Insurance-related drug standardization digital platform based on artificial intelligence

Abstract

The invention discloses an insurance related medicine standardization digital platform based on artificial intelligence, and the platform comprises an insurance related medicine standardization digital platform. The insurance-related drug standardization digital platform comprises a drug information management module, an intelligent auditing module, an insurance docking module, a data analysis and visualization module, a user management and authority control module and a system maintenance and upgrade module. The rule establishing and learning module defines auditing rules of insurance claim, the auditing rules include medicine catalogs and use conditions, complex medicine use conditions are intelligently judged and predicted through a machine learning algorithm, auditing decisions are automatically made according to the rules and model results, the intelligent auditing module can achieve medicine standardization, the management efficiency is improved, and the safety of insurance claim payment is improved. And due to the unified data specification and standard, the medicine information is effectively integrated and utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of insurance digital platforms, and more specifically to a digital platform for insurance-related drug standardization based on artificial intelligence. Background Art

[0002] First, the standardization of insurance-related drugs involves the scientific classification of drugs. This includes the division of clinically necessary basic drugs, insured drugs and non-insured drugs. Clinically necessary basic drugs are the cornerstone of the medical insurance system, with a wide range of indications, definite therapeutic effects, high safety and reasonable prices. Insured drugs refer to the range of drugs that meet the provisions of medical insurance, which can be used by insured persons in specified medical institutions and enjoy reimbursement from medical insurance. Non-insured drugs are not covered by medical insurance reimbursement, and the cost must be borne by the patient. Secondly, drug access management is an important part of the standardization of insurance-related drugs. This includes processes such as drug registration, evaluation and approval, and procurement. Drug registration requires drug manufacturers to apply to the national drug supervision and administration department for drug marketing authorization in accordance with the procedures and requirements prescribed by the state. Evaluation and approval is a scientific and strict evaluation and approval of the efficacy, safety, quality and reasonable use of drugs to ensure the safety and effectiveness of drug use. The procurement process is to purchase drugs from manufacturers that meet the requirements through public bidding and other means to ensure the quality and reasonable price of drugs.

[0003] At present, the digital platform of the insurance industry has limited drug standardization, resulting in opaque information, inefficient management, and lack of unified data specifications and standards, making it difficult to effectively integrate and utilize drug information. Therefore, a new technical solution is needed to solve this problem. Summary of the invention

[0004] The purpose of the present invention is to provide a digital platform for insurance-related drug standardization based on artificial intelligence, which solves the problem that the current digital platform in the insurance industry has a limited degree of drug standardization, resulting in information opacity, low management efficiency, lack of unified data specifications and standards, and makes it difficult to effectively integrate and utilize drug information.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a digital platform for insurance-related drug standardization based on artificial intelligence, comprising: a digital platform for insurance-related drug standardization, the digital platform for insurance-related drug standardization comprising a drug information management module, an intelligent review module, an insurance docking module, a data analysis and visualization module, a user management and authority control module and a system maintenance and upgrade module;

[0006] The drug information management module collects and organizes detailed information on various drugs, including name, ingredients, dosage form, usage, dosage, contraindications, etc., to achieve standardization and unification of drug information, which is convenient for processing and analysis by subsequent modules. By constructing a deep convolutional neural network, it extracts features and classifies drug images to achieve automatic recognition of drug names, manufacturers, and dosages. It uses data enhancement methods such as image rotation, cropping, and scaling to expand the drug image data set and improve the generalization ability and recognition accuracy of the model.

[0007] The intelligent audit module includes a standardized management execution and monitoring module, an intelligent analysis and decision support module, and a rule establishment and learning module;

[0008] The standardized management execution and monitoring module formulates standardized management processes for insurance drugs in accordance with relevant national laws and industry norms to ensure business compliance. Through automated and intelligent technical means, it monitors and schedules the execution of standardized processes in real time to ensure efficient execution of the processes, establishes risk assessment models, monitors potential risks in business operations in real time, and issues early warnings and responses;

[0009] The intelligent analysis and decision support module uses machine learning algorithms and deep learning algorithms to conduct in-depth mining and analysis of pre-processed data, discover potential associations and patterns in the data, and build an insurance drug business model based on the data analysis results to provide a scientific basis for decision support. Through intelligent recommendation, predictive analysis and other functions, it provides decision support for insurance drug business and continuously optimizes business processes and strategies;

[0010] The rule establishment and learning module defines the review rules for insurance claims, including drug catalogs and usage conditions. It uses machine learning algorithms to make intelligent judgments and predictions on complex medication situations, and automatically makes review decisions based on the rules and model results. Its intelligent review module can achieve drug standardization and increase management efficiency. Its unified data specifications and standards enable the effective integration and utilization of drug information.

[0011] As a preferred embodiment of the present invention, the drug information management module includes a data collection and preprocessing module, and the data collection and preprocessing module includes data source integration, data cleaning and conversion, and data storage and management.

[0012] As a preferred implementation mode of the present invention, the standardized management execution and monitoring module includes standardized process formulation, process execution and monitoring, and risk assessment and early warning.

[0013] As a preferred implementation mode of the present invention, the intelligent analysis and decision support module includes data mining and analysis, business model construction, and decision support and optimization.

[0014] As a preferred implementation of the present invention, the rule establishment and learning module includes a rule engine module, a machine learning module and an intelligent decision-making module.

[0015] As a preferred implementation mode of the present invention, the insurance docking module includes an interface management module, a data conversion module and a data transmission module.

[0016] As a preferred implementation of the present invention, the data analysis and visualization module includes a visualization design module and a report generation module.

[0017] As a preferred implementation mode of the present invention, the user management and authority control module includes a user information management module, a role management module and an authority verification module.

[0018] As a preferred implementation mode of the present invention, the system maintenance and upgrade module includes a daily maintenance module, a fault handling module and an upgrade and transformation module.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The digital platform for insurance-related drug standardization of the present invention includes a drug information management module, an intelligent audit module, an insurance docking module, a data analysis and visualization module, a user management and authority control module, and a system maintenance and upgrade module. The drug information management module collects and organizes detailed information on various types of drugs, including name, ingredients, dosage form, usage and dosage, contraindications, etc., to achieve standardization and unification of drug information, which is convenient for processing and analysis by subsequent modules. By constructing a deep convolutional neural network, feature extraction and classification recognition are performed on drug images to achieve automatic recognition of drug names, manufacturers, and dosages. Data enhancement methods such as image rotation, cropping, and scaling are used to expand the drug image data set and improve the generalization ability and recognition accuracy of the model. The intelligent audit module includes a standardized management execution and monitoring module, an intelligent analysis and decision support module, and a rule establishment and learning module. The standardized management execution and monitoring module formulates a standardized management process for insurance drugs in accordance with relevant national laws and regulations and industry specifications to ensure business compliance. Through automated and intelligent technical means, the execution of standardized processes is monitored and scheduled in real time to ensure the efficient execution of the processes, establish risk assessment models, monitor potential risks in business operations in real time, and provide early warnings and responses. The intelligent analysis and decision support module uses machine learning algorithms and deep learning algorithms to conduct in-depth mining and analysis of pre-processed data, discover potential associations and patterns in the data, and build an insurance drug business model based on the data analysis results to provide a scientific basis for decision support. Through intelligent recommendations, predictive analysis and other functions, it provides decision support for insurance drug business and continuously optimizes business processes and strategies. The rule establishment and learning module defines the review rules for insurance claims, including drug catalogs and usage conditions. It uses machine learning algorithms to make intelligent judgments and predictions on complex drug use situations, and automatically makes review decisions based on rules and model results. Its intelligent review module can achieve drug standardization and increase management efficiency. Its unified data specifications and standards enable effective integration and utilization of drug information. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a digital platform for standardization of insurance-related drugs of the present invention;

[0022] Figure 2 This is a schematic diagram of the drug information management module of the present invention;

[0023] Figure 3 This is a schematic diagram of the intelligent audit module of the present invention;

[0024] Figure 4 This is a schematic diagram of the insurance docking module of the present invention;

[0025] Figure 5 This is a schematic diagram of the data analysis and visualization module of the present invention;

[0026] Figure 6 This is a schematic diagram of the user management and authority control module of the present invention;

[0027] Figure 7 It is a schematic diagram of the system maintenance and upgrade module of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1-7 , the present invention provides a technical solution: a digital platform for insurance-related drug standardization based on artificial intelligence, comprising: a digital platform for insurance-related drug standardization, the digital platform for insurance-related drug standardization comprising a drug information management module, an intelligent review module, an insurance docking module, a data analysis and visualization module, a user management and authority control module and a system maintenance and upgrade module;

[0030] The drug information management module collects and organizes detailed information on various drugs, including name, ingredients, dosage form, usage, dosage, contraindications, etc., to achieve standardization and unification of drug information, which is convenient for processing and analysis by subsequent modules. By constructing a deep convolutional neural network, it extracts features and classifies drug images to achieve automatic recognition of drug names, manufacturers, and dosages. It uses data enhancement methods such as image rotation, cropping, and scaling to expand the drug image data set and improve the generalization ability and recognition accuracy of the model.

[0031] The intelligent audit module includes a standardized management execution and monitoring module, an intelligent analysis and decision support module, and a rule establishment and learning module;

[0032] The standardized management execution and monitoring module formulates standardized management processes for insurance drugs in accordance with relevant national laws and industry norms to ensure business compliance. Through automated and intelligent technical means, it monitors and schedules the execution of standardized processes in real time to ensure efficient execution of the processes, establishes risk assessment models, monitors potential risks in business operations in real time, and issues early warnings and responses;

[0033] The intelligent analysis and decision support module uses machine learning algorithms and deep learning algorithms to conduct in-depth mining and analysis of pre-processed data, discover potential associations and patterns in the data, and build an insurance drug business model based on the data analysis results to provide a scientific basis for decision support. Through intelligent recommendation, predictive analysis and other functions, it provides decision support for insurance drug business and continuously optimizes business processes and strategies;

[0034] The rule establishment and learning module defines the review rules for insurance claims, including drug catalogs and usage conditions. It uses machine learning algorithms to make intelligent judgments and predictions on complex medication situations, and automatically makes review decisions based on the rules and model results. Its intelligent review module can achieve drug standardization and increase management efficiency. Its unified data specifications and standards enable the effective integration and utilization of drug information.

[0035] Further improvements, such as Figure 2 As shown: the drug information management module includes a data acquisition and preprocessing module, which includes data source integration, data cleaning and conversion, and data storage and management. The data source integration is responsible for integrating insurance drug-related data from different channels and systems, including drug information, insurance policies, and claims records. The data cleaning and conversion cleans, removes duplications, and converts the format of the collected original data to ensure data quality and consistency. The data storage and management adopts technical means such as distributed databases to achieve efficient storage and management of massive data.

[0036] Further improvements, such as Figure 3 As shown: the standardized management execution and monitoring module includes standardized process formulation, process execution and monitoring, and risk assessment and early warning. The standardized process formulation is based on relevant national laws and regulations and industry specifications to formulate standardized management processes for insurance drugs to ensure business compliance. The process execution and monitoring uses automated and intelligent technical means to monitor and schedule the execution of standardized processes in real time to ensure efficient execution of the process. The risk assessment and early warning establishes a risk assessment model to monitor potential risks in business operations in real time, and provide early warnings and responses.

[0037] Further improvements, such as Figure 3 As shown: the intelligent analysis and decision support module includes data mining and analysis, business model construction and decision support and optimization. The data mining and analysis uses machine learning, deep learning and other algorithms to conduct deep mining and analysis on the pre-processed data to discover potential associations and patterns in the data. The business model is constructed based on the data analysis results to build an insurance drug business model to provide a scientific basis for decision support. The decision support and optimization provides decision support for the insurance drug business through intelligent recommendation, predictive analysis and other functions, and continuously optimizes business processes and strategies.

[0038] Further improvements, such as Figure 3 As shown: the rule establishment and learning module includes a rule engine module, a machine learning module and an intelligent decision-making module. The rule engine module defines the review rules for insurance claims, including drug catalogs and usage conditions. The machine learning module uses machine learning algorithms to make intelligent judgments and predictions on complex medication situations. The intelligent decision-making module automatically makes review decisions based on the rules and model results.

[0039] Further improvements, such as Figure 4 As shown: the insurance docking module includes an interface management module, a data conversion module and a data transmission module. The interface management module is responsible for managing the data exchange interface with the insurance company system. The data conversion module converts and formats the drug review results according to the insurance company's data format requirements. The data transmission module is responsible for transmitting the converted data to the insurance company system in real time.

[0040] Further improvements, such as Figure 5 As shown: the data analysis and visualization module includes a visualization design module and a report generation module. The visualization design module designs corresponding visualization charts and interfaces according to the analysis results, and the report generation module integrates the analysis results and visualization charts into a report for users to view and make decisions.

[0041] Further improvements, such as Figure 6 As shown: the user management and permission control module includes a user information management module, a role management module and a permission verification module. The user information management module is responsible for operations such as creating, modifying and deleting user accounts, and managing the user's basic information and account status. The role management module defines different user roles and assigns corresponding permissions to them. When a user requests to access a platform function, the permission verification module verifies the user's permissions and decides whether to allow access.

[0042] Further improvements, such as Figure 7 As shown: the system maintenance and upgrade module includes a daily maintenance module, a fault handling module and an upgrade and transformation module. The daily maintenance module regularly executes maintenance plans, including data backup, performance monitoring and optimization operations. The fault handling module quickly responds and handles faults when a platform fails to ensure the stable operation of the platform. The upgrade and transformation module upgrades the platform's functions and improves its technology according to the needs of business and technological development to adapt to new needs and environments.

[0043] Working principle: The digital platform for insurance-related drug standardization includes drug information management module, intelligent review module, insurance docking module, data analysis and visualization module, user management and authority control module and system maintenance and upgrade module. The drug information management module collects and organizes detailed information of various drugs, including name, ingredients, dosage form, usage and dosage, contraindications, etc., to achieve standardization and unification of drug information, which is convenient for processing and analysis of subsequent modules. By constructing a deep convolutional neural network, feature extraction and classification recognition of drug images are performed to achieve automatic recognition of drug names, manufacturers, and dosages. Image rotation, cropping, scaling and other data enhancement methods are used to expand the drug image data set and improve the generalization ability and recognition accuracy of the model. The intelligent review module includes standardized management execution and monitoring module, intelligent analysis and decision support module and rule establishment and learning module. The standardized management execution and monitoring module formulates standardized management processes for insurance drugs in accordance with relevant national laws and regulations and industry specifications to ensure business compliance. Through automated and intelligent technical means, the execution of standardized processes is monitored and scheduled in real time to ensure efficient execution of the processes, establish risk assessment models, monitor potential risks in business operations in real time, and provide early warnings and responses. The intelligent analysis and decision support module uses machine learning algorithms and deep learning algorithms to conduct in-depth mining and analysis of pre-processed data, discover potential associations and patterns in the data, and build an insurance drug business model based on data analysis results to provide a scientific basis for decision support. Through intelligent recommendations, predictive analysis and other functions, it provides decision support for insurance drug business and continuously optimizes business processes and strategies. The rule establishment and learning module defines the review rules for insurance claims, including drug catalogs and usage conditions. It uses machine learning algorithms to make intelligent judgments and predictions on complex drug use situations, and automatically makes review decisions based on rules and model results. Its intelligent review module can achieve drug standardization and increase management efficiency. Its unified data specifications and standards enable effective integration and utilization of drug information.

[0044] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0045] Finally, a few points should be explained: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, and can be mechanical or electrical connections, or internal connectivity between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may change.

[0046] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital platform for insurance-related drug standardization based on artificial intelligence, characterized by: include: A digital platform for insurance-related drug standardization, the digital platform for insurance-related drug standardization comprising a drug information management module, an intelligent review module, an insurance docking module, a data analysis and visualization module, a user management and authority control module, and a system maintenance and upgrade module; The drug information management module collects and organizes detailed information on various drugs, including name, ingredients, dosage form, usage, dosage, contraindications, etc., to achieve standardization and unification of drug information, which is convenient for processing and analysis by subsequent modules. By constructing a deep convolutional neural network, it extracts features and classifies drug images to achieve automatic recognition of drug names, manufacturers, and dosages. It uses data enhancement methods such as image rotation, cropping, and scaling to expand the drug image data set and improve the generalization ability and recognition accuracy of the model. The intelligent audit module includes a standardized management execution and monitoring module, an intelligent analysis and decision support module, and a rule establishment and learning module; The standardized management execution and monitoring module formulates standardized management processes for insurance drugs in accordance with relevant national laws and industry norms to ensure business compliance. Through automated and intelligent technical means, it monitors and schedules the execution of standardized processes in real time to ensure efficient execution of the processes, establishes risk assessment models, monitors potential risks in business operations in real time, and issues early warnings and responses; The intelligent analysis and decision support module uses machine learning algorithms and deep learning algorithms to conduct in-depth mining and analysis of pre-processed data, discover potential associations and patterns in the data, and build an insurance drug business model based on the data analysis results to provide a scientific basis for decision support. Through intelligent recommendation, predictive analysis and other functions, it provides decision support for insurance drug business and continuously optimizes business processes and strategies; The rule establishment and learning module defines the review rules for insurance claims, including drug catalogs and usage conditions. It uses machine learning algorithms to make intelligent judgments and predictions on complex medication situations, and automatically makes review decisions based on the rules and model results. Its intelligent review module can achieve drug standardization and increase management efficiency. Its unified data specifications and standards enable the effective integration and utilization of drug information.

2. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The drug information management module includes a data collection and preprocessing module, which includes data source integration, data cleaning and conversion, and data storage and management.

3. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The standardized management execution and monitoring module includes standardized process formulation, process execution and monitoring, and risk assessment and early warning.

4. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The intelligent analysis and decision support module includes data mining and analysis, business model construction, and decision support and optimization.

5. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The rule building and learning module includes a rule engine module, a machine learning module and an intelligent decision-making module.

6. The digital platform for insurance-related drug standardization based on artificial intelligence according to claim 1, characterized in that: The insurance docking module includes an interface management module, a data conversion module and a data transmission module.

7. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The data analysis and visualization module includes a visualization design module and a report generation module.

8. According to claim 1, a digital platform for insurance-related drug standardization based on artificial intelligence is characterized by: The user management and authority control module includes a user information management module, a role management module and an authority verification module.

9. The digital platform for insurance-related drug standardization based on artificial intelligence according to claim 1, characterized in that: The system maintenance and upgrade module includes a daily maintenance module, a fault handling module and an upgrade and transformation module.

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