Automatic follow-up visit strategy optimization method and system based on big data analysis

Abstract

The invention belongs to the technical field of health management, and provides an automatic follow-up visit strategy optimization method and system based on big data analysis. The method comprises the steps of determining an illness state type, a disease degree and a treatment means of a follow-up visit patient, obtaining disease record big data according to the illness state type, the disease degree and the treatment means, and performing analysis based on the disease record big data to obtain a first follow-up visit strategy; obtaining the medical knowledge level and the compliance evaluation value of the follow-up visit patient, determining a first adjustment coefficient according to the medical knowledge level and the compliance evaluation value, performing optimization adjustment on the first quantity by using the first adjustment coefficient to obtain a second quantity, and adding a plurality of follow-up visit time in the first follow-up visit strategy to obtain a second follow-up visit strategy. The preliminary follow-up visit strategy is automatically generated based on big data analysis, then a more adaptive follow-up visit strategy is formulated based on the medical knowledge level and the compliance evaluation value of the follow-up visit patient, and it is ensured that the follow-up visit patient has a better health recovery degree.

Description

Technical Field

[0001] The present invention relates to the field of health management technology, and in particular to an automatic follow-up strategy optimization method and system based on big data analysis. Background Art

[0002] In the medical field, health follow-up of patients after disease treatment is a crucial link. In the traditional medical model, patients often only seek medical treatment again when they feel obvious discomfort or relapse of the disease after receiving treatment. This model leads to irrational allocation of medical resources and may miss the best time to intervene in the patient's health status in a timely manner. With the advancement of medical technology and the evolution of health management concepts, more and more medical institutions have begun to pay attention to health follow-up after treatment, aiming to improve treatment effects, reduce disease recurrence, and improve patients' quality of life by continuously monitoring the patient's health status and timely discovering and dealing with potential health problems.

[0003] However, existing health follow-up methods mostly rely on manual telephone interviews, patient self-reporting or regular outpatient review, which have problems such as low efficiency, limited coverage, inaccurate data and low patient participation. For example, manual telephone interviews require a lot of human resources and are subject to time constraints; patient self-reporting may omit important information due to lack of professional knowledge; regular outpatient review may bring additional financial burden and time cost to patients, especially for patients in remote areas or with limited mobility, which is even more difficult to achieve.

[0004] Therefore, there is an urgent need for an efficient, convenient and comprehensive health follow-up system that can automatically collect and analyze patients' health data, provide personalized health management suggestions based on the data analysis results, and promote effective communication between doctors and patients to ensure that patients can receive continuous and professional attention and guidance after treatment. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides an automatic follow-up strategy optimization method, system, electronic device, computer storage medium and computer program product based on big data analysis.

[0006] The present invention provides an automatic follow-up strategy optimization method based on big data analysis, the method comprising the following steps: determining the disease type, disease severity and treatment means of a follow-up patient, obtaining disease record big data according to the disease type, disease severity and treatment means, and obtaining a first follow-up strategy based on the disease record big data analysis; wherein the first follow-up strategy includes a first number of follow-up times; obtaining the medical knowledge level and compliance assessment value of the follow-up patient, determining a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, optimizing and adjusting the first number using the first adjustment coefficient to obtain a second number, adding a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein the second follow-up strategy includes the second number of the follow-up times; and performing follow-up operations on the follow-up patients at each of the follow-up times according to the second follow-up strategy.

[0007] In some embodiments, the acquisition of disease record big data based on the disease type, the disease severity and the treatment means, and the analysis of the disease record big data to obtain a first follow-up strategy include: performing preliminary searches in multiple sources according to the disease type, the disease type and the disease severity, and the disease type, the disease severity and the treatment means, respectively, to obtain three preliminary search result numbers, performing weighted summation on the three preliminary search result numbers, and obtaining a target search result number; determining a first target duration according to the target search result number, and performing a formal search in multiple sources according to the first target duration, the disease type, the disease severity and the treatment means to obtain the disease record big data; wherein the first target duration is positively correlated with the target search result number; extracting multiple groups of follow-up record data from the disease record big data, performing data aggregation on each group of the follow-up record data, analyzing to obtain the first number of each of the follow-up times, and generating the first follow-up strategy.

[0008] In some embodiments, the determining of the first adjustment coefficient based on the medical knowledge level and the compliance assessment value includes: obtaining the second adjustment coefficient based on the medical knowledge level and the compliance assessment value; calculating the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and determining the compliance difficulty coefficient based on the ratio of the third number to the second target duration and the first number; and calculating the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

[0009] In some embodiments, the adding of a number of follow-up times to the first follow-up strategy to obtain a second follow-up strategy includes: calculating the difference between the second number and the first number to obtain a fourth number; dividing the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configuring a first percentage, a second percentage, and a third percentage for them respectively, multiplying the fourth number with the first percentage, the second percentage, and the third percentage respectively and rounding them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and increasing the fifth number, the sixth number, and the seventh number of follow-up times in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

[0010] The present invention also discloses an automatic follow-up strategy optimization system based on big data analysis, the system comprising a first acquisition and analysis module, a second acquisition and analysis module, and a follow-up control module; the first acquisition and analysis module is used to determine the disease type, disease severity and treatment means of the follow-up patient, obtain disease record big data according to the disease type, disease severity and treatment means, and obtain a first follow-up strategy based on the disease record big data analysis; wherein the first follow-up strategy includes a first number of follow-up times; the second acquisition and analysis module is used to obtain the medical knowledge level and compliance assessment value of the follow-up patient, determine a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, optimize and adjust the first number using the first adjustment coefficient to obtain a second number, add a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein the second follow-up strategy includes a second number of the follow-up times; the follow-up control module is used to perform follow-up operations on the follow-up patients according to the second follow-up strategy, i.e. at each follow-up time.

[0011] In some embodiments, the first acquisition and analysis module also includes a first analysis module, which is used to: perform preliminary searches in multiple sources according to the disease type, the disease type and the disease severity, the disease type, the disease severity and the treatment methods, respectively, to obtain three preliminary search result numbers, perform weighted summation on the three preliminary search result numbers, and obtain the target search result number; determine a first target duration according to the target search result number, perform a formal search in multiple sources according to the first target duration, the disease type, the disease severity and the treatment methods, and obtain the disease record big data; wherein the first target duration is positively correlated with the target search result number; extract multiple groups of follow-up record data from the disease record big data, perform data aggregation on each group of the follow-up record data, analyze the first number of each of the follow-up times, and generate the first follow-up strategy.

[0012] In some embodiments, the second acquisition and analysis module also includes a second analysis module, which is used to: obtain a second adjustment coefficient based on the medical knowledge level and the compliance assessment value; calculate the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and determine the compliance difficulty coefficient based on the ratio of the third number to the second target duration and the first number; calculate the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

[0013] In some embodiments, the second acquisition and analysis module also includes a third analysis module, which is used to: calculate the difference between the second number and the first number to obtain a fourth number; divide the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configure a first percentage, a second percentage, and a third percentage for them respectively, multiply the fourth number by the first percentage, the second percentage, and the third percentage respectively, and round them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and increase the follow-up time of the fifth number, the sixth number, and the seventh number in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

[0014] The present invention also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0016] The present invention also provides a computer program product, wherein the computer program product includes computer program codes, and when the computer program codes are executed by a processor of an electronic device, the steps of any one of the above methods are implemented.

[0017] The beneficial effects of the present invention are as follows: on the one hand, the above scheme of the present invention obtains the follow-up times required for follow-up based on big data analysis involving similar diseases, without the need for medical staff to manually set the follow-up time. On the other hand, the above scheme of the present invention can also decide whether to increase the number of follow-up visits by analyzing the medical knowledge level and compliance evaluation value of the follow-up patients, thereby achieving timely intervention for those patients who are not sufficiently compliant with the standard treatment method, thereby ensuring that the follow-up patients have a better degree of health recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of an automatic follow-up strategy optimization method based on big data analysis disclosed in an embodiment of the present invention.

[0020] Figure 2 It is a structural schematic diagram of an automatic follow-up strategy optimization system based on big data analysis disclosed in an embodiment of the present invention.

[0021] Figure 3 It is a structural diagram of the first acquisition and analysis module disclosed in an embodiment of the present invention.

[0022] Figure 4 It is a structural diagram of the second acquisition and analysis module disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0024] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0025] like Figure 1 As shown, in view of the above technical problems, an embodiment of the present invention discloses an automatic follow-up strategy optimization method based on big data analysis, the method comprising the following steps: determining the disease type, disease severity and treatment means of the follow-up patient, obtaining disease record big data according to the disease type, disease severity and treatment means, and deriving a first follow-up strategy based on the disease record big data analysis; wherein, the first follow-up strategy includes a first number of follow-up times; obtaining the medical knowledge level and compliance assessment value of the follow-up patient, determining a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, optimizing and adjusting the first number using the first adjustment coefficient to obtain a second number, adding a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein, the second follow-up strategy includes a second number of the follow-up times; and performing follow-up operations on the follow-up patients at each of the follow-up times according to the second follow-up strategy.

[0026] In the above scheme, after the follow-up patient completes the diagnosis and treatment of a disease, the disease record big data is obtained from multiple sources according to the disease type, disease severity and treatment methods received by the follow-up patient. The multiple sources refer to the case database of the medical system, professional medical forums, professional medical journals, etc. Of course, the acquisition of these data requires corresponding authorization. The first follow-up strategy for the follow-up patient can be obtained based on the disease record big data, that is, when the first number of follow-up times is reached, the follow-up of the follow-up patient is started. The follow-up here can be home follow-up, telephone follow-up, APP follow-up, etc., which is not specifically limited in the present invention.

[0027] However, the first follow-up strategy obtained above is based on cases of various different disease conditions, which actually represents the general follow-up strategy that should be adopted. It can only be used as a basic follow-up strategy, and the actual situation of the follow-up patient needs to be considered on this basis. Specifically, the medical knowledge level and compliance evaluation value of the follow-up patient are further obtained. Among them, the medical knowledge level refers to the amount of medical knowledge mastered by the follow-up patient himself, which can be obtained by analyzing and evaluating the educational experience of the follow-up patient, Internet access big data (mainly records of access to disease information and medical knowledge), etc.; and the compliance evaluation value is obtained through evaluation by doctors, patients' families, and the historical compliance evaluation values ​​of the follow-up patient (for example, based on treatment records of other diseases), etc., which is used to characterize the degree of compliance of the follow-up patient with the standard treatment method, such as whether to take medicine on time, change medicine on time, etc. According to the medical knowledge level and the compliance evaluation value, a first adjustment coefficient can be determined, and the first adjustment coefficient is used to optimize and adjust the first quantity in the aforementioned first follow-up strategy to obtain a second quantity.

[0028] Then, the number of follow-up times in the first follow-up strategy is increased from the first number to the second number, thereby obtaining a new second follow-up strategy. At this time, the follow-up operation is performed on the follow-up patients at each follow-up time according to the second follow-up strategy.

[0029] On the one hand, the above scheme of the present invention is based on the big data analysis of similar diseases to obtain the follow-up time required for follow-up, without the need for medical staff to manually set the follow-up time. On the other hand, the above scheme of the present invention can also analyze the medical knowledge level and compliance evaluation value of the follow-up patients to decide whether to increase the number of follow-up visits, thereby achieving timely intervention for those patients who are not compliant with the standard treatment method, thereby ensuring that the follow-up patients have a better degree of health recovery.

[0030] In some embodiments, the acquisition of disease record big data based on the disease type, the disease severity and the treatment means, and the analysis of the disease record big data to obtain a first follow-up strategy include: performing preliminary searches in multiple sources according to the disease type, the disease type and the disease severity, and the disease type, the disease severity and the treatment means, respectively, to obtain three preliminary search result numbers, performing weighted summation on the three preliminary search result numbers, and obtaining a target search result number; determining a first target duration according to the target search result number, and performing a formal search in multiple sources according to the first target duration, the disease type, the disease severity and the treatment means to obtain the disease record big data; wherein the first target duration is positively correlated with the target search result number; extracting multiple groups of follow-up record data from the disease record big data, performing data aggregation on each group of the follow-up record data, analyzing to obtain the first number of each of the follow-up times, and generating the first follow-up strategy.

[0031] In this embodiment, in order to ensure that the first follow-up strategy determined is consistent with the current follow-up treatment method, this embodiment of the present invention is used to determine a reasonable time span for obtaining disease record big data. Specifically, first, according to the three retrieval methods of disease type, disease type and disease degree, disease type and disease degree and treatment means, a preliminary search is performed in a multi-source approach (but no data analysis is performed), and correspondingly, three preliminary search result numbers are obtained respectively, and the preliminary search result number refers to the number of search results obtained by the corresponding search method. Then, the three preliminary search result numbers are weighted and summed to obtain the target search result number. Among them, the weighted coefficients of the retrieval methods "disease type", "disease type and disease degree", "disease type and disease degree and treatment means" are increased in sequence, for example, 0.2, 0.3, and 0.5 respectively.

[0032] After obtaining the number of target search results, it is also necessary to match the first target duration according to the number of target search results. The first target duration is the determined acquisition duration of the disease record big data, that is, the disease record data from the current moment to the corresponding historical moment is obtained from multiple sources. Specifically, when the number of target search results obtained is larger, it means that the type of disease is a common disease. At this time, the first target duration can be set longer, so that disease record big data containing more disease record data can be obtained, which is conducive to analyzing and obtaining a more accurate first follow-up strategy. When the number of target search results obtained is smaller, it means that the type of disease is uncommon or relatively uncommon. At this time, the first target duration can be set shorter, so that only more "recent" disease record big data can be obtained. For "new" diseases, more "recent" disease record big data is more valuable for reference, and the follow-up strategy involved in the early disease record big data may not be perfect and should not be adopted. That is, there is a positive correlation between the first target duration and the number of target search results.

[0033] At this point, we can conduct a formal search in the multi-source pathway according to the first target duration based on the disease type, severity and treatment methods to obtain disease record big data. Then, multiple groups of follow-up record data are extracted from the disease record big data. Each group of follow-up record data corresponds to a case. Each group of follow-up record data includes multiple follow-up times of the same patient, that is, the first follow-up time, the second follow-up time... All Nth follow-up times are aggregated separately, and the corresponding first follow-up aggregation time, second follow-up aggregation time... These follow-up aggregation times are the first number of follow-up times, thereby generating the first follow-up strategy.

[0034] In some embodiments, the determining of the first adjustment coefficient based on the medical knowledge level and the compliance assessment value includes: obtaining a second adjustment coefficient based on the medical knowledge level and the compliance assessment value; calculating a third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and a second target duration of the initial follow-up stage, and determining a compliance difficulty coefficient based on a ratio of the third number to the second target duration and the first number; wherein the initial follow-up stage is manually specified; and obtaining the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

[0035] In this embodiment, the determination of the first adjustment coefficient includes two steps, namely, first evaluating the second adjustment coefficient based on the medical knowledge level and the compliance evaluation value. The evaluation process can be obtained through a preset evaluation rule or model, such as a model obtained by fine-tuning a general large model, or a model constructed based on CNN / RNN. Among them, the higher the medical knowledge level of the follow-up patient, the lower the second adjustment coefficient obtained by the evaluation, which indicates that the follow-up patient has a higher level of medical knowledge, and the higher the probability that the doctor will implement the rehabilitation strategy formulated for this treatment at a high standard; and the lower the medical knowledge level of the follow-up patient, the higher the second adjustment coefficient obtained by the evaluation, which indicates that the follow-up patient has a lower level of medical knowledge, and the lower the probability that the doctor will implement the rehabilitation strategy formulated for this treatment at a high standard. At the same time, the compliance evaluation value obtained based on the statements of others and the actual medical records of the follow-up patient can also directly represent the probability that the follow-up patient will implement the post-treatment rehabilitation strategy formulated by the doctor with high standards. Specifically: the higher the compliance evaluation value of the follow-up patient, the lower the second adjustment coefficient obtained by the evaluation, which means that the follow-up patient always complies with the doctor's advice, and the higher the probability that the follow-up patient will implement the post-treatment rehabilitation strategy formulated by the doctor with high standards; and the lower the compliance evaluation value of the follow-up patient, the higher the second adjustment coefficient obtained by the evaluation, which means that the stronger the habit of the follow-up patient not to comply with the doctor's advice, the lower the probability that the follow-up patient will implement the post-treatment rehabilitation strategy formulated by the doctor with high standards. Therefore, the second adjustment coefficient is negatively correlated with both the medical knowledge level and the compliance evaluation value, and the second adjustment coefficient can be used to change the increment of the follow-up time of the second follow-up strategy compared with the first follow-up strategy.

[0036] In addition, it is also necessary to analyze the difficulty of executing the first follow-up strategy itself, because when the execution difficulty is too high, even people with high medical knowledge levels and compliance assessment values ​​will find it difficult to perform high-standard execution. Therefore, when the first follow-up strategy is generated, the initial follow-up stage is determined by manual designation (for example, by a doctor). The initial follow-up stage is the most critical for health recovery. The more serious the condition, the longer the corresponding initial follow-up stage, and the more follow-up times it contains. On this basis, the present invention is arranged to first calculate the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and then calculate the ratio of the third number to the second target duration, so as to determine the compliance difficulty coefficient (a value greater than 1) based on the ratio and the first number.

[0037] The higher the ratio of the third quantity to the second target duration, the more intensive medication and dressing changes are needed in the initial follow-up phase, which makes it more difficult to implement. The lower the ratio of the third quantity to the second target duration, the less medication and dressing changes are needed in the initial follow-up phase, which makes it easier to implement. It can be seen that there is a positive correlation between the compliance difficulty coefficient and the above ratio.

[0038] At the same time, when the total number of follow-up times included in the first follow-up strategy, i.e., the first number, is larger, the difficulty of following up patients with high standards is greater; when the total number of follow-up times included in the first follow-up strategy, i.e., the first number, is smaller, the difficulty of following up patients with high standards is smaller. It can be seen that the compliance difficulty coefficient is also positively correlated with the above-mentioned first number.

[0039] In some embodiments, the adding of a number of follow-up times to the first follow-up strategy to obtain a second follow-up strategy includes: calculating the difference between the second number and the first number to obtain a fourth number; dividing the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configuring a first percentage, a second percentage, and a third percentage for them respectively, multiplying the fourth number with the first percentage, the second percentage, and the third percentage respectively and rounding them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and increasing the fifth number, the sixth number, and the seventh number of follow-up times in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

[0040] In this embodiment, the difference between the second number and the first number (i.e., the fourth number) is the number of follow-up times that need to be added in the first follow-up strategy, and a reasonable allocation method of the fourth number needs to be determined. In this regard, the present invention divides the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage. Since the treatment of a disease is often more critical in the early stage, more follow-up time is configured in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage, that is, the percentage of the fourth number divided into the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage gradually decreases.

[0041] Among them, the initial follow-up stage, mid-term follow-up stage, and late follow-up stage can all be specified in advance by the doctor or the health management platform, and the details will not be repeated here.

[0042] like Figure 2As shown, an embodiment of the present invention further discloses an automatic follow-up strategy optimization system based on big data analysis, the system comprising a first acquisition and analysis module, a second acquisition and analysis module, and a follow-up control module; the first acquisition and analysis module is used to determine the disease type, disease severity and treatment means of the follow-up patient, obtain disease record big data according to the disease type, disease severity and treatment means, and obtain a first follow-up strategy based on the disease record big data analysis; wherein the first follow-up strategy includes a first number of follow-up times; the second acquisition and analysis module is used to obtain the medical knowledge level and compliance assessment value of the follow-up patient, determine a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, optimize and adjust the first number using the first adjustment coefficient to obtain a second number, add a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein the second follow-up strategy includes a second number of the follow-up times; the follow-up control module is used to perform follow-up operations on the follow-up patients according to the second follow-up strategy, i.e. at each follow-up time.

[0043] In some embodiments, Figure 3 As shown, the first acquisition and analysis module also includes a first analysis module, which is used to: perform preliminary searches in multi-source channels according to the disease type, the disease type and the disease severity, the disease type, the disease severity and the treatment methods, respectively, to obtain three preliminary search result numbers, perform weighted summation on the three preliminary search result numbers, and obtain the target search result number; determine a first target duration according to the target search result number, perform a formal search in multi-source channels according to the first target duration, the disease type, the disease severity and the treatment methods, and obtain the disease record big data; wherein the first target duration is positively correlated with the target search result number; extract multiple groups of follow-up record data from the disease record big data, perform data aggregation on each group of the follow-up record data, analyze the first number of each of the follow-up times, and generate the first follow-up strategy.

[0044] In some embodiments, Figure 4 As shown, the second acquisition and analysis module also includes a second analysis module, which is used to: obtain a second adjustment coefficient based on the medical knowledge level and the compliance assessment value; calculate the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and determine the compliance difficulty coefficient based on the ratio of the third number to the second target duration and the first number; calculate the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

[0045] In some embodiments, the second acquisition and analysis module also includes a third analysis module, which is used to: calculate the difference between the second number and the first number to obtain a fourth number; divide the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configure a first percentage, a second percentage, and a third percentage for them respectively, multiply the fourth number by the first percentage, the second percentage, and the third percentage respectively, and round them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and increase the follow-up time of the fifth number, the sixth number, and the seventh number in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

[0046] The system in this embodiment adopts all the technical solutions of the above embodiments, and therefore has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0047] An embodiment of the present invention further discloses an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0048] An embodiment of the present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0049] An embodiment of the present invention further discloses a computer program product, wherein the computer program product includes computer program codes, and when the computer program codes are executed by a processor of an electronic device, the steps of any one of the above methods are implemented.

[0050] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. An automatic follow-up strategy optimization method based on big data analysis, characterized in that: The method comprises the following steps: determining the disease type, disease severity and treatment methods of the follow-up patient, obtaining disease record big data according to the disease type, disease severity and treatment methods, and obtaining a first follow-up strategy based on the disease record big data analysis; wherein the first follow-up strategy includes a first number of follow-up times; obtaining the medical knowledge level and compliance assessment value of the follow-up patient, determining a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, optimizing and adjusting the first number using the first adjustment coefficient to obtain a second number, adding a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein the second follow-up strategy includes a second number of the follow-up times; and performing follow-up operations on the follow-up patients at each of the follow-up times according to the second follow-up strategy.

2. The automatic follow-up strategy optimization method based on big data analysis according to claim 1, characterized in that: Obtain disease record big data according to the disease type, the disease severity and the treatment means, and derive a first follow-up strategy based on the analysis of the disease record big data, including: performing preliminary searches in multiple sources according to the disease type, the disease type and the disease severity, and the disease type, the disease severity and the treatment means, respectively, to obtain three preliminary search result numbers, performing weighted summation on the three preliminary search result numbers, and obtaining a target search result number; determining a first target duration according to the target search result number, performing a formal search in multiple sources according to the first target duration, the disease type, the disease severity and the treatment means, and obtaining the disease record big data; wherein the first target duration is positively correlated with the target search result number; extracting multiple groups of follow-up record data from the disease record big data, performing data aggregation on each group of the follow-up record data, analyzing to obtain the first number of each of the follow-up times, and generating the first follow-up strategy.

3. The automatic follow-up strategy optimization method based on big data analysis according to claim 2, characterized in that: The first adjustment coefficient is determined according to the medical knowledge level and the compliance assessment value, including: obtaining the second adjustment coefficient according to the medical knowledge level and the compliance assessment value; calculating the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and determining the compliance difficulty coefficient according to the ratio of the third number to the second target duration and the first number; and obtaining the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

4. The automatic follow-up strategy optimization method based on big data analysis according to claim 3 is characterized in that: Adding a number of follow-up times to the first follow-up strategy to obtain a second follow-up strategy includes: calculating the difference between the second number and the first number to obtain a fourth number; dividing the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configuring a first percentage, a second percentage, and a third percentage for them respectively, multiplying the fourth number with the first percentage, the second percentage, and the third percentage respectively and rounding them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and adding the fifth number, the sixth number, and the seventh number of follow-up times to the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

5. An automatic follow-up strategy optimization system based on big data analysis, the system comprising a first acquisition and analysis module, a second acquisition and analysis module, and a follow-up control module; characterized in that: The first acquisition and analysis module is used to determine the type of disease, the degree of disease and the treatment methods of the follow-up patient, obtain disease record big data according to the type of disease, the degree of disease and the treatment methods, and obtain a first follow-up strategy based on the analysis of the disease record big data; wherein, the first follow-up strategy includes a first number of follow-up times; the second acquisition and analysis module is used to obtain the medical knowledge level and compliance assessment value of the follow-up patient, determine a first adjustment coefficient according to the medical knowledge level and the compliance assessment value, use the first adjustment coefficient to optimize and adjust the first number to obtain a second number, add a number of the follow-up times to the first follow-up strategy to obtain a second follow-up strategy; wherein, the second follow-up strategy includes a second number of the follow-up times; the follow-up control module is used to follow up the follow-up patients according to the second follow-up strategy, i.e., at each follow-up time.

6. The automatic follow-up strategy optimization system based on big data analysis according to claim 5, characterized in that: The first acquisition and analysis module further includes a first analysis module, which is used to: perform preliminary search in a multi-source approach according to the disease type, the disease type and the disease degree, the disease type, the disease degree and the treatment means, respectively, to obtain three preliminary search result quantities, and perform weighted summation on the three preliminary search result quantities to obtain a target search result quantity; A first target duration is determined according to the number of target search results, and a formal search is performed in a multi-source channel according to the first target duration, the disease type, the disease severity and the treatment methods to obtain the disease record big data; wherein the first target duration is positively correlated with the number of target search results; multiple groups of follow-up record data are extracted from the disease record big data, data aggregation is performed on each group of the follow-up record data, and the first number of each follow-up time is analyzed to generate the first follow-up strategy.

7. The automatic follow-up strategy optimization system based on big data analysis according to claim 6, characterized in that: The second acquisition and analysis module also includes a second analysis module, which is used to: obtain a second adjustment coefficient based on the medical knowledge level and the compliance assessment value; calculate the third number of each follow-up time belonging to the initial follow-up stage in the first follow-up strategy and the second target duration of the initial follow-up stage, and determine the compliance difficulty coefficient based on the ratio of the third number to the second target duration and the first number; calculate the first adjustment coefficient based on the compliance difficulty coefficient and the second adjustment coefficient.

8. The automatic follow-up strategy optimization system based on big data analysis according to claim 7, characterized in that: The second acquisition and analysis module also includes a third analysis module, which is used to: calculate the difference between the second number and the first number to obtain a fourth number; divide the follow-up stage into an initial follow-up stage, a mid-term follow-up stage, and a late follow-up stage, and configure a first percentage, a second percentage, and a third percentage for them respectively, multiply the fourth number by the first percentage, the second percentage, and the third percentage respectively, and round them to obtain a fifth number, a sixth number, and a seventh number; wherein the first percentage is greater than the second percentage, and the second percentage is greater than the third percentage; and increase the follow-up time of the fifth number, the sixth number, and the seventh number in the initial follow-up stage, the mid-term follow-up stage, and the late follow-up stage respectively.

9. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, characterized in that the processor implements the steps of any one of the methods of claims 1-4 when executing the computer program.

10. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

Images