A multi-source data verification and reach method and system for uninsured personnel

Abstract

The application provides a multi-source data verification and reach method and system for non-insured personnel, and belongs to the technical field of data processing, and specifically comprises the following steps: determining a reach processing strategy of non-insured personnel in a target area according to the similarity of the residential addresses of the non-insured personnel and the difficult-to-visit personnel in the target area, determining the insured change data of the difficult-to-visit personnel in different monitoring periods, determining the distribution data of the non-insured difficult-to-visit personnel in different target addresses and the residential addresses in different data sources when the target area needs to be subjected to optimization processing of the reach processing strategy, and combining the overlap of the residential addresses of the non-insured difficult-to-visit personnel in the target addresses with other target areas to determine the reach optimization processing strategy of the non-insured difficult-to-visit personnel in the target addresses, thereby improving the reliability of the reach processing of the non-insured information.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to a multi-source data verification and reaching method and system for non-insured personnel. BACKGROUND

[0002] In order to realize the identification and reaching processing of the non-insured personnel of medical insurance, the address information field, address word segmentation and code classification of the obtained expandable non-insured data are extracted in the invention patent application CN202410974835.0 "Insured expansion personnel screening and positioning method and system based on multi-source data fusion", the standard address information of the expandable non-insured personnel is obtained, the obtained standard address information is compared and matched with the medical insurance agency information maintained in the local database, the medical insurance agency to which the expandable non-insured personnel belongs is obtained, and the positioning of the insured expansion personnel is completed, but the following technical problems exist:

[0003] In the management of medical insurance, the existing technical solutions often use WeChat group sending, telephone notification and other ways to realize the reaching processing of the non-insured users, and finally realize the reaching processing of the non-insured personnel through unified visiting. Once the initial reaching processing strategy does not match, on the one hand, it may lead to a large number of non-insured personnel, so that the efficiency and difficulty of the visiting processing cannot meet the requirements, and on the other hand, it may lead to the fact that some personnel miss the insurance time due to not obtaining the relevant notification. Therefore, how to realize the targeted optimization processing of the reaching processing strategy of different management areas becomes a technical problem to be solved.

[0004] To solve the above technical problems, the application provides a multi-source data verification and reaching method and system for non-insured personnel. SUMMARY

[0005] To achieve the purpose of the application, the application adopts the following technical solutions:

[0006] Specifically, in a first aspect, the application is a multi-source data verification and reaching method for non-insured personnel, which specifically comprises:

[0007] S1 determining the deviation of the residence addresses of the non-insured personnel in the target area in different data sources based on the analysis results of the multi-source data, and determining the visiting difficult personnel among the non-insured personnel in combination with the interval distance between the residence addresses of different data sources;

[0008] S2 obtaining the number of the visiting difficult personnel in the target area, and determining whether the target area meets the requirement of visiting processing difficulty in combination with the similarity of the residence addresses of different visiting difficult personnel in different data sources, and if so, entering the next step;

[0009] S3 determines the outreach strategy for uninsured persons in the target area based on the similarity of their residential addresses to those of persons with difficulty in being visited. When the outreach strategy for the target area needs to be optimized based on the insurance change data of persons with difficulty in being visited in different monitoring periods, the process proceeds to the next step.

[0010] S4 uses the distribution data of uninsured individuals with difficulty in being visited in different target addresses and their residential addresses in different data sources, and combines the overlap between the residential addresses of uninsured individuals with difficulty in being visited in the target addresses and other target areas to determine the outreach optimization strategy for uninsured individuals with difficulty in being visited in the target addresses.

[0011] The beneficial effects of this invention are as follows:

[0012] By using data on enrollment changes of people with difficulty in accessing insurance during different monitoring periods, it is possible to determine whether the outreach strategy needs to be optimized. This not only takes into account the number of people with difficulty in accessing insurance, but also the changes in the number of people with difficulty in accessing insurance during different monitoring periods. This enables the screening of target areas with decreasing enrollment numbers and low enrollment numbers, and also lays the foundation for further dynamic adjustment of the outreach optimization strategy.

[0013] Based on the distribution data of uninsured individuals with difficulties in reaching out to people in the target address, as well as the overlap between their residential addresses in different data sources, the residential addresses of uninsured individuals with difficulties in reaching out to people in the target address, and other target areas, an optimized outreach strategy for uninsured individuals with difficulties in reaching out to people in the target address was determined. This strategy fully considers the number of uninsured individuals with difficulties in reaching out to people in the target address, the marketing situation regarding the difficulty of screening for individuals with difficulties in reaching out to people in the target address after excluding those who do not have difficulties in reaching out to people in the target address, and the marketing situation regarding the difficulty of screening for individuals in difficulties in reaching out to people in other target areas. This strategy achieves an optimized outreach strategy for uninsured individuals with difficulties in reaching out to people in the target address from multiple perspectives, thereby improving the reliability of the outreach process.

[0014] A further technical solution is that the data source includes pharmacies, hospitals, and medical insurance systems.

[0015] A further technical solution is that the deviation of the residential address of the uninsured person in different data sources includes the number of data sources corresponding to different residential addresses.

[0016] A further technical solution involves identifying individuals among the uninsured persons who are difficult to reach through home visits as follows:

[0017] Based on the discrepancies in the residential addresses of the uninsured individuals across different data sources, uninsured individuals with inconsistent residential addresses across different data sources are identified and designated as target insured individuals.

[0018] The maximum interval distance of the target insured person is determined based on the maximum interval distance between the residential addresses of the target insured person in different data sources;

[0019] Based on the maximum distance between the target insured persons, it is determined whether the uninsured persons are people who are difficult to visit.

[0020] A further technical solution is that when the maximum interval distance of the target insured persons is greater than a preset interval distance threshold, the uninsured persons are determined to be persons who are difficult to visit.

[0021] A further technical solution involves identifying individuals among the uninsured persons who are difficult to reach through home visits as follows:

[0022] Based on the discrepancies in the residential addresses of the uninsured individuals across different data sources, uninsured individuals with inconsistent residential addresses across different data sources are identified and designated as target insured individuals.

[0023] Residential addresses whose distance from other data source residential addresses is greater than a preset distance threshold are used as the discrete addresses of the target insured persons.

[0024] Based on the number of discrete addresses of the target insured persons, it is determined whether the uninsured persons are those who are difficult to visit.

[0025] A further technical solution is that when the number of discrete addresses of the target insured person is greater than a preset threshold for the number of discrete addresses, the uninsured person is determined to be a person who is difficult to visit.

[0026] A further technical solution involves using online notifications to inform all uninsured individuals of their insurance information when optimization of the outreach strategy is required.

[0027] A further technical solution involves determining the method for optimizing the outreach strategy to uninsured individuals with difficulty in making home visits to the target address as follows:

[0028] Based on the distribution data of uninsured individuals with difficulty in visiting the target address, determine the number of uninsured individuals with difficulty in visiting the target address. Based on the proportion of uninsured individuals with difficulty in visiting the target address whose residential addresses exist in other target areas, determine the exclusion reliability coefficient.

[0029] Based on the residential addresses of different uninsured individuals with difficulties in being visited in the target address from different data sources, determine the number of different residential addresses of uninsured individuals with difficulties in being visited, and determine the visitation demand coefficient of different uninsured individuals with difficulties in being visited based on the reciprocal of the number of different residential addresses of uninsured individuals with difficulties in being visited.

[0030] The visitation demand value is determined based on the average of the exclusion reliability coefficient and the visitation demand coefficient of different uninsured people with visitation difficulties in the target address. Based on the number of uninsured people with visitation difficulties and the visitation demand value, the reach optimization processing strategy for uninsured people with visitation difficulties in the target address is determined.

[0031] A further technical solution is that the other target areas are areas other than the target areas.

[0032] A further technical solution involves determining an optimized outreach strategy for uninsured individuals facing difficulties in reaching out to them at the target address, based on the number of such individuals and their outreach needs. This strategy specifically includes:

[0033] When the number of uninsured individuals with difficulty in being visited at the target address exceeds the threshold for the number of uninsured individuals with difficulty in being visited, the optimized outreach strategy for these individuals at the target address is determined to be on-site visits.

[0034] When the number of uninsured individuals with difficulty in being visited at the target address is not greater than the threshold for the number of uninsured individuals with difficulty in being visited, the visitation demand value is used to determine the outreach optimization strategy for the uninsured individuals with difficulty in being visited at the target address.

[0035] A further technical solution involves using the visitation demand value to determine an optimized outreach strategy for uninsured individuals with difficulty in being visited at the target address, specifically including:

[0036] When the visitation demand value of the target address is greater than the preset demand threshold, the optimization strategy for reaching the uninsured and difficult-to-visit individuals at the target address is determined to be on-site visits.

[0037] When the visitation demand value of the target address is not greater than the preset demand threshold, the optimization strategy for reaching uninsured individuals with difficulty in visiting the target address is determined to be based on the on-site visit results of other residential addresses to determine the number of uninsured individuals with difficulty in visiting who do not reside in other residential addresses besides the target address. When the number of uninsured individuals with difficulty in visiting who do not reside in other residential addresses besides the target address is greater than the preset verification number, the optimization strategy for reaching uninsured individuals with difficulty in visiting the target address is determined to be on-site visit processing. Otherwise, there is no need to perform optimization processing for reaching uninsured individuals with difficulty in visiting the target address.

[0038] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for multi-source data verification and outreach to uninsured persons when running the computer program.

[0039] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0042] Figure 1 This is a flowchart of a multi-source data verification and outreach method for uninsured individuals;

[0043] Figure 2 This is a flowchart illustrating the method for identifying individuals with difficulties in visiting uninsured individuals.

[0044] Figure 3 This is a flowchart for determining whether the difficulty of the site visit and processing in the target area meets the requirements;

[0045] Figure 4 It is a flowchart for determining the optimization process of the outreach strategy. Detailed Implementation

[0046] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0047] In this application, by identifying individuals with inconsistent residential addresses across different data sources who are difficult to reach, the strategy for reaching and processing their medical insurance information is optimized based on monitoring changes in their insurance enrollment, thereby improving the enrollment rate.

[0048] Example 1

[0049] like Figure 1As shown, this application provides a multi-source data verification and outreach method for uninsured individuals, specifically including:

[0050] S1 uses the analysis results of multi-source data to determine the deviation of the residential addresses of uninsured persons in the target area in different data sources, and combines the distance between the residential addresses of different data sources to identify the uninsured persons who are difficult to visit.

[0051] Furthermore, the data sources include pharmacies, hospitals, and medical insurance systems.

[0052] Specifically, the discrepancy in the residential address of the uninsured person across different data sources includes the number of data sources corresponding to different residential addresses.

[0053] Specifically, such as Figure 2 As shown, the method for identifying those who are difficult to visit among the uninsured individuals is as follows:

[0054] Based on the discrepancies in the residential addresses of the uninsured individuals across different data sources, uninsured individuals with inconsistent residential addresses across different data sources are identified and designated as target insured individuals.

[0055] The maximum interval distance of the target insured person is determined based on the maximum interval distance between the residential addresses of the target insured person in different data sources;

[0056] Based on the maximum distance between the target insured persons, it is determined whether the uninsured persons are people who are difficult to visit.

[0057] It should be noted that when the maximum interval distance of the target insured persons is greater than the preset interval distance threshold, the uninsured persons are determined to be people who are difficult to visit.

[0058] In another possible embodiment, the method for identifying those uninsured individuals who are difficult to reach is as follows:

[0059] Based on the discrepancies in the residential addresses of the uninsured individuals across different data sources, uninsured individuals with inconsistent residential addresses across different data sources are identified and designated as target insured individuals.

[0060] Residential addresses whose distance from other data source residential addresses is greater than a preset distance threshold are used as the discrete addresses of the target insured persons.

[0061] Based on the number of discrete addresses of the target insured persons, it is determined whether the uninsured persons are those who are difficult to visit.

[0062] Furthermore, when the number of discrete addresses of the target insured person exceeds a preset threshold for the number of discrete addresses, the uninsured person is determined to be a person who is difficult to visit.

[0063] In another possible embodiment, the method for identifying those uninsured individuals who are difficult to reach is as follows:

[0064] Based on the discrepancies in the residential addresses of the uninsured individuals across different data sources, uninsured individuals with inconsistent residential addresses across different data sources are identified and designated as target insured individuals. If an uninsured individual is not among the target insured individuals, then it is determined that the uninsured individual is not considered a person with difficulty in being visited.

[0065] When the aforementioned uninsured individuals are among the target insured individuals:

[0066] When the target insured person has an updated residential address within the most recent preset time period:

[0067] Based on the update time of the residential address of the target insured person, determine the update result of the residential address within the most recent preset time period. When the update results of the residential address within the most recent preset time period are consistent, it is determined that the uninsured person is not a person with difficulty in being visited.

[0068] If the updated residential address results are inconsistent within the most recent preset time period, the uninsured person is determined to be a person with difficulty in being visited.

[0069] When the target insured person does not have an updated residential address within the most recent preset time period:

[0070] Obtain the number of residential addresses of the target insured persons. If the number of residential addresses of the target insured persons does not meet the requirements, then the uninsured persons are determined to be persons who are difficult to visit.

[0071] When the number of residential addresses of the target insured persons meets the requirements:

[0072] Based on the maximum distance between the residential addresses of the target insured persons in different data sources, the maximum distance between the target insured persons is determined. If the maximum distance between the target insured persons does not meet the requirements, the uninsured persons are determined to be people who are difficult to visit.

[0073] When the maximum interval distance of the target insured persons meets the requirements:

[0074] The residential address distribution deviation coefficient of the uninsured person is determined by the distance between different residential addresses and the number of data sources corresponding to different residential addresses. Based on the residential address distribution deviation coefficient, it is determined whether the uninsured person is a person who is difficult to visit.

[0075] It should be noted that when the residential address distribution deviation coefficient is greater than the preset distribution deviation coefficient threshold, the uninsured person is determined to be a person with difficulty in being visited.

[0076] S2 obtains the number of people with difficulty in visiting the target area, and combines the similarity of the residential addresses of different people with difficulty in visiting the target area in different data sources. When it is determined that the difficulty of visiting the target area meets the requirements, proceed to the next step.

[0077] It is understandable that, such as Figure 3 As shown, determining that the difficulty of the visit and processing of the target area meets the requirements specifically includes:

[0078] Based on the similarity of the residential addresses of people with difficulty in being visited in the target area across different data sources, people with difficulty in being visited corresponding to different residential addresses are identified and used as the matched persons for those residential addresses.

[0079] Based on the number of residential addresses of different matched individuals in different data sources, the matching coefficient between the matched individuals and the residential addresses is determined. Based on the sum of the matching coefficients of matched individuals at different residential addresses, the clustering matching coefficient of different residential addresses is determined.

[0080] The difficulty coefficient for visiting individuals in the target area is determined by the ratio of the number of individuals with difficulty in visiting to the average clustering matching coefficient of different residential addresses. Based on the difficulty coefficient, it is determined whether the difficulty of visiting individuals in the target area meets the requirements.

[0081] Furthermore, when the difficulty coefficient of the visit is greater than the preset difficulty coefficient threshold, it is determined that the difficulty of the visit processing in the target area does not meet the requirements.

[0082] In another possible embodiment, determining that the difficulty of visiting and processing the target area meets the requirements specifically includes:

[0083] Based on the similarity of the residential addresses of people with difficulty in being visited in the target area across different data sources, people with difficulty in being visited corresponding to different residential addresses are identified and used as the matched persons for those residential addresses.

[0084] Based on the number of residential addresses of different matched individuals in different data sources, the matching coefficient between the matched individuals and the residential addresses is determined. Based on the sum of the matching coefficients of matched individuals at different residential addresses, the clustering matching coefficient of different residential addresses is determined.

[0085] Based on the clustering matching coefficients of different residential addresses, it is determined whether the difficulty of the visit processing of the target area meets the requirements.

[0086] Furthermore, the matching coefficient between the matched person and the residential address is the reciprocal of the number of residential addresses of the matched person in different data sources.

[0087] It is understandable that when the number of residential addresses exceeds a preset threshold for the number of residential addresses and the average value of the aggregation matching coefficients of different residential addresses is within a preset aggregation allocation coefficient range, it is determined that the difficulty of the visit processing of the target area does not meet the requirements.

[0088] It should be noted that when the difficulty of handling visits to the target area does not meet the requirements, all personnel in the target area will be notified of their insurance information online, and all personnel who are difficult to visit will be notified of their insurance information through offline visits.

[0089] In another possible embodiment, determining that the difficulty of visiting and processing the target area meets the requirements specifically includes:

[0090] The number of people with difficulties in visiting the target area is obtained. When the number of people with difficulties in visiting the target area is greater than a preset threshold for the number of people with difficulties, it is determined that the difficulty of visiting the target area does not meet the requirements.

[0091] When the number of people with difficulty in visiting the target area is not greater than a preset threshold for the number of people with difficulty:

[0092] Based on the similarity of the residential addresses of people with difficulty in being visited in the target area across different data sources, the total number of residential addresses of such people with difficulty in being visited is determined. When the total number of residential addresses of such people with difficulty in being visited is within a preset range, the difficulty of handling visits in the target area is determined to meet the requirements.

[0093] When the total number of residential addresses of the people who are difficult to visit is not within the preset range of address numbers:

[0094] Identify individuals with difficulty in visiting different residential addresses and use them as matching individuals for those addresses. If there are no residential addresses with a number of matching individuals greater than a preset threshold, then the difficulty of visiting the target area is determined to be insufficient.

[0095] When the number of matching individuals for a residential address exceeds a preset threshold for the number of matching individuals:

[0096] Based on the number of residential addresses of different matching personnel in different data sources, the matching coefficient between the matching personnel and the residential address is determined. Based on the sum of the matching coefficients of the matching personnel at different residential addresses, the clustering matching coefficient of different residential addresses is determined. When the clustering allocation coefficients of different residential addresses are all less than the preset clustering allocation coefficient threshold, it is determined that the difficulty of the visit processing of the target area does not meet the requirements.

[0097] When there are residential addresses with a clustering allocation coefficient not less than a preset clustering allocation coefficient threshold:

[0098] The difficulty coefficient for visiting individuals in the target area is determined by the ratio of the number of individuals with difficulty in visiting to the average clustering matching coefficient of different residential addresses. Based on the difficulty coefficient, it is determined whether the difficulty of visiting individuals in the target area meets the requirements.

[0099] Furthermore, the outreach strategy for uninsured individuals is determined based on the number of uninsured individuals with the same residential address who are difficult to reach. When the number of uninsured individuals with the same residential address who are difficult to reach is not greater than the preset value for uninsured individuals, all online notification methods are used to inform the uninsured individuals of their insurance information. When the number of uninsured individuals with the same residential address who are difficult to reach is greater than the preset value for uninsured individuals, only centralized online notification methods are used to inform the uninsured individuals of their insurance information.

[0100] S3 determines the outreach strategy for uninsured persons in the target area based on the similarity of their residential addresses to those of persons with difficulty in being visited. When the outreach strategy for the target area needs to be optimized based on the insurance change data of persons with difficulty in being visited in different monitoring periods, the process proceeds to the next step.

[0101] Specifically, the online notification methods include personalized push notifications from real-time communication software, unified push processing in the user's group, telephone, SMS, and email.

[0102] It is understood that the centralized online notification method refers to real-time communication software uniformly pushing notifications to the user's group.

[0103] It should be noted that the process of informing uninsured individuals about their insurance enrollment information was conducted entirely through online notifications.

[0104] Furthermore, the monitoring period is determined based on a preset unit duration.

[0105] Specifically, such as Figure 4 As shown, the optimization of the outreach strategy needs to be determined, specifically including:

[0106] The number of people with difficulties in accessing insurance was determined by the data on changes in their insurance enrollment during different monitoring periods.

[0107] The amount of change in the number of insured persons between different adjacent monitoring periods is determined based on the number of insured persons, and the monitoring period in which the amount of decrease in the number of insured persons between adjacent monitoring periods is greater than a preset decrease threshold is taken as the abnormal change monitoring period.

[0108] Based on the number of abnormal change monitoring cycles and the number of uninsured individuals who are currently difficult to reach, it is determined whether the outreach strategy needs to be optimized.

[0109] Furthermore, when the number of abnormal change monitoring cycles exceeds the preset threshold for the number of abnormal change dates and the number of uninsured individuals who are currently difficult to reach exceeds the preset threshold for the number of uninsured individuals, it is determined that the outreach processing strategy needs to be optimized.

[0110] Understandably, when there is no need to optimize the outreach strategy, the current outreach strategy will continue to be used to reach different uninsured individuals.

[0111] In another possible embodiment, determining that an optimization of the outreach strategy is needed specifically includes:

[0112] S31 uses the enrollment change data of people with difficulty in visiting insurance in different monitoring periods to determine the number of people with difficulty in visiting insurance in different monitoring periods, and combines the current number of people with difficulty in visiting insurance who are not enrolled and the remaining available enrollment time to determine the contact and processing abnormality coefficient.

[0113] S32 determines the change in the number of insured persons between different adjacent monitoring periods based on the number of insured persons, and takes the monitoring period in which the decrease in the number of insured persons between adjacent monitoring periods is greater than a preset decrease threshold as the abnormal change monitoring period. Based on the number of abnormal change monitoring periods and the change in the number of insured persons between different abnormal change monitoring periods and adjacent monitoring periods, the abnormal coefficient of the change in the number of insured persons is determined.

[0114] S33 determines the outreach strategy matching deviation coefficient based on the change anomaly coefficient and the outreach processing anomaly coefficient, and uses the outreach strategy matching deviation coefficient to determine whether outreach processing strategy optimization is required.

[0115] Furthermore, when the matching deviation coefficient of the reach strategy is greater than the preset matching deviation coefficient threshold, it is determined that the target area needs to undergo optimization of the reach processing strategy.

[0116] Optionally, step S31 above includes the following:

[0117] S311 Obtain the remaining available insurance duration. If the remaining available insurance duration is greater than the preset insurance duration threshold, it is determined that no optimization of the outreach processing strategy is required. If the remaining available insurance duration is not greater than the preset insurance duration threshold, proceed to step S312.

[0118] S312 Obtain the number of uninsured persons currently being visited who are experiencing difficulties. If the number of uninsured persons currently being visited who are experiencing difficulties is less than the preset value for the number of persons experiencing difficulties, it is determined that no optimization of the outreach strategy is required. If the number of uninsured persons currently being visited who are experiencing difficulties is not less than the preset value for the number of persons experiencing difficulties, proceed to step S313.

[0119] S313 When the number of uninsured persons with current difficulties in visiting them does not meet the requirements, it is determined that the outreach processing strategy needs to be optimized. When the number of uninsured persons with current difficulties in visiting them meets the requirements, proceed to step S314.

[0120] S314 uses the enrollment change data of people with difficulty in visiting insurance in different monitoring periods to determine the number of people with difficulty in visiting insurance in different monitoring periods. When the number of people with difficulty in visiting insurance in different monitoring periods is less than the preset enrollment number threshold, it is determined that the outreach processing strategy needs to be optimized. When there is a monitoring period in which the number of enrollments is not less than the preset enrollment number threshold, proceed to step S315.

[0121] S315 When the number of monitoring cycles with an insured number not less than the preset insured number threshold is greater than the preset monitoring cycle number threshold, it is determined that no optimization of the outreach processing strategy is required. When the number of monitoring cycles with an insured number not less than the preset insured number threshold is not greater than the preset monitoring cycle number threshold, proceed to step S316.

[0122] S316 determines the contact processing anomaly coefficient based on the number of people with difficulty in accessing insurance during different monitoring periods, the number of people with difficulty in accessing insurance who are not currently enrolled, and the remaining available enrollment time. If the contact processing anomaly coefficient does not meet the requirements, it is determined that the contact processing strategy needs to be optimized. If the contact processing anomaly coefficient meets the requirements, proceed to step S32.

[0123] Optionally, step S32 above includes the following:

[0124] S321 determines the change in the number of insured persons between different adjacent monitoring periods based on the number of insured persons. When there is no monitoring period in which the number of insured persons decreases more than a preset threshold for the number of insured persons decreases between adjacent monitoring periods, it is determined that no optimization of the outreach processing strategy is required. When there is a monitoring period in which the number of insured persons decreases more than a preset threshold for the number of insured persons decreases between adjacent monitoring periods, the process proceeds to step S322.

[0125] S322 defines the monitoring period in which the number of participants decreases more than a preset threshold between adjacent monitoring periods as the abnormal change monitoring period. When the number of abnormal change monitoring periods does not meet the requirements, it is determined that the outreach processing strategy needs to be optimized. When the number of abnormal change monitoring periods meets the requirements, the process proceeds to step S323.

[0126] S323 determines the abnormal coefficient of the number of insured persons based on the number of abnormal change monitoring cycles and the amount of change in the number of insured persons between different abnormal change monitoring cycles and adjacent monitoring cycles. When the abnormal coefficient of the number of insured persons is greater than the preset abnormal coefficient threshold, it is determined that the reach processing strategy needs to be optimized. When the abnormal coefficient of the number of insured persons is not greater than the preset abnormal coefficient threshold, proceed to step S33.

[0127] Furthermore, when it is necessary to optimize the outreach strategy, all uninsured individuals will be notified of their insurance information through online notification.

[0128] S4 uses the distribution data of uninsured individuals with difficulty in being visited in different target addresses and their residential addresses in different data sources, and combines the overlap between the residential addresses of uninsured individuals with difficulty in being visited in the target addresses and other target areas to determine the outreach optimization strategy for uninsured individuals with difficulty in being visited in the target addresses.

[0129] Specifically, the method for determining the optimized outreach strategy for uninsured individuals with difficulty in making home visits to the target addresses is as follows:

[0130] Based on the distribution data of uninsured individuals with difficulty in visiting the target address, determine the number of uninsured individuals with difficulty in visiting the target address. Based on the proportion of uninsured individuals with difficulty in visiting the target address whose residential addresses exist in other target areas, determine the exclusion reliability coefficient.

[0131] Based on the residential addresses of different uninsured individuals with difficulties in being visited in the target address from different data sources, determine the number of different residential addresses of uninsured individuals with difficulties in being visited, and determine the visitation demand coefficient of different uninsured individuals with difficulties in being visited based on the reciprocal of the number of different residential addresses of uninsured individuals with difficulties in being visited.

[0132] The visitation demand value is determined based on the average of the exclusion reliability coefficient and the visitation demand coefficient of different uninsured people with visitation difficulties in the target address. Based on the number of uninsured people with visitation difficulties and the visitation demand value, the reach optimization processing strategy for uninsured people with visitation difficulties in the target address is determined.

[0133] Furthermore, the other target areas are areas excluding the target areas.

[0134] Additionally, it should be noted that, based on the number of uninsured individuals facing difficulties in home visits and their needs for home visits, an optimized outreach strategy for these individuals at the target addresses is determined, specifically including:

[0135] When the number of uninsured individuals with difficulty in being visited at the target address exceeds the threshold for the number of uninsured individuals with difficulty in being visited, the optimized outreach strategy for these individuals at the target address is determined to be on-site visits.

[0136] When the number of uninsured individuals with difficulty in being visited at the target address is not greater than the threshold for the number of uninsured individuals with difficulty in being visited, the visitation demand value is used to determine the outreach optimization strategy for the uninsured individuals with difficulty in being visited at the target address.

[0137] It is understandable that the strategy for optimizing outreach to uninsured individuals with difficulty in being reached at the target address, determined using the aforementioned visitation demand value, specifically includes:

[0138] When the visitation demand value of the target address is greater than the preset demand threshold, the optimization strategy for reaching the uninsured and difficult-to-visit individuals at the target address is determined to be on-site visits.

[0139] When the visitation demand value of the target address is not greater than the preset demand threshold, the optimization strategy for reaching uninsured individuals with difficulty in visiting the target address is determined to be based on the on-site visit results of other residential addresses to determine the number of uninsured individuals with difficulty in visiting who do not reside in other residential addresses besides the target address. When the number of uninsured individuals with difficulty in visiting who do not reside in other residential addresses besides the target address is greater than the preset verification number, the optimization strategy for reaching uninsured individuals with difficulty in visiting the target address is determined to be on-site visit processing. Otherwise, there is no need to perform optimization processing for reaching uninsured individuals with difficulty in visiting the target address.

[0140] Example 2

[0141] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for multi-source data verification and outreach to uninsured persons when running the computer program.

[0142] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0143] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0144] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A multi-source data verification outreach method for uninsured individuals, characterized in that, Specifically comprising: With the analysis result of the multi-source data, the deviation of the residence addresses of the uninsured personnel in the target region in different data sources is determined, and combined with the interval distance between the residence addresses of different data sources, the difficult-to-visit personnel among the uninsured personnel is determined; The number of the difficult-to-visit personnel in the target region is obtained, and combined with the similarity of the residence addresses of different difficult-to-visit personnel in different data sources, it is determined that when the visit processing difficulty of the target region meets the requirements, the next step is entered; According to the similarity of the residence addresses of the uninsured personnel and the difficult-to-visit personnel in the target region, the touch processing strategy of the uninsured personnel in the target region is determined, and according to the insured change data of the difficult-to-visit personnel in different monitoring periods, it is determined that when the target region needs to be optimized for the touch processing strategy, the next step is entered; With the distribution data of the uninsured difficult-to-visit personnel in different target regions and the residence addresses in different data sources, and combined with the coincidence of the residence addresses of the uninsured difficult-to-visit personnel in the target region and other target regions, the touch optimization processing strategy of the uninsured difficult-to-visit personnel in the target region is determined.

2. The multi-source data validation outreach method for uninsured persons as claimed in claim 1, wherein, The data sources include pharmacies, hospitals and medical insurance systems.

3. The multi-source data validation outreach method for uninsured persons as claimed in claim 1, wherein, The deviation of the residence addresses of the uninsured personnel in different data sources includes the number of data sources corresponding to different residence addresses.

4. The multi-source data validation outreach method for uninsured persons as claimed in claim 1, wherein, The method for determining the difficult-to-visit personnel among the uninsured personnel is: With the deviation of the residence addresses of the uninsured personnel in different data sources, the uninsured personnel with inconsistent residence addresses in different data sources is determined, and it is used as the target insured personnel; According to the maximum value of the interval distance of the residence addresses of the target insured personnel in different data sources, the maximum interval distance of the target insured personnel is determined; Based on the maximum interval distance of the target insured personnel, it is determined whether the uninsured personnel is a difficult-to-visit personnel.

5. The multi-source data verification and outreach method for uninsured persons as claimed in claim 4, wherein, When the maximum interval distance of the target insured personnel is greater than a preset interval distance threshold, the uninsured personnel is determined to be a difficult-to-visit personnel.

6. The multi-source data validation outreach method for uninsured persons as claimed in claim 1, wherein, When the visit processing difficulty of the target region does not meet the requirements, all personnel in the target region are informed of the insured information by using the whole online notification method, and all difficult-to-visit personnel are informed of the insured information by using the offline visit method.

7. The multi-source data validation touch method for uninsured persons of claim 1, wherein, The monitoring period is determined according to a preset unit time length.

8. The multi-source data validation outreach method for uninsured persons as claimed in claim 1, wherein, The method for determining the touch optimization processing strategy of the uninsured difficult-to-visit personnel in the target region is: With the distribution data of the uninsured difficult-to-visit personnel in the target region, the number of the uninsured difficult-to-visit personnel in the target region is determined, and according to the proportion of the number of the uninsured difficult-to-visit personnel with residence addresses in other target regions, the reliable coefficient is determined. According to the different residence addresses of the different uninsured and difficult-to-visit personnel in the target area in different data sources, the number of residence addresses of the different uninsured and difficult-to-visit personnel is determined, and the visit demand coefficient of the different uninsured and difficult-to-visit personnel is determined based on the reciprocal of the number of residence addresses of the different uninsured and difficult-to-visit personnel; Based on the average value of the exclusion reliable coefficient and the visit demand coefficient of the different uninsured and difficult-to-visit personnel in the target area, a visit demand value is determined, and based on the number of uninsured and difficult-to-visit personnel and the visit demand value, a reach optimization processing strategy for the uninsured and difficult-to-visit personnel in the target area is determined.

9. The multi-source data validation touch method for uninsured persons of claim 8, wherein, Based on the number of uninsured and difficult-to-visit personnel and the visit demand value, a reach optimization processing strategy for the uninsured and difficult-to-visit personnel in the target area is determined, specifically including: When the number of uninsured and difficult-to-visit personnel in the target area is greater than the uninsured and difficult person number threshold, the reach optimization processing strategy for the uninsured and difficult-to-visit personnel in the target area is determined to be on-site visit processing; When the number of uninsured and difficult-to-visit personnel in the target area is not greater than the uninsured and difficult person number threshold, the reach optimization processing strategy for the uninsured and difficult-to-visit personnel in the target area is determined using the visit demand value.

10. A computer system comprising: The memory and processor connected in communication, and the computer program stored on the memory and capable of running on the processor, characterized in that the processor executes the computer program to perform the multi-source data verification reach method for uninsured personnel according to any one of claims 1-9.

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