Medical insurance data management method and system based on cloud platform

Abstract

This invention discloses a cloud-based method and system for managing medical insurance data. The method relates to the field of medical insurance data management technology and includes the following steps: data collaboration efficiency monitoring, data collaboration efficiency optimization, data quality compliance monitoring, and quality compliance optimization. This invention uses distributed access nodes on a cloud platform to obtain the cross-institutional data collaboration efficiency of medical insurance based on effective collaboration parameters and determine whether dynamic optimization is triggered. Subsequently, data quality compliance is verified, and resource scheduling optimization is determined based on the compliance rate. Finally, continuous monitoring is initiated, forming a closed-loop management system from collaboration efficiency to data quality. This improves the reliability of medical insurance data management and solves the problem of low reliability in existing medical insurance data management technologies.

Description

Technical Field

[0001] This invention relates to the field of medical insurance data management technology, and in particular to a medical insurance data management method and system based on a cloud platform. Background Technology

[0002] First, during the intelligent data collection and preprocessing process, a plug-in architecture automatically connects to heterogeneous platforms such as hospital HIS (Hospital Information System) and pharmacy ERP (Enterprise Resource Planning), utilizing standard interfaces such as HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) to collect patient visit and medication purchase data in real time. Compared to the traditional manual data entry method, dynamic form technology automatically generates differentiated data entry interfaces, combined with OCR (Optical Character Recognition) to quickly extract document information. After the data is uploaded to the cloud, a rules engine automatically cleans and standardizes it.

[0003] Secondly, in the process of tiered storage and governance, the cloud platform adopts a tiered storage strategy for hot, warm, and cold data, and automatically migrates data through lifecycle management. A unified data model is built based on master data management technology to integrate and correlate scattered medical, pharmaceutical, and reimbursement data, forming a 360° view of insured individuals. Graph databases are used to mine data relationships, replacing traditional manual statistical methods.

[0004] Finally, in the process of intelligent analysis and application, abnormal settlement behavior is identified in milliseconds through real-time computing frameworks, and AI (Artificial Intelligence) models accurately identify fraudulent patterns such as insurance fraud, changing the inefficient situation of traditional manual spot checks; personalized services such as chronic disease management and out-of-town medical treatment are proactively pushed based on the insured's profile, realizing the transformation from "passive response" to "proactive service", and the fund's income and expenditure trends are predicted through multi-dimensional analysis, providing data support for policy formulation.

[0005] For example, Chinese invention patent CN114943439B discloses a smart city medical insurance data assessment method and system based on the Internet of Things, which includes: obtaining user risk query requests through a user platform based on a service platform, the risk query requests being used to assess the insurance risk of the assessed object; obtaining the associated persons of the assessed object based on a population information platform; obtaining the medical characteristics and insurance characteristics of the assessed object and associated persons based on a medical information platform; determining target medical characteristics and target insurance characteristics based on the medical characteristics and insurance characteristics of the assessed object and associated persons; determining the assessment value of the insurance risk based on the target medical characteristics and target insurance characteristics; and feeding back the assessment value to the user through the user platform based on the service platform.

[0006] For example, Chinese invention patent CN119168785B discloses a medical insurance data analysis method and system, including: a medical insurance prospective purchaser information collection module, a medical insurance prospective purchaser qualification management module, and a medical insurance purchase demand and processing management module. It accurately collects the opinions of the prospective purchaser's relatives online remotely using mobile terminals based on the relatives' identity information; simultaneously, it performs precise qualification analysis of the relatives' willingness to purchase medical insurance by combining keyword parameters of approval, achieving intelligent assessment of the relatives' willingness and qualification, realizing a comprehensive and scientific evaluation of the prospective purchaser's eligibility, and providing a more humanized and secure service for medical insurance processing.

[0007] The above-mentioned technology has at least the following technical problems:

[0008] In the smart healthcare management system, cross-institutional data collaboration mechanisms are severely lacking. Due to unclear data responsibilities and inconsistent technical standards, medical institutions generally adopt a data silo strategy, resulting in information silos between clinical data and medical insurance reimbursement data. This manifests in several ways: examination and test data for the same insured person cannot be shared between different medical institutions, making it difficult for the medical insurance cloud platform to identify duplicate treatments; in cross-regional medical treatment scenarios, the insured person's historical health data is separated from the clinical data of the treatment location, leading to a lack of continuity in treatment plans. Simultaneously, there are systemic risks to data quality. Primary healthcare institutions, due to weak IT infrastructure, still rely heavily on manual data entry, resulting in missing key fields and logical errors; existing data verification mechanisms can only perform basic format verification, lacking the ability to deeply verify medical business logic and failing to identify data anomalies in specialized scenarios such as "excessive medication dosage in children," indicating structural defects in data dimensions.

[0009] Existing AI models, due to a lack of training data and outdated technical architecture, can only achieve basic risk control based on rule engines and cannot effectively identify new violations such as "prescription splitting" and "treatment path variation." In the health service sector, the immaturity of multi-source data fusion technology leads to a lack of accurate data support for personalized health intervention plans. Regarding system performance, traditional monolithic architectures and static resource allocation mechanisms cannot cope with the instantaneous concurrency pressure during peak medical insurance settlement periods. Insufficient application of distributed computing technology results in low efficiency in analyzing petabyte-scale historical data, leading to low reliability in medical insurance data management. Summary of the Invention

[0010] To address the technical problem of low reliability in existing medical insurance data management technologies, this invention provides a cloud-based medical insurance data management method and system. The technical solution is as follows:

[0011] On the one hand, a cloud-based medical insurance data management method is provided. This method includes: collecting effective parameters of cross-institutional collaboration in the medical insurance process through distributed access nodes of the cloud platform; obtaining the cross-institutional data collaboration effectiveness rate based on the effective parameters to quantify the effectiveness of cross-institutional data interoperability and sharing; determining whether to perform dynamic optimization of collaboration effectiveness based on the cross-institutional data collaboration effectiveness rate; if yes, conducting medical data quality compliance verification after dynamic optimization; otherwise, directly conducting medical data quality compliance verification. Dynamic optimization of collaboration effectiveness includes a data caching collaboration optimization sub-process and a sharing authorization approval timeliness sub-process; obtaining quality compliance parameters during the medical data quality compliance verification process; obtaining the medical data quality compliance rate based on the quality compliance parameters to quantify the compliance degree of medical data quality with preset standards; determining whether to perform dynamic optimization of quality compliance based on the medical data quality compliance rate; if yes, entering the continuous monitoring stage of medical insurance data quality after dynamic optimization; otherwise, directly entering the continuous monitoring stage of medical insurance data quality. Dynamic optimization of quality compliance includes a flexible expansion trigger threshold sub-process and a granularity sub-process of sharding processing units.

[0012] On the other hand, a cloud-based medical insurance data management system is provided. This system includes: a data collaboration efficiency monitoring module, a data collaboration efficiency optimization module, a data quality compliance monitoring module, and a quality compliance optimization module. The data collaboration efficiency monitoring module collects effective collaboration parameters during the cross-institutional collaboration process of medical insurance through distributed access nodes on the cloud platform. Based on these parameters, it obtains the cross-institutional data collaboration efficiency rate, which quantifies the effectiveness of cross-institutional data interoperability and sharing. The data collaboration efficiency optimization module determines whether to perform dynamic optimization based on the cross-institutional data collaboration efficiency rate. If so, it performs medical data quality compliance verification after dynamic optimization; otherwise, it directly performs medical data quality compliance verification. The system includes a compliance inspection and collaborative dynamic optimization sub-process, which includes data caching collaborative optimization and shared authorization approval timeliness sub-process; a data quality compliance monitoring module, which is used to obtain quality compliance parameters during the medical data quality compliance inspection process, and obtain the medical data quality compliance rate based on the quality compliance parameters, which is used to quantify whether the medical data quality meets the preset standards; and a quality compliance optimization module, which is used to determine whether to perform quality compliance dynamic optimization based on the medical data quality compliance rate. If yes, the system will enter the medical insurance data quality continuous monitoring stage after quality compliance dynamic optimization; otherwise, it will directly enter the medical insurance data quality continuous monitoring stage. Quality compliance dynamic optimization includes an elastic expansion trigger threshold sub-process and a sharding processing unit granularity sub-process.

[0013] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0014] 1. By utilizing distributed access nodes on a cloud platform, effective parameters of cross-institutional collaboration in medical insurance are collected. This enables unified monitoring and data collection across the entire cross-institutional collaboration process, achieving observability and quantitative evaluation of cross-system collaboration performance. This provides precise data support for subsequent optimization decisions. Based on the effective collaboration parameters, the cross-institutional data collaboration efficiency is obtained, quantifying the effectiveness of cross-institutional data sharing. The efficiency of cross-institutional data collaboration determines whether to implement dynamic optimization. This mechanism establishes an intelligent control closed loop for the data collaboration system, automatically triggering optimization processes through quantitative evaluation results. This shifts from passive monitoring to proactive intervention, ensuring cross-institutional collaboration effectiveness. Institutional data exchange remains highly efficient; quality and compliance parameters are acquired during the medical data quality compliance inspection process, transforming subjective data quality assessments into objective, quantifiable compliance parameters. This provides a scientific basis for establishing a data quality measurement system and achieving precise governance. Based on these parameters, a medical data quality compliance rate is obtained, which quantifies whether medical data quality meets preset standards. The compliance rate determines whether dynamic quality compliance optimization should be implemented, and precise optimization strategies are automatically triggered through compliance rate thresholds. This achieves closed-loop management from quality monitoring to root cause governance, continuously improving the usability and reliability of medical data, thereby enhancing the reliability of medical insurance data management.

[0015] 2. The mechanism determines whether to execute a data caching collaborative optimization sub-process based on the duplicate data collection rate of medical insurance data. This mechanism establishes an intelligent correlation between caching strategies and data reuse efficiency. It automatically triggers caching optimization based on the duplicate collection rate, effectively reducing redundant data transmission and system load, and improving the efficiency of medical insurance data flow. The mechanism also determines whether to execute a shared authorization approval timeliness sub-process based on the effective interoperability of medical insurance target data. This mechanism achieves intelligent linkage between business efficiency and approval efficiency. It automatically triggers approval timeliness adjustments based on the effective interoperability, significantly shortening business waiting time while ensuring data security, improving the response speed of medical insurance services, and thus improving the reliability of medical insurance data management.

[0016] 3. By predicting the concurrent processing rate, the mechanism determines whether to execute the elastic expansion trigger threshold sub-process. This mechanism shifts from passive response to proactive prediction, adjusting the expansion strategy in advance by predicting data traffic. This ensures system stability while achieving precise pre-allocation of computing resources and cost optimization. Furthermore, the mechanism determines whether to execute the granular sub-process of sharding processing units based on message queue backlog latency. This mechanism enables intelligent diagnosis and adaptive adjustment of data processing bottlenecks. By dynamically optimizing the sharding strategy through real-time latency monitoring, it finds the optimal balance between computational density and scheduling overhead, continuously ensuring the data throughput efficiency of high-concurrency medical insurance settlement, thereby improving the reliability of medical insurance data management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a cloud-based medical insurance data management method provided in an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of the elastic expansion trigger threshold sub-process of the medical insurance data management method based on a cloud platform provided in this embodiment of the invention;

[0020] Figure 3 This is a flowchart of the granularity of the data fragmentation processing unit in the cloud-based medical insurance data management method provided in this embodiment of the invention.

[0021] Figure 4 This is a schematic diagram of the structure of the medical insurance data management system based on a cloud platform provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0024] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0025] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0026] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0027] This invention provides a cloud-based method for managing medical insurance data, such as... Figure 1 The flowchart shown is for a cloud-based medical insurance data management method. The processing flow of this method may include the following steps:

[0028] As the first step in a cloud-based medical insurance data management approach, this method collects effective parameters of cross-institutional collaboration during the medical insurance process through distributed access nodes on the cloud platform. Based on these parameters, the cross-institutional data collaboration efficiency is calculated, quantifying the effectiveness of cross-institutional data sharing. By deploying distributed access nodes on the cloud platform, a real-time monitoring system for cross-institutional collaboration in medical insurance is constructed. This system continuously collects key collaboration parameters such as data interoperability success rate, response latency, and institutional access coverage, establishing a multi-dimensional evaluation model to calculate the cross-institutional data collaboration efficiency. This quantitative indicator achieves three major technical effects: first, it integrates previously scattered and isolated collaboration status information into a measurable and traceable unified performance indicator; second, it accurately reflects the real-time health of cross-system data flow through a data-driven approach; and third, it provides accurate and reliable judgment criteria for subsequent optimization decisions, fundamentally changing the traditional model that relies on manual experience to evaluate collaboration effectiveness, and realizing standardized management and continuous improvement of medical insurance data sharing efficiency.

[0029] It needs to be explained that the specific steps to obtain the efficiency of cross-institutional data collaboration in medical insurance are as follows: Effective collaboration parameters include the completion rate of defining the responsibilities and rights of medical insurance data, the response latency of medical insurance data interoperability, and the total amount of target data requiring collaboration. Within a preset single evaluation period for cross-institutional data collaboration in medical insurance, the completion rate of defining the responsibilities and rights of medical insurance data is obtained, and its arithmetic mean is calculated as the average completion rate of defining responsibilities and rights. Within a preset single evaluation period for cross-institutional data collaboration in medical insurance, the response latency of each medical insurance data interoperability request is collected, and its arithmetic mean is calculated as the average response latency of transactional data interoperability. Within a preset single evaluation period for cross-institutional data collaboration in medical insurance, the total amount of target data requiring collaboration for each medical insurance item is collected, and its arithmetic mean is calculated as the average total amount of target data for collaboration. The weighted geometric average of the average completion rate of defining responsibilities and rights, the reciprocal of the average response latency of transactional data interoperability, and the average total amount of target data for collaboration is taken to obtain the efficiency of cross-institutional data collaboration in medical insurance. Among them, the completion rate of defining the rights and responsibilities of medical insurance data refers to the proportion of scenarios in which data rights and responsibilities have been clearly defined through pre-setting methods to the total number of collaborative scenarios in the context of cross-institutional collaboration of medical insurance. The response latency for medical insurance data interoperability refers to the average time from when the initiator (e.g., hospital, medical insurance platform) submits a data sharing request to when the recipient (e.g., remote medical insurance platform, local hospital) returns data that can be normally used for medical insurance business processing. The total amount of medical insurance data to be collaboratively requested refers to the total amount of medical insurance data that needs to be shared based on actual business needs (e.g., reimbursement review, remote settlement, fund supervision) in cross-institutional collaboration scenarios within a preset period. The specific constraint expression for the efficiency of cross-institutional data collaboration of medical insurance is as follows:

[0030] ;

[0031] In the formula, Y represents the efficiency of cross-institutional data collaboration in medical insurance, Pavg represents the average completion rate of responsibility definition, Tavg represents the average transaction data interoperability response latency, Xavg represents the average total amount of collaborative target data, v1 represents the completion rate weight obtained from the data management database, v2 represents the interoperability response latency weight obtained from the data management database, and v3 represents the target total amount weight obtained from the data management database.

[0032] It should be understood that the higher the completion rate of defining the rights and responsibilities of medical insurance data, the less "disputes over rights and responsibilities" will hinder the data sharing process. There is no need to temporarily negotiate permissions when requesting data, avoiding the time-consuming process of "initiator requesting - receiver questioning permissions - manual communication confirmation - re-initiating the request", which will reduce the response latency of medical insurance data interoperability. A higher completion rate of defining the rights and responsibilities of medical insurance data means that "the risks of data sharing are controllable", and institutions are more willing to expand the scope of collaboration and the amount of data. The total amount of target data that medical insurance needs to collaborate with is greater. The greater the total amount of target data that medical insurance needs to collaborate with, the higher the load on the transmission channel and the longer the queuing time, which will lead to a longer response latency of medical insurance data interoperability. Meanwhile, there is a positive correlation between the completion rate of defining the responsibilities for medical insurance data and the efficiency of cross-institutional data collaboration in medical insurance. The higher the completion rate of defining the responsibilities for medical insurance data, the more collaborative scenarios (such as cross-regional settlement, fund supervision, and chronic disease management) have clearly defined the "data collection responsibility party, usage permission boundaries, and security accountability subject." Institutions do not need to avoid data sharing due to "ambiguity of responsibilities," and the efficiency of cross-institutional data collaboration in medical insurance is higher. There is a negative correlation between the response latency of medical insurance data interoperability and the efficiency of cross-institutional data collaboration in medical insurance. The longer the response latency of medical insurance data interoperability, the more the data transmission and parsing time exceeds the tolerance threshold of medical insurance business, resulting in data being unusable during the business decision-making window, and the lower the efficiency of cross-institutional data collaboration in medical insurance. There is also a negative correlation between the total amount of target data that medical insurance needs to collaborate on and the efficiency of cross-institutional data collaboration in medical insurance. The larger the total amount of target data that medical insurance needs to collaborate on, the more the data volume will surge, leading to increased data queuing time if bandwidth and server concurrency capacity are not expanded simultaneously, and the lower the efficiency of cross-institutional data collaboration in medical insurance.

[0033] As the second step in the cloud-based medical insurance data management method, the effectiveness of cross-institutional data collaboration is used to determine whether to perform dynamic optimization for effective collaboration. If so, medical data quality compliance verification is conducted after dynamic optimization; otherwise, medical data quality compliance verification is performed directly. Dynamic optimization for effective collaboration includes a data caching collaboration optimization sub-process and a shared authorization approval timeliness sub-process. By establishing a closed loop of intelligent decision-making and optimization based on quantitative assessment, the medical insurance data collaboration system has achieved a leap from "passive response" to "proactive governance." By calculating the effectiveness of cross-institutional data collaboration in real time, the system can automatically and accurately identify collaboration bottlenecks and intelligently trigger targeted dynamic optimization measures: when collaboration efficiency is insufficient, the system executes data caching collaboration optimization (reducing duplicate data transmission and improving flow efficiency by adjusting caching strategies) and shared authorization approval timeliness optimization in parallel (dynamically adjusting the approval process according to business pressure to balance security and efficiency), improving system efficiency from both technical and process dimensions; when collaboration efficiency meets the standard, the system skips the optimization step and directly enters the subsequent quality inspection process, avoiding resource waste. This mechanism not only ensures that system resources are always accurately allocated to the most needed areas, but also builds a complete governance chain of "efficiency monitoring - intelligent decision-making - collaborative optimization - quality control", continuously driving the maximization of medical insurance data collaboration efficiency while ensuring data quality.

[0034] Furthermore, the specific steps for determining whether to perform collaborative and effective dynamic optimization are as follows: compare the cross-institutional data collaboration efficiency of medical insurance with the preset efficiency threshold; if the cross-institutional data collaboration efficiency of medical insurance is lower than the preset efficiency threshold, determine whether to perform the data caching collaborative optimization sub-process based on the medical insurance duplicate collection rate; if so, after performing the data caching collaborative optimization sub-process, determine whether to perform the shared authorization approval timeliness sub-process; if not, directly determine whether to perform the shared authorization approval timeliness sub-process; if the cross-institutional data collaboration efficiency of medical insurance is higher than or equal to the preset efficiency threshold, then collaborative and effective dynamic optimization is not performed.

[0035] As further explained in detail, the specific process of the data caching collaborative optimization sub-process is as follows:

[0036] S101, Monitor and calculate the duplicate data collection rate of medical insurance. The duplicate data collection rate is the proportion of the number of duplicate medical insurance data requests initiated within a preset unit time to the total number of medical insurance data collection requests. This step realizes the quantitative perception of the degree of data redundancy and provides reliable data support for identifying resource waste.

[0037] S102 compares the duplicate medical insurance data collection rate with a reference value and establishes an automated early warning mechanism based on a preset business rationality threshold. This comparison operation transforms raw data into actionable information, providing a clear basis for the system's autonomous decision-making.

[0038] S103. If the duplicate collection rate of medical insurance data is higher than the duplicate collection rate threshold, then based on the duplicate collection rate offset and the efficiency offset, the medical insurance data cache survival time weight factor and the medical insurance data cache capacity weight factor are obtained from the constructed collection rate-cache weight mapping table. The duplicate collection rate offset represents the positive difference between the duplicate collection rate of medical insurance data and the duplicate collection rate threshold. The collection rate-cache weight mapping table stores the combination of the duplicate collection rate offset and the efficiency offset, and the correspondence between them and the medical insurance data cache survival time weight factor and the medical insurance data cache capacity weight factor. The efficiency offset represents the negative difference between the efficiency of cross-institutional data collaboration in medical insurance and the preset efficiency threshold. When the duplicate collection rate is detected to be excessive, the system accurately obtains the adjustment parameters from the pre-trained collection rate-cached weight mapping table based on the dual-dimensional deviation (duplicate collection rate offset + efficiency offset). The duplicate collection rate offset ensures that the optimization intensity is positively correlated with the severity of the problem, and the efficiency offset links the optimization measures with the overall collaborative efficiency, avoiding the impact of local optimization on global performance. Through the joint regulation of dual factors (cached time + capacity), the storage resources are configured in a three-dimensional manner. The final technical solution establishes a complete autonomous closed loop of "monitoring-evaluation-decision-execution", which not only effectively reduces the network load and computing resource waste caused by duplicate collection, but also ensures data access performance through intelligent caching mechanism.

[0039] In this embodiment, the implemented data caching collaborative optimization sub-process constructs a closed-loop control system based on multi-dimensional indicator perception and intelligent decision-making. Its overall technical effect lies in significantly improving the efficiency and resource utilization of medical insurance data flow through precise quantification and dynamic response. Specifically, the system first transforms the data redundancy problem from a qualitative description into a quantifiable management indicator by real-time monitoring and calculation of the medical insurance data duplication rate. Then, through intelligent comparison with preset thresholds, it automatically identifies abnormal states requiring optimization intervention. In the decision-making stage, the system innovatively introduces dual deviations (duplication rate offset and collaborative efficiency offset) as joint query conditions to obtain the optimal caching control parameters from a pre-trained mapping table. This ensures that the optimization strategy can not only specifically address the local problem of data duplication but also take into account the overall efficiency of cross-institutional collaboration in medical insurance, avoiding a decline in global performance due to local optimization. Ultimately, by dynamically adjusting cache lifetime and cache capacity weighting factors, the system achieves precise adaptation and intelligent scheduling of storage resources. This effectively reduces network transmission load and source system pressure, while also ensuring real-time data access by improving cache hit rate. As a result, the medical insurance data collaboration system is driven to operate continuously in an optimal state of high efficiency, stability, and reliability.

[0040] As a further explanation, the data caching collaborative optimization sub-process also includes:

[0041] S104, based on the medical insurance data cache survival time weighting factor, adjust the baseline cache survival time of medical insurance data. Specifically: obtain the baseline cache survival time, scale the baseline cache survival time with the medical insurance data cache survival time weighting factor to obtain the target cache survival time, update the corresponding medical insurance data cache survival time to the target cache survival time, and start a timer corresponding to the target cache survival time; when the timer exceeds the preset timeout, mark the corresponding medical insurance data as expired and trigger a new round of medical insurance data collection and medical insurance data cache update. Dynamically scaling the baseline cache time based on the medical insurance data cache survival time weighting factor transforms the abstract caching strategy into a quantifiable time control parameter. By establishing a timer-driven cache invalidation mechanism, it ensures both the long-term residence of frequently reused data and timely data updates (forced data update is triggered when the timer expires), thereby reducing the data duplication rate while maintaining the timeliness and accuracy of cached data.

[0042] S105, based on the medical insurance data cache capacity weighting factor, adjust the capacity of the dedicated cache pool allocated to frequently requested medical insurance data. Specifically: obtain the current available cache space for medical insurance data; scale the medical insurance data cache capacity weighting factor and the current available cache space to obtain the target expansion capacity of the medical insurance data cache; based on the target expansion capacity, allocate additional medical insurance data cache space from the pre-set public medical insurance cache resource pool, and allocate this additional cache space to the dedicated cache pool corresponding to the frequently requested medical insurance data. Based on the cache capacity weighting factor and the real-time available cache space, elastically expand the dedicated cache pool. This design achieves two key effects: first, the weighting factor ensures that the expansion range accurately matches business needs; second, by dynamically allocating space from the public resource pool, it achieves global optimization and allocation of cache resources within the system, giving priority to frequently requested data and significantly improving cache hit rate and system response speed.

[0043] S106: If the duplicate data collection rate of medical insurance is lower than or equal to the duplicate collection rate threshold, the data caching collaborative optimization sub-process will not be executed. This reflects the system's resource-saving characteristics. When the duplicate collection rate is detected to be within a reasonable threshold, the optimization process is automatically skipped to avoid unnecessary consumption of computational resources and ensure that the system only initiates optimization intervention when necessary.

[0044] In this embodiment, the data caching collaborative optimization sub-process implemented by this technical solution achieves multi-dimensional resource optimization goals by constructing a complete "monitoring-decision-execution" closed-loop control system. Specific technical effects are reflected in the following: the system automatically triggers a hierarchical response mechanism based on real-time calculated duplicate collection rates. When data redundancy is detected, a dual-factor control strategy simultaneously optimizes cache resource configuration in both time and space dimensions—dynamically extending the cache lifetime of high-frequency data based on weight factors to reduce duplicate requests, and intelligently expanding the capacity of the dedicated cache pool to improve the service capability of hot data. Simultaneously, a timer mechanism is introduced to ensure the timeliness of data updates, and a dynamic balance between public and dedicated resources is achieved through resource allocation. This collaborative optimization mode reduces the load on the source system while improving the cache hit rate, and avoids unnecessary resource consumption through an intelligent stop mechanism, ultimately forming an intelligent cache governance system that can adapt to business fluctuations and is both efficient and economical.

[0045] As further detailed, the specific steps for determining whether to execute the shared authorization approval time limit sub-process are as follows:

[0046] This system monitors and calculates the effective data exchange volume of medical insurance target data during the current cross-institutional data collaboration cycle. By calculating the effective data exchange volume of medical insurance target data in real time, the collaboration efficiency is transformed into quantifiable management indicators, providing precise data support for intelligent decision-making.

[0047] If the effective interoperability of the medical insurance target data is less than the lower limit of the interoperability reference, an approval time reduction instruction is generated based on the interoperability offset and the efficiency offset. The target shortened approval time value is retrieved from the constructed interoperability-approval time reduction mapping table according to the generated instruction, and the current medical insurance data sharing authorization approval time is updated to the target approval time value. The interoperability offset represents the negative difference between the effective interoperability of the medical insurance target data and the lower limit of the interoperability reference. Generating the approval time reduction instruction based on a two-dimensional deviation (interoperability offset + efficiency offset) achieves a dual optimization effect: on the one hand, the interoperability offset ensures that the optimization intensity is positively correlated with the degree of lack of actual business needs; on the other hand, the efficiency offset links approval optimization to overall system efficiency. The target shortened approval time value retrieved from the interoperability-approval time reduction mapping table effectively alleviates the data flow congestion problem caused by approval delays by reducing unnecessary waiting time.

[0048] If the effective interoperability of the medical insurance target data exceeds the reference upper limit for interoperability, an approval time extension instruction is generated based on the interoperability correction and the effectiveness offset. The target gain approval time limit value is retrieved from the constructed interoperability-approval time limit mapping table based on this instruction, and the current medical insurance data sharing authorization approval time limit is updated to the target gain approval time limit value. The interoperability correction represents the positive difference between the effective interoperability of the medical insurance target data and the reference upper limit for interoperability. This innovative design, which initiates the approval time extension mechanism based on the interoperability correction and the effectiveness offset, breaks away from the traditional single-speed-up model. By appropriately extending the approval time, it allows reviewers to more fully evaluate high-risk requests, achieving a balance between efficiency and risk control while ensuring data security.

[0049] If the effective interoperability volume of medical insurance target data is within the interoperability reference range, the shared authorization approval timeliness sub-process will not be executed. The interoperability reference range represents the closed interval formed by the lower limit and upper limit of the interoperability reference range. Maintaining a stable approval strategy when the effective interoperability volume of medical insurance target data is within the interoperability reference range prevents unnecessary system adjustments caused by minor fluctuations, effectively improving overall operational efficiency.

[0050] The interoperability-approval timeliness mapping table stores the correspondence between input parameters (composed of interoperability offset and efficiency offset, or interoperability correction and efficiency offset) and the target shortened approval timeliness value and the target increased approval timeliness value. This established interoperability-approval timeliness mapping table serves as the core of the system's intelligent decision-making. By storing the optimal approval timeliness configuration for different business scenarios, it transforms complex business decisions into precise parameter adjustments, avoiding the subjectivity of manual intervention and ensuring the consistency of system response.

[0051] In this embodiment, the shared authorization approval timeliness dynamic control mechanism implemented by this technical solution significantly improves the overall efficiency of medical insurance data sharing by constructing a closed-loop control system of "quantitative monitoring - intelligent decision-making - precise execution" while ensuring data security. Ultimately, this solution achieves dynamic adaptation between the approval process and business needs, forming a continuous self-optimization capability for the optimal solution of data security and circulation efficiency.

[0052] As the third step in the cloud-based medical insurance data management methodology, the quality compliance parameters include the efficiency of cross-institutional data collaboration in medical insurance, the number of non-empty key fields, and the accuracy of the patient master index. The efficiency correction factor and efficiency score are interacted to obtain the efficiency correction coefficient, where the efficiency score represents the result of the analysis of the ratio of cross-institutional data collaboration efficiency to the efficiency reference value. Similarly, the field quantity correction factor and field quantity score are interacted to obtain the field quantity correction coefficient, where the field quantity score represents the result of the analysis of the ratio of non-empty key fields to the field quantity reference value. Finally, the accuracy correction factor and accuracy score are interacted to obtain the accuracy correction coefficient, where the accuracy score represents the result of the analysis of the ratio of the patient master index accuracy to the accuracy reference value. Finally, the efficiency correction coefficient, field quantity correction coefficient, and accuracy correction coefficient are coupled to obtain the medical data quality compliance rate. The number of non-empty key fields refers to the total number of pre-defined key fields (such as patient ID, diagnosis code, surgery date, medication name, etc.) in medical insurance data records that have been filled with valid information and do not contain null values. The patient master index accuracy refers to the ratio of unique and accurately matched patient master index records to the total number of patient master index records. The constraint expression for the medical data quality compliance rate is:

[0053] ;

[0054] In the formula, R represents the cross-institutional data collaboration efficiency of medical insurance, U represents the cross-institutional data collaboration efficiency of medical insurance, U0 represents the efficiency reference value obtained from the data management database, Q represents the number of non-empty key fields, Q0 represents the field number reference value obtained from the data management database, M represents the patient master index accuracy, M0 represents the accuracy reference value obtained from the data management database, w1 represents the efficiency correction factor obtained from the data management database, w2 represents the field number correction factor obtained from the data management database, and w3 represents the accuracy correction factor obtained from the data management database.

[0055] It should be understood that there is a positive correlation between the number of non-empty key fields and the accuracy of the patient master index. The more non-empty key fields there are, the more comprehensive the identity information that can be cross-verified, the smaller the matching error, and the higher the accuracy of the patient master index. There is also a positive correlation between the accuracy of the patient master index and the efficiency of cross-institutional data collaboration in medical insurance. The higher the accuracy of the patient master index, the more accurately patient data from different institutions can be linked through a unique index, resulting in complete and unconfused data retrieval, smooth collaboration processes, and higher efficiency of cross-institutional data collaboration in medical insurance. Finally, there is a positive correlation between the number of non-empty key fields and the efficiency of cross-institutional data collaboration in medical insurance. The more non-empty key fields there are, the more effective cross-institutional collaboration in medical insurance will be due to "precise identity matching," and the higher the efficiency of cross-institutional data collaboration in medical insurance. Meanwhile, there is a positive correlation between the efficiency of cross-institutional data collaboration in medical insurance and the compliance rate of medical data quality. The higher the efficiency of cross-institutional data collaboration in medical insurance, the higher the scores in the dimensions of uniqueness and consistency in the compliance rate, the better the overall compliance rate, and the higher the compliance rate of medical data quality. There is also a positive correlation between the number of non-empty key fields and the compliance rate of medical data quality. The more non-empty key fields there are, the higher the key field filling rate, and the higher the compliance rate of medical data quality. Furthermore, there is a positive correlation between the accuracy of the patient master index and the compliance rate of medical data quality. The higher the accuracy of the patient master index, the more usable, accurate, and consistent the data is in the cross-institutional scenario of medical insurance, and the more it meets the preset compliance standards, thus the higher the compliance rate of medical data quality.

[0056] As the fourth step in the cloud-based medical insurance data management methodology, the decision to perform dynamic quality compliance optimization is based on the medical data quality compliance rate. If so, the process proceeds to the continuous monitoring phase of medical insurance data quality after dynamic quality compliance optimization; otherwise, it directly enters the continuous monitoring phase. Dynamic quality compliance optimization includes sub-processes for triggering elastic expansion thresholds and sub-processes at the granularity of sharding processing units. Using the medical data quality compliance rate as the core criterion, a closed-loop management mechanism of "judgment - optimization (optional) - monitoring" is constructed. This ensures both the accuracy and flexibility of medical insurance data quality control and achieves efficient connection and continuous assurance throughout the entire process. By first determining whether to initiate dynamic quality compliance optimization based on the compliance rate, resource waste caused by ineffective optimization operations can be avoided, ensuring that optimization actions are accurately implemented only for data scenarios that have not met compliance standards. The elastic expansion trigger threshold sub-process included in dynamic quality compliance optimization can dynamically adjust resource allocation thresholds according to actual conditions such as data volume and growth rate, ensuring resource supply and demand matching during data processing and avoiding processing delays due to insufficient resources or redundant consumption due to excess resources. The granularity sub-process of sharded processing units can improve the parallelism and efficiency of data processing by reasonably dividing the granularity of processing units, ensuring that compliance optimization actions are quickly implemented and accurately effective. Regardless of whether dynamic optimization is performed, the final stage is the continuous monitoring stage of medical insurance data quality, which enables full-cycle tracking of data quality, timely capture of quality fluctuations in subsequent data flow, and forms a full-chain technical support of "pre-judgment, in-process optimization, and post-monitoring". This effectively ensures the accuracy, integrity, and compliance of medical insurance data, providing reliable data support for core scenarios such as medical insurance business accounting, auditing, and supervision. At the same time, through process standardization and closed-loop design, the cost of manual intervention is reduced, and the automation and intelligence level of data quality control are improved.

[0057] Furthermore, the specific steps for determining whether to perform dynamic optimization for quality compliance are as follows: compare the medical data quality compliance rate with the preset quality compliance rate threshold; if the medical data quality compliance rate is not lower than the preset quality compliance rate threshold, then dynamic optimization for quality compliance will not be performed; if the medical data quality compliance rate is lower than the preset quality compliance rate threshold, then based on the compliance rate offset, trigger and execute the elastic expansion trigger threshold sub-process and the granularity sub-process of the sharding processing unit, where the compliance rate offset represents the negative difference between the medical data quality compliance rate and the preset quality compliance rate threshold.

[0058] It should be noted that, as Figure 2The diagram shows the flowchart of the elastic scaling trigger threshold sub-process of the cloud-based medical insurance data management method provided in this embodiment of the invention. The specific logic is as follows: Real-time monitoring of the current concurrent medical insurance data processing rate and node resource utilization; calculation of the predicted concurrent processing rate within a preset period using a long short-term memory network model based on the compliance rate offset and the current concurrent medical insurance data processing rate; comparison of the predicted concurrent processing rate with a preset baseline concurrent processing rate; if the predicted concurrent processing rate is greater than the preset baseline concurrent processing rate, an assessment of the potential load pressure level is obtained; if the potential load pressure level is high, then based on the potential load pressure level and node resource utilization... The elastic expansion trigger threshold reduction amount is obtained by querying the existing load-threshold mapping table. The difference between the current elastic expansion trigger threshold and the reduction amount is processed to obtain the target elastic expansion trigger threshold. If the potential load pressure level is low, the elastic expansion trigger threshold increase amount is obtained by querying the existing load-threshold mapping table based on the potential load pressure level and node resource utilization. The current elastic expansion trigger threshold and the increase amount are coupled to obtain the target elastic expansion trigger threshold. If the predicted concurrent processing rate is less than or equal to the preset baseline concurrent processing rate, the elastic expansion trigger threshold subprocess is not executed.

[0059] As further explained in detail, the specific steps for executing the elastic expansion trigger threshold sub-process are as follows:

[0060] Real-time monitoring of current concurrent medical insurance data processing rate and node resource utilization.

[0061] Based on the compliance rate offset and the current concurrent medical insurance data processing rate, a predicted concurrent processing rate within a preset period is calculated using a Long Short-Term Memory (LSTM) network model. This predicted concurrent processing rate is then compared to a preset baseline concurrent processing rate. The introduction of the LTM network model allows it to learn complex temporal patterns in historical data, achieving a fundamental shift from passive response to proactive prediction. Simultaneously, using the "compliance rate offset," a business health indicator, as a predictive factor ensures that the prediction results not only consider traffic volume but also data quality. This effectively avoids interference from invalid traffic caused by a surge in validation failures, improving the accuracy and business relevance of the prediction.

[0062] If the predicted concurrent processing rate exceeds the preset baseline concurrent processing rate, a potential load stress level is assessed, including high and low levels. This comparison operation sets the "start switch" for intelligent decision-making. By comparing the forward-looking predicted value with the performance baseline defined based on the service level agreement, it enables early identification and quantification of system expansion needs, providing a clear basis for taking preventative measures. It also introduces a "tiered assessment" mechanism, enabling refined differentiation of potential risks. This avoids a crude "one-size-fits-all" decision-making approach, allowing the system to distinguish between different scenarios such as "severe overload" and "slight overload," laying the foundation for subsequently developing differentiated and most suitable resource control strategies. This is a key step in achieving precise elasticity.

[0063] If the potential load pressure level is high, the system retrieves the reduction amount of the elastic expansion trigger threshold from the existing load-threshold mapping table based on the potential load pressure level and node resource utilization. The difference between the current elastic expansion trigger threshold and the reduction amount is then calculated to obtain the target elastic expansion trigger threshold. In high-risk scenarios, the system adopts a proactive "prevention is better than cure" strategy. By actively lowering the expansion trigger threshold, it essentially provides more lead time for expansion operations, enabling the system to complete resource expansion before the actual traffic surge arrives. This effectively ensures the continuity and stability of medical insurance services during peak processing periods, preventing service "avalanche."

[0064] If the potential load pressure level is low, the elastic scaling trigger threshold adjustment amount is retrieved from the existing load-threshold mapping table based on the potential load pressure level and node resource utilization. The current elastic scaling trigger threshold and the adjustment amount are then coupled to obtain the target elastic scaling trigger threshold. In low-risk scenarios, this strategy achieves an optimized balance between cost and performance. By appropriately increasing the scaling threshold, the system can tolerate higher resource utilization, suppress unnecessary scaling under slight fluctuations, thereby significantly reducing cloud resource costs and mitigating the service jitter risk caused by frequent scaling, thus improving the overall economy and smoothness of the system.

[0065] The load-threshold mapping table stores the correspondence between the combined input parameters of potential load pressure levels and node resource utilization, and the corresponding adjustments to the elastic scaling trigger thresholds. This mapping table acts as an "intelligent decision engine" for the entire adjustment process. It couples the two key dimensions of "load pressure" and "current resource status," making the threshold adjustment strategy no longer static and fixed, but dynamic, multi-dimensional, and context-aware. For example, even under the same high load, the reduction range will differ depending on whether the current resource utilization is already high or still has surplus, which greatly improves the scientific nature and accuracy of the decision-making.

[0066] If the predicted concurrent processing rate is less than or equal to the preset baseline concurrent processing rate, the elastic scaling trigger threshold sub-process will not be executed. This ensures that the system maintains the silence and stability of the strategy under risk-free or controllable conditions, avoiding unnecessary intervention and threshold adjustments.

[0067] In this embodiment, by real-time monitoring of the current concurrent medical insurance data processing rate and node resource utilization, the system's operational status is instantly perceived, providing dynamic and accurate basic data support for subsequent decision-making. Based on the compliance rate offset and the current concurrent processing rate, a Long Short-Term Memory (LSTM) network model is used to calculate the predicted concurrent processing rate within a preset period. Leveraging its excellent fitting and prediction capabilities for time-series data, the accuracy of rate prediction is significantly improved, providing a reliable basis for forward-looking judgment of load pressure. By comparing the predicted concurrent processing rate with a preset benchmark concurrent processing rate, it is possible to quickly determine whether the system faces potential load pressure, enabling early identification of load risks. The overall process, through real-time monitoring, intelligent prediction, tiered decision-making, and dynamic threshold adjustment, achieves precise and scenario-based adaptation of elastic expansion trigger thresholds, significantly improving the flexibility and resilience of the medical insurance data processing system in dealing with concurrent fluctuations. Simultaneously, the structured association of the load-threshold mapping table ensures the consistency and traceability of adjustment logic, ultimately maximizing resource utilization efficiency and reducing operation and maintenance costs while ensuring efficient and stable processing of medical insurance data.

[0068] It should be noted that, as Figure 3 The diagram shows the granularity sub-flow of the sharding processing unit in the cloud-based medical insurance data management method provided in this embodiment of the invention. The specific logic is as follows: If the message queue backlog latency is greater than the backlog latency threshold, the sharding refinement granularity factor is obtained from the constructed latency-granularity mapping table based on the backlog latency offset and compliance rate offset. The result of the interaction processing between the current sharding processing unit granularity and the sharding refinement granularity factor, rounded up, is used as the target sharding processing unit granularity. If the message queue backlog latency is less than or equal to the backlog latency threshold, the sharding processing unit granularity sub-flow is not executed.

[0069] As further explained in detail, the specific process of the granular sub-process of the fragmentation processing unit is as follows:

[0070] Real-time monitoring of message queue backlog latency and granularity of sharded processing units in the medical insurance settlement data stream enables dual monitoring of the "health" and "processing capacity" of the data processing pipeline. Monitoring message queue backlog latency allows for real-time perception of whether the system is facing processing bottlenecks from a business smoothness perspective, providing a more intuitive reflection of the end-user experience compared to monitoring only basic indicators such as CPU / memory. Simultaneously, tracking the current granularity of sharded processing units provides a crucial decision-making basis for subsequent precise adjustments to computing power that match the current load.

[0071] If the message queue backlog latency exceeds the backlog latency threshold, the sharding refinement granularity factor is retrieved from the constructed latency-granularity mapping table based on the backlog latency offset and compliance rate offset. This two-factor decision-making mechanism ensures that the system can distinguish between pure business surges and bottlenecks caused by data anomalies (such as slowed processing due to numerous incorrect message formats) when dealing with backlogs. This provides a precise basis for selecting the most suitable sharding strategy. The target sharding processing unit granularity is obtained by interacting with the current sharding processing unit granularity and the sharding refinement granularity factor and rounding the result. The backlog latency offset refers to the positive difference between the message queue backlog latency and the backlog latency threshold. This is the core step in performing dynamic resource scheduling. By interacting with the current processing granularity and the retrieved factor (multiplication), macroscopic, coarse-grained data processing tasks are automatically "split" into more microscopic, fine-grained parallel subtasks. The rounding operation ensures that the split granularity is the smallest executable unit in terms of business logic. Its direct effect is that the system can improve the parallelism of its internal processing, break down the accumulated data into smaller parts and divide and conquer them, thus quickly digesting the queue backlog, just like adding tollbooth lanes, and effectively reducing data processing latency.

[0072] If the message queue backlog latency is less than or equal to the backlog latency threshold, the sharding processing unit granularity sub-process will not be executed. This ensures that the system maintains its current efficient and stable processing architecture while running smoothly and meeting performance standards, avoiding any unnecessary sharding operations that may introduce additional overhead (such as task splitting and scheduling overhead), thereby guaranteeing the system's operating efficiency and resource economy under normal load.

[0073] The latency-granularity mapping table stores the correspondence between the combined input of accumulated latency offset and compliance rate offset and the granularity factor for sharding. This mapping table is the "brain" or "strategy center" of the entire dynamic sharding mechanism. It scientifically and predefinedly correlates the two key dimensions of business bottlenecks (latency offset) and data quality (compliance rate offset) with the specific sharding intensity. This makes the system's response no longer rigid or singular, but capable of adopting differentiated and most appropriate sharding strategies based on different emergency situations and business health. For example, when latency is high but the compliance rate is normal, an aggressive sharding may be adopted to quickly recover; while when latency is high and the compliance rate is low, a conservative sharding may be adopted to avoid wasting too much computing power on abnormal data.

[0074] In this embodiment, the solution constructs an intelligent dynamic sharding system based on real-time queue latency and business compliance rate, achieving precise elastic scaling of medical insurance settlement data stream processing capabilities. Its core technological advantage lies in transforming the traditional, static resource allocation model into an integrated adaptive processing model of "perception-decision-execution." Specifically, by monitoring message queue backlog latency in real time, the system can directly and sensitively perceive the occurrence of processing bottlenecks from the perspective of business smoothness. Once latency exceeds the standard, it does not simply perform horizontal scaling, but introduces a two-factor decision engine (latency-granularity mapping table), simultaneously considering the severity of pressure represented by the "backlog latency offset" and the data quality health status reflected by the "compliance rate offset." This allows the system to intelligently distinguish the root cause of the bottleneck—whether it is a pure business surge or a decrease in processing efficiency due to data anomalies—and thus query the most matching sharding refinement granularity factor. Finally, by interacting with the current processing granularity and rounding up the factor, the system automatically and accurately breaks down macroscopic, coarse-grained data processing tasks into more microscopic, fine-grained parallel subtasks. This "divide and conquer" strategy directly and instantly boosts the parallel processing capabilities of the data processing pipeline, rapidly absorbing backlogged data and effectively ensuring high throughput and low latency for medical insurance settlement services under high concurrency scenarios. Simultaneously, when system pressure is within normal thresholds, this mechanism remains silent, avoiding unnecessary data fragmentation overhead and ensuring the system's operational efficiency and resource economy under normal conditions, ultimately achieving an optimal balance between processing performance, cost, and data quality.

[0075] like Figure 4The diagram shows the structure of a cloud-based medical insurance data management system provided in this embodiment of the invention. Specifically, it includes: a data collaboration efficiency monitoring module, a data collaboration efficiency optimization module, a data quality compliance monitoring module, and a quality compliance optimization module. The data collaboration efficiency monitoring module collects effective collaboration parameters during the cross-institutional collaboration process of medical insurance through distributed access nodes on the cloud platform. Based on these parameters, it obtains the cross-institutional data collaboration efficiency rate, which quantifies the effectiveness of cross-institutional data sharing. The data collaboration efficiency optimization module determines whether to perform dynamic optimization based on the cross-institutional data collaboration efficiency rate. If so, it performs medical data quality compliance verification after dynamic optimization; otherwise, it directly performs medical data quality compliance verification. Data quality compliance verification and collaborative dynamic optimization include a data caching collaborative optimization sub-process and a shared authorization approval timeliness sub-process; Data quality compliance monitoring module: used to obtain quality compliance parameters during the medical data quality compliance verification process, and obtain the medical data quality compliance rate based on the quality compliance parameters, used to quantify whether the medical data quality meets the degree of compliance of the preset standards; Quality compliance optimization module: used to determine whether to perform quality compliance dynamic optimization based on the medical data quality compliance rate. If yes, after quality compliance dynamic optimization, it enters the medical insurance data quality continuous monitoring stage; if no, it directly enters the medical insurance data quality continuous monitoring stage. Quality compliance dynamic optimization includes an elastic expansion trigger threshold sub-process and a sharding processing unit granularity sub-process.

[0076] In this embodiment, a closed-loop management architecture integrating "efficiency monitoring, efficiency optimization, compliance monitoring, and compliance optimization" is constructed to achieve intelligent governance and precise efficiency improvement of medical insurance data across the entire cross-institutional collaboration and processing chain. Specifically, the system first uses the data collaboration efficiency monitoring module to quantitatively perceive the effectiveness of cross-institutional data exchange, providing a precise basis for subsequent optimization. Once the collaboration efficiency fails to meet the standard, the data collaboration efficiency optimization module is immediately activated. Through process optimization such as data caching collaboration and shared authorization approval, bottlenecks are broken down at the collaboration level, significantly improving data flow efficiency. Subsequently, the process seamlessly connects to the data quality compliance monitoring module, which performs real-time quality assessment of the data being processed to ensure that it meets preset standards. If a deviation in the compliance rate is detected, the quality compliance optimization module will immediately intervene. By dynamically adjusting the system's underlying resource thresholds (elastic expansion trigger thresholds) and task processing granularity (sharding processing unit granularity), precise intervention is made at the computing power allocation level to ensure that the system has sufficient processing capacity and flexibility to guarantee output quality when facing data surges or quality fluctuations. The closed loop formed by these four modules works together to ultimately achieve adaptive optimization of the entire process from data access and collaborative exchange to quality verification, thereby maximizing the business efficiency and processing throughput of cross-institutional data collaboration while ensuring the compliance of medical data.

[0077] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cloud-based medical insurance data management method, characterized in that: Includes the following steps: By using distributed access nodes on the cloud platform, effective parameters of cross-institutional collaboration in medical insurance are collected. Based on these effective parameters, the efficiency of cross-institutional data collaboration in medical insurance is obtained, which is used to quantify the effectiveness of cross-institutional data interoperability and sharing. The effectiveness of cross-institutional data collaboration in medical insurance is used to determine whether to perform dynamic optimization for effective collaboration. If yes, then after dynamic optimization for effective collaboration, medical data quality compliance inspection is carried out. If no, then medical data quality compliance inspection is carried out directly. The dynamic optimization for effective collaboration includes a data caching collaboration optimization sub-process and a shared authorization approval timeliness sub-process. The quality compliance parameters are obtained during the medical data quality compliance inspection process. Based on the quality compliance parameters, the medical data quality compliance rate is obtained, which is used to quantify whether the medical data quality meets the degree of compliance with the preset standards. The decision on whether to perform dynamic optimization of quality compliance is based on the medical data quality compliance rate. If yes, the medical insurance data quality continuous monitoring stage will be entered after dynamic optimization of quality compliance. If no, the medical insurance data quality continuous monitoring stage will be entered directly. The dynamic optimization of quality compliance includes a sub-process of elastic expansion trigger threshold and a sub-process of granularity of sharding processing unit. The specific steps to obtain the efficiency of cross-institutional data collaboration in medical insurance are as follows: The effective parameters for collaboration include the completion rate of defining the rights and responsibilities of medical insurance data, the response latency of medical insurance data interoperability, and the total amount of target data that medical insurance needs to collaborate on. Within a pre-defined single medical insurance cross-institutional data collaboration evaluation cycle, the completion rate of medical insurance data rights and responsibilities definition is obtained, and its arithmetic mean is calculated as the average rights and responsibilities definition completion rate. Within a preset single medical insurance cross-institutional data collaboration evaluation cycle, the transaction data interoperability response latency of each medical insurance data interoperability request is collected, and its arithmetic mean is calculated as the average transaction data interoperability response latency. Within a pre-set single medical insurance cross-institutional data collaboration evaluation cycle, the total amount of data required for collaboration for each medical insurance item is collected, and its arithmetic mean is calculated as the average total amount of collaborative target data. The weighted geometric average of the average completion rate of responsibility definition, the reciprocal of the average transaction data interoperability response latency, and the average total amount of collaborative target data is used to obtain the efficiency of cross-institutional data collaboration in medical insurance. The specific steps for determining whether to perform collaborative effective dynamic optimization are as follows: The efficiency of cross-institutional data collaboration in medical insurance is compared with a preset efficiency threshold. If the efficiency of cross-institutional data collaboration in medical insurance is lower than the preset efficiency threshold, then it is determined whether to execute the data caching collaboration optimization sub-process based on the medical insurance duplicate collection rate. If yes, then after executing the data caching collaboration optimization sub-process, it is determined whether to execute the shared authorization approval timeliness sub-process. If no, then it is determined directly whether to execute the shared authorization approval timeliness sub-process. If the cross-institutional data collaboration efficiency of medical insurance is higher than or equal to the preset efficiency threshold, then dynamic optimization of collaboration efficiency will not be performed. The specific process of the data caching collaborative optimization sub-process is as follows: S101, Monitor and calculate the duplicate collection rate of medical insurance data, wherein the duplicate collection rate is the ratio of the number of duplicate medical insurance data requests initiated within a preset unit time to the total number of medical insurance data collection requests. S102, compare the duplicate medical insurance collection rate with the reference value for duplicate collection rate; S103, if the duplicate collection rate of medical insurance data is higher than the duplicate collection rate threshold, then based on the duplicate collection rate offset and the efficiency offset, the medical insurance data cache survival time weight factor and the medical insurance data cache capacity weight factor are obtained from the constructed collection rate-cache weight mapping table. The duplicate collection rate offset represents the degree of positive deviation between the duplicate collection rate of medical insurance data and the duplicate collection rate threshold. The collection rate-cache weight mapping table stores the combination of the duplicate collection rate offset and the efficiency offset, and the correspondence between them and the medical insurance data cache survival time weight factor and the medical insurance data cache capacity weight factor. The efficiency offset represents the degree of negative deviation between the efficiency of cross-institutional data collaboration of medical insurance and the preset efficiency threshold.

2. The medical insurance data management method based on a cloud platform according to claim 1, characterized in that, The data caching collaborative optimization sub-process also includes: S104, adjust the baseline cache survival time of medical insurance data according to the weighting factor of medical insurance data cache survival time. Specifically, obtain the baseline cache survival time, scale the baseline cache survival time with the weighting factor of medical insurance data cache survival time to obtain the target cache survival time, update the corresponding medical insurance data cache survival time to the target cache survival time, and start a timer corresponding to the target cache survival time; when the timer exceeds the preset timeout, mark the corresponding medical insurance data as expired and trigger a new round of medical insurance data collection and medical insurance data cache update. S105, adjust the capacity of the dedicated cache pool allocated to high-frequency repeated requests for medical insurance data according to the medical insurance data cache capacity weight factor. Specifically, obtain the current available cache balance of medical insurance data, scale the medical insurance data cache capacity weight factor and the current available cache balance of medical insurance data to obtain the target expansion capacity of medical insurance data cache, and based on the target expansion capacity of medical insurance data cache, allocate additional medical insurance data cache space from the pre-set medical insurance public cache resource pool, and add the additional medical insurance data cache space to the dedicated cache pool corresponding to the high-frequency repeated requests for medical insurance data. S106 If the duplicate collection rate of medical insurance data is lower than or equal to the duplicate collection rate threshold, the data caching collaborative optimization sub-process will not be executed.

3. The medical insurance data management method based on a cloud platform according to claim 1, characterized in that, The specific steps for determining whether to execute the shared authorization approval time limit sub-process are as follows: Monitor and calculate the effective data exchange volume of the medical insurance target during the current medical insurance cross-institutional data collaboration cycle; If the effective interoperability of the medical insurance target data is less than the lower limit of the interoperability reference, an approval time reduction instruction is generated based on the interoperability offset and the effective rate offset. The target shortened approval time value is obtained from the constructed interoperability-approval time reduction mapping table according to the generated approval time reduction instruction, and the current medical insurance data sharing authorization approval time is updated to the target approval time value. The interoperability offset represents the degree of negative deviation between the effective interoperability of the medical insurance target data and the lower limit of the interoperability reference. If the effective interoperability of the medical insurance target data is greater than the upper limit of the interoperability reference, an approval time extension instruction is generated based on the interoperability correction amount and the effective rate offset. The target gain approval time value is obtained from the constructed interoperability-approval time value according to the generated approval time extension instruction, and the current medical insurance data sharing authorization approval time is updated to the target gain approval time value. The interoperability correction amount represents the degree of positive deviation between the effective interoperability of the medical insurance target data and the upper limit of the interoperability reference. If the effective interoperability of medical insurance target data is within the interoperability reference range, the sharing authorization approval timeliness sub-process will not be executed. The interoperability reference range refers to the closed interval formed by the lower limit of the interoperability reference and the upper limit of the interoperability reference. The interoperability-approval timeliness mapping table stores the correspondence between input parameters, which are composed of a combination of interoperability offset and efficiency offset or a combination of interoperability correction and efficiency offset, and the target shortened approval timeliness value and the target increased approval timeliness value.

4. The medical insurance data management method based on a cloud platform according to claim 1, characterized in that, The quality compliance parameters include the efficiency of cross-institutional data collaboration in medical insurance, the number of non-empty key fields, and the accuracy of the patient master index. The efficiency correction factor and the efficiency score are interacted to obtain the efficiency correction coefficient. The efficiency score represents the result of the analysis of the ratio of the efficiency of cross-institutional data collaboration in medical insurance to the efficiency reference value. The field quantity correction factor and the field quantity score are interacted to obtain the field quantity correction coefficient. The field quantity score represents the result of the analysis of the ratio of the number of non-empty key fields to the field quantity reference value. The accuracy correction factor and the accuracy score are interacted to obtain the accuracy correction coefficient. The accuracy score represents the result of the analysis of the ratio of the patient's master index accuracy to the accuracy reference value. By coupling the efficiency correction coefficient, the field quantity correction coefficient, and the accuracy correction coefficient, the medical data quality compliance rate is obtained.

5. The medical insurance data management method based on a cloud platform according to claim 1, characterized in that, The specific steps for determining whether to perform dynamic optimization for quality compliance are as follows: Compare the medical data quality compliance rate with the preset quality compliance rate threshold; If the medical data quality compliance rate is not lower than the preset quality compliance rate threshold, dynamic quality compliance optimization will not be performed. If the medical data quality compliance rate is lower than the preset quality compliance rate threshold, then based on the compliance rate offset, the elastic expansion trigger threshold sub-process and the granularity sub-process of the sharding processing unit are triggered and executed. The compliance rate offset represents the degree of negative deviation between the medical data quality compliance rate and the preset quality compliance rate threshold.

6. The medical insurance data management method based on a cloud platform according to claim 5, characterized in that, The specific steps of the sub-process for executing the elastic expansion trigger threshold are as follows: Real-time monitoring of current concurrent medical insurance data processing rate and node resource utilization; Based on the compliance rate offset and the current concurrent medical insurance data processing rate, the predicted concurrent processing rate within a preset period is calculated using a long short-term memory network model, and the predicted concurrent processing rate is compared with the preset benchmark concurrent processing rate. If the predicted concurrent processing rate is greater than the preset baseline concurrent processing rate, the potential load stress level is evaluated, which includes a high level and a low level. If the potential load pressure level is high, then based on the potential load pressure level and node resource utilization, the elastic expansion trigger threshold reduction amount is obtained from the constructed load-threshold mapping table. The difference between the current elastic expansion trigger threshold and the elastic expansion trigger threshold reduction amount is processed to obtain the target elastic expansion trigger threshold. If the potential load pressure level is low, then based on the potential load pressure level and node resource utilization, the elastic expansion trigger threshold adjustment amount is obtained from the constructed load-threshold mapping table. The current elastic expansion trigger threshold and the elastic expansion trigger threshold adjustment amount are coupled to obtain the target elastic expansion trigger threshold. The load-threshold mapping table stores the combined input parameters of potential load pressure level and node resource utilization, and the correspondence between them and the amount of downward adjustment and upward adjustment of the elastic expansion trigger threshold. If the predicted concurrent processing rate is less than or equal to the preset baseline concurrent processing rate, the elastic scaling trigger threshold sub-process will not be executed.

7. The medical insurance data management method based on a cloud platform according to claim 5, characterized in that, The specific process of the granular sub-process of the segmentation processing unit is as follows: Real-time monitoring of message queue backlog latency and fragmentation unit granularity in medical insurance settlement data stream; If the message queue backlog latency is greater than the backlog latency threshold, then based on the backlog latency offset and compliance rate offset, the sharding refinement granularity factor is obtained from the constructed latency-granularity mapping table. The result of the interactive processing of the current sharding processing unit granularity and the sharding refinement granularity factor is taken as the target sharding processing unit granularity. The backlog latency offset refers to the degree of positive deviation between the message queue backlog latency and the backlog latency threshold. If the message queue backlog latency is less than or equal to the backlog latency threshold, the granular sub-process of the sharding processing unit will not be executed. The latency-granularity mapping table stores the correspondence between the combined input of stacked latency offset and compliance rate offset and the fragmentation refinement granularity factor.

8. A cloud-based medical insurance data management system, wherein the cloud-based medical insurance data management system is used to implement the cloud-based medical insurance data management method as described in any one of claims 1-7, characterized in that, The cloud-based medical insurance data management system includes: a data collaboration efficiency monitoring module, a data collaboration efficiency optimization module, a data quality compliance monitoring module, and a quality compliance optimization module; The data collaboration efficiency monitoring module is used to collect effective collaboration parameters during the cross-institutional collaboration process of medical insurance through distributed access nodes on the cloud platform, and obtain the cross-institutional data collaboration efficiency based on the effective collaboration parameters, which is used to quantify the effectiveness of cross-institutional data interoperability and sharing. The data collaboration efficiency optimization module is used to determine whether to perform collaborative dynamic optimization based on the cross-institutional data collaboration efficiency of medical insurance. If yes, then after collaborative dynamic optimization, medical data quality compliance inspection is performed. If no, then medical data quality compliance inspection is performed directly. The collaborative dynamic optimization includes a data caching collaborative optimization sub-process and a shared authorization approval timeliness sub-process. The data quality compliance monitoring module is used to acquire quality compliance parameters during the medical data quality compliance inspection process, and to obtain the medical data quality compliance rate based on the quality compliance parameters, which is used to quantify whether the medical data quality meets the degree of compliance with preset standards. The quality compliance optimization module is used to determine whether to perform dynamic quality compliance optimization based on the medical data quality compliance rate. If yes, it will enter the continuous monitoring stage of medical insurance data quality after dynamic quality compliance optimization. If no, it will directly enter the continuous monitoring stage of medical insurance data quality. The dynamic quality compliance optimization includes a sub-process for triggering thresholds for elastic expansion and a sub-process for granularity of sharding processing units.

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