CN113706320A - Intelligent claims settlement method and device, computer equipment and storage medium - Google Patents
Intelligent claims settlement method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent claim settlement method, which is applied to the field of data processing and is used for improving the claim settlement efficiency. The method provided by the invention comprises the following steps: the method comprises the steps of obtaining user information and insurance information, constructing an insurance association network, inquiring user reimburseable medicines in a preset medicine knowledge base based on the insurance association network, generating a candidate medicine list, recommending historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommendation model and a user operation record when a medicine purchasing request of a user is received, obtaining a medicine recommending and purchasing list, sending the medicine recommending and purchasing list to a client, purchasing medicines and generating a corresponding claim settlement request according to a medicine purchasing selection result fed back by the client based on the medicine recommending and purchasing list, identifying the claim settlement type of the claim settlement request, determining the claim settlement type, obtaining preset claim settlement rules from the insurance association network based on the claim settlement type, and performing insurance claim settlement based on the preset claim settlement rules.
Description
Technical Field
The invention relates to the field of data processing, in particular to an intelligent claims settlement method, an intelligent claims settlement device, computer equipment and a storage medium.
Background
With the increasing importance on health of people and the increasing number of people who purchase insurance, the existing medicine insurance claim settlement mode can start an insurance claim settlement process after a user purchases medicine, but the user needs to check on-site insurance claim settlement by holding information such as related invoices issued by a pharmacy, the insurance claim settlement needs to be checked by a claimant, the claim settlement process is initiated after the check is successful, and after the claim settlement process passes, the insurance company pays the claim settlement money after the check passes.
In the existing mode, the claims settlement is carried out by the artificial intelligence algorithm, but because the claim settlement process has more problems, more manpower is still needed to participate in the process, the claim settlement application mode is single, and when the multi-demand claim settlement scene is faced, the existing mode does not have good adaptability and expansibility, so that the multi-demand claim scene is still judged and audited mainly by the manpower. In addition, the existing medicines are various in types and quick to update, and the names of the medicines are different from each other only in different characters at many times, so that a user does not know the names. Due to the asymmetry of the information, various problems of claim settlement failure are easily caused.
Therefore, the drug insurance claims in the prior art have the problems of excessive dependence on manpower and low claim settlement efficiency caused by information asymmetry.
Disclosure of Invention
The embodiment of the invention provides an intelligent claim settlement method and device, computer equipment and a storage medium, so as to improve the claim settlement efficiency.
An intelligent claims settlement method, comprising:
acquiring user information and insurance information, and constructing an insurance association network, wherein the user information comprises historical medicine purchasing records;
based on the insurance association network, inquiring the drug which can be reimbursed by the user in a preset drug knowledge base to generate a candidate drug list;
when a medicine purchasing request of a user is received, recommending the historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommendation model and a user operation record to obtain a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored by a server within a preset time period;
according to a medicine purchasing selection result fed back by the client based on the medicine recommending and purchasing list, medicine purchasing processing is carried out, and a corresponding claim settlement request is generated;
carrying out claim type identification on the claim settlement request, and determining the claim type;
and acquiring preset claim settlement rules from the insurance associated network based on the claim settlement types, and performing insurance claim settlement based on the preset claim settlement rules.
An intelligent claims settlement device, comprising:
the insurance associated network building module is used for obtaining user information and insurance information and building an insurance associated network, wherein the user information comprises historical medicine purchasing records;
the candidate medicine list generating module is used for inquiring the user reimburseable medicines in a preset medicine knowledge base based on the insurance association network and generating a candidate medicine list;
the medicine recommending and purchasing module is used for recommending the historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommending model and a user operation record when a medicine purchasing request of a user is received, obtaining a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored by a server within a preset time period;
the claim settlement request acquisition module is used for processing medicine purchasing and generating a corresponding claim settlement request according to a medicine purchasing selection result fed back by the client based on the medicine recommendation purchase list;
the claim type determining module is used for carrying out claim type identification on the claim request and determining the claim type;
and the claim settlement module is used for acquiring preset claim settlement rules from the insurance association network based on the claim settlement types and carrying out insurance claim settlement based on the preset claim settlement rules.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the intelligent claims settlement method when executing the computer program.
A computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the intelligent claims settlement method described above.
The intelligent claim settlement method, the intelligent claim settlement device, the computer equipment and the storage medium, provided by the embodiment of the invention, are used for acquiring user information and insurance information and constructing an insurance association network, wherein the user information comprises historical medicine purchasing records; based on an insurance association network, inquiring a user reimburseable medicine in a preset medicine knowledge base to generate a candidate medicine list; when a medicine purchasing request of a user is received, recommending historical medicine purchasing records and a candidate medicine list based on a medicine purchasing recommendation model and a user operation record to obtain a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored by a server within a preset time period; according to a medicine purchasing selection result fed back by the client based on the medicine recommending and purchasing list, medicine purchasing processing is carried out, and a corresponding claim settlement request is generated; carrying out claim type identification on the claim settlement request, and determining the claim type; and acquiring preset claim settlement rules from the insurance association network based on the claim settlement type, and performing insurance claim settlement based on the preset claim settlement rules. Through the steps, the three ends of the user, the insurance and the pharmacy are fused into the insurance claim settlement system, the insurance claim settlement system can intelligently recommend the user to purchase the medicine and automatically settle the claim, and a series of on-line automatic audits are carried out on the claim settlement process, so that the manual processing process is reduced, and the claim settlement efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a diagram of an application environment of an intelligent claim settlement method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an intelligent claims settlement method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an intelligent claim settlement device according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The intelligent claim settlement method provided by the application can be applied to the application environment shown in fig. 1, wherein the computer device communicates with the server through a network. The computer device may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, among others. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
In an embodiment, as shown in fig. 2, an intelligent claims settlement method is provided, which is described by taking the server in fig. 1 as an example, and includes the following steps S101 to S106:
s101, obtaining user information and insurance information, and constructing an insurance association network, wherein the user information comprises historical medicine purchasing records.
In step S101, the user information refers to information related to the user, and the user information includes, but is not limited to, user identification, user basic information, user location information, user insurance information purchased by the user, user claim information, user pending claim information, and user medicine purchasing information. The user identification refers to a unique identification code registered by a user in an insurance claim settlement system, the basic information of the user includes but is not limited to user identification card information and a user mobile phone number, and the positioning information of the user refers to the current longitude and latitude position or the selected longitude and latitude position of the user.
The insurance information refers to information related to existing insurance contents, and includes, but is not limited to, insurance types, payable medicines, and medicine paying proportions. The insurance information is adaptively changed according to the insurance types pushed by the insurance company.
The method for constructing the insurance association network comprises but is not limited to big data dictionary and keyword matching.
The insurance claim settlement system binds the user with the insurance purchased by the user by acquiring the user information and the insurance information, so that an insurance associated network is constructed, and according to the constructed insurance associated network, the user can clearly check the insurance payable medicines purchased by the user, so that the degree of information symmetry between the user and the insurance is improved, the problem of low claim settlement efficiency caused by asymmetric information can be effectively solved, the user experience is optimized, and the claim settlement efficiency is improved.
S102, inquiring the reimburseable drugs of the user in a preset drug knowledge base based on the insurance association network, and generating a candidate drug list.
In step S102, the preset drug knowledge base is constructed by methods including, but not limited to, a big data dictionary and a knowledge map. A Knowledge Graph (knowledgegraph) is a mesh Knowledge base formed by linking entities with attributes through relationships, and is essentially a concept network, in which nodes represent entities (or concepts) in the physical world, and various semantic relationships between the entities form edges in the network.
Preferably, the drug knowledge base is obtained by using the drug, the insurance and the pharmacy as three entities and generating a knowledge base with a connection relation based on the relation among the three entities.
The list of candidate drugs includes, but is not limited to, price information of drugs, address information of drug stores corresponding to drugs, and drug reimbursement ratio.
Through the association of the insurance management network and the medicines, all medicine information can be stored in the medicine knowledge base, and when medicine recommendation is carried out, medicine information positioning can be carried out through the medicine knowledge base quickly, so that medicine recommendation service is provided for users efficiently and accurately, the medicine recommendation speed is improved, the probability of claim settlement failure caused by medicine purchase errors is reduced, and the claim settlement efficiency is improved laterally.
S103, when a medicine purchasing request of a user is received, recommending historical medicine purchasing records and a candidate medicine list based on a medicine purchasing recommendation model and a user operation record to obtain a medicine recommending and purchasing list, and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored in a server within a preset time period.
In step S103, the historical purchase record includes, but is not limited to, user location information, user preference information, and medicine information, wherein the user preference information refers to index fields preferred by the user, and the index fields include, but are not limited to, price fields and distance fields.
The client refers to a terminal where a user performs operation processing.
The candidate medicine list is a list for storing various information of the candidate recommended medicines.
The medicine recommending and purchasing list is a list formed by selecting a plurality of medicines which best meet the requirements of the user and are most likely to be purchased by the user from the candidate medicine list.
Methods for implementing the above-described drug purchase recommendation model include, but are not limited to, content-based recommendation algorithms, collaboration-based recommendation algorithms, association-based recommendation algorithms, weighted-mix recommendation algorithms, and merged-mix recommendation algorithms.
Preferably, the medicine purchase recommendation model is implemented by using a weighted mixing recommendation algorithm, wherein the weighted mixing recommendation algorithm is an algorithm for generating recommendations by combining calculation results of multiple recommendation technologies in a weighted mixing manner. For example, the price is used as a recommendation factor, the candidate drug lists are sorted to generate a price priority drug list with the price as a reference, the distance is used as a recommendation factor, the candidate drug lists are sorted to generate a distance priority drug list with the distance as a reference, user preference information is acquired by combining the historical purchase records of the user, different weights are given to the price priority drug list and the distance priority drug list, and the corresponding weights are added to obtain a drug purchase recommendation list.
The medicine purchase recommendation is carried out through the medicine purchase recommendation model and the relevant information of the user, personalized recommendation can be effectively realized for individuals, medicine recommendation service is efficiently and accurately provided for any user, the medicine recommendation speed is increased, the probability of claim settlement failure caused by medicine purchase errors is reduced, and therefore the claim settlement efficiency is improved.
And S104, according to a medicine purchasing selection result fed back by the client based on the medicine recommending and purchasing list, performing medicine purchasing processing and generating a corresponding claim settlement request.
The medicine purchasing selection result refers to that a user carries out a medicine purchasing operation according to the self requirement based on a medicine recommending and purchasing list generated by a medicine purchasing recommending model. The user may select all or a portion of the medications on the medication recommendation purchase list for purchase.
The claim settlement request refers to a request related to claim settlement reimbursement for purchased medicines. The claim settlement request is in the form of, but not limited to, automatic generation in the background and active application by the user. The background automatic generation means that the background receives a medicine purchasing selection result fed back by the client and generates a claim settlement request according to the property of purchased medicines after medicine purchasing processing is carried out.
After the medicine is purchased, the claim settlement request is generated, so that a user can carry out claim settlement and reimbursement on line at any time and any place, the time cost and the labor cost brought by the on-line reimbursement and the corresponding time consumption are reduced, the progress of the claim settlement process is accelerated, and the claim settlement efficiency is improved.
And S105, carrying out claim type identification on the claim request, and determining the claim type.
In step S105, the claim types include, but are not limited to, pay-through claims and post-event claims. The direct payment claim is a claim type which can be directly paid when a user purchases a medicine in the insurance claim system. Post-event claims refer to the type of claim that the user proposes after not purchasing a drug in the insurance claims system, but rather at another location.
The method for determining the claim type includes but is not limited to type matching and content query. The content query mode is to query whether the insurance claim settlement system has user purchase records, if yes, the insurance claim settlement system is directly regarded as direct payment claim settlement, and if not, the insurance claim settlement system is judged as post-settlement claim settlement.
By determining the claim types firstly, different claim settlement processes can be selected effectively aiming at different claim settlement scenes in the multi-demand claim settlement scenes, the adaptability of the insurance claim settlement system is enhanced, and the efficiency corresponding to each claim settlement scene is not influenced.
S106, acquiring preset claim settlement rules from the insurance association network based on the claim settlement types, and carrying out insurance claim settlement based on the preset claim settlement rules.
In step S106, the preset claim rule refers to a claim rule corresponding to a claim type, and the method for obtaining the preset claim rule includes, but is not limited to, a type matching method.
By acquiring the preset claim settlement rules, insurance claim payment is carried out on claim settlement requests of different claim settlement types, and the efficiency of insurance claim settlement can be effectively improved in a multi-demand claim settlement scene.
The intelligent claim settlement method provided by the embodiment of the invention acquires the user information and the insurance information, and constructs an insurance association network. Based on an insurance association network, inquiring user reimburseable medicines in a preset medicine knowledge base to generate a candidate medicine list, recommending historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommendation model and a user operation record when a medicine purchasing request of a user is received, obtaining a medicine recommended purchasing list, sending the medicine recommended purchasing list to a client, purchasing medicines and generating a corresponding claim settlement request according to a medicine purchasing selection result fed back by the client based on the medicine recommended purchasing list; and performing claim type identification on the claim request, and determining the claim type. And acquiring preset claim settlement rules from the insurance association network based on the claim settlement type, and performing insurance claim settlement based on the preset claim settlement rules. Through the steps, the three ends of the user, the insurance and the pharmacy are fused into the insurance claim settlement system, the insurance claim settlement system can intelligently recommend the user to purchase the medicine and automatically settle the claim, a series of on-line automatic audits are carried out on the claim settlement process, and meanwhile, the information symmetry is realized, so that the manual processing process is reduced, and the claim settlement efficiency is improved.
In some optional implementations of this embodiment, step S101 includes the following steps S1010 to S1012:
s1010, obtaining user information, and extracting keywords from the user information to obtain user keywords.
S1011, acquiring insurance information, and extracting keywords from the insurance information to obtain insurance keywords.
S1012, mapping the user information and the insurance information based on the user keywords and the insurance keywords, and constructing an insurance association network.
For the step S1010, the user keywords include, but are not limited to, the policy label purchased by the user, and the insurance risk number corresponding to the policy.
For the step S1011, the insurance keyword includes, but is not limited to, an insurance type number, and drug information that can be reimbursed for the insurance type.
The insurance association network is constructed by acquiring insurance risk codes corresponding to the policy in the user keywords and mapping the insurance risk codes with the insurance risk codes in the insurance keywords.
In some optional implementations of this embodiment, step S101 includes, before step S001:
s001, storing the user information into the block chain network node.
Through block chain storage, sharing of data information among different platforms is achieved, and data can be prevented from being tampered.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.
In some optional implementations of this embodiment, step S102 includes step S201 to step S203 before:
s201, determining the drug attribute according to the drug knowledge and the policy claim settlement condition, and taking the drug attribute as a drug entity.
S202, medicine information in a pharmacy database is obtained.
And S203, mapping the medicine information and the corresponding medicine entity to obtain a preset medicine knowledge base.
In step S201, the insurance claim condition refers to a claim condition and a corresponding claim proportion of the medicines in different insurance types.
The above-mentioned medicine knowledge refers to knowledge related to the properties of the medicine, and is used for classifying the medicine. For example, the medicines are classified into prescription/non-prescription medicines, oral administration/external application, classification of diseases according to the treatment with the medicines, and the like.
The drug attributes include, but are not limited to, drug name, drug classification, reimburseable or reimburseable rate among different insurance categories.
The drug attributes are used as the entities of the knowledge graph to construct the knowledge graph corresponding to the drug entities, for example, the drug name is compound borax, the drug classification can be Chinese patent drug and western drug, the drug can be reimbursed in the insurance risk species coded as 001, and the reimbursement proportion is total reimbursement. The relationship of the compound borax entity is' compound borax: western medicine, 001, reimbursement entirely.
In step S202, the pharmacy database is a database for storing pharmacies supporting online medicine purchase in the insurance claim settlement system.
The medicine information includes medicine name and medicine classification.
It should be understood that the pharmacy can configure the medicines and the prices of the medicines in real time according to the inventory and actual conditions of the pharmacy, and further update the pharmacy database.
In step S203, knowledge for constructing the drug database is obtained from the obtained pharmacy database by using a method for constructing a knowledge map, and the drug information and the drug entities in the pharmacy database are used to generate a preset drug knowledge base.
Preferably, the drug entities are classified by using a pre-trained classification model to obtain a drug knowledge base.
The medicine knowledge base is established in a knowledge graph mode, the intelligent recommendation of the medicine purchasing list of the user is achieved, the problem that information is asymmetric due to the fact that the medicine is various and is updated quickly by the user is solved, the accuracy rate of the user for purchasing the medicine meeting the insurance claim settlement condition is improved, the probability of claim settlement failure caused by the fact that the medicine does not meet the insurance claim settlement condition is reduced, and the efficiency of the insurance claim settlement is further improved.
In some optional implementations of this embodiment, step S103 includes the following steps S301 to S305:
s301, when a medicine purchasing request of a user is received, acquiring a historical purchasing record.
S302, determining an interest factor based on the historical purchase record, wherein the interest factor is a first influence factor of purchase of the user.
S303, inputting the interest factor and a candidate medicine list into a medicine purchase recommendation model, wherein the candidate medicine list comprises candidate medicines and at least two fields.
S304, selecting a field corresponding to the interest factor in the candidate medicine list, taking the field corresponding to the interest factor as an interest field, and taking a field corresponding to a non-interest factor in the candidate medicine list as other fields.
S305, fusing the interest fields and other fields of the candidate medicine list to obtain a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to the client.
For the step S301, the historical purchase record includes, but is not limited to, user location information, user preference information, and medicine information, where the user preference information refers to index fields preferred by the user, and the index fields include, but are not limited to, price fields and distance fields.
For the step S302, the interest factor refers to a first influence factor of the user' S purchase. For example, when the user preference information is a price, the price is a first influencing factor of the user for purchasing the medicine, namely the price is an interest factor.
For the step S303, the medicine purchase recommendation model may be a neural network-based recommendation model. The medicine purchase recommendation model can comprise functions of a rule engine, an updating mechanism, a confusable reminder and the like.
It should be understood that, for the candidate drug list, there is an initial ordering manner in the candidate drug list, the ordering manner may be based on the interest factor corresponding to the last purchase, and the ordering manner may also be a random ordering.
For the above step S305, the fusion process includes, but is not limited to, weighted fusion, random fusion, and the like.
Through the steps, a candidate medicine list is subjected to a series of processing, medicines meeting the user interest are selected, medicines are intelligently recommended, the time and the energy of the user for going to a pharmacy for screening the medicines are reduced, the medicine purchasing accuracy is improved, and the time that follow-up insurance claim examination does not meet claim settlement conditions is reduced.
In some optional implementations of this embodiment, step S305 further includes the following steps S3051 to S3055:
s3051, determining interest weight corresponding to the interest field and secondary weight corresponding to other fields.
S3052, updating the content corresponding to the interest fields in the candidate medicine list based on the interest weight to obtain the interest list corresponding to the interest fields.
S3053, updating the contents corresponding to other fields in the candidate medicine list based on the secondary weight to obtain a secondary list corresponding to other fields.
S3054, selecting the candidate medicines corresponding to the interest list and the secondary list as fusion medicines.
S3055, combining the fields corresponding to the interest list and the secondary list based on the fused medicines to obtain a medicine purchase recommendation list and sending the medicine purchase recommendation list to the client.
For the step S3052, the updating process is a process of multiplying the content corresponding to the interest field in the candidate drug list by the interest weight, and updating the content corresponding to the interest field according to the obtained result.
In step S3053, the update process is a process of multiplying the contents corresponding to other fields in the candidate drug list by the secondary weight, and updating the contents corresponding to other fields according to the obtained result.
By the preset weight assignment mode, factors which are preferentially considered by the users can be more accurately identified for different users, so that medicine recommendation is intelligentized and personalized, the medicine purchasing accuracy is improved, and the time that follow-up insurance claim examination does not accord with claim conditions is shortened.
In some optional implementations of this embodiment, step S106 further includes the following steps S601 to S604:
s601, if the claim type is a post claim, acquiring a preset post claim rule, and taking the preset post claim rule as a claim rule.
S602, if the claim type is a direct payment claim, acquiring a preset direct payment claim rule, and taking the preset direct payment claim rule as the claim rule.
S603, obtaining user materials from the user information, wherein the user materials are materials required by the user for carrying out insurance claim settlement.
And S604, carrying out insurance claim settlement on the user based on the claim settlement rule and the user material.
For the above step S601, the post-claim is a claim type proposed after the user purchases the drug in the insurance claim system but purchases the drug in another place
In step S602, the direct payment claim is a claim type that can be directly paid when the user purchases a medicine in the insurance claim system.
For the step S603, the user material refers to material related to insurance claims. The user materials include, but are not limited to, medical records, and user medical insurance card information.
In a specific example, when the claim type is a post-claim, the preset post-claim rule is obtained. And acquiring user materials based on the preset post-event claim settlement rule and the user information, and performing verification processing on the user materials to obtain a first verification result. If the first check result is that the check is passed, the claim is passed, and the user needs to wait for the insurance company to check with the bank and then carry out claim settlement. And if the first check result shows that the missing material exists, feeding back the supplement method of the missing material to the user for the user to supplement the material.
In another specific example, when the claim type is a direct payment claim, the preset direct payment claim rule is obtained. And acquiring user materials based on a preset pay-through-payment claim rule, and performing verification processing on the user materials to obtain a second verification result. And if the second check result is that the check is passed, paying the payment for the user according to the payment proportion. And if the second check result is that the information is not passed, the user is allowed to update the information so as to carry out direct payment.
It should be understood that in the process of making insurance claims, when a user applies for a claim for the first time, the user needs to submit relevant materials for review. The insurance claim settlement system can record the materials submitted by the user, when the claim settlement application is carried out subsequently, the insurance claim settlement system can directly bring the materials uploaded once, and if the materials need to be modified, the user can repeatedly submit the materials on the original basis. The insurance claim settlement system can modify the basic information of users, medicines, drug stores and the like according to the content filled in before, if the basic information is not needed, the original materials can be directly submitted, and important materials such as medical records and the like which relate to reimbursement only need to be manually submitted by the users.
By determining the claim type and then carrying out insurance claim settlement, the method has adaptability of a multi-demand claim settlement scene. If the claim types are increased, the claim rules are directly imported, the claim process can be carried out, and the diversification of claim application is facilitated.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, an intelligent claim settlement device is provided, and the intelligent claim settlement device corresponds to the intelligent claim settlement method in the above embodiment one to one. As shown in fig. 3, the intelligent claim settlement device comprises an insurance association network building module 11, a candidate drug inventory generating module 12, a drug recommendation purchasing module 13, a claim settlement request acquiring module 14, a claim type determining module 15 and a claim settlement module 16. The functional modules are explained in detail as follows:
and the insurance associated network building module 11 is used for acquiring user information and insurance information and building an insurance associated network, wherein the user information comprises historical medicine purchasing records.
And the candidate medicine list generating module 12 is configured to query the user reimburseable medicines in a preset medicine knowledge base based on the insurance association network, and generate a candidate medicine list.
The medicine recommending and purchasing module 13 is configured to, when a medicine purchasing request of a user is received, recommend the historical medicine purchasing record and the candidate medicine list based on the medicine purchasing recommendation model and the user operation record to obtain a medicine recommending and purchasing list, and send the medicine recommending and purchasing list to the client, where the user operation record is operation record information stored in the server within a preset time period.
The claim settlement request obtaining module 14 is configured to perform medicine purchasing processing and generate a corresponding claim settlement request according to a medicine purchasing selection result fed back by the client based on the medicine recommendation purchase list.
The claim type determining module 15 is configured to perform claim type identification on the claim request, and determine the claim type.
And the claim settlement module 16 is configured to obtain a preset claim settlement rule from the insurance associated network based on the claim settlement type, and perform insurance claim settlement based on the preset claim settlement rule.
In one embodiment, the insurance association network building module 11 further includes:
and the user keyword acquisition unit is used for acquiring the user information and extracting keywords from the user information to obtain the user keywords.
And the insurance keyword acquisition unit is used for acquiring insurance information and extracting keywords from the insurance information to obtain insurance keywords.
And the insurance associated network construction unit is used for mapping the user information and the insurance information based on the user keywords and the insurance keywords to construct an insurance associated network.
In one embodiment, before the candidate drug list generating module 12, the intelligent claims device further includes:
and the drug entity determining module is used for determining drug attributes according to the drug knowledge and the policy claim settlement conditions, and taking the drug attributes as drug entities.
And the medicine information acquisition module is used for acquiring the medicine information in the pharmacy database.
And the medicine knowledge base acquisition module is used for mapping the medicine information and the corresponding medicine entities to obtain a preset medicine knowledge base.
In one embodiment, the medication recommendation purchasing module 13 further comprises:
and the historical purchase record acquisition unit is used for acquiring the historical purchase record when a medicine purchase request of the user is received.
And the interest factor determining unit is used for determining an interest factor based on the historical purchase record, wherein the interest factor is a first influence factor of the purchase made by the user.
An input unit for inputting the interest factor and a candidate drug list into the medication purchase recommendation model, wherein the candidate drug list comprises candidate drugs and at least two fields.
And the field determining unit is used for selecting a field corresponding to the interest factor in the candidate medicine list, taking the field corresponding to the interest factor as an interest field, and taking a field corresponding to a non-interest factor in the candidate medicine list as other fields.
And the fusion unit is used for carrying out fusion processing on the interest fields and other fields of the candidate medicine list to obtain a medicine recommendation purchase list and sending the medicine recommendation purchase list to the client.
In one embodiment, the fusion unit further comprises:
and the weight assignment unit is used for determining the interest weight corresponding to the interest field and the secondary weight corresponding to other fields.
And the interest list acquiring unit is used for updating the content corresponding to the interest fields in the candidate medicine list based on the interest weight to obtain the interest list corresponding to the interest fields.
And the secondary list acquiring unit is used for updating the contents corresponding to other fields in the candidate medicine list based on the secondary weight to obtain the secondary list corresponding to other fields.
And the fusion medicine determining unit is used for selecting the corresponding candidate medicines in the interest list and the secondary list as fusion medicines.
And the field fusion unit is used for combining the fields corresponding to the interest list and the secondary list based on the fusion medicine to obtain a medicine purchase recommendation list and sending the medicine purchase recommendation list to the client.
In one embodiment, the claim settlement module 16 further comprises:
the first claim settlement unit is used for acquiring preset post-claim settlement rules if the claim type is post-claim settlement, and taking the preset post-claim settlement rules as the claim settlement rules.
And the second claim settlement unit is used for acquiring a preset direct payment claim rule if the claim type is a direct payment claim, and taking the preset direct payment claim rule as the claim rule.
And the user material acquisition unit is used for acquiring user materials from the user information, wherein the user materials are materials required by the user for carrying out insurance claim settlement.
And the insurance claim settlement unit is used for performing insurance claim settlement on the user based on the claim settlement rules and the user materials.
Wherein the meaning of "first" and "second" in the above modules/units is only to distinguish different modules/units, and is not used to define which module/unit has higher priority or other defining meaning. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such process, method, article, or apparatus, and such that a division of modules presented in this application is merely a logical division and may be implemented in a practical application in a further manner.
For specific limitations of the intelligent claim settlement device, reference may be made to the above limitations of the intelligent claim settlement method, which are not described herein again. The modules in the intelligent claims device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data related to the intelligent claims settlement method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an intelligent claims settlement method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the intelligent claims settlement method in the above embodiments, such as the steps S101 to S106 shown in fig. 2 and other extensions of the method and related steps. Alternatively, the processor executes the computer program to realize the functions of the modules/units of the intelligent claims settlement device in the above embodiments, such as the modules 11 to 16 shown in fig. 3. To avoid repetition, further description is omitted here.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc.
The memory may be integrated in the processor or may be provided separately from the processor.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the intelligent claims settlement method in the above-described embodiments, such as the steps S101 to S106 shown in fig. 2 and extensions of other extensions and related steps of the method. Alternatively, the computer program is used to implement the functions of the modules/units of the intelligent claims settlement device in the above embodiments, such as the modules 11 to 16 shown in fig. 3, when being executed by the processor. To avoid repetition, further description is omitted here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An intelligent claims settlement method, comprising:
acquiring user information and insurance information, and constructing an insurance association network, wherein the user information comprises historical medicine purchasing records;
based on the insurance association network, inquiring the drug which can be reimbursed by the user in a preset drug knowledge base to generate a candidate drug list;
when a medicine purchasing request of a user is received, recommending the historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommendation model and a user operation record to obtain a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored by a server within a preset time period;
according to a medicine purchasing selection result fed back by the client based on the medicine recommending and purchasing list, medicine purchasing processing is carried out, and a corresponding claim settlement request is generated;
carrying out claim type identification on the claim settlement request, and determining the claim type;
and acquiring preset claim settlement rules from the insurance associated network based on the claim settlement types, and performing insurance claim settlement based on the preset claim settlement rules.
2. The method according to claim 1, wherein the step of obtaining user information and insurance information to construct an insurance association network, wherein the user information comprises historical drug purchasing records comprises:
acquiring user information, and extracting keywords from the user information to obtain user keywords;
acquiring insurance information, and extracting keywords of the insurance information to obtain insurance keywords;
and mapping the user information and the insurance information based on the user keywords and the insurance keywords to construct an insurance association network.
3. The method of claim 1, wherein before querying a pre-defined drug repository for user reimburseable drugs based on the insurance association network to generate a candidate drug list, further comprising:
determining the drug attribute according to the drug knowledge and the policy claim settlement condition, and taking the drug attribute as a drug entity;
acquiring medicine information in a pharmacy database;
and mapping the medicine information and the corresponding medicine entity to obtain the medicine knowledge base.
4. The method according to claim 1, wherein when a user medicine purchasing request is received, recommendation processing is performed on the historical medicine purchasing record and the candidate medicine list based on a medicine purchasing recommendation model and a user operation record to obtain a medicine recommending and purchasing list, and the medicine recommending and purchasing list is sent to a client, wherein the step of the user operation record being operation record information stored by a server within a preset time period comprises the following steps of:
when a medicine purchasing request of a user is received, acquiring a historical purchasing record;
determining an interest factor based on the historical purchase record, wherein the interest factor is a first influence factor of purchase of a user;
inputting the interest factor and the list of candidate drugs into the medication purchase recommendation model, wherein the list of candidate drugs includes a candidate drug and at least two fields;
selecting a field corresponding to the interest factor in the candidate medicine list, taking the field corresponding to the interest factor as an interest field, and taking a field which is not corresponding to the interest factor in the candidate medicine list as other fields;
and fusing the interest fields and other fields of the candidate drug list to obtain a drug recommendation purchase list and sending the drug recommendation purchase list to a client.
5. The method of claim 4, wherein the step of fusing the interest field and other fields of the candidate drug list to obtain a drug recommendation purchase list and sending the drug recommendation purchase list to a client comprises:
determining an interest weight corresponding to the interest field and a secondary weight corresponding to the other fields;
updating the content corresponding to the interest field in the candidate medicine list based on the interest weight to obtain an interest list corresponding to the interest field;
updating the contents corresponding to other fields in the candidate medicine list based on the secondary weight to obtain secondary lists corresponding to other fields;
selecting the candidate medicines corresponding to the interest list and the secondary list as fusion medicines;
and combining the fields corresponding to the interest list and the secondary list based on the fused medicines to obtain a medicine purchase recommendation list and sending the medicine purchase recommendation list to a client.
6. The method according to claim 1, wherein the step of obtaining a preset claim settlement rule from the insurance associated network based on the claim settlement type, and performing an insurance claim settlement based on the preset claim settlement rule comprises:
if the claim type is post-claim, acquiring preset post-claim rules, and taking the preset post-claim rules as the claim rules;
if the claim type is direct payment claim, acquiring a preset direct payment claim rule, and taking the preset direct payment claim rule as a claim rule;
obtaining user materials from the user information, wherein the user materials are materials required by the user for carrying out insurance claim settlement;
and performing insurance claim on the user based on the claim settlement rule and the user material.
7. An intelligent claim settlement device, comprising:
the insurance associated network building module is used for obtaining user information and insurance information and building an insurance associated network, wherein the user information comprises historical medicine purchasing records;
the candidate medicine list generating module is used for inquiring the user reimburseable medicines in a preset medicine knowledge base based on the insurance association network and generating a candidate medicine list;
the medicine recommending and purchasing module is used for recommending the historical medicine purchasing records and the candidate medicine list based on a medicine purchasing recommending model and a user operation record when a medicine purchasing request of a user is received, obtaining a medicine recommending and purchasing list and sending the medicine recommending and purchasing list to a client, wherein the user operation record is operation record information stored by a server within a preset time period;
the claim settlement request acquisition module is used for processing medicine purchasing and generating a corresponding claim settlement request according to a medicine purchasing selection result fed back by the client based on the medicine recommendation purchase list;
the claim type determining module is used for carrying out claim type identification on the claim request and determining the claim type;
and the claim settlement module is used for acquiring preset claim settlement rules from the insurance association network based on the claim settlement types and carrying out insurance claim settlement based on the preset claim settlement rules.
8. The apparatus of claim 7, wherein the medication recommendation purchase module comprises:
a historical purchase record acquisition unit, which is used for acquiring a historical purchase record when a medicine purchase request of a user is received;
the interest factor determining unit is used for determining an interest factor based on the historical purchase record, wherein the interest factor is a first influence factor for the purchase of the user;
an input unit for inputting the interest factor and the candidate drug list into the medication purchase recommendation model, wherein the candidate drug list comprises candidate drugs and at least two fields;
a field determining unit, configured to select a field corresponding to the interest factor in the candidate drug list, and use the field corresponding to the interest factor as an interest field, and use a field not corresponding to the interest factor in the candidate drug list as another field;
and the fusion unit is used for carrying out fusion processing on the interest field and other fields of the candidate medicine list to obtain a medicine recommendation purchase list and sending the medicine recommendation purchase list to a client.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the intelligent claims method of any one of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the intelligent claims settlement method according to any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023124772A1 (en) * | 2021-12-27 | 2023-07-06 | 北京京东拓先科技有限公司 | Information processing method and apparatus, and storage medium |
| CN117934177A (en) * | 2024-03-22 | 2024-04-26 | 湖南多层次商保科技有限公司 | Method and system for constructing insurance intelligent responsibility determination model |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023124772A1 (en) * | 2021-12-27 | 2023-07-06 | 北京京东拓先科技有限公司 | Information processing method and apparatus, and storage medium |
| CN117934177A (en) * | 2024-03-22 | 2024-04-26 | 湖南多层次商保科技有限公司 | Method and system for constructing insurance intelligent responsibility determination model |
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