalization fees and the like, by calling data of the medical system.
In this technical solution, optionally, after obtaining the claim information, the method further includes:
performing data intelligent quality inspection on the claim information to determine whether the claim information has a data quality problem or not;
If the data exists, the prompt information of the data quality problem is returned so as to intelligently correct the data and timely find out the potential risk of claim settlement.
The data quality problem may be a problem of data missing, incomplete data, or data error. For example, in the disease type, the input disease type is incomplete, or is input as a treatment for the disease, or the user does not input the disease type field. The arrangement avoids the limited field range of the input field of the existing claim settlement operation system, the intelligent quality inspection function of the data is lost, and the intelligent quality inspection and intelligent correction of the data are realized by an intelligent means, so that the potential claim settlement risk is found in time.
S120, inputting the claim case information into a pre-trained claim intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim intelligent wind control model.
The intelligent air control model for claim settlement can be obtained by training historical claim case information. The model may be used to determine the risk level of the case information, e.g., the risk level of directly outputting the case information is high risk or low risk. In addition, the model can also identify information in several aspects, such as whether there is a situation of responsibility relief, whether there is a situation of abnormality of the therapeutic subject, and the like, and finally determine the risk level of the case information according to the identification result of each dimension.
In this scheme, specifically, the training process of the intelligent wind control model for claim settlement includes:
Acquiring a preset number of historical claim case data, and taking the historical claim case data as a training sample;
And inputting the training sample into an initial model, and training the initial model according to the output result of the initial model and the historical risk evaluation result of the training sample to obtain the intelligent air control model for claim settlement.
The preset number can be 1000 or more, and can be divided for each dangerous seed independently. After the training sample is determined, whether a label is added to the training sample can be selected according to the final evaluation result so as to achieve the mode of supervised training for training. The initial model can be built or selected according to requirements, and whether the parameters of the initial model are trained can be determined according to whether the output result and the historical risk assessment result are converged or not by taking training samples as input data through the initial model.
In this scheme, optionally, the intelligent wind control model for claim settlement includes at least five classifiers for outputting responsibility relief, whether the treatment subject is abnormal, whether the treatment track is abnormal, whether the disease medication is abnormal and whether hospitalization is abnormal.
The intelligent wind control model for the claim comprises five main categories of responsibility relief, treatment subject abnormality, treatment track abnormality, disease medication abnormality and hospitalization abnormality, and intelligent identification capability of 19 risk types. The first is the large class of liability relief risks, and intelligently identifies whether the insurance accident belongs to the scope of liability relief clauses. Secondly, the main body abnormal risk is treated, and whether the disease diagnosis and treatment projects are consistent with the age and sex of the adventure is intelligently identified. Thirdly, the risk of abnormal treatment track is large, and whether the treatment, the medicine cost and the treatment times exceed the common level of similar claim cases is intelligently identified. Fourth, the risks of abnormal medication of diseases are large, and whether medication or treatment modes accord with general rules is intelligently identified. Fifthly, the general risk of hospitalization is classified into the abnormal risk category, and physical examination admission, low standard admission and hanging bed admission are intelligently identified.
And S130, if the risk level of the claim case information does not meet the preset condition, the claim case information is sent to a manual risk check queue.
The preset condition may be a case with low risk of evaluating the risk of the claim case information, and if the risk does not meet the preset condition, it is indicated that the claim case information may have high risk, and in this case, whether the claim risk settlement behavior exists may be determined by means of manual verification.
In this scheme, artifical risk check queue can be the queue that is used for carrying out the storage to the claim case information of high risk, can be used for the staff to carry out artifical check one by one.
S140, carrying out claim case processing according to the checking result of the manual risk checking queue.
If the manual verification is that there is indeed a risk of settling a claim, then the claim case information needs to be reclassified and a determination is made as to whether the payoff criteria are met. If the claim risk behavior is not considered to exist, the reasonable cost can be determined, the claim case processing and the subsequent flow are carried out, and the related information is recorded.
In this technical solution, optionally, after determining the risk level of the claim information according to the result output by the claim intelligent wind control model, the method further includes:
And if the risk level of the claim case information accords with a preset condition, carrying out claim case processing and subsequent processes.
It can be appreciated that if the risk level of the claim information is low, the claim processing and subsequent processes can be directly performed.
The subsequent process may be an operation process of recording the claim information, storing the record information in a system, and the like.
Specifically, the claim case processing comprises three steps of automatically settling insurance policy, processing and submitting and approving the reasonable cost after eliminating the unreasonable cost.
Through such setting, the very big reduction staff's work load, only discern promptly and just need the manual work to check under the circumstances that has high risk, very big improvement the efficiency of medical insurance reimbursement to staff's labour cost has been reduced.
According to the technical scheme provided by the embodiment of the application, if the medical insurance claim settlement event which accords with the application range of the preconfigured identification method is detected, the claim case information is obtained; wherein the claim information includes application information and claim settlement request information; inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model; if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue; and carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue. The technical scheme provided by the application can realize intelligent identification of the high-risk cases, simplify the working flow of staff and improve the processing efficiency of health risk claim cases.
On the basis of the above technical solutions, optionally, after sending the claim information to a manual risk check queue, the method further includes:
judging whether the output result of the claim settlement intelligent wind control model is accurate or not according to the checking result of the manual risk checking queue;
if the output result of the claim intelligent wind control model is not accurately identified, updating the claim intelligent wind control model according to the checking result.
On the basis of the above technical solutions, optionally, updating the claim intelligent wind control model includes:
and if the error feedback times of the check result to the target rule corresponding to the output result reach a preset threshold value, rejecting and updating the target rule of the intelligent air control model of the claim.
Specifically, the model can perform self-updating according to the manual feedback of the claimant, and the self-updating comprises three modes. Firstly, the updating is removed, the updating is carried out according to the error feedback times of the claimant, the error times standard is reached, and the rule is not used for prompting. And secondly, carrying out statistical updating, wherein after 100 new types of claim samples are added according to the statistical rules, the model automatically updates the model rules and parameter values. And thirdly, updating the model, and re-calculating the model and the rule value in each month according to the statistical rule, and intelligently updating the wind control model knowledge graph.
Meanwhile, a manager can regularly export and confirm the rule for eliminating the update, so that the irregular feedback of the claimant can be repaired in time, and the rule for eliminating the error is wrongly eliminated.
The following is a specific embodiment provided by the examples of the present application:
In the scheme, the risk identification system of the medical insurance can be selected to comprise an claim settlement operation system and an claim settlement intelligent wind control model.
Fig. 2 is a schematic diagram of a risk identification system for medical insurance according to an embodiment of the present invention. As shown in FIG. 2, the invention comprises five links of model configuration, claim settlement risk identification, manual feedback, model self-updating and claim case processing, which are respectively deployed in a claim settlement operation system and a claim settlement intelligent wind control model. The model configuration is configured individually by each branch company according to business characteristics and risk identification management and control requirements, after the claim settlement worker carries out risk identification on the claim case by the claim settlement intelligent wind control model, the model identification accuracy degree is fed back manually, intelligent iteration can be carried out by the model according to manual feedback, and a claim settlement experience knowledge base is continuously perfected. By introducing the intelligent model, the pain point with high cost of health risk settlement and risk checking can be effectively solved.
The model configuration comprises three dimensions, namely, risk scenes and responsibility exemption rules which are applicable to the risk configuration are universal to the risk; and secondly, configuring the group list special about on the basis of dangerous seed configuration, and if the configured group list special about rule is preferentially applicable to the group list configuration rule. Thirdly, the product combination is configured according to the product codes on the basis of dangerous seed configuration, and the products which are specially appointed for the responsibility relief scope by the applicable school insurance do not need to be configured by policy. The applicable priority of the three dimensions is policy > product > risk. The configuration mode is divided into an input mode and an introduction mode, and the problems that a big bill is input, special wind control rules and responsibility relief ranges of learning and leveling insurance business cannot be configured quickly and the workload is large are solved.
Specifically, the dangerous seed general wind control model configuration can support full-scale query and accurate query, and the scene supports switch configuration and disease responsibility scene-free configuration.
Specifically, the group list special constraint wind control model configuration can support full-quantity query and accurate query, and the risk scene supports switch configuration.
Specifically, the single product combination wind control model configuration can support full-quantity query and accurate query, and support risk scene configuration.
Aiming at the claim risk identification step, the current claim intelligent wind control model comprises five major categories of responsibility relief, treatment main body abnormality, treatment track abnormality, disease medication abnormality and hospitalization abnormality, and the intelligent identification capability of 19 claim risk types. The first is the large class of liability relief risks, and intelligently identifies whether the insurance accident belongs to the scope of liability relief clauses. Secondly, the main body abnormal risk is treated, and whether the disease diagnosis and treatment projects are consistent with the age and sex of the adventure is intelligently identified. Thirdly, the risk of abnormal treatment track is large, and whether the treatment, the medicine cost and the treatment times exceed the common level of similar claim cases is intelligently identified. Fourth, the risks of abnormal medication of diseases are large, and whether medication or treatment modes accord with general rules is intelligently identified. Fifthly, the general risk of hospitalization is classified into the abnormal risk category, and physical examination admission, low standard admission and hanging bed admission are intelligently identified.
And in the manual feedback step, feedback of the claim intelligent wind control model is the most important link in model operation, and feedback quality directly influences the application effect of the model. And checking the image data, the client identity information and the past claim settlement information by the claim settlement personnel according to the prompt of the intelligent wind control model, and feeding back whether the risk scene identification is accurate or not and the case risk processing result.
And in the model self-updating step, the model can perform self-updating according to the manual feedback of the claimant, and the self-updating comprises three modes. Firstly, the updating is removed, the updating is carried out according to the error feedback times of the claimant, the error times standard is reached, and the rule is not used for prompting. And secondly, carrying out statistical updating, wherein after 100 new types of claim samples are added according to the statistical rules, the model automatically updates the model rules and parameter values. And thirdly, updating the model, and re-calculating the model and the rule value in each month according to the statistical rule, and intelligently updating the wind control model knowledge graph.
Meanwhile, a manager can regularly export and confirm the rule for eliminating the update, so that the irregular feedback of the claimant can be repaired in time, and the rule for eliminating the error is wrongly eliminated.
Aiming at the claim case processing step, the claimant carries out the rejection of unreasonable claim payouts according to the prompt of the intelligent wind control model, such as the processing of making a rejection claim case, agreeing pay, claim rejected bill, rejecting item and the like. As the wind control model prompts: epileptic seizures are a disease of the (psychotic) class, which is the relief of liability. The claimant finds that the accident is mental diseases according to the prompt, and belongs to the scope of responsibility relief of the clause convention. During the investigation, it is verified that the customer is informed of the insurance clauses and disclaimer clauses in the insurance sales process, and the customer is not informed of the past admission records of the insured life. Make the decision of rejecting the claim case for the reason of responsibility relief and release the insurance contract.
The current commercial medical insurance claim risk intelligent system already comprises 5 major categories of 19 risk scenes, and more than 11 ten thousand rules are built in the system. The following is an introduction to the typical case of application:
In the first claim, the wind control model prompts: disease diagnosis [ choledocholithiasis ] is a (congenital genetic) disease, and belongs to the responsibility relief.
The claimant inquires whether the past visit records exist or not according to the prompt, and knows whether congenital or not to the main doctor, and verifies whether sales and underwriting are standard or not. The investigation conclusion is that a plurality of hospitals with larger local scale have no past visit records; secondly, the disease belongs to the congenital genetic disease; thirdly, the group insurance business, the business staff does not inform parents about the contents of the clauses when in underwriting, sales flaws exist, the investigation is qualitative as a general positive part, and the decision of agreement payment is made through agreement with clients.
And (3) in the second claim, prompting by the wind control model: the probability of use of glimepiride in metatarsal fracture is very low, please verify the therapeutic rationality.
According to the prompt, the claimant finds that the medicine for treating the chronic diabetes mellitus is used in the treatment of the fracture, and eliminates the medical cost for treating the diabetes mellitus by accidently using the reason of the medicine for treating the disease.
The invention aims to effectively solve the business problem in the background technology, and is suitable for the requirements of rapid and accurate risk identification of health insurance claim cases under the background of client refinement requirements. After the patent of the invention is applied, the following technical effects can be achieved:
Firstly, realize the intelligent recognition of high risk claim case. Big data artificial intelligence technology is introduced, based on the rapid processing and strong learning ability, the diagnosis records which are deviated from general medicine and insurance rules in the process of the claim settlement are analyzed, the high-risk claim cases are accurately identified, and the rapid processing of the low-risk claim cases is ensured.
Secondly, the experience of claim settlement is inherited. The claim settlement operators of the insurance company are distributed in each branch, the medical experience and the claim settlement processing experience are greatly different, the requirements of complex health insurance claim case processing cannot be completely met, and the processing experience library of the advanced claim settlement operators can be subjected to structural processing by an intelligent means, so that effective inheritance is obtained.
Thirdly, the method is suitable for the different risk management and control requirements of various places, and the health risk claim case cannot be subjected to risk control through nationally unified standards in the processing process due to different forms of insurance products and different medical environments of various places, and the method is realized by means of new technical means.
And fourthly, the quality of the basic data is effectively improved. The short board of the existing claim settlement operation system is limited in field range, the intelligent quality inspection function of data is lost, intelligent quality inspection and intelligent correction of data are realized by an intelligent means, and potential claim settlement risks are found in time.
Fifth, avoid company insider concealing or making false claims. According to analysis of past data, partial claim risk claim cases exist in the insurance company, personnel or medical staff partner cases can be screened out to a certain extent through intelligent risk screening means.
Sixth, realize the cost reduction and efficiency enhancement of company operation. The intelligent wind control model is introduced to thoroughly change the mode that the original claims are subjected to risk check one by one and consume manpower, realize cost reduction and efficiency improvement of the claim settlement service, promote the steady development of business and promote subversion change of customer service and operation management of insurance enterprises.
Example two
Fig. 3 is a schematic structural diagram of a risk identification device for medical insurance according to a second embodiment of the present invention. As shown in fig. 3, the risk identification device for medical insurance includes:
the claim information obtaining module 310 is configured to obtain claim information if a medical insurance claim settlement event is detected; wherein the claim information includes application information and claim settlement request information;
the risk level output module 320 is configured to input the claim information into a pre-trained claim settlement intelligent wind control model, and determine a risk level of the claim information according to a result output by the claim settlement intelligent wind control model;
The checking module 330 is configured to send the claim information to a manual risk checking queue if the risk level of the claim information does not meet a preset condition;
And the claim case processing module 340 is configured to perform claim case processing and subsequent flows according to the checking result of the manual risk checking queue.
The patent of the invention provides an intelligent claim settlement wind control model based on manual operation feedback, which converts service experience into unified wind control rules by applying a front edge technology to check standardized data. The intelligent air control model for the claim settlement thoroughly changes the mode that the original claim cases are subjected to risk check one by one and consume manpower, realizes cost reduction and efficiency improvement of the claim settlement service, and promotes the steady development of business.
The intelligent air control model for the claim settlement enables the air control of the health insurance claim settlement to be more suitable for business characteristics and differentiated risk management and control requirements of various places. The risk identification types of the intelligent wind control model of the current health risk claim are divided into 5 major categories of 19 types, and the data types suitable for evaluating the risk comprise a insurance layer, a client layer, a risk information layer, a medical data layer, a rule layer and an organization layer, and the rules cover the medical knowledge graph of a total disease catalog library, a social security medical standard catalog library, a medicine applicability rule library and a social security foundation auditing library. Identified scenarios include medical violation risk, unreasonable hospitalization risk, abuse risk, and risk not belonging to insurance liability, etc. Meanwhile, due to the fact that the deep learning technology is introduced, incorrect interception rules can be automatically corrected according to feedback of service personnel on model identification effects, and model identification accuracy is continuously improved. After a short period of trial, the intelligent wind control model for health risk claim settlement is operated nationwide, so that the unreasonable claim expenditure is reduced by more than two tens of millions of yuan for the company.
The product can execute the method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Embodiments of the present application also provide a storage medium containing computer executable instructions, which when executed by a computer processor, are for performing a risk identification method of medical insurance, the method comprising:
If the medical insurance claim settlement event is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information;
Inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model;
if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue;
And carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue.
Storage media—any of various types of memory electronic devices or storage electronic devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, lanbas (Rambus) RAM, etc.; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present application is not limited to the risk identification operation of the online medical insurance described above, and may also perform the related operations in the risk identification method of the medical insurance provided in any embodiment of the present application.
Example IV
The embodiment of the application provides electronic equipment, and the risk identification device of the medical insurance provided by the embodiment of the application can be integrated in the electronic equipment. Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; a storage device 410, configured to store one or more programs that, when executed by the one or more processors 420, cause the one or more processors 420 to implement a risk identification method for medical insurance provided by an embodiment of the present application, the method includes:
If the medical insurance claim settlement event is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information;
Inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model;
if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue;
And carrying out claim case processing and subsequent processes according to the checking result of the manual risk checking queue.
The electronic device 400 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of processors 420 in the electronic device may be one or more, one processor 420 being taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic device may be connected by a bus or other means, as exemplified by connection via a bus 450 in fig. 4.
The storage device 410 is used as a computer readable storage medium for storing a software program, a computer executable program, and a module unit, such as program instructions corresponding to the risk identification method of medical insurance in the embodiment of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, the storage 410 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, storage device 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may be used to receive input numeric, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include an electronic device such as a display screen, a speaker, etc.
The electronic equipment provided by the embodiment of the application can realize quick response to the user, fully utilizes the elasticity, the elasticity and the economy of public cloud resources, and aims to ensure the service safety and the continuity.
The risk identification device, the medium and the electronic equipment of the medical insurance provided in the embodiment can operate the risk identification method of the medical insurance provided in any embodiment of the application, and have the corresponding functional modules and beneficial effects of operating the method. Technical details not described in detail in the above embodiments may be referred to the risk identification method of medical insurance provided in any embodiment of the present application.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (6)
1. A method of risk identification for medical insurance comprising:
If the medical insurance claim settlement event is detected, obtaining claim case information; wherein the claim information includes application information and claim settlement request information;
Inputting the claim case information into a pre-trained claim settlement intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim settlement intelligent wind control model;
if the risk level of the claim case information does not accord with the preset condition, the claim case information is sent to a manual risk checking queue;
According to the checking result of the manual risk checking queue, carrying out claim case processing;
wherein, the training process of the claim intelligent wind control model comprises the following steps:
Acquiring a preset number of historical claim case data, and taking the historical claim case data as a training sample;
inputting the training sample into an initial model, and training the initial model according to the output result of the initial model and the historical risk evaluation result of the training sample to obtain an intelligent air control model for claim settlement;
The intelligent wind control model for claim settlement comprises at least five classifiers, wherein the at least five classifiers are used for outputting responsibility relief, whether a treatment subject is abnormal, whether a treatment track is abnormal, whether disease medication is abnormal and whether hospitalization is abnormal;
Wherein, the responsibility exemption classifier is used for intelligently identifying whether the insurance accident belongs to the scope of the responsibility exemption clause; the treatment subject abnormality classifier is used for intelligently identifying whether the disease diagnosis and treatment project is consistent with the age and sex of the adventure; the treatment track abnormality classifier intelligently identifies whether the treatment, the medicine cost and the treatment times exceed the general level of similar claim cases; the disease medication abnormality classifier is used for intelligently identifying whether medication or treatment modes accord with a general rule; the hospitalization abnormality classifier is used for intelligently identifying physical examination admission, low-standard admission and hanging bed admission;
Wherein after sending the claim information to the manual risk verification queue, the method further comprises:
judging whether the output result identification of the claim settlement intelligent wind control model is accurate or not according to the checking result of the manual risk checking queue;
If the output result of the claim intelligent wind control model is not accurately identified, updating the claim intelligent wind control model according to the checking result;
Wherein, update the intelligent wind control model of claim, include:
and if the error feedback times of the check result to the target rule corresponding to the output result reach a preset threshold value, rejecting and updating the target rule of the intelligent air control model of the claim.
2. The method of claim 1, wherein after obtaining the claim information, the method further comprises:
performing data intelligent quality inspection on the claim information to determine whether the claim information has a data quality problem or not;
If the data exists, the prompt information of the data quality problem is returned so as to intelligently correct the data, and the potential risk of claim settlement is found in time.
3. The method of claim 1, wherein after determining a risk level of the claim information based on the result output by the claim intelligent wind control model, the method further comprises:
And if the risk level of the claim case information accords with a preset condition, carrying out claim case processing and subsequent processes.
4. A risk identification device for medical insurance comprising:
The claim case information acquisition module is used for acquiring claim case information if the medical insurance claim event is detected; wherein the claim information includes application information and claim settlement request information;
The risk level output module is used for inputting the claim case information into a pre-trained claim intelligent wind control model, and determining the risk level of the claim case information according to the result output by the claim intelligent wind control model;
the checking module is used for sending the claim case information to a manual risk checking queue if the risk level of the claim case information does not accord with a preset condition;
the claim case processing module is used for carrying out claim case processing according to the checking result of the manual risk checking queue;
wherein, the training process of the claim intelligent wind control model comprises the following steps:
Acquiring a preset number of historical claim case data, and taking the historical claim case data as a training sample;
inputting the training sample into an initial model, and training the initial model according to the output result of the initial model and the historical risk evaluation result of the training sample to obtain an intelligent air control model for claim settlement;
The intelligent wind control model for claim settlement comprises at least five classifiers, wherein the at least five classifiers are used for outputting responsibility relief, whether a treatment subject is abnormal, whether a treatment track is abnormal, whether disease medication is abnormal and whether hospitalization is abnormal;
Wherein, the responsibility exemption classifier is used for intelligently identifying whether the insurance accident belongs to the scope of the responsibility exemption clause; the treatment subject abnormality classifier is used for intelligently identifying whether the disease diagnosis and treatment project is consistent with the age and sex of the adventure; the treatment track abnormality classifier intelligently identifies whether the treatment, the medicine cost and the treatment times exceed the general level of similar claim cases; the disease medication abnormality classifier is used for intelligently identifying whether medication or treatment modes accord with a general rule; the hospitalization abnormality classifier is used for intelligently identifying physical examination admission, low-standard admission and hanging bed admission;
wherein the device is further for:
After the claim case information is sent to a manual risk checking queue, whether the output result identification of the claim intelligent wind control model is accurate or not is judged according to the checking result of the manual risk checking queue;
If the output result of the claim intelligent wind control model is not accurately identified, updating the claim intelligent wind control model according to the checking result;
Wherein, update the intelligent wind control model of claim, include:
and if the error feedback times of the check result to the target rule corresponding to the output result reach a preset threshold value, rejecting and updating the target rule of the intelligent air control model of the claim.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a risk identification method of a medical insurance according to any of claims 1-3.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the risk identification method of a medical insurance according to any of claims 1-3 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011490453.9A CN112541831B (en) | 2020-12-16 | 2020-12-16 | Medical insurance risk identification method, device, medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011490453.9A CN112541831B (en) | 2020-12-16 | 2020-12-16 | Medical insurance risk identification method, device, medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112541831A CN112541831A (en) | 2021-03-23 |
| CN112541831B true CN112541831B (en) | 2024-06-28 |
Family
ID=75018915
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011490453.9A Active CN112541831B (en) | 2020-12-16 | 2020-12-16 | Medical insurance risk identification method, device, medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112541831B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283995B (en) * | 2021-05-20 | 2022-07-12 | 支付宝(杭州)信息技术有限公司 | Insurance public estimation remote access summary method, device and equipment |
| CN113658009A (en) * | 2021-07-22 | 2021-11-16 | 北京健康之家科技有限公司 | Claim settlement information processing method and device |
| CN113469826B (en) * | 2021-07-22 | 2022-12-09 | 阳光人寿保险股份有限公司 | Information processing method, device, equipment and storage medium |
| CN113724094A (en) * | 2021-08-31 | 2021-11-30 | 平安养老保险股份有限公司 | Risk information checking method, device, equipment and medium |
| CN113793226A (en) * | 2021-09-30 | 2021-12-14 | 上海健交科技服务有限责任公司 | Health insurance wind control rule compiling method based on big data |
| CN115374190B (en) * | 2022-10-25 | 2023-03-24 | 支付宝(杭州)信息技术有限公司 | Method and device for searching class case, storage medium and electronic equipment |
| CN115439033A (en) * | 2022-11-09 | 2022-12-06 | 浙江浙大网新软件产业集团有限公司 | Risk user screening method and device based on social security data and computing equipment |
| CN117934177B (en) * | 2024-03-22 | 2024-06-28 | 湖南多层次商保科技有限公司 | Method and system for constructing insurance intelligent responsibility determination model |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909299A (en) * | 2017-12-11 | 2018-04-13 | 凯泰铭科技（北京）有限公司 | People hinders Claims Resolution data risk checking method and system |
| CN111986039A (en) * | 2020-09-02 | 2020-11-24 | 平安养老保险股份有限公司 | Claims wind control identification method and device, computer equipment and readable storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408547B (en) * | 2014-10-30 | 2017-09-15 | 浙江网新恒天软件有限公司 | A kind of detection method of the medical insurance fraud based on data mining |
| CN107798618A (en) * | 2017-04-11 | 2018-03-13 | 平安科技（深圳）有限公司 | A kind of intelligent Claims Resolution treating method and apparatus |
| CN108257029A (en) * | 2017-09-13 | 2018-07-06 | 平安科技（深圳）有限公司 | Insurance data checking method, device, computer equipment and readable storage medium storing program for executing |
| CN108492196B (en) * | 2018-03-08 | 2020-11-10 | 平安医疗健康管理股份有限公司 | Wind control method for deducing medical insurance violation behavior through data analysis |
| CN109636632A (en) * | 2018-12-13 | 2019-04-16 | 平安医疗健康管理股份有限公司 | Settlement of insurance claim method, apparatus, equipment and storage medium based on machine learning |
| CN110033382B (en) * | 2019-02-12 | 2020-09-04 | 阿里巴巴集团控股有限公司 | Insurance service processing method, device and equipment |
-
2020
- 2020-12-16 CN CN202011490453.9A patent/CN112541831B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909299A (en) * | 2017-12-11 | 2018-04-13 | 凯泰铭科技（北京）有限公司 | People hinders Claims Resolution data risk checking method and system |
| CN111986039A (en) * | 2020-09-02 | 2020-11-24 | 平安养老保险股份有限公司 | Claims wind control identification method and device, computer equipment and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112541831A (en) | 2021-03-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112541831B (en) | Medical insurance risk identification method, device, medium and electronic equipment | |
| CN110263024B (en) | Data processing method, terminal device and computer storage medium | |
| JP5586373B2 (en) | Computer-readable storage medium storing a program for causing a computer system to realize the function of a component that processes a payment request, and a method of operating a computer system that causes a computer system to process a payment request | |
| US20160267396A1 (en) | System and Method for Using Machine Learning to Generate a Model from Audited Data | |
| CN110110093A (en) | A kind of recognition methods, device, electronic equipment and the storage medium of knowledge based map | |
| US20130179314A1 (en) | Risk Based Data Assessment | |
| CN109636564A (en) | Information verification mechanism, device, equipment and storage medium for air control | |
| CN113343058B (en) | Voice session supervision method, device, computer equipment and storage medium | |
| US20190066248A1 (en) | Method and system for identifying potential fraud activity in a tax return preparation system to trigger an identity verification challenge through the tax return preparation system | |
| CN111180065A (en) | Insurance user evaluation method and device, electronic equipment and computer readable medium | |
| CN112330432B (en) | Risk level identification model training method, risk level identification method, terminal and storage medium | |
| CN112000862B (en) | Data processing method and device | |
| CN109409648A (en) | Claims Resolution air control method, apparatus, computer equipment and storage medium | |
| Tan et al. | A model-based approach to generate dynamic synthetic test data: A conceptual model | |
| CN116843481A (en) | Knowledge graph analysis method, device, equipment and storage medium | |
| CN117010892A (en) | Payment risk detection method, device, electronic equipment and readable medium | |
| CN115600877A (en) | Hidden danger troubleshooting system and method based on dual prevention mechanism | |
| CN113487440A (en) | Model generation method, health insurance claim settlement determination method, device, equipment and medium | |
| CN114723570B (en) | Agricultural risk monitoring method and device, storage medium and computer equipment | |
| CN110782360A (en) | Settlement data processing method and device, storage medium and electronic equipment | |
| CN111582754B (en) | Risk investigation method, apparatus, device and computer readable storage medium | |
| CN116974858A (en) | Information generation method, system, device, equipment and storage medium | |
| CN114819440A (en) | Transaction processing method and device based on artificial intelligence and electronic equipment | |
| AU2015207809A1 (en) | Risk based data assessment | |
| CN117012384A (en) | Disease risk identification model training and disease risk identification method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |