Medical care data sharing method and system

Abstract

The invention relates to the technical field of medical data processing, and provides a medical care data sharing method and system, and the method comprises the steps: obtaining the medical care data of a plurality of patients, and the related data of each medical node; classifying the same type of diagnosis and treatment data of different patients, and quantifying the analysis necessity of each type of diagnosis and treatment data of each patient; according to the time sequence change of each type of diagnosis and treatment data of the patient, the analysis timeliness of each type of diagnosis and treatment data of the patient for each medical node is determined; analyzing a corresponding relation between each medical node and each type of diagnosis and treatment data of the patient, combining analysis necessity and distribution of the medical nodes, performing limitation through analysis timeliness, and determining sharing necessity of each type of diagnosis and treatment data of the patient to each medical node; therefore, the medical care data of a plurality of patients can be shared. The invention aims to solve the problem that sharing and calling among medical institutions are affected due to timeliness difference of medical care data.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to a medical care data sharing method and system. Background Art

[0002] Medical care data includes patients' medical data and nursing data. Medical data mainly includes various types of physiological data covered by patients' medical records and medical information, while nursing data includes various types of physiological data during the patient's post-treatment care process and related data on some of the patient's living habits. By sharing medical care data, multiple departments or medical institutions can analyze the medical data and nursing data of a large number of patients, which is conducive to communication between hospital departments and the assessment of disease risks, and then specify relevant treatment plans and follow-up care plans for different patients.

[0003] In the process of sharing medical care data, the medical care data of patients is usually continuous monitoring of multiple types of physiological data, and the data volume is huge. It is necessary to selectively distribute and store the medical care data according to the needs of each node (department, medical institution) for the medical care data, so as to improve the efficiency of calling the medical care data in the analysis process, and thus improve the efficiency of sharing the medical care data; and the medical care data required by the departments or medical institutions corresponding to different nodes are different, that is, different types of medical care data are required, and different types of medical care data have different timeliness based on the patient's condition. Therefore, it is necessary to avoid the impact of the difference in the timeliness of medical care data on the calling of various types of medical care data by different nodes, thereby reducing the efficiency of sharing medical care data. Summary of the invention

[0004] The present invention provides a medical care data sharing method and system to solve the problem that the existing medical care data is affected by the timeliness difference between medical institutions. The technical solution adopted is as follows: The present invention proposes a medical care data sharing method, which comprises the following steps: Acquire medical care data of several patients and relevant data of each medical node, wherein the medical care data of the patients includes several types of diagnosis and treatment data; Classify the same type of diagnosis and treatment data of different patients, analyze the differences between the classification results of different types of diagnosis and treatment data of the same patient, and quantify the necessity of analyzing different types of diagnosis and treatment data of each patient; determine the timeliness of the analysis of different types of diagnosis and treatment data of each patient for each medical node based on the temporal changes of different types of diagnosis and treatment data of patients and the relevant data of each medical node; Analyze the correspondence between the relevant data of each medical node and the various types of diagnosis and treatment data of patients, combine the necessity of analyzing the various types of diagnosis and treatment data of each patient and the distribution of medical nodes, and limit the timeliness of the analysis of each medical node through the various types of diagnosis and treatment data, and determine the necessity of sharing the various types of diagnosis and treatment data of patients with each medical node; The medical care data of several patients are shared based on the sharing necessity.

[0005] Optionally, the classification of the same type of diagnosis and treatment data of different patients includes the following specific methods: For the diagnosis and treatment data of the corresponding type of physiological data obtained by a one-time test, the diagnosis and treatment data of this type for all patients are digitized and used as representative data of the diagnosis and treatment data of this type for each patient; For the diagnosis and treatment data of the type corresponding to the physiological data obtained by continuous monitoring, the median of the standard range of the diagnosis and treatment data of this type is obtained and used as the standard value of the diagnosis and treatment data of this type. According to the deviation value between the diagnosis and treatment data of this type of any patient and the standard value, the numerical processing result corresponding to the deviation value with the largest absolute value is selected through numerical processing as the representative data of the diagnosis and treatment data of this type of the patient; Density clustering is performed on the representative data of any type of diagnosis and treatment data of all patients. The distance measurement uses the absolute value of the difference between the representative data of the patients to obtain several clusters of diagnosis and treatment data of this type. The cluster with the largest number of representative data among all clusters is used as the standard cluster of diagnosis and treatment data of this type.

[0006] Optionally, the analysis of the differences between the classification results of different types of diagnosis and treatment data of the same patient and quantification of the necessity of analyzing different types of diagnosis and treatment data of each patient include the following specific methods: in, Indicates The patient's The necessity of analyzing various types of medical data, Indicates The number of types of medical data for each patient, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, represents the linear normalization function.

[0007] Optionally, the specific method for obtaining the analysis timeliness of each type of diagnosis and treatment data of the patient for each medical node is as follows: Medical care data from The average time from consultation to discharge of several patients collected by the medical nodes is taken as the first Retention time of medical care data under each medical node; According to the time series changes of various types of diagnosis and treatment data of patients within the retention time of medical care data under each medical node, the analysis timeliness of various types of diagnosis and treatment data of patients for each medical node is obtained.

[0008] Optionally, the specific method of obtaining the analysis timeliness of each type of diagnosis and treatment data of the patient for each medical node based on the time series change of each type of diagnosis and treatment data of the patient within the retention time of the medical care data at each medical node is as follows: From From the first patient visit to the The retention period of medical care data under each medical node ends, and the retention period of the medical care data under the medical node within this period ends. The patient's Type of medical data, recorded as The patient's Types of medical data are in The diagnosis and treatment sequence under each medical node; Count the number of patients who exceed the first Patient No. The number of treatment data in the standard range of the type of treatment data is set, and the last treatment data in the treatment sequence is used as the right boundary of the window, and a sequence is intercepted with the reference window as the first The patient's Types of medical data are in The diagnosis and treatment reference sequence under each medical node; Based on The retention time of medical care data under each medical node, combined with the time that exceeds the first Patient No. The proportion of the standard range of the diagnosis and treatment data of each type, as well as the fluctuation of the diagnosis and treatment data in the diagnosis and treatment reference sequence, are obtained. The patient's Types of medical data for The analysis timeliness of each medical node; the analysis timeliness is positively correlated with the retention time, the proportion and the fluctuation.

[0009] Optionally, the analysis of the correspondence between the relevant data of each medical node and each type of diagnosis and treatment data of the patient is combined with the necessity of analyzing each type of diagnosis and treatment data of each patient and the distribution of the medical nodes, and the analysis timeliness of each type of diagnosis and treatment data for each medical node is limited by each type of diagnosis and treatment data, and the necessity of sharing each type of diagnosis and treatment data of the patient for each medical node is determined, including the specific method of: Based on the corresponding relationship between the relevant data of each medical node and the various types of diagnosis and treatment data of the patient and the necessity of analysis, the necessity of storing the various types of diagnosis and treatment data of the patient for each medical node is obtained; Based on the differences in the timeliness of analysis of various types of medical data for different medical nodes and the distribution of medical nodes, the sharing factors of various types of medical data between different medical nodes are quantified; On the basis of the storage necessity, the sharing factor is analyzed to determine the sharing necessity of each type of diagnosis and treatment data of the patient for each medical node.

[0010] Optionally, the obtaining of the necessity of storing each type of diagnosis and treatment data of the patient for each medical node includes the following specific methods: Based on the inclusion relationship of the patient's diagnosis and treatment data type on the medical node, and the proportion of the diagnosis and treatment data type contained in both the patient and the medical node in the total diagnosis and treatment data type of the patient and the medical node, combined with the necessity of analyzing each type of diagnosis and treatment data of the patient, the necessity of storing each type of diagnosis and treatment data of the patient for each medical node is obtained; the storage necessity is positively correlated with the proportion and the analysis necessity.

[0011] Optionally, the specific method of quantifying the sharing factor of each type of diagnosis and treatment data between different medical nodes includes: Based on the inclusion relationship between the two medical nodes for the same type of diagnosis and treatment data of the patient and the difference in the necessity of analyzing this type of diagnosis and treatment data for the two medical nodes, combined with the spatial distance between the two medical nodes and the proportion of the number of commonly included types of diagnosis and treatment data, the sharing factor of the same type of diagnosis and treatment data of the patient between the two medical nodes is obtained; the sharing factor is negatively correlated with the difference in analysis necessity and the spatial distance, and the sharing factor is positively correlated with the proportion of the number.

[0012] Optionally, the necessity of sharing each type of diagnosis and treatment data of the patient with each medical node, and the specific acquisition method is as follows: First The patient's Taking a type of medical data as an example, the medical nodes whose storage necessity of this type of medical data for all medical nodes is not equal to 0 are arranged in order of storage necessity from small to large, and the node storage sequence of this type of medical data is obtained. The shared necessity of this type of medical data for the first medical node in its node storage sequence is The calculation method is: in, Indicates The patient's The necessity of storing each type of medical treatment data for the first medical node in its node storage sequence. Indicates The patient's The number of medical nodes in the node storage sequence of each type of diagnosis and treatment data, Indicates The patient's For each type of medical treatment data, the first medical node in the node storage sequence and the The sharing factor of medical nodes, Indicates The patient's For each type of medical data, the first The storage necessity of each medical node; represents the weight normalization function; After obtaining the corresponding sharing necessity for the first medical node in the node storage sequence, the sharing necessity corresponding to the second medical node is calculated, wherein the medical node that has been updated from storage necessity to sharing necessity during the calculation process participates in the calculation with sharing necessity; By analogy, we get The patient's The sharing necessity of each type of diagnosis and treatment data for each medical node in its node storage sequence.

[0013] The present invention also proposes a medical care data sharing system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0014] The beneficial effects of the present invention are as follows: the present invention firstly analyzes the deviation of the same type of diagnosis and treatment data to reflect the characteristic performance of the diagnosis and treatment data for the patient's symptoms and quantifies the necessity of the analysis of the diagnosis and treatment data; at the same time, the temporal changes of the diagnosis and treatment data and the retention time of the corresponding medical nodes are analyzed to determine the timeliness of the analysis of the diagnosis and treatment data for each medical node, and provide a basis for judgment on the sharing of the diagnosis and treatment data between subsequent medical nodes; by analyzing the correspondence between the diagnosis and treatment data of various types of patients and the relevant data of the medical nodes, as well as the characteristic performance of the diagnosis and treatment data reflected by the necessity of analysis, the storage relationship of the diagnosis and treatment data in a single medical node is quantified, and then by analyzing the distribution of different medical nodes and combining the differences in the timeliness of the analysis of the diagnosis and treatment data, the transmission relationship of the diagnosis and treatment data of different medical nodes is determined, so as to adjust the storage relationship of the diagnosis and treatment data in the medical nodes and obtain the necessity of sharing, so as to improve the utilization efficiency of the sharing of medical care data; in this way, the diagnosis and treatment data in the patient's medical care data is shared and stored, which effectively improves the communication between the medical nodes and the utilization of their storage space, and at the same time gives full play to the shared transmission relationship between the medical care data, so as to realize the efficient and effective sharing of the medical care data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 A schematic flow chart of a medical care data sharing method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1, which shows a flow chart of a medical care data sharing method provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Obtain medical care data of several patients and relevant data of each medical node.

[0019] The purpose of this embodiment is to realize that during the storage of medical care data, different medical nodes (departments or medical institutions) have different requirements for different types of diagnosis and treatment data of patients. At the same time, during the continuous monitoring of various types of diagnosis and treatment data, there are differences in the analysis timeliness of each medical node. Therefore, it is necessary to obtain the patient's medical care data, including continuous monitoring of various types of physiological data in the patient's medical records and treatment records, and various types of care data composed of living habits during the patient's follow-up care. At the same time, it is necessary to record in which department the patient's medical care data is collected, as well as the physiological data and nursing data required by each department (medical institution).

[0020] Specifically, after a patient is admitted to the hospital for treatment, multiple types of data collected from his or her medical records, treatment records, and post-treatment nursing process together constitute the patient's medical care data; the personal information in the medical records includes multiple types of data, which are directly used as the patient's multiple types of diagnosis and treatment data, and because the personal information will not change, in the subsequent processing process, the retention time of the multiple types of diagnosis and treatment data corresponding to the personal information is processed at the maximum retention time; and in the medical records and treatment records, the patient is required to test multiple types of physiological data in the corresponding department for treatment, and the frequency of the test is obtained according to the collection frequency of the corresponding type of physiological data, and the multiple types of physiological data are used as the patient's multiple types of diagnosis and treatment data; in addition to the various types of physiological data continuously monitored in the medical records and treatment records, the post-treatment nursing process also includes the patient's diet type (liquid food, infusion, normal diet, taboos, etc., which are presented as data through classification and labeling), toilet-related records, etc., all of which are used as the patient's multiple types of diagnosis and treatment data, so as to obtain multiple types of diagnosis and treatment data in the patient's medical care data.

[0021] Furthermore, multiple hospitals and medical institutions jointly share medical care data to conduct medical care big data analysis and joint consultations, and each department and medical institution in each hospital is regarded as a medical node. Based on the departments and medical institutions corresponding to each medical node, they obtain the types of diagnosis and treatment data they need, that is, the types of diagnosis and treatment data of patients needed by the departments or medical institutions in the diagnosis and treatment process, and use them as relevant data for each medical node.

[0022] It should be noted that different departments have different retention times for the same type of medical data, which is affected by the correspondence between the physical signs reflected by each type of medical data and the department; for example, compared with general departments, the emergency department needs to monitor physiological data that can more intuitively reflect the patient's physical condition, such as blood pressure, blood oxygen, body temperature and other physiological data, while different departments need to monitor more professional physiological data on the basis of conventional physiological data. At the same time, compared with general departments, the emergency department retains patients' medical care data for a shorter time, while general departments require long-term monitoring, so as to effectively share medical care data, improve the effective use of patients' medical care data, and the efficiency of each medical node in calling medical care data.

[0023] Step S002: classify the same type of diagnosis and treatment data of different patients, analyze the differences between the classification results of different types of diagnosis and treatment data of the same patient, and quantify the necessity of analyzing each type of diagnosis and treatment data of each patient; based on the temporal changes of each type of diagnosis and treatment data of the patient and combined with the relevant data of each medical node, determine the timeliness of the analysis of each type of diagnosis and treatment data of the patient for each medical node.

[0024] Preferably, in one embodiment of the present invention, the same type of diagnosis and treatment data of different patients are classified, the differences between the classification results of different types of diagnosis and treatment data of the same patient are analyzed, and the necessity of analyzing the diagnosis and treatment data of each type of each patient is quantified, including the specific method of: It should be noted that under normal circumstances, the values ​​of the same physiological indicator are close. The larger the deviation, the more it can reflect certain physiological characteristics or disease characteristics of the patient. Therefore, by clustering the same type of diagnosis and treatment data (physiological indicators), and based on the clustering results, combined with the changes in the patient's belonging clusters in different types of diagnosis and treatment data, the greater the deviation compared to the normal cluster (the cluster with the largest number of data points in the same type of diagnosis and treatment data), and the greater the change in the belonging clusters of different types of diagnosis and treatment data of the same patient, the more likely the corresponding type of diagnosis and treatment data is to be the manifestation of certain disease symptoms, and accordingly, it is more necessary to analyze it.

[0025] Specifically, each type of diagnosis and treatment data of patients is divided into physiological data obtained by a one-time test and physiological data obtained by continuous monitoring; for the diagnosis and treatment data of the type corresponding to the physiological data obtained by a one-time test, the diagnosis and treatment data of this type for all patients are digitized and used as the representative data of the diagnosis and treatment data of this type for each patient; and for the diagnosis and treatment data of the type corresponding to the physiological data obtained by continuous monitoring, the median of the standard range of the diagnosis and treatment data of this type is obtained and used as the standard value of the diagnosis and treatment data of this type, and the standard value is subtracted from the diagnosis and treatment data of this type for any patient to obtain several deviation values ​​of the diagnosis and treatment data of this type for the patient (the difference is obtained by subtracting the standard value from the patient's data), the deviation values ​​of the diagnosis and treatment data of this type for all patients are digitized, and the digitization result corresponding to the deviation value with the largest absolute value among the several deviation values ​​of the diagnosis and treatment data of this type for the patient is used as the representative data of the diagnosis and treatment data of this type for the patient.

[0026] Furthermore, taking any type of medical data as an example, DBSCAN clustering is performed on the representative data of this type of medical data of all patients (if the patient does not have this type of medical data, no clustering is performed), and the distance measurement uses the absolute value of the difference between the patient's representative data to obtain several clusters of this type of medical data; the cluster with the largest number of representative data among all clusters is used as the standard cluster of this type of medical data.

[0027] Furthermore, for the The patient's Types of medical data and their necessity for analysis The calculation method is: in, Indicates The number of types of medical data for each patient, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, Represents a linear normalization function, and the normalized object is the Patients except All other types of medical data .

[0028] It should be noted that the greater the difference between the representative data and the standard cluster, and the greater the difference between the representative data and the standard cluster compared with other types, the more this type of diagnosis and treatment data can reflect the patient's disease symptoms, and the more necessary it is for analysis; at the same time, the difference in the degree of outliers of the representative data of different types of diagnosis and treatment data in their corresponding clusters is used as a weight. The greater the corresponding outlier degree of this type compared with the corresponding outlier degree of other types, the higher the credibility of the deviation between the corresponding representative data and the standard cluster. Conversely, the smaller it is, or even smaller than the corresponding outlier degree of other types, the weaker the deviation between the corresponding representative data and the standard cluster reflects the characteristics of the disease symptoms, and the smaller the corresponding weight.

[0029] Preferably, in one embodiment of the present invention, according to the time series changes of various types of diagnosis and treatment data of patients, combined with the relevant data of each medical node, the analysis timeliness of various types of diagnosis and treatment data of patients for each medical node is determined, and the specific method includes: It should be noted that each type of diagnosis and treatment data of patients is time-sensitive for each medical node, that is, each medical node sets a different retention time for each type of diagnosis and treatment data of patients, which depends on the correspondence between the type of diagnosis and treatment data and the relevant data of the medical node. The more the corresponding type of diagnosis and treatment data can reflect the characteristics of the symptoms covered by the relevant medical node, the stronger the correspondence, and the greater the impact on timeliness. At the same time, the time series changes of the corresponding type of diagnosis and treatment data of the physiological data obtained by continuous monitoring also have a certain timeliness. The deviation of the diagnosis and treatment data exceeds the standard range of the corresponding type of diagnosis and treatment data, and the diagnosis and treatment data fluctuates greatly, and its analysis is more difficult. When the fluctuation gradually decreases and the deviation is within the standard range, after a period of time, the corresponding type of diagnosis and treatment data tends to be normal, and its analysis is reduced. Combined with the correspondence between the type of diagnosis and treatment data and the medical node, the timeliness is quantitatively analyzed.

[0030] Specifically, Take the medical node as an example, the medical care data is sent from the Several patients collected by the medical node, that is, The medical node corresponds to a number of patients who visit the department or medical structure, and the average time from visit to discharge is taken as the first The retention time of medical care data under each medical node.

[0031] Furthermore, a reference window is preset. In this embodiment, the reference window is described by taking the length of 10 diagnosis and treatment data as an example; Patient No. For example, from the first From the first patient visit to the The retention time of medical care data under each medical node ends (a period of time is obtained by starting time and a duration), and the retention time of the medical care data under each medical node within this period of time is The patient's Type of medical data, recorded as The patient's Types of medical data are in The diagnosis and treatment sequence under each medical node; it should be noted that Patient No. The type of diagnosis and treatment data is the type of physiological data that is continuously monitored; the number of patients who exceed the first Patient No. The number of treatment data in the standard range of the type of treatment data is set, and the last treatment data in the treatment sequence is used as the right boundary of the window, and a sequence is intercepted with the reference window as the first The patient's Types of medical data are in The diagnosis and treatment reference sequence under each medical node.

[0032] Furthermore, based on The retention time of medical care data under each medical node, combined with the time that exceeds the first Patient No. The proportion of the standard range of the diagnosis and treatment data of each type, as well as the fluctuation of the diagnosis and treatment data in the diagnosis and treatment reference sequence, are obtained. The patient's Types of medical data for The analysis timeliness of each medical node.

[0033] As an example, The patient's Types of medical data for Analysis timeliness of medical nodes The calculation method is: in, Indicates The retention time of medical care data under each medical node, Indicates The patient's Types of medical data are in The number of diagnosis and treatment data in the diagnosis and treatment sequence under the medical node, Indicates that the diagnosis and treatment sequence exceeds the Patient No. The number of medical data within the standard range for each type of medical data, Indicates The patient's Types of medical data are in The variance of all diagnosis and treatment data in the diagnosis and treatment reference sequence under the medical node; represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation; is a linear normalization function, and the normalization object is the retention time of medical care data under each medical node; in particular, if any type of diagnosis and treatment data of a patient is obtained once, then its analysis timeliness for the corresponding medical node is the normalized result of the retention time of the medical care data under the corresponding node, with the first For example, a medical node .

[0034] It should be noted that in the process of quantifying the timeliness of analysis, the longer the retention time of the medical node itself, the greater the deviation of the patient's diagnosis and treatment data during the retention time, and the longer the duration of abnormal data monitoring, the more analytical value the corresponding type of diagnosis and treatment data has, and the greater the timeliness of analysis. At the same time, if the last segment of diagnosis and treatment data in the diagnosis and treatment sequence still fluctuates greatly, that is, the larger the variance, the analytical value of the corresponding type of diagnosis and treatment data will still be improved, and the timeliness of analysis will be greater.

[0035] So far, we first analyze the deviations of the same type of medical data to reflect the characteristic manifestations of the medical data for the patient's symptoms and quantify the necessity of analyzing the medical data. At the same time, we analyze the temporal changes of the medical data and its retention time at the corresponding medical nodes to determine the timeliness of the analysis of the medical data for each medical node, providing a basis for judgment on the sharing of medical data between subsequent medical nodes.

[0036] Step S003, analyze the correspondence between the relevant data of each medical node and the various types of diagnosis and treatment data of the patient, combine the necessity of analyzing the various types of diagnosis and treatment data of each patient and the distribution of medical nodes, and limit the timeliness of the analysis of each medical node through each type of diagnosis and treatment data, and determine the necessity of sharing the various types of diagnosis and treatment data of the patient with each medical node.

[0037] Preferably, in one embodiment of the present invention, the specific method included in this step is: Based on the corresponding relationship between the relevant data of each medical node and the various types of diagnosis and treatment data of the patient and the necessity of analysis, the necessity of storing the various types of diagnosis and treatment data of the patient for each medical node is obtained; Based on the differences in the timeliness of analysis of various types of medical data for different medical nodes and the distribution of medical nodes, the sharing factors of various types of medical data between different medical nodes are quantified; On the basis of the storage necessity, the sharing factor is analyzed to determine the sharing necessity of each type of diagnosis and treatment data of the patient for each medical node.

[0038] As an example, based on the correspondence between the relevant data of each medical node and each type of diagnosis and treatment data of the patient and the necessity of analysis, the necessity of storing each type of diagnosis and treatment data of the patient for each medical node is obtained, including the specific method of: It should be noted that the necessity of analyzing various types of diagnosis and treatment data of patients itself reflects the characteristic manifestations of the corresponding types of diagnosis and treatment data for the patient's disease and symptoms. On this basis, if the relevant data of each medical node contains the corresponding type of diagnosis and treatment data, and the closer the patient's other types of diagnosis and treatment data are to the relevant data of the corresponding medical node, the more the medical node needs to store the patient's corresponding type of diagnosis and treatment data in order to analyze the patient's relevant medical care data and enrich the patient database sample of the medical institution or department, thereby obtaining the necessity of storage.

[0039] Specifically, based on the inclusion relationship of the patient's diagnosis and treatment data type on the medical node, and the proportion of the diagnosis and treatment data type contained in both the patient and the medical node in the total diagnosis and treatment data type of the patient and the medical node, combined with the necessity of analyzing each type of diagnosis and treatment data of the patient, the necessity of storing each type of diagnosis and treatment data of the patient for each medical node is obtained.

[0040] As an example, The patient's Types of medical data and Take the medical node as an example. The medical care data of each patient includes Types of medical data, The relevant data of medical nodes include Types of medical data, then The patient's Types of medical data for Storage necessity of medical nodes The calculation method is: in, Indicates The patient's The necessity of analyzing various types of medical data, Indicates The medical nodes include The patient's Parameters of the diagnosis and treatment data of each type, if included , otherwise it does not include ; Indicates The patient and The number of types of diagnosis and treatment data contained in each medical node, Indicates The number of types of diagnosis and treatment data included in the relevant data of each medical node, Indicates The number of types of diagnosis and treatment data contained in the medical care data of each patient.

[0041] It should be noted that, during the storage necessity analysis process, if the patient's medical data of this type is not included in the corresponding medical node, the storage necessity is directly 0, that is, no storage analysis is required; and the more similar the medical data contained in the patient's medical care data is to the medical data contained in the relevant data of the medical node, that is, the greater the proportion of both included, and the greater the necessity of analyzing the medical data itself, the greater the corresponding storage necessity, and the more the corresponding node needs to store the medical data for analysis.

[0042] It should be noted that the necessity of storage refers to the characteristics of whether various types of medical data of a single medical node are stored, and the sharing of medical care data can ensure data transmission between multiple medical nodes. It is necessary to analyze the distribution between medical nodes and the similarity between related data, which can reflect the data transmission relationship between different medical nodes; at the same time, there are differences in the timeliness of analysis of the same type of medical data of the same patient by different medical nodes. The greater the difference in analysis timeliness, the more it will interfere with the normal data transmission relationship, that is, the corresponding type of medical data in a medical node is close to losing timeliness, and the role of other medical nodes in sharing and analyzing this type of medical data will become worse, and the sharing factor will be reduced on the basis of the transmission relationship.

[0043] As an example, based on the differences in the timeliness of analysis of various types of medical data for different medical nodes and combined with the distribution of medical nodes, the sharing factors of various types of medical data for different medical nodes are quantified, including the following specific methods: Specifically, based on the inclusion relationship between the two medical nodes for the same type of medical data for the patient and the difference in the necessity of analyzing this type of medical data for the two medical nodes, combined with the spatial distance between the two medical nodes and the proportion of the commonly included types of medical data, the sharing factor of the same type of medical data for the patient between the two medical nodes is obtained.

[0044] As an example, The medical node and Medical nodes, and The patient's Take the diagnosis and treatment data of the first type as an example, and obtain The medical node and The spatial distance (actual geographical location distance) between the medical nodes, then The patient's Types of medical data for The medical node and Sharing factor between medical nodes The calculation method is: in, Indicates The medical node and Each medical node includes The patient's Parameters of various types of diagnosis and treatment data, if all are included, then , otherwise if there is a medical node that does not include ; Indicates The medical node and The spatial distance between medical nodes; To avoid hyperparameters with exponential function values ​​that are too small, this embodiment adopts to give a narrative; represents an exponential function with a natural constant as the base. In this embodiment, Model to present inverse proportional relationship and normalization, As the input of the model, the implementer can set the inverse proportional function and normalization function according to the actual situation; Indicates The number of types of diagnosis and treatment data included in the relevant data of each medical node, Indicates The number of types of diagnosis and treatment data included in the relevant data of each medical node, Indicates The medical node and The number of types of diagnosis and treatment data included in the relevant data of each medical node; represents the absolute value function, and Respectively represent The patient's Types of medical data for Medical nodes and The analysis timeliness of each medical node.

[0045] It should be noted that if two medical nodes have different inclusion relationships for the same type of diagnosis and treatment data, then the two do not have a sharing relationship for this type of diagnosis and treatment data, and the sharing factor is 0; the smaller the spatial distance between the two medical nodes, the more convenient the data transmission between the two medical nodes, which is conducive to the sharing of diagnosis and treatment data; the more types of diagnosis and treatment data the two medical nodes have in common, and the larger their proportion in the total number and proportion of the two medical nodes' overall diagnosis and treatment data, the closer it is to 1, the closer the diseases treated by the two medical nodes themselves are, the more conducive it is to communication between the medical nodes, and the larger the corresponding sharing factor; at the same time, the greater the difference in the timeliness of analysis between medical nodes, the more unfavorable it is for the sharing of medical care data, and they need to be stored separately in a timely manner to ensure the timeliness of the diagnosis and treatment data.

[0046] As an example, based on the storage necessity, the sharing factor is analyzed to determine the necessity of sharing various types of diagnosis and treatment data of patients for each medical node, including the specific method of: It should be noted that the sharing factor reflects the data transmission relationship between different medical nodes. The storage necessity of the medical node can be adjusted based on the sharing factor for the transmission relationship between a medical node and other medical nodes. If the sharing factors with multiple medical nodes are large, and the storage necessity of other medical nodes for the corresponding types of medical data of the corresponding patients is also large, the storage necessity of the corresponding medical node can be appropriately reduced. In combination with the sharing relationship of medical care data, the utilization efficiency of data transmission between medical nodes can be improved, and finally the sharing necessity of various types of medical data of patients for each medical node can be obtained.

[0047] Specifically, The patient's Take a type of medical data as an example, arrange the medical nodes whose storage necessity of this type of medical data for all medical nodes is not equal to 0 in the order of storage necessity from small to large, and obtain the node storage sequence of this type of medical data. Then the shared necessity of this type of medical data for the first medical node in its node storage sequence is The calculation method is: in, Indicates The patient's The necessity of storing each type of medical treatment data for the first medical node in its node storage sequence. Indicates The patient's The number of medical nodes in the node storage sequence of each type of diagnosis and treatment data, Indicates The patient's For each type of medical treatment data, the first medical node in the node storage sequence and the The sharing factor of medical nodes, Indicates The patient's For each type of medical data, the first The storage necessity of each medical node; Represents the weight normalization function, the normalized object is The patient's The shared factor between the first medical node and each medical node in the node storage sequence of each type of diagnosis and treatment data; during the traversal process It means starting from the first medical node but not traversing the first medical node, that is, skipping the first medical node and starting from the second medical node directly.

[0048] Furthermore, after obtaining the corresponding sharing necessity for the first medical node in the node storage sequence, the sharing necessity corresponding to the second medical node is calculated, wherein the medical node that has been updated from storage necessity to sharing necessity during the calculation process participates in the calculation with sharing necessity, and at the same time, during the traversal of other medical nodes in the node storage sequence, the medical node being calculated itself is not traversed, that is, the second medical node is not traversed during the calculation of the sharing necessity of the second medical node; and so on, the first medical node is obtained. The patient's The sharing necessity of each type of medical data for each medical node in its node storage sequence, and the sharing necessity of the medical node with a storage necessity of 0 is also 0.

[0049] Furthermore, the necessity of sharing each type of diagnosis and treatment data of each patient with each medical node is obtained according to the above method.

[0050] It should be noted that, based on the storage necessity, the sharing factor is used as a weight to quantify the impact of the storage necessity of other medical nodes on the medical node. The larger the sharing factor and the greater the storage necessity of the corresponding medical node, the more able the medical node is to analyze the corresponding type of medical data through sharing. In this case, the storage necessity needs to be reduced, and the average of the difference between the storage necessity and 1 minus the sharing relationship is used as the corresponding sharing necessity.

[0051] At this point, through the analysis of the correspondence between various types of diagnosis and treatment data of patients and the relevant data of medical nodes, as well as the characteristic performance of diagnosis and treatment data reflected by the necessity of analysis, the storage relationship of diagnosis and treatment data in a single medical node is quantified. Then, by analyzing the distribution of different medical nodes and combining the differences in the timeliness of their analysis of diagnosis and treatment data, the transmission relationship of diagnosis and treatment data in different medical nodes is determined. In this way, the storage relationship of diagnosis and treatment data in medical nodes is adjusted and the necessity of sharing is obtained, so as to improve the utilization efficiency of medical care data sharing.

[0052] Step S004: sharing the medical care data of several patients based on the necessity of sharing.

[0053] It should be noted that the necessity of sharing is to judge whether each medical node needs to share various types of diagnosis and treatment data. There is a large demand for a certain type of diagnosis and treatment data between medical nodes (departments), and the retention time (analysis timeliness) of this type of diagnosis and treatment data in different medical nodes varies greatly. In this way, it is necessary to share and store it in multiple medical nodes in a timely manner. Based on the necessity of sharing, the storage of various types of diagnosis and treatment data in medical nodes can be judged, and the storage under multiple medical nodes can realize the sharing of corresponding medical care data.

[0054] Specifically, a sharing threshold is preset. In this embodiment, the sharing threshold is described as 0.7. For any type of diagnosis and treatment data of any patient, if the sharing necessity of this type of diagnosis and treatment data for any medical node is greater than or equal to the sharing threshold, then this type of diagnosis and treatment data needs to be shared with the medical node. In the process of monitoring the patient's medical care data, this type of diagnosis and treatment data is transmitted to the medical node and stored, thereby realizing the sharing of the patient's medical care data. It should be noted that if the sharing necessity is less than the threshold but the patient's medical care data is collected and obtained at the medical node, there is no need to delete any type of diagnosis and treatment data in the patient's medical care data from the medical node, that is, during the sharing of medical care data, it is necessary to ensure that the corresponding data source medical node still stores the corresponding medical care data.

[0055] At this point, by analyzing various types of diagnosis and treatment data in the patient's medical care data, combined with the relevant data of each medical node, quantifying the characteristic performance of the diagnosis and treatment data itself for the patient's symptoms, and determining its timeliness performance for different medical nodes, the storage and sharing of diagnosis and treatment data at each medical node is judged, and finally the necessity of sharing diagnosis and treatment data for medical nodes is obtained, and the shared storage of diagnosis and treatment data in the patient's medical care data is carried out, which effectively improves the communication between each medical node and the utilization of its storage space, and at the same time gives full play to the shared transmission relationship between medical care data to realize efficient and effective sharing of medical care data.

[0056] Another embodiment of the present invention provides a medical care data sharing system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the above-mentioned method steps S001 to S004 are implemented.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A medical care data sharing method, characterized in that: The method comprises the following steps: Acquire medical care data of several patients and relevant data of each medical node, wherein the medical care data of the patients includes several types of diagnosis and treatment data; Classify the same type of diagnosis and treatment data of different patients, analyze the differences between the classification results of different types of diagnosis and treatment data of the same patient, and quantify the necessity of analyzing different types of diagnosis and treatment data of each patient; determine the timeliness of the analysis of different types of diagnosis and treatment data of each patient for each medical node based on the temporal changes of different types of diagnosis and treatment data of patients and the relevant data of each medical node; Analyze the correspondence between the relevant data of each medical node and the various types of diagnosis and treatment data of patients, combine the necessity of analyzing the various types of diagnosis and treatment data of each patient and the distribution of medical nodes, and limit the timeliness of the analysis of each medical node through the various types of diagnosis and treatment data, and determine the necessity of sharing the various types of diagnosis and treatment data of patients with each medical node; The medical care data of several patients are shared based on the sharing necessity.

2. A medical care data sharing method according to claim 1, characterized in that: The specific method of classifying the same type of diagnosis and treatment data of different patients includes: For the diagnosis and treatment data of the corresponding type of physiological data obtained by a one-time test, the diagnosis and treatment data of this type for all patients are digitized and used as representative data of the diagnosis and treatment data of this type for each patient; For the diagnosis and treatment data of the type corresponding to the physiological data obtained by continuous monitoring, the median of the standard range of the diagnosis and treatment data of this type is obtained and used as the standard value of the diagnosis and treatment data of this type. According to the deviation value between the diagnosis and treatment data of this type of any patient and the standard value, the numerical processing result corresponding to the deviation value with the largest absolute value is selected through numerical processing as the representative data of the diagnosis and treatment data of this type of the patient; Density clustering is performed on the representative data of any type of diagnosis and treatment data of all patients. The distance measurement uses the absolute value of the difference between the representative data of the patients to obtain several clusters of diagnosis and treatment data of this type. The cluster with the largest number of representative data among all clusters is used as the standard cluster of diagnosis and treatment data of this type.

3. A medical care data sharing method according to claim 2, characterized in that: The specific method of analyzing the differences between the classification results of different types of diagnosis and treatment data of the same patient and quantifying the necessity of analyzing different types of diagnosis and treatment data of each patient is as follows: in, Indicates The patient's The necessity of analyzing various types of medical data, Indicates The number of types of medical data for each patient, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's The absolute value of the difference between the representative data of each type of medical data in the cluster to which it belongs and the centroid of the cluster, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, Indicates The patient's Representative data of each type of medical data and The patient's The absolute value of the difference between the means of all representative data in the standard cluster of the type of diagnosis and treatment data, represents the linear normalization function.

4. A medical care data sharing method according to claim 2, characterized in that: The specific acquisition method of the analysis timeliness of each type of diagnosis and treatment data of the patient for each medical node is as follows: Medical care data from The average time from consultation to discharge of several patients collected by the medical nodes is taken as the first Retention time of medical care data under each medical node; According to the time series changes of various types of diagnosis and treatment data of patients within the retention time of medical care data under each medical node, the analysis timeliness of various types of diagnosis and treatment data of patients for each medical node is obtained.

5. A medical care data sharing method according to claim 4, characterized in that: The specific method of obtaining the analysis timeliness of each type of diagnosis and treatment data of the patient for each medical node based on the time series change of each type of diagnosis and treatment data of the patient within the retention time of the medical care data at each medical node is as follows: From From the first patient visit to the The retention period of medical care data under each medical node ends, and the retention period of the medical care data under the medical node within this period ends. The patient's Type of medical data, recorded as The patient's Types of medical data are in The diagnosis and treatment sequence under each medical node; Count the number of patients who exceed the first Patient No. The number of treatment data in the standard range of the type of treatment data is set, and the last treatment data in the treatment sequence is used as the right boundary of the window, and a sequence is intercepted with the reference window as the first The patient's Types of medical data are in The diagnosis and treatment reference sequence under each medical node; Based on The retention time of medical care data under each medical node, combined with the time that exceeds the first Patient No. The proportion of the standard range of the diagnosis and treatment data of each type, as well as the fluctuation of the diagnosis and treatment data in the diagnosis and treatment reference sequence, are obtained. The patient's Types of medical data for The analysis timeliness of each medical node; the analysis timeliness is positively correlated with the retention time, the proportion and the fluctuation.

6. A medical care data sharing method according to claim 1, characterized in that: The corresponding relationship between the analysis of the relevant data of each medical node and the various types of diagnosis and treatment data of the patient is combined with the necessity of analyzing the various types of diagnosis and treatment data of each patient and the distribution of the medical nodes, and the analysis timeliness of each medical node is limited by each type of diagnosis and treatment data, and the necessity of sharing the various types of diagnosis and treatment data of the patient for each medical node is determined, including the specific method of: Based on the corresponding relationship between the relevant data of each medical node and the various types of diagnosis and treatment data of the patient and the necessity of analysis, the necessity of storing the various types of diagnosis and treatment data of the patient for each medical node is obtained; Based on the differences in the timeliness of analysis of various types of medical data for different medical nodes and the distribution of medical nodes, the sharing factors of various types of medical data between different medical nodes are quantified; On the basis of the storage necessity, the sharing factor is analyzed to determine the sharing necessity of each type of diagnosis and treatment data of the patient for each medical node.

7. A medical care data sharing method according to claim 6, characterized in that: The specific method of obtaining the necessity of storing various types of diagnosis and treatment data of patients for each medical node includes: Based on the inclusion relationship of the patient's diagnosis and treatment data type on the medical node, and the proportion of the diagnosis and treatment data type contained in both the patient and the medical node in the total diagnosis and treatment data type of the patient and the medical node, combined with the necessity of analyzing each type of diagnosis and treatment data of the patient, the necessity of storing each type of diagnosis and treatment data of the patient for each medical node is obtained; the storage necessity is positively correlated with the proportion and the analysis necessity.

8. A medical care data sharing method according to claim 6, characterized in that: The specific method of quantifying the sharing factor of various types of diagnosis and treatment data between different medical nodes includes: Based on the inclusion relationship between the two medical nodes for the same type of diagnosis and treatment data of the patient and the difference in the necessity of analyzing this type of diagnosis and treatment data for the two medical nodes, combined with the spatial distance between the two medical nodes and the proportion of the number of commonly included types of diagnosis and treatment data, the sharing factor of the same type of diagnosis and treatment data of the patient between the two medical nodes is obtained; the sharing factor is negatively correlated with the difference in analysis necessity and the spatial distance, and the sharing factor is positively correlated with the proportion of the number.

9. A medical care data sharing method according to claim 6, characterized in that: The necessity of sharing various types of diagnosis and treatment data of the patient with each medical node, and the specific acquisition method is as follows: First The patient's Taking a type of medical data as an example, the medical nodes whose storage necessity of this type of medical data for all medical nodes is not equal to 0 are arranged in order of storage necessity from small to large, and the node storage sequence of this type of medical data is obtained. The shared necessity of this type of medical data for the first medical node in its node storage sequence is The calculation method is: in, Indicates The patient's The necessity of storing each type of medical treatment data for the first medical node in its node storage sequence. Indicates The patient's The number of medical nodes in the node storage sequence of each type of diagnosis and treatment data, Indicates The patient's For each type of medical treatment data, the first medical node in the node storage sequence and the The sharing factor of medical nodes, Indicates The patient's For each type of medical data, the first The storage necessity of each medical node; represents the weight normalization function; After obtaining the corresponding sharing necessity for the first medical node in the node storage sequence, the sharing necessity corresponding to the second medical node is calculated, wherein the medical node that has been updated from storage necessity to sharing necessity during the calculation process participates in the calculation with sharing necessity; By analogy, we get The patient's The sharing necessity of each type of diagnosis and treatment data for each medical node in its node storage sequence.

10. A medical care data sharing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a medical care data sharing method as described in any one of claims 1-9 are implemented.

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