CN116433391A - Method and device for processing claim information, electronic equipment and storage medium - Google Patents
Method and device for processing claim information, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116433391A CN116433391A CN202310459220.XA CN202310459220A CN116433391A CN 116433391 A CN116433391 A CN 116433391A CN 202310459220 A CN202310459220 A CN 202310459220A CN 116433391 A CN116433391 A CN 116433391A
- Authority
- CN
- China
- Prior art keywords
- information
- item
- self
- invoice
- fee
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000012545 processing Methods 0.000 title claims abstract description 28
- 201000010099 disease Diseases 0.000 claims abstract description 48
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 48
- 238000011156 evaluation Methods 0.000 claims abstract description 35
- 238000012502 risk assessment Methods 0.000 claims abstract description 31
- 238000007781 pre-processing Methods 0.000 claims abstract description 28
- 230000010365 information processing Effects 0.000 claims description 30
- 230000011218 segmentation Effects 0.000 claims description 20
- 238000000605 extraction Methods 0.000 claims description 17
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000003672 processing method Methods 0.000 claims description 5
- 238000013473 artificial intelligence Methods 0.000 abstract description 7
- 238000003745 diagnosis Methods 0.000 description 16
- 230000006870 function Effects 0.000 description 13
- 239000003814 drug Substances 0.000 description 12
- 238000004422 calculation algorithm Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 10
- 229940079593 drug Drugs 0.000 description 6
- 208000010392 Bone Fractures Diseases 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 206010017076 Fracture Diseases 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 208000024891 symptom Diseases 0.000 description 3
- BSYNRYMUTXBXSQ-UHFFFAOYSA-N Aspirin Chemical group CC(=O)OC1=CC=CC=C1C(O)=O BSYNRYMUTXBXSQ-UHFFFAOYSA-N 0.000 description 2
- 208000034656 Contusions Diseases 0.000 description 2
- 208000031294 Upper limb fractures Diseases 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 2
- 229960001138 acetylsalicylic acid Drugs 0.000 description 2
- 239000002537 cosmetic Substances 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000012015 optical character recognition Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 208000029725 Metabolic bone disease Diseases 0.000 description 1
- 206010049088 Osteopenia Diseases 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 210000003127 knee Anatomy 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 231100000572 poisoning Toxicity 0.000 description 1
- 230000000607 poisoning effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Technology Law (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of artificial intelligence, and provides a method and a device for processing claim information, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring medical record information and invoice information from the claim information; performing first preprocessing on medical record information to obtain disease information, and performing second preprocessing on invoice information to obtain treatment information; inquiring a preset medical knowledge base to obtain a self-fee proportion; performing risk assessment on the self-fee proportion through traversing the created medical knowledge graph to obtain an assessment result; and carrying out the settlement of the claims based on the evaluation result and the self-fee proportion of each item to obtain the final result of the claims. According to the invention, the medical knowledge base is queried, the medical knowledge map is traversed, the evaluation result and the self-fee proportion are obtained, and the claim settlement is carried out, so that the accuracy of the final claim settlement result is improved. In addition, the invention also relates to the technical field of blockchain, and claim information is stored in the blockchain node.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for processing claim information, electronic equipment and a storage medium.
Background
Currently, when a user applies for a medical claim, the user is generally required to provide personal identification, a medical fee invoice and other data to upload to a medical insurance claim settlement system.
However, the medical insurance claim settlement system needs to extract relevant information through manual or OCR (Optical CharacterRecognition ) and other technologies after receiving the data, needs to manually check the proof file, relies on manual processing too much, has high professional requirements on the claimant, results in long claim settlement period and low claim settlement efficiency, and meanwhile, has low claim settlement accuracy due to a certain error rate caused by manual settlement.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, an apparatus, an electronic device, and a storage medium for processing claim information, which acquire the self-cost ratio of each item by querying a preset medical knowledge base, and traverse the created medical knowledge graph to perform risk assessment on the self-cost ratio of each item, and perform claim settlement based on the assessment result and the corresponding self-cost ratio, thereby improving the accuracy of the final claim result obtained by calculation.
A first aspect of the present invention provides a method of processing claim information, the method comprising:
Responding to the received information processing request, and acquiring claim settlement information;
acquiring medical record information and invoice information from the claim information;
performing first preprocessing on the medical record information to obtain disease information, and performing second preprocessing on the invoice information to obtain treatment information;
inquiring a preset medical knowledge base to obtain the self-fee proportion of each item based on the disease information and the treatment information;
traversing the created medical knowledge graph, and performing risk assessment on the self-fee proportion of each item to obtain an assessment result of each item;
and carrying out claim settlement based on the evaluation result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
Optionally, the first preprocessing is performed on the medical record information to obtain disease information, and the second preprocessing is performed on the invoice information to obtain treatment information, where the obtaining includes:
identifying the medical record information and converting the medical record information into first text information; extracting a plurality of first indexes of a user from the first text information; converting the plurality of first indexes according to a preset first conversion rule to obtain a first standard index corresponding to each first index; determining a plurality of first criteria indicators as disease information;
Identifying the invoice information and converting the invoice information into second text information; extracting a plurality of second indexes of the invoice from the second text information; converting the plurality of second indexes according to a preset second conversion rule to obtain a second standard index corresponding to each second index; a plurality of second criteria indicators is determined as treatment information.
Optionally, before performing risk assessment on the self-fee proportion of each item in the medical knowledge graph created by traversing to obtain an assessment result of each item, the method further includes:
acquiring historical claim settlement information from a plurality of preset data sources;
adopting a preset information extraction strategy to extract information of the historical claim information to obtain a plurality of entities, association relations among the entities and attribute information;
and creating a medical knowledge graph based on the entities and the association relation and attribute information among the entities.
Optionally, the information extraction of the historical claim information by using a preset information extraction policy, and obtaining a plurality of entities, association relationships among the entities, and attribute information include:
entity identification is carried out on the historical claim information, so that a plurality of key information are obtained; performing word segmentation processing on the plurality of key information by using a word segmentation tool to obtain a plurality of segmented words; counting the frequency of each word segmentation, reserving the word segmentation with the frequency being greater than or equal to a preset frequency threshold value, and determining the word segmentation as a plurality of entities;
And carrying out semantic recognition on the historical claim information, and extracting the association relationship and attribute information among the entities.
Optionally, the obtaining medical record information and invoice information from the claim information includes:
identifying an interface identification code of each piece of claim information in the claim information;
if the interface identification code is a first interface code, determining claim information corresponding to the first interface code as medical record information;
and if the interface identification code is a second interface code, determining claim information corresponding to the second interface code as invoice information.
Optionally, traversing the created medical knowledge graph, and performing risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, where the assessment result includes:
traversing the created medical knowledge graph to inquire about project relations;
if the relation exists among any item in the medical knowledge graph, determining that the evaluation result is that the claim is settled according to the self-fee proportion of the corresponding item;
if no relation exists between any item in the medical knowledge graph, determining that the self-fee proportion of the corresponding item is all self-fees according to the evaluation result.
Optionally, the calculating the claim settlement based on the evaluation result of each item and the corresponding self-fee proportion, and obtaining the final claim settlement result includes:
Acquiring an invoice list of each item from the invoice information;
calculating a cost item in the invoice list of each item to obtain the actual cost of the corresponding item;
acquiring an evaluation result of each item;
if the evaluation result of any item is that the claim is settled according to the self-fee proportion of the corresponding item, calculating the product between the actual fee and the self-fee proportion of the corresponding item, and obtaining the claim settlement result;
if the evaluation result of any one item is all self-fees, the claim result of the corresponding item is determined to be zero.
A second aspect of the present invention provides a claim information processing apparatus, the apparatus comprising:
the first acquisition module is used for responding to the received information processing request and acquiring claim information;
the second acquisition module is used for acquiring medical record information and invoice information from the claim information;
the preprocessing module is used for carrying out first preprocessing on the medical record information to obtain disease information, and carrying out second preprocessing on the invoice information to obtain treatment information;
the query module is used for querying a preset medical knowledge base to obtain the self-fee proportion of each item based on the disease information and the treatment information;
The traversing module is used for traversing the created medical knowledge graph, and carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item;
and the calculation module is used for carrying out the claim settlement based on the evaluation result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
A third aspect of the present invention provides an electronic device including a processor and a memory, the processor being configured to implement the claim information processing method when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements the claim information processing method.
In summary, the method, the device, the electronic equipment and the storage medium for processing the claim information can promote the construction of smart cities, are applied to the fields of smart buildings, smart security, smart communities, smart life, the Internet of things and the like, divide the claim information from two dimensions of medical records and invoices by acquiring the medical record information and the invoice information from the claim information, and further improve the accuracy of the claim settlement by considering the medical record and the invoice information when the claim settlement is carried out subsequently. And when the medical record information is subjected to the first pretreatment and the invoice information is subjected to the second pretreatment, the medical record information and the invoice information are standardized, and the disease information and the treatment information are unified. Based on the disease information and the treatment information, a preset medical knowledge base is queried to acquire the self-charge proportion of each item, and the medical knowledge base is combined with different medical insurance policies of each region to record the self-charge proportion of all the items, so that the accuracy of the acquired self-charge proportion is improved. And traversing the created medical knowledge graph, carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, and carrying out risk assessment on the self-fee proportion by traversing the medical knowledge graph to improve the accuracy of the obtained risk assessment result because medical insurance policies of each region are covered in the medical knowledge graph, and further carrying out claim settlement on the basis of the assessment result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
Drawings
Fig. 1 is a flowchart of a method for processing claim information according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a medical knowledge graph according to an embodiment of the invention.
Fig. 3 is a block diagram of a claim information processing apparatus according to a second embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.
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 invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example 1
Fig. 1 is a flowchart of a method for processing claim information according to an embodiment of the present invention.
In this embodiment, the method for processing claim information may be applied to an electronic device, and for an electronic device that needs to perform claim information processing, the function of claim information processing provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a software development kit (Software Development Kit, SDK).
The embodiment of the invention 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.
As shown in fig. 1, the method for processing claim information specifically includes the following steps, and the order of the steps in the flowchart may be changed according to different requirements, and some may be omitted.
And 101, acquiring claim settlement information in response to the received information processing request.
In this embodiment, in the digital medical field, when a medical claim is paid to a user, an information processing request is sent to an electronic device, the electronic device analyzes the information processing request message, and claim information is obtained from the information processing request message, where the claim information includes a case picture and an invoice picture uploaded by the user and an interface identification code of each claim information.
It is emphasized that the claims information may also be stored in a blockchain node in order to further ensure the privacy and security of the claims information.
102, obtaining medical record information and invoice information from the claim information.
In this embodiment, the case information includes age, sex, diagnosis information, and the like of the user; the invoice information comprises diagnosis and treatment projects, medicines, consumable names, fees and the like.
In an alternative embodiment, the obtaining medical record information and invoice information from the claim information includes:
identifying an interface identification code of each piece of claim information in the claim information;
if the interface identification code is a first interface code, determining claim information corresponding to the first interface code as medical record information;
and if the interface identification code is a second interface code, determining claim information corresponding to the second interface code as invoice information.
In this embodiment, the interface identifier is configured to uniquely identify a content type of the claim information, and if the interface identifier is a first interface code, determine that the claim information obtained from the first interface code is medical record information; and if the interface identification code is the second interface code, determining that the claim information acquired from the second interface code is invoice information.
In this embodiment, by dividing the interface identifier code into the first interface code and the second interface code, the problem that medical record information and invoice information are disordered when acquiring claim information is avoided, and the accuracy of the acquired medical record information and invoice information is improved.
In this embodiment, the claim settlement information is divided from two dimensions of the medical record and the invoice, and the medical record and the invoice information are considered when the claim settlement is performed subsequently, so that the accuracy of the claim settlement is improved.
103, performing first preprocessing on the medical record information to obtain disease information, and performing second preprocessing on the invoice information to obtain treatment information.
In this embodiment, the medical record information is obtained from a medical record picture uploaded by a user, and the medical record picture is subjected to first preprocessing to obtain disease information, where the disease information includes age, gender, disease symptoms and the like of the user.
In this embodiment, the invoice information is from an invoice picture uploaded by the user, and the medical record picture is subjected to second preprocessing to obtain treatment information, where the treatment information includes medicines taken by the user, diagnosis and treatment detection performed by the user, consumable names used by the user, and the like.
In an optional embodiment, the first preprocessing the medical record information to obtain disease information includes:
identifying the medical record information and converting the medical record information into first text information;
extracting a plurality of first indexes of a user from the first text information;
converting the plurality of first indexes according to a preset first conversion rule to obtain a first standard index corresponding to each first index;
a plurality of first criteria indicators is determined as disease information.
In this embodiment, a text recognition algorithm is used to recognize the medical record information, the recognition result is converted into first text information, and a named entity recognition algorithm is used to extract a plurality of first indexes of the user from the first text information, where the first indexes include age of the user, gender of the user, disease symptoms of the user, and the like.
Illustratively, if the first indicator is the age of the user: 20 years old, converting the first index 20 years old into a first standard index according to a preset first conversion rule: young adults are identified as disease information.
In an optional embodiment, the performing the second preprocessing on the invoice information to obtain treatment information includes:
Identifying the invoice information and converting the invoice information into second text information;
extracting a plurality of second indexes of the invoice from the second text information;
converting the plurality of second indexes according to a preset second conversion rule to obtain a second standard index corresponding to each second index;
a plurality of second criteria indicators is determined as treatment information.
In this embodiment, a text recognition algorithm is adopted to recognize the invoice information, the recognition result is converted into second text information, and a named entity recognition algorithm is used to extract a plurality of second indexes of the invoice from the second text information, wherein the second indexes comprise medicine information, diagnosis and treatment detection names, consumable names and the like.
Illustratively, if the second index is aspirin, converting the second index aspirin into a second labeling index according to a preset second conversion rule: and determining the medicinal materials as treatment information.
In this embodiment, the text recognition algorithm and the named entity recognition algorithm are related art, and the detailed description of this embodiment is omitted here.
In this embodiment, the medical record information and the invoice information are standardized, so that the disease information and the treatment information are unified.
104, inquiring a preset medical knowledge base to obtain the self-fee proportion of each item based on the disease information and the treatment information.
In this embodiment, the treatment information in the invoice information and the disease information in the medical record information are associated, and a preset medical knowledge base is queried based on the associated disease information and treatment information to obtain the self-charge proportion of each item, wherein the preset medical knowledge base includes all diagnosis and treatment services, consumables and drug items related to medical insurance, and the self-charge proportion of all items is recorded in combination with different medical insurance policies of each region, and the table is referred to in the following table.
| Project | Encoding | Cost name | Region of | Self-charge ratio |
| A diagnosis and treatment service | 11111 | Outpatient service | M | 10 |
| B consumable | 22222 | Hospitalization | N | 10 |
| C medicine | 33333 | Special clinic | P | 10 |
Form-medical knowledge base
In this embodiment, the self-fee proportion of the corresponding item can be obtained by querying a preset medical knowledge base, so that the subsequent claim settlement processing is facilitated.
And 105, traversing the created medical knowledge graph, and carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item.
In this embodiment, a medical knowledge graph may be created in advance, after the self-fee proportion of each item is obtained by querying a medical knowledge base, risk assessment is performed on the self-fee proportion of each item by traversing the medical knowledge graph created in advance, if a relationship exists in each item is queried in the medical knowledge graph, it is determined that the relationship accords with a medical insurance reimbursement rule, and an assessment result of the risk assessment is that no risk exists; if the medical knowledge graph inquires that any item does not have a relation, determining that the medical insurance reimbursement rule is not met, and evaluating the risk as risk, wherein all the cost users related to the item are self-charged.
In this embodiment, before the medical knowledge graph created by the traversal performs risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, the medical knowledge graph needs to be created.
Specifically, the medical knowledge graph creation process includes:
acquiring historical claim settlement information from a plurality of preset data sources;
adopting a preset information extraction strategy to extract information of the historical claim information to obtain a plurality of entities, association relations among the entities and attribute information;
and creating a medical knowledge graph based on the entities and the association relation and attribute information among the entities.
In this embodiment, the historical claim information includes claim information of each region, and the claim information of each region includes medical insurance policies of the corresponding region.
In this embodiment, the preset information extraction policy may be created based on a historical information extraction policy, where the information extraction policy includes an entity recognition algorithm and a semantic recognition model, specifically, a named entity recognition algorithm may be used to perform entity recognition on the historical claim information to obtain a plurality of key information, and an existing semantic recognition model may be used to perform semantic recognition on the historical claim information to obtain an association relationship and attribute information between the plurality of entities.
In this embodiment, entity extraction, relationship extraction and attribute extraction are performed from the historical claim information through an information extraction policy, and a medical knowledge graph is created based on the extracted entities, relationships and attributes, as shown in fig. 2, where the entities include diseases, symptoms, crowds, diagnosis and treatment services, consumables and medicines; the relationship includes a consumable-crowd relationship, a diagnosis and treatment service-crowd relationship, a medicine-crowd relationship, a diagnosis and treatment service-disease relationship, a medicine-disease relationship, a crowd-crowd relationship, and a disease-crowd relationship; the attribute refers to an attribute of an entity, for example, a disease has an senile osteopenia, and a consumable has a bandage.
Exemplary crowds include children, females, males, pregnant women and the like, specifically, the information of the drug adaptation crowd and the diagnosis service item are analyzed through a preset information extraction strategy (under the age, above the age, inadaptation, and the like), the age range distribution is extracted, and then all data are combined and de-duplicated, wherein the combination rule is realized based on a synonym word stock for age description, such as 'greater than', 'no less than' as a group of words, and 'school age' and 'under 7 years' as a group of words, and the combination and de-duplication are carried out.
Further, the step of extracting information from the historical claim information by using a preset information extraction policy to obtain a plurality of entities, association relationships among the entities, and attribute information includes:
entity identification is carried out on the historical claim information, so that a plurality of key information are obtained; performing word segmentation processing on the plurality of key information by using a word segmentation tool to obtain a plurality of segmented words; counting the frequency of each word segmentation, reserving the word segmentation with the frequency being greater than or equal to a preset frequency threshold value, and determining the word segmentation as a plurality of entities;
and carrying out semantic recognition on the historical claim information, and extracting the association relationship and attribute information among the entities.
In this embodiment, a named entity recognition algorithm may be used to perform entity recognition on the historical claim information to obtain a plurality of key information, and an existing semantic recognition model may be used to perform semantic recognition on the historical claim information to obtain association relationships and attribute information between the plurality of entities.
Exemplary, the named entity recognition algorithm is used to perform entity recognition on the disease information in the historical claim information to obtain a plurality of key information, determine a plurality of entities, for example, poisoning, upper level, miseating cosmetics, fracture, bone fracture, left arm fracture, abrasion, knee abrasion, male, young, etc., after obtaining the plurality of entities, perform semantic recognition on the disease information in the historical claim information, calculate an edit distance between the entities, obtain an entity with a minimum distance according to the edit distance, find association relations and attribute information between the minimum entity and other entities, and obtain association relations and attribute information between the plurality of entities, for example: poisoning-upper level-miseating of cosmetic; fracture-superior-fracture; fracture-superior-left arm fracture; bruising-superior-knee bruising; male-upper-young; female-upper position-pregnant woman.
In the embodiment, the medical insurance policies of all areas are covered in the created medical knowledge graph, so that implicit relation calculation of an invoice list during reimbursement can be effectively realized, and accurate risk assessment is improved.
In an optional embodiment, the traversing the created medical knowledge graph performs risk assessment on the self-fee proportion of each item, and obtaining an assessment result of each item includes:
traversing the created medical knowledge graph to inquire about project relations;
if the relation exists among any item in the medical knowledge graph, determining that the evaluation result is that the claim is settled according to the self-fee proportion of the corresponding item;
if no relation exists between any item in the medical knowledge graph, determining that the self-fee proportion of the corresponding item is all self-fees according to the evaluation result.
For example, if the project is a medicine, each medicine needs to judge the relation of medicine-crowd and the relation of medicine-disease, judge whether the corresponding medicine has the relation with crowd, if the medicine belongs to the corresponding crowd, the explanation accords with the medical insurance reimbursement rule, and the evaluation result is determined to be the claim according to the self-charge proportion of the corresponding project; if the drug is not taken by the corresponding group, the project involves all the cost self-fees.
For example, if the project is a disease, each disease needs to perform a disease-crowd relationship judgment to judge whether the corresponding disease has a relationship with crowd, if the disease belongs to the corresponding crowd, the explanation accords with a medical insurance reimbursement rule, and the evaluation result is determined to be the claim according to the self-charge proportion of the corresponding project; if the disease does not belong to the corresponding group, the project involves all fee self-fees.
For example, if the project is a diagnosis and treatment service, each diagnosis and treatment service needs to perform diagnosis and treatment service-crowd relationship judgment, whether the diagnosis and treatment service has a relationship with crowd is judged, if the diagnosis and treatment service corresponds to crowd, the explanation accords with a medical insurance reimbursement rule, and the evaluation result is determined to be the claim according to the self-charge proportion of the corresponding project; if the diagnosis and treatment service does not serve the corresponding crowd, the project involves all fee self-fees.
If the item is a consumable, each consumable is subjected to consumable-crowd relationship judgment, whether the consumable has a relationship with crowd is judged, if the consumable belongs to the corresponding crowd, the description accords with medical insurance reimbursement rules, and an evaluation result is determined to be subjected to claim settlement according to the self-charge proportion of the corresponding item; if the consumable does not belong to the corresponding group, the project involves all the fee self-fees.
In this embodiment, the risk assessment is performed on the self-fee proportion of each item by traversing the created medical knowledge graph, that is, the self-fee proportion of each item is checked, so that the accuracy of the self-fee proportion of each item is ensured, the items which have the self-fee proportion but have no relationship and need to be self-fee for the corresponding item are eliminated, and meanwhile, whether the self-fee proportion is accurate or not does not need to be checked manually, thereby improving the accuracy and efficiency of claim settlement.
And 106, carrying out claim settlement based on the evaluation result of each item and the corresponding self-fee proportion, and obtaining a final claim settlement result.
In this embodiment, after the evaluation result and the self-fee ratio are obtained, the information processing request needs to be executed to perform the settlement of the claim, where the settlement of the claim is mainly to calculate the amount of the claim of each item, and the amount of the claim is equal to the product between the actual fee and the self-fee ratio of each item.
In an optional embodiment, the performing the settlement of the claim based on the evaluation result of each item and the corresponding self-fee proportion, and obtaining the final result of the claim includes:
acquiring an invoice list of each item from the invoice information;
calculating a cost item in the invoice list of each item to obtain the actual cost of the corresponding item;
Acquiring an evaluation result of each item;
if the evaluation result of any item is that the claim is settled according to the self-fee proportion of the corresponding item, calculating the product between the actual fee and the self-fee proportion of the corresponding item, and obtaining the claim settlement result; or if the evaluation result of any item is all self-fees, determining that the claim settlement result of the corresponding item is zero and the claim settlement amount is zero;
and counting the claim settlement results of all the projects to obtain the final claim settlement result.
In this embodiment, when the final claim settlement result is calculated, the self-fee proportion of each item obtained by inquiring the preset medical knowledge base is considered, and the created medical knowledge graph is traversed, and risk assessment is performed on the self-fee proportion of each item to obtain the assessment result of each item.
In summary, according to the claim information processing method of the embodiment, the medical record information and the invoice information are obtained from the claim information, the claim information is divided from the two dimensions of the medical record and the invoice, and the medical record and the invoice information are considered when the claim settlement is carried out subsequently, so that the accuracy of the claim settlement is improved. And when the medical record information is subjected to the first pretreatment and the invoice information is subjected to the second pretreatment, the medical record information and the invoice information are standardized, and the disease information and the treatment information are unified. Based on the disease information and the treatment information, a preset medical knowledge base is queried to acquire the self-charge proportion of each item, and the medical knowledge base is combined with different medical insurance policies of each region to record the self-charge proportion of all the items, so that the accuracy of the acquired self-charge proportion is improved. And traversing the created medical knowledge graph, carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, and carrying out risk assessment on the self-fee proportion by traversing the medical knowledge graph to improve the accuracy of the obtained risk assessment result because medical insurance policies of each region are covered in the medical knowledge graph, and further carrying out claim settlement on the basis of the assessment result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
Example two
Fig. 3 is a block diagram of a claim information processing apparatus according to a second embodiment of the present invention.
In some embodiments, the claim information processing apparatus 20 may include a plurality of functional modules that are comprised of program code segments. Program code for each program segment in the claim information processing apparatus 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform (see fig. 1 for details) the function of claim information processing.
In this embodiment, the claim information processing apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: the system comprises a first acquisition module 201, a second acquisition module 202, a preprocessing module 203, a query module 204, a traversal module 205 and a calculation module 206. The module referred to herein is a series of computer readable instructions capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The first obtaining module 201 is configured to obtain claim information in response to the received information processing request.
A second obtaining module 202, configured to obtain medical record information and invoice information from the claim information.
The preprocessing module 203 is configured to perform a first preprocessing on the medical record information to obtain disease information, and perform a second preprocessing on the invoice information to obtain treatment information.
And a query module 204, configured to query a preset medical knowledge base to obtain a self-fee ratio of each item based on the disease information and the treatment information.
And the traversing module 205 is configured to traverse the created medical knowledge graph, and perform risk assessment on the self-fee proportion of each item to obtain an assessment result of each item.
And the calculating module 206 is configured to perform the claim settlement based on the evaluation result of each item and the corresponding self-fee proportion, so as to obtain a final claim settlement result.
In an alternative embodiment, the first obtaining module 201 is configured to: identifying an interface identification code of each piece of claim information in the claim information; if the interface identification code is a first interface code, determining claim information corresponding to the first interface code as medical record information; and if the interface identification code is a second interface code, determining claim information corresponding to the second interface code as invoice information.
In this embodiment, by dividing the interface identifier code into the first interface code and the second interface code, the problem that medical record information and invoice information are disordered when acquiring claim information is avoided, and the accuracy of the acquired medical record information and invoice information is improved.
In an alternative embodiment, the preprocessing module 203 is configured to: identifying the medical record information and converting the medical record information into first text information; extracting a plurality of first indexes of a user from the first text information; converting the plurality of first indexes according to a preset first conversion rule to obtain a first standard index corresponding to each first index; determining a plurality of first criteria indicators as disease information; identifying the invoice information and converting the invoice information into second text information; extracting a plurality of second indexes of the invoice from the second text information; converting the plurality of second indexes according to a preset second conversion rule to obtain a second standard index corresponding to each second index; a plurality of second criteria indicators is determined as treatment information.
In an optional embodiment, before performing risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, acquiring historical claim settlement information from a plurality of preset data sources; adopting a preset information extraction strategy to extract information of the historical claim information to obtain a plurality of entities, association relations among the entities and attribute information; and creating a medical knowledge graph based on the entities and the association relation and attribute information among the entities.
Further, the step of extracting information from the historical claim information by using a preset information extraction policy to obtain a plurality of entities, association relationships among the entities, and attribute information includes: entity identification is carried out on the historical claim information, so that a plurality of key information are obtained; performing word segmentation processing on the plurality of key information by using a word segmentation tool to obtain a plurality of segmented words; counting the frequency of each word segmentation, reserving the word segmentation with the frequency being greater than or equal to a preset frequency threshold value, and determining the word segmentation as a plurality of entities; and carrying out semantic recognition on the historical claim information, and extracting the association relationship and attribute information among the entities.
In an alternative embodiment, the traversal module 205 is configured to: traversing the created medical knowledge graph to inquire about project relations; if the relation exists among any item in the medical knowledge graph, determining that the evaluation result is that the claim is settled according to the self-fee proportion of the corresponding item; if no relation exists between any item in the medical knowledge graph, determining that the self-fee proportion of the corresponding item is all self-fees according to the evaluation result.
In this embodiment, the risk assessment is performed on the self-fee proportion of each item by traversing the created medical knowledge graph, that is, the self-fee proportion of each item is checked, so that the accuracy of the self-fee proportion of each item is ensured, the items which have the self-fee proportion but have no relationship and need to be self-fee for the corresponding item are eliminated, and meanwhile, whether the self-fee proportion is accurate or not does not need to be checked manually, thereby improving the accuracy and efficiency of claim settlement.
In an alternative embodiment, the computing module 206 is configured to: acquiring an invoice list of each item from the invoice information; calculating a cost item in the invoice list of each item to obtain the actual cost of the corresponding item; acquiring an evaluation result of each item; if the evaluation result of any item is that the claim is settled according to the self-fee proportion of the corresponding item, calculating the product between the actual fee and the self-fee proportion of the corresponding item, and obtaining the claim settlement result; if the evaluation result of any one item is all self-fees, the claim result of the corresponding item is determined to be zero.
In this embodiment, when the final claim settlement result is calculated, the self-fee proportion of each item obtained by inquiring the preset medical knowledge base is considered, and the created medical knowledge graph is traversed, and risk assessment is performed on the self-fee proportion of each item to obtain the assessment result of each item.
In summary, the claim information processing device according to the embodiment obtains medical record information and invoice information from the claim information, divides the claim information from two dimensions of medical record and invoice, and considers the medical record and invoice information when the claim settlement is performed subsequently, thereby improving the accuracy of the claim settlement. And when the medical record information is subjected to the first pretreatment and the invoice information is subjected to the second pretreatment, the medical record information and the invoice information are standardized, and the disease information and the treatment information are unified. Based on the disease information and the treatment information, a preset medical knowledge base is queried to acquire the self-charge proportion of each item, and the medical knowledge base is combined with different medical insurance policies of each region to record the self-charge proportion of all the items, so that the accuracy of the acquired self-charge proportion is improved. And traversing the created medical knowledge graph, carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item, and carrying out risk assessment on the self-fee proportion by traversing the medical knowledge graph to improve the accuracy of the obtained risk assessment result because medical insurance policies of each region are covered in the medical knowledge graph, and further carrying out claim settlement on the basis of the assessment result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
Example III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 4 is not limiting of the embodiments of the present invention, and that either a bus-type configuration or a star-type configuration is possible, and that the electronic device 3 may also include more or less other hardware or software than that shown, or a different arrangement of components.
In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may further include a client device, where the client device includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, a mouse, a remote control, a touch pad, or a voice control device, such as a personal computer, a tablet computer, a smart phone, a digital camera, etc.
It should be noted that the electronic device 3 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
In some embodiments, the memory 31 is used to store program codes and various data, such as the claim information processing apparatus 20 installed in the electronic device 3, and to implement high-speed, automatic access to programs or data during operation of the electronic device 3. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
In some embodiments, the at least one processor 32 may be comprised of an integrated circuit, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects the respective components of the entire electronic device 3 using various interfaces and lines, and executes various functions of the electronic device 3 and processes data by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power source (such as a battery) for powering the various components, and optionally, the power source may be logically connected to the at least one processor 32 via a power management device, thereby implementing functions such as managing charging, discharging, and power consumption by the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) or a processor (processor) to perform portions of the methods described in the various embodiments of the invention.
In a further embodiment, in conjunction with fig. 3, the at least one processor 32 may execute the operating device of the electronic device 3, as well as various installed applications (e.g., the claim information processing device 20), program code, etc., such as the various modules described above.
The memory 31 has program code stored therein, and the at least one processor 32 can invoke the program code stored in the memory 31 to perform related functions. For example, each of the modules depicted in fig. 3 is a program code stored in the memory 31 and executed by the at least one processor 32 to perform the functions of each of the modules for the purpose of claim information processing.
Illustratively, the program code may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 32 to complete the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing the specified functions, which instruction segments describe the execution of the program code in the electronic device 3. For example, the program code may be partitioned into a first acquisition module 201, a second acquisition module 202, a preprocessing module 203, a query module 204, a traversal module 205, and a computation module 206.
In one embodiment of the invention, the memory 31 stores a plurality of computer readable instructions that are executed by the at least one processor 32 to perform the function of claim information processing.
Specifically, the specific implementation method of the above instruction by the at least one processor 32 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 and fig. 2, which are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. The units or means stated in the invention may also be implemented by one unit or means, either by software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of processing claim information, the method comprising:
responding to the received information processing request, and acquiring claim settlement information;
acquiring medical record information and invoice information from the claim information;
performing first preprocessing on the medical record information to obtain disease information, and performing second preprocessing on the invoice information to obtain treatment information;
inquiring a preset medical knowledge base to obtain the self-fee proportion of each item based on the disease information and the treatment information;
traversing the created medical knowledge graph, and performing risk assessment on the self-fee proportion of each item to obtain an assessment result of each item;
and carrying out claim settlement based on the evaluation result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
2. The method of claim 1, wherein the first preprocessing the medical record information to obtain disease information, and the second preprocessing the invoice information to obtain treatment information comprises:
Identifying the medical record information and converting the medical record information into first text information; extracting a plurality of first indexes of a user from the first text information; converting the plurality of first indexes according to a preset first conversion rule to obtain a first standard index corresponding to each first index; determining a plurality of first criteria indicators as disease information;
identifying the invoice information and converting the invoice information into second text information; extracting a plurality of second indexes of the invoice from the second text information; converting the plurality of second indexes according to a preset second conversion rule to obtain a second standard index corresponding to each second index; a plurality of second criteria indicators is determined as treatment information.
3. The method for processing claim 1, wherein before performing risk assessment on the self-rating scale of each item to obtain an assessment result of each item, the method further comprises:
acquiring historical claim settlement information from a plurality of preset data sources;
adopting a preset information extraction strategy to extract information of the historical claim information to obtain a plurality of entities, association relations among the entities and attribute information;
And creating a medical knowledge graph based on the entities and the association relation and attribute information among the entities.
4. The claim 3 further comprising a step of extracting information from the historical claim information by using a predetermined information extraction policy, wherein the step of obtaining a plurality of entities, association relationships between the plurality of entities, and attribute information comprises:
entity identification is carried out on the historical claim information, so that a plurality of key information are obtained; performing word segmentation processing on the plurality of key information by using a word segmentation tool to obtain a plurality of segmented words; counting the frequency of each word segmentation, reserving the word segmentation with the frequency being greater than or equal to a preset frequency threshold value, and determining the word segmentation as a plurality of entities;
and carrying out semantic recognition on the historical claim information, and extracting the association relationship and attribute information among the entities.
5. The claim 1 wherein the obtaining medical record information and invoice information from the claim information comprises:
identifying an interface identification code of each piece of claim information in the claim information;
if the interface identification code is a first interface code, determining claim information corresponding to the first interface code as medical record information;
And if the interface identification code is a second interface code, determining claim information corresponding to the second interface code as invoice information.
6. The method for processing claim 1, wherein traversing the created medical knowledge graph, performing risk assessment on the self-cost ratio of each item, and obtaining an assessment result of each item comprises:
traversing the created medical knowledge graph to inquire about project relations;
if the relation exists among any item in the medical knowledge graph, determining that the evaluation result is that the claim is settled according to the self-fee proportion of the corresponding item;
if no relation exists between any item in the medical knowledge graph, determining that the self-fee proportion of the corresponding item is all self-fees according to the evaluation result.
7. The method for processing claim 1, wherein the obtaining a final claim result comprises:
acquiring an invoice list of each item from the invoice information;
calculating a cost item in the invoice list of each item to obtain the actual cost of the corresponding item;
Acquiring an evaluation result of each item;
if the evaluation result of any item is that the claim is settled according to the self-fee proportion of the corresponding item, calculating the product between the actual fee and the self-fee proportion of the corresponding item, and obtaining the claim settlement result;
if the evaluation result of any one item is all self-fees, the claim result of the corresponding item is determined to be zero.
8. An apparatus for processing claim information, the apparatus comprising:
the first acquisition module is used for responding to the received information processing request and acquiring claim information;
the second acquisition module is used for acquiring medical record information and invoice information from the claim information;
the preprocessing module is used for carrying out first preprocessing on the medical record information to obtain disease information, and carrying out second preprocessing on the invoice information to obtain treatment information;
the query module is used for querying a preset medical knowledge base to obtain the self-fee proportion of each item based on the disease information and the treatment information;
the traversing module is used for traversing the created medical knowledge graph, and carrying out risk assessment on the self-fee proportion of each item to obtain an assessment result of each item;
and the calculation module is used for carrying out the claim settlement based on the evaluation result of each item and the corresponding self-fee proportion to obtain a final claim settlement result.
9. An electronic device comprising a processor and a memory, wherein the processor is configured to implement the claim information processing method of any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the claim information processing method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310459220.XA CN116433391A (en) | 2023-04-19 | 2023-04-19 | Method and device for processing claim information, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310459220.XA CN116433391A (en) | 2023-04-19 | 2023-04-19 | Method and device for processing claim information, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116433391A true CN116433391A (en) | 2023-07-14 |
Family
ID=87094306
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310459220.XA Pending CN116433391A (en) | 2023-04-19 | 2023-04-19 | Method and device for processing claim information, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116433391A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118152548A (en) * | 2024-05-13 | 2024-06-07 | 杭州律途科技有限公司 | Medical insurance data tracing method and system based on question-answer type picture text extraction model |
-
2023
- 2023-04-19 CN CN202310459220.XA patent/CN116433391A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118152548A (en) * | 2024-05-13 | 2024-06-07 | 杭州律途科技有限公司 | Medical insurance data tracing method and system based on question-answer type picture text extraction model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CA3046247C (en) | Data platform for automated data extraction, transformation, and/or loading | |
| CN109542966B (en) | Data fusion method and device, electronic equipment and computer readable medium | |
| CN113782125B (en) | Clinic scoring method and device based on artificial intelligence, electronic equipment and medium | |
| WO2022222943A1 (en) | Department recommendation method and apparatus, electronic device and storage medium | |
| CN111986744B (en) | Patient interface generation method and device for medical institution, electronic equipment and medium | |
| CN112614578A (en) | Doctor intelligent recommendation method and device, electronic equipment and storage medium | |
| CN113345577A (en) | Diagnosis and treatment auxiliary information generation method, model training method, device, equipment and storage medium | |
| CN116433391A (en) | Method and device for processing claim information, electronic equipment and storage medium | |
| CN115206512B (en) | Hospital information management method and device based on Internet of things | |
| CN114663223A (en) | Credit risk assessment method, device and related equipment based on artificial intelligence | |
| CN114117226A (en) | Product recommendation method, system, device and medium | |
| CN112634889A (en) | Electronic case logging method, device, terminal and medium based on artificial intelligence | |
| CN116341523A (en) | Text error correction method, device, computer equipment and storage medium | |
| CN111143394A (en) | Knowledge data processing method, knowledge data processing device, knowledge data processing medium and electronic equipment | |
| CN112686683A (en) | Cost accounting system, and corresponding apparatus and storage medium | |
| CN116741358A (en) | Inquiry registration recommendation method, inquiry registration recommendation device, inquiry registration recommendation equipment and storage medium | |
| CN116108276A (en) | Information recommendation method and device based on artificial intelligence and related equipment | |
| CN113722324B (en) | Report generation method and device based on artificial intelligence, electronic equipment and medium | |
| CN113435975B (en) | Wheelchair leasing processing method and device and related equipment | |
| CN113780675B (en) | Consumption prediction method and device, storage medium and electronic equipment | |
| US20140278527A1 (en) | Large scale identification and analysis of population health risks | |
| CN114240677A (en) | Medical data risk identification method and device, electronic equipment and storage medium | |
| CN113935328A (en) | Text abstract generation method and device, electronic equipment and storage medium | |
| CN114490590A (en) | Data warehouse quality evaluation method and device, electronic equipment and storage medium | |
| CN113792681B (en) | Information acquisition method and device based on point cloud matching, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |