CN117422270A

CN117422270A - Material auditing method, device, equipment and storage medium thereof

Info

Publication number: CN117422270A
Application number: CN202311453358.5A
Authority: CN
Inventors: 罗菁秋
Original assignee: Ping An Health Insurance Company of China Ltd
Current assignee: Ping An Health Insurance Company of China Ltd
Priority date: 2023-11-02
Filing date: 2023-11-02
Publication date: 2024-01-19

Abstract

The embodiment of the application belongs to the technical field of digital medical treatment, is applied to a medical claim scene of a digital medical treatment platform, and relates to a material auditing method, a device, equipment and a storage medium thereof, wherein the method comprises the steps of carrying out field content identification on all collected medical documents, generating a strategy based on a field content identification result and a preset template, and generating a claim settlement template sample group; combining a preset claim settlement list table to train a claim settlement material auditing model; acquiring a medical document uploaded by a target patient; and inputting the sample set to an examination model of the claim material, identifying a target claim template sample set corresponding to the target patient, and predicting the risk of the claim and examining the integrity of the claim application material according to the target claim template sample set. By generating the claim settlement template sample group and the claim settlement material auditing model, the method is convenient for the subsequent prediction of the risk settlement and the auditing of the integrity of the claim settlement application material can be rapidly carried out when the medical document uploaded by the target patient is acquired, and the influence of missed transmission on the claim settlement progress of the user is reduced as much as possible.

Description

Material auditing method, device, equipment and storage medium thereof

Technical Field

The application relates to the technical field of digital medical treatment, and is applied to a medical claim settlement scene of a digital medical treatment platform, in particular to a material auditing method, a device, equipment and a storage medium thereof.

Background

Along with the development of the computer industry and artificial intelligence and the coming of the big data age, the traditional medical mode is gradually converted into the digital medical mode. Because, health risks often exist in a variety of dangerous types depending on the type of disease and the individual variability of the patient.

At present, when a digital medical platform carries out health insurance claim declaration, on one hand, bill types are complicated due to various reasons (different hospitals, different dangerous types and different bill styles), on the other hand, a plurality of users often apply for users for new claims, the preparation of claim application materials is not familiar, and the users are easy to upload errors or carry out supplementary transmission for many times, so that the claim settlement progress of the users is influenced.

Disclosure of Invention

The embodiment of the application aims to provide a material auditing method, device and equipment and a storage medium thereof, so as to solve the problem that when the existing digital medical platform performs health insurance claim reporting, the user is easy to upload errors or perform benefit transmission for many times, and the user claim settlement progress is affected.

In order to solve the above technical problems, the embodiment of the present application provides a material auditing method, which adopts the following technical scheme:

a method of material auditing comprising the steps of:

acquiring all medical documents collected by a target public medical database;

performing field content identification on all the medical documents, and generating a strategy based on a field content identification result and a preset template to generate a claim settlement template sample group;

training an examination model of the claim material based on the claim template sample group and a preset claim list, wherein the preset claim list comprises claim application material lists respectively required for carrying out claim settlement on different dangerous types;

acquiring all medical documents uploaded by a target patient;

inputting all medical documents uploaded by the target patient into the claim material auditing model, identifying a target claim template sample group corresponding to the target patient according to a model output result, and

and according to the target claim template sample group, predicting the risk of the claim and checking the integrity of the claim application material.

Further, the step of identifying field content of all the medical documents and generating a claim template sample group based on the field content identification result and a preset template generation strategy specifically includes:

Identifying field contents respectively contained in all medical receipts through an OCR text scanning technology;

respectively carrying out templated arrangement on all the medical receipts based on the field content to generate receipt templates corresponding to different medical receipts respectively;

carrying out distinguishing marking on the bill templates according to the differences of the bill templates, and generating a first-order template assembly library based on the bill templates after the distinguishing marking is completed;

identifying hospitals corresponding to the document templates after the distinguishing marks are completed respectively through the field content, introducing a hospital name field into the first-order template component library, and generating a second-order template component library;

based on the difference of dangerous types and medical receipts required for the claim settlement of different dangerous types, sorting out target medical receipts required for the claim settlement of different dangerous types and all receipt templates corresponding to the target medical receipts from the second-order template component library, wherein the medical receipts required for the claim settlement of different dangerous types are sorted out according to actual claim settlement business requirements;

and generating claim template sample groups corresponding to different risk types according to all bill templates corresponding to the target medical bill and the distinguishing marks and the hospital names corresponding to all bill templates respectively.

Further, the step of generating document templates corresponding to different medical documents respectively includes the steps of:

sorting field contents respectively contained in all medical documents to obtain a field content sorting result;

dividing medical receipts containing the same field content as the same receipt group based on the field content sorting result to obtain grouping dividing results corresponding to all the medical receipts;

the medical receipts in each receipt grouping are subjected to templated arrangement according to the same field content contained in the medical receipts in the grouping, and the receipt templates corresponding to each receipt grouping are obtained;

according to grouping division results corresponding to all medical receipts and receipt templates corresponding to each receipt grouping respectively, establishing association corresponding relations among the receipt groupings, the receipt templates and the medical receipts, wherein the receipt groupings and the receipt templates are in one-to-one relation, the receipt groupings and the medical receipts are in one-to-many relation, and the receipt templates and the medical receipts are in one-to-many relation;

and determining the bill templates corresponding to different medical bills respectively based on the association corresponding relation between the bill templates and the medical bills.

Further, the step of identifying hospitals to which the document templates after the completion of the distinguishing mark respectively correspond through the field content, introducing a hospital name field into the first-order template component library, and generating a second-order template component library specifically comprises the following steps:

identifying the medical receipts respectively contained in the receipt templates after the distinguishing marks are completed according to the association corresponding relation between the receipt templates and the medical receipts;

identifying a hospital name field from field contents respectively contained in all medical documents as a hospital name identification result;

determining all hospitals corresponding to the bill templates after all distinguishing marks are completed according to the hospital name recognition result;

and constructing association corresponding relations between hospital name fields and the bill templates after the completion of all the distinguishing marks based on all hospitals corresponding to the bill templates after the completion of all the distinguishing marks respectively, and generating a second-order template component library, wherein the association corresponding relations between the hospital name fields and the bill templates after the completion of all the distinguishing marks are many-to-many relations.

Further, the step of sorting out the target medical documents required for the claim settlement for the different dangerous types and all document templates corresponding to the target medical documents from the second-order template component library based on the different dangerous types and the medical documents required for the claim settlement for the different dangerous types specifically includes:

Identifying medical receipts required for the settlement of different dangerous types according to the settlement claim list table, and obtaining an identification result, wherein the settlement claim list table also records the medical receipts respectively required for the settlement of different dangerous types;

acquiring target medical documents respectively required when the claims are settled for different dangerous types from the second-order template component library according to the identification result;

screening all bill templates respectively required by different dangerous types for claim settlement from the second-order template component library based on the association corresponding relation between the bill templates and the medical bill;

the step of generating claim template sample groups corresponding to different risk types according to all bill templates corresponding to the target medical bill and the distinguishing marks and the hospital names corresponding to all bill templates respectively, specifically comprises the following steps:

and constructing structural identification information of corresponding claim template sample groups by taking the distinguishing marks and hospital names corresponding to each bill template in all bill templates as identification information, and generating claim template sample groups corresponding to different risk types by taking all bill templates as template items in the corresponding claim template sample groups.

Further, the step of training an audit model of the claim material based on the claim template sample group and a preset claim list table specifically includes:

identifying claim application bill of materials required for respectively carrying out claim settlement on different dangerous types according to the claim list, wherein the claim application bill of materials comprises a personal information bill of materials, an insurance agreement bill of materials and a medical bill of materials;

and taking the claim settlement template sample groups corresponding to different risk types respectively and the claim settlement application bill of materials required by the different risk types when the claim settlement is carried out as model learning knowledge, inputting the model learning knowledge into a pre-built claim settlement material auditing model for learning training, and obtaining a claim settlement material auditing model with the training completed, wherein the claim settlement material auditing model with the training completed can identify the target claim settlement template sample group corresponding to the target patient according to all medical documents uploaded by the target patient, and carrying out claim settlement risk prediction and claim settlement application material integrity auditing according to the target claim settlement template sample group.

Further, the step of inputting all the medical documents uploaded by the target patient into the claim material auditing model and identifying the target claim template sample group corresponding to the target patient according to the model output result specifically includes:

Identifying bill templates corresponding to all medical bills uploaded by the target patient respectively, and distinguishing marks and hospital names corresponding to each bill template respectively according to the association corresponding relation between the bill templates and the medical bills;

identifying a target claim template sample group corresponding to the target patient according to the structured identification information of all claim template sample groups and the distinguishing marks and hospital names corresponding to each bill template respectively;

the steps of predicting the risk of the claim and checking the integrity of the claim application material are carried out according to the target claim template sample group, and specifically comprise the following steps:

predicting the risk type corresponding to the target risk type template sample group based on the claim template sample groups corresponding to different risk types respectively, and taking the risk type corresponding to the target risk type template sample group as the target risk type corresponding to the target patient;

identifying the claim application bill of materials required by the target dangerous type for the claim settlement based on the claim application bill of materials required by the different dangerous types for the claim settlement;

comparing the claim application material uploaded by the target patient with the materials in the personal information bill of materials, the insurance agreement bill of materials and the medical bill of materials to obtain a comparison result;

And determining whether the types of the claim application materials uploaded by the target patient are complete according to the comparison result.

In order to solve the technical problems, the embodiment of the application also provides a material auditing device, which adopts the following technical scheme:

a material auditing apparatus, comprising:

the medical receipt first acquisition module is used for acquiring all medical receipts collected by the target public medical database;

the claim template sample group generation module is used for carrying out field content identification on all the medical documents and generating a claim template sample group based on a field content identification result and a preset template generation strategy;

the model training module is used for training out an examination model of the claim material based on the claim template sample group and a preset claim list table, wherein the preset claim list table comprises claim application material lists respectively required for carrying out claim settlement on different dangerous types;

the medical receipt second acquisition module is used for acquiring all medical receipts uploaded by the target patient;

the model identification module is used for inputting all medical receipts uploaded by the target patient into the claim material auditing model, identifying a target claim template sample group corresponding to the target patient according to a model output result, and

And the dangerous seed prediction and material auditing module is used for predicting the dangerous seed of the claim and auditing the integrity of the claim application material according to the target claim settlement template sample group.

In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:

a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor perform the steps of the material audit method described above.

In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:

a computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of a material audit method as described above.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

according to the material auditing method, all medical documents collected through the target public medical database are acquired; performing field content identification, and generating a claim settlement template sample group based on a field content identification result and a preset template generation strategy; training an audit model of the claim material based on the claim settlement template sample group and a preset claim settlement list table; acquiring all medical documents uploaded by a target patient; and inputting all medical receipts uploaded by the target patient into an examination model of the claim settlement materials, identifying a target claim settlement template sample group corresponding to the target patient according to the output result of the model, and carrying out claim settlement dangerous seed prediction and claim settlement application material integrity examination according to the target claim settlement template sample group. By generating the claim settlement template sample group and the claim settlement material auditing model, the method is convenient for the subsequent prediction of the risk settlement and the auditing of the integrity of the claim settlement application material can be rapidly carried out when the medical document uploaded by the target patient is acquired, and the influence of missed transmission on the claim settlement progress of the user is reduced as much as possible.

Drawings

For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a material audit method according to the present application;

FIG. 3 is a flow chart of one embodiment of step 202 of FIG. 2;

FIG. 4 is a flow chart of one embodiment of step 302 shown in FIG. 3;

FIG. 5 is a flow chart of one embodiment of step 304 of FIG. 3;

FIG. 6 is a flow chart of one embodiment of step 206 of FIG. 2;

FIG. 7 is a schematic structural view of one embodiment of a material audit device according to the present application;

FIG. 8 is a schematic structural diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the material auditing method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the material auditing apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a material audit method according to the present application is shown. The material auditing method comprises the following steps:

Step 201, all medical documents collected by the target public medical database are acquired.

In this embodiment, the target public medical database includes a medical receipt database disclosed by a related digital medical platform, in which a large number of related medical receipts, such as an outpatient reimbursement receipt, an inspection report receipt, a diagnosis proof receipt, a doctor case receipt, and the like, are stored.

The medical receipts have differences according to different hospitals and different hospital service types, wherein the hospital service types comprise an outpatient reimbursement service, an inspection report service, a diagnosis proving service and a medical case service, and different service types correspond to different medical receipts, such as an outpatient reimbursement receipt corresponding to the outpatient reimbursement service, an inspection report receipt corresponding to the inspection report service, a diagnosis proving receipt corresponding to the diagnosis proving service, a medical case receipt corresponding to the medical case service and the like.

In this embodiment, the specific way of acquiring all the medical documents collected by the target public medical database is to acquire all the medical documents by adopting a document image scanning or shooting mode.

When medical claims are settled for different dangerous types, medical documents required by the different dangerous types are different, and all medical documents collected through a target public medical database are acquired to serve as existing documents, so that a follow-up processing program can be combined with the existing documents to generate a claim settlement template sample group.

And 202, carrying out field content identification on all the medical documents, and generating a strategy based on a field content identification result and a preset template to generate a claim settlement template sample group.

With continued reference to FIG. 3, FIG. 3 is a flow chart of one embodiment of step 202 shown in FIG. 2, comprising:

step 301, identifying field contents respectively contained in all medical receipts through an OCR text scanning technology;

step 302, respectively carrying out templated arrangement on all the medical receipts based on the field content to generate receipt templates corresponding to different medical receipts respectively;

with continued reference to FIG. 4, FIG. 4 is a flow chart of one embodiment of step 302 shown in FIG. 3, including:

step 401, sorting field contents respectively contained in all medical documents to obtain a field content sorting result;

step 402, dividing medical receipts containing the same field content as the same receipt group based on the field content arrangement result, and obtaining group division results corresponding to all the medical receipts;

step 403, performing templated arrangement on the medical receipts in each receipt group according to the same field content contained in the medical receipts in the group, and obtaining receipt templates corresponding to each receipt group respectively;

Step 404, according to grouping and dividing results corresponding to all medical receipts and receipt templates corresponding to each receipt grouping respectively, establishing association corresponding relations among the receipt groupings, the receipt templates and the medical receipts, wherein the receipt groupings and the receipt templates are in one-to-one relation, the receipt groupings and the medical receipts are in one-to-many relation, and the receipt templates and the medical receipts are also in one-to-many relation;

step 405, determining document templates corresponding to different medical documents respectively based on association correspondence between the document templates and the medical documents.

And respectively carrying out templated arrangement on all the medical receipts through the field content to generate receipt templates corresponding to different medical receipts, so that when the medical receipts uploaded by a target patient are acquired later, the receipt templates corresponding to the medical receipts can be directly acquired, and templated recognition and processing are facilitated.

Step 303, distinguishing and marking the bill templates according to the different bill templates, and generating a first-order template assembly library based on the bill templates after distinguishing and marking;

step 304, identifying hospitals corresponding to the document templates after the distinguishing marks are completed respectively according to the field content, introducing a hospital name field into the first-order template component library, and generating a second-order template component library;

With continued reference to fig. 5, fig. 5 is a flow chart of one embodiment of step 304 shown in fig. 3, comprising:

step 501, identifying medical receipts respectively contained in the receipt templates after the distinguishing mark is completed according to the association corresponding relation between the receipt templates and the medical receipts;

step 502, identifying a hospital name field from field contents respectively contained in all medical documents as a hospital name identification result;

step 503, determining all hospitals corresponding to the document templates after all distinguishing marks are completed according to the hospital name recognition result;

and step 504, constructing association corresponding relations between hospital name fields and the bill templates after the completion of all the distinguishing marks based on all hospitals corresponding to the bill templates after the completion of all the distinguishing marks respectively, and generating a second-order template component library, wherein the association corresponding relations between the hospital name fields and the bill templates after the completion of all the distinguishing marks are many-to-many relations.

Because the different medical receipts comprise corresponding hospital name fields, the hospital name fields are introduced on the basis of a first-order template component library, a second-order template component library is generated, and the follow-up program can conveniently and simultaneously combine the distinguishing marks corresponding to the receipt templates and the hospital name fields to identify claim settlement template sample groups respectively corresponding to different risk types.

Step 305, sorting out target medical receipts required for different dangerous types and all receipt templates corresponding to the target medical receipts when the different dangerous types are subjected to the claim settlement from the second-order template component library based on the different dangerous types and the medical receipts required for the claim settlement of the different dangerous types, wherein the medical receipts required for the claim settlement of the different dangerous types are obtained according to actual claim settlement business requirements;

in this embodiment, the steps of sorting out, from the second-order template component library, the target medical documents required for the claim settlement for different risk types and all document templates corresponding to the target medical documents based on the difference of the risk types and the medical documents required for the claim settlement for different risk types specifically include: identifying medical receipts required for the settlement of different dangerous types according to the settlement claim list table, and obtaining an identification result, wherein the settlement claim list table also records the medical receipts respectively required for the settlement of different dangerous types; acquiring target medical documents respectively required when the claims are settled for different dangerous types from the second-order template component library according to the identification result; and screening all bill templates respectively required by different dangerous types for claim settlement from the second-order template component library based on the association corresponding relation between the bill templates and the medical bill.

And 306, generating claim template sample groups corresponding to different risk types according to all bill templates corresponding to the target medical bill and the distinguishing marks and the hospital names corresponding to all bill templates respectively.

In this embodiment, the step of generating the claim template sample group corresponding to different risk types according to all document templates corresponding to the target medical document and the distinguishing marks and the hospital names corresponding to all the document templates respectively specifically includes: and constructing structural identification information of corresponding claim template sample groups by taking the distinguishing marks and hospital names corresponding to each bill template in all bill templates as identification information, and generating claim template sample groups corresponding to different risk types by taking all bill templates as template items in the corresponding claim template sample groups.

Specifically, each claim template sample group may be formed by one or more document templates, so that in order to show the difference of each claim template sample group in the dimension of the document template and the hospital name, the distinguishing mark and the hospital name corresponding to each document template in all the document templates are directly used as identification information to construct the structural identification information of the corresponding claim template sample group, and all the document templates are used as template items in the corresponding claim template sample group to generate claim template sample groups corresponding to different dangerous types. The method and the system have the advantages that visual association relations are generated between the dangerous types and the distinguishing marks and the hospital names corresponding to the bill templates respectively, so that the dangerous types of the target patients required to be subjected to claim settlement can be predicted more quickly when all medical bills uploaded by the target patients are acquired later.

And 203, training out an examination model of the claim material based on the claim template sample group and a preset claim list, wherein the preset claim list comprises claim application material lists respectively required for carrying out claim settlement on different dangerous types.

In this embodiment, the step of training the claim material audit model based on the claim template sample set and a preset claim list table specifically includes: identifying claim application bill of materials required for respectively carrying out claim settlement on different dangerous types according to the claim list, wherein the claim application bill of materials comprises a personal information bill of materials, an insurance agreement bill of materials and a medical bill of materials; and taking the claim settlement template sample groups corresponding to different dangerous types respectively and the claim settlement application bill of materials required by the different dangerous types when the claims are settled as model learning knowledge, and inputting the model learning knowledge into a pre-built claim settlement material auditing model for learning training to obtain a trained claim settlement material auditing model.

The trained claim material auditing model can identify a target claim template sample group corresponding to the target patient according to all medical documents uploaded by the target patient, and predict the risk of claim and audit the integrity of claim application materials according to the target claim template sample group.

In this embodiment, the pre-built and completed claim material auditing model includes an NLP-based natural language learning model, where the NLP-based natural language learning model can learn, according to claim template sample groups corresponding to different risk types, and claim application bill of materials required for claim settlement when the different risk types perform claim settlement, claim application bill of materials corresponding to all claim template sample groups.

Through regard the claim template sample group that different dangerous types correspond respectively to and the claim application bill of materials that needs respectively when different dangerous types carry out the claim is as model learning knowledge, combine the model learning mode to obtain the claim material audit model, be convenient for after obtaining all medical documents that the target patient uploaded, directly adopt the claim material audit model, discernment target patient corresponds the claim template sample group, carry out the prediction of claim dangerous types and claim application material integrality audit, more automaticly and intelligent, avoided the time consumption when artifical audit, improved audit efficiency.

Step 204, all medical documents uploaded by the target patient are acquired.

And 205, inputting all medical receipts uploaded by the target patient into the claim material auditing model, and identifying a target claim template sample group corresponding to the target patient according to a model output result.

In this embodiment, the step of inputting all the medical documents uploaded by the target patient into the claim material auditing model and identifying the target claim template sample group corresponding to the target patient according to the model output result specifically includes: identifying bill templates corresponding to all medical bills uploaded by the target patient respectively, and distinguishing marks and hospital names corresponding to each bill template respectively according to the association corresponding relation between the bill templates and the medical bills; and identifying the target claim template sample group corresponding to the target patient according to the structured identification information of all claim template sample groups and the distinguishing marks and the hospital names corresponding to each bill template.

The method can directly acquire the distinguishing marks of the bill templates corresponding to all the medical bills uploaded by the target patient according to the first-order template component library, and the hospital names can also be directly acquired from all the medical bills uploaded by the target patient in a recognition mode, so that the target claim template sample group corresponding to the target patient is recognized by considering the distinguishing marks and the hospital names, complex analysis and logic reasoning are not needed, the method is more direct and rapid, and the recognition efficiency of the target claim template sample group is improved.

And 206, predicting the risk of the claim and checking the integrity of the claim application material according to the target claim template sample group.

With continued reference to fig. 6, fig. 6 is a flow chart of one embodiment of step 206 shown in fig. 2, comprising:

step 601, predicting the risk type corresponding to the target risk type of the target risk template sample group based on the claim template sample groups corresponding to different risk types respectively, wherein the risk type is used as the target risk type corresponding to the target patient;

step 602, identifying a claim application bill of materials required for the target dangerous type to perform claim settlement based on claim application bill of materials required for the different dangerous types to perform claim settlement;

in this embodiment, after executing the steps of applying for the bill of materials for claims to be required for claims to be paid based on different risk types and identifying the bill of materials for claims to be paid required for claims to be paid based on the target risk type, the method further includes: constructing a display item according to a claim application bill of materials required by the target dangerous seed type in the claim settlement; and feeding back the display item to the receiving terminal of the target patient in a preset communication mode, wherein the preset communication mode comprises a short message mode, a mail mode and an applet message pushing mode.

Specifically, constructing a display item according to a claim application bill of materials required by the target dangerous seed type in the claim settlement; the display items are fed back to the receiving terminal of the target patient in a preset communication mode, so that the target patient can prepare all application materials at one time according to the display items when the application materials are filled, missing of the application materials of the claims due to the fact that the claims are not applied for the history period of the user is avoided, the situation that the user performs multiple material reporting operations due to one claim item is avoided, and the business experience of the user is improved.

Step 603, comparing the claim application material uploaded by the target patient with the materials in the personal information bill of materials, the insurance agreement bill of materials and the medical bill of materials to obtain a comparison result;

step 604, determining whether the type of the claim application material uploaded by the target patient is complete according to the comparison result.

In this embodiment, the step of determining whether the type of the claim application material uploaded by the target patient is complete according to the comparison result specifically includes: constructing a material comparison list according to the materials in the personal information bill of materials, the insurance agreement bill of materials and the medical bill of materials; judging whether the claim application materials uploaded by the target patient all contain the materials in the material comparison list; if the claim application materials uploaded by the target patient all contain the materials in the material comparison list, the types of the claim application materials uploaded by the target patient are complete; if the claim application material uploaded by the target patient does not fully contain the material in the material comparison list, the type of the claim application material uploaded by the target patient is incomplete.

In this embodiment, after the step of determining whether the type of the claim application material uploaded by the target patient is complete according to the comparison result is performed, the method further includes: if the types of the claim application materials uploaded by the target patient are incomplete, screening target materials which are not contained in the claim application materials uploaded by the target patient, and sending a material supplement prompt to a receiving terminal of the target patient by taking the target materials as prompt fields.

Specifically, on the basis that the user is prevented from reporting the claim materials for multiple times according to the display item, after reporting, a corresponding verification program is set to verify whether the claim materials uploaded by the user are complete or not, so that the service experience of the user is improved, and when the application materials are missed, the target user is reminded to supplement in time.

All medical documents collected through the target public medical database are acquired; performing field content identification, and generating a claim settlement template sample group based on a field content identification result and a preset template generation strategy; training an audit model of the claim material based on the claim settlement template sample group and a preset claim settlement list table; acquiring all medical documents uploaded by a target patient; and inputting all medical receipts uploaded by the target patient into an examination model of the claim settlement materials, identifying a target claim settlement template sample group corresponding to the target patient according to the output result of the model, and carrying out claim settlement dangerous seed prediction and claim settlement application material integrity examination according to the target claim settlement template sample group. By generating the claim settlement template sample group and the claim settlement material auditing model, the method is convenient for the subsequent prediction of the risk settlement and the auditing of the integrity of the claim settlement application material can be rapidly carried out when the medical document uploaded by the target patient is acquired, and the influence of missed transmission on the claim settlement progress of the user is reduced as much as possible.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

In the embodiment of the application, all medical documents collected through a target public medical database are acquired; performing field content identification, and generating a claim settlement template sample group based on a field content identification result and a preset template generation strategy; training an audit model of the claim material based on the claim settlement template sample group and a preset claim settlement list table; acquiring all medical documents uploaded by a target patient; and inputting all medical receipts uploaded by the target patient into an examination model of the claim settlement materials, identifying a target claim settlement template sample group corresponding to the target patient according to the output result of the model, and carrying out claim settlement dangerous seed prediction and claim settlement application material integrity examination according to the target claim settlement template sample group. Through generating the claim template sample group and the claim material auditing model, the method is convenient for the subsequent prediction of the claim dangerous seed and the auditing of the claim application material integrity can be rapidly carried out when the medical document uploaded by the target patient is acquired, and is more automatic and intelligent.

With further reference to fig. 7, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a material auditing apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 7, the material auditing apparatus 700 according to this embodiment includes: a medical document first acquisition module 701, an claim template sample group generation module 702, a model training module 703, a medical document second acquisition module 704, a model identification module 705 and a risk prediction and material auditing module 706.

Wherein:

a medical receipt first acquiring module 701, configured to acquire all medical receipts collected by the target public medical database;

the claim template sample group generating module 702 is configured to identify field content of all the medical documents, and generate a claim template sample group based on a field content identification result and a preset template generating policy;

the model training module 703 is configured to train out an audit model of the claim material based on the set of claim template samples and a preset claim list table, where the preset claim list table includes claim application bill of materials required for respectively performing claim settlement on different dangerous types;

A medical receipt second acquiring module 704, configured to acquire all medical receipts uploaded by the target patient;

the model identifying module 705 is configured to input all medical documents uploaded by the target patient into the claim material auditing model, and identify a target claim template sample group corresponding to the target patient according to a model output result;

and the risk prediction and material auditing module 706 is used for predicting the risk of the claim and auditing the integrity of the claim application material according to the target claim template sample group.

Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 8, fig. 8 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 8 comprises a memory 8a, a processor 8b, a network interface 8c communicatively connected to each other via a system bus. It should be noted that only computer device 8 having components 8a-8c is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may alternatively be implemented. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 8a includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 8a may be an internal storage unit of the computer device 8, such as a hard disk or a memory of the computer device 8. In other embodiments, the memory 8a may also be an external storage device of the computer device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 8. Of course, the memory 8a may also comprise both an internal memory unit of the computer device 8 and an external memory device. In this embodiment, the memory 8a is typically used to store an operating system and various application software installed on the computer device 8, such as computer readable instructions of a material auditing method. Further, the memory 8a may be used to temporarily store various types of data that have been output or are to be output.

The processor 8b may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 8b is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is configured to execute computer readable instructions stored in the memory 8a or process data, such as computer readable instructions for executing the material audit method.

The network interface 8c may comprise a wireless network interface or a wired network interface, which network interface 8c is typically used to establish a communication connection between the computer device 8 and other electronic devices.

The computer equipment provided by the embodiment belongs to the technical field of digital medical treatment and is applied to a medical claim settlement scene of a digital medical platform. All medical documents collected through the target public medical database are acquired; performing field content identification, and generating a claim settlement template sample group based on a field content identification result and a preset template generation strategy; training an audit model of the claim material based on the claim settlement template sample group and a preset claim settlement list table; acquiring all medical documents uploaded by a target patient; and inputting all medical receipts uploaded by the target patient into an examination model of the claim settlement materials, identifying a target claim settlement template sample group corresponding to the target patient according to the output result of the model, and carrying out claim settlement dangerous seed prediction and claim settlement application material integrity examination according to the target claim settlement template sample group. By generating the claim settlement template sample group and the claim settlement material auditing model, the method is convenient for the subsequent prediction of the risk settlement and the auditing of the integrity of the claim settlement application material can be rapidly carried out when the medical document uploaded by the target patient is acquired, and the influence of missed transmission on the claim settlement progress of the user is reduced as much as possible.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by a processor to cause the processor to perform the steps of a material audit method as described above.

The computer readable storage medium provided by the embodiment belongs to the technical field of digital medical treatment and is applied to the medical claim settlement scene of the digital medical platform. All medical documents collected through the target public medical database are acquired; performing field content identification, and generating a claim settlement template sample group based on a field content identification result and a preset template generation strategy; training an audit model of the claim material based on the claim settlement template sample group and a preset claim settlement list table; acquiring all medical documents uploaded by a target patient; and inputting all medical receipts uploaded by the target patient into an examination model of the claim settlement materials, identifying a target claim settlement template sample group corresponding to the target patient according to the output result of the model, and carrying out claim settlement dangerous seed prediction and claim settlement application material integrity examination according to the target claim settlement template sample group. By generating the claim settlement template sample group and the claim settlement material auditing model, the method is convenient for the subsequent prediction of the risk settlement and the auditing of the integrity of the claim settlement application material can be rapidly carried out when the medical document uploaded by the target patient is acquired, and the influence of missed transmission on the claim settlement progress of the user is reduced as much as possible.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.

Claims

1. A method of material auditing comprising the steps of:

acquiring all medical documents collected by a target public medical database;

acquiring all medical documents uploaded by a target patient;

2. The material auditing method according to claim 1, wherein the step of identifying field content of all medical documents and generating a claim template sample group based on a field content identification result and a preset template generation policy specifically comprises the following steps:

3. The material auditing method according to claim 2, wherein the step of respectively performing templated arrangement on all the medical documents based on the field content to generate document templates respectively corresponding to different medical documents specifically includes:

4. The material auditing method according to claim 3, wherein the step of identifying hospitals to which the document templates after the completion of the distinguishing mark respectively correspond by the field content, introducing a hospital name field into the first-order template component library, and generating a second-order template component library specifically comprises the steps of:

5. The material auditing method according to claim 3 or 4, wherein the step of sorting out target medical documents required for the claim settlement for different risk types and all document templates corresponding to the target medical documents from the second-order template component library based on the difference of the risk types and the medical documents required for the claim settlement for different risk types specifically comprises:

6. The material auditing method according to claim 5, wherein the step of training a claim material auditing model based on the claim template sample group and a preset claim list table specifically comprises:

7. The material auditing method according to claim 6, wherein the step of inputting all medical documents uploaded by the target patient into the claim material auditing model and identifying the target claim template sample group corresponding to the target patient according to the model output result specifically comprises the following steps:

8. A material auditing device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the material audit method according to any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the material audit method according to any of claims 1 to 7.