Patient data integration and management method and system

Abstract

The invention discloses a patient data integration and management method and system, and relates to the field of data management, and the method comprises the steps: obtaining the basic information of a patient, searching the historical treatment data of the patient in the historical data of a plurality of hospitals, integrating the historical treatment data, and obtaining the historical disease data; acquiring current initial doctor seeing data of the patient, and acquiring initial disease data in combination with the historical disease data; acquiring a disease type of the patient based on the initial disease data, searching similar disease data in the disease data of the plurality of hospitals based on the disease type, and integrating the similar disease data to obtain disease integrated data; and acquiring behavior data of the patient, predicting disease change of the patient in combination with the behavior data, the disease integration data and the initial disease data, and generating a prediction result. The method has the effect of improving the reference to the disease data of the patient and the disease data of the same type as the patient and the comprehensiveness of predicting the condition of the patient.

Description

Technical Field

[0001] The present application relates to the field of data management, and in particular to a method and system for integrating and managing patient data. Background Art

[0002] With the rapid development and changes of current medical technology, hospitals are becoming more and more efficient and accurate in testing patients. At the same time, with the advancement of informatization in the medical industry, medical data has ushered in a blowout growth. With a large amount of medical data, it plays a positive role in the detection and judgment of patient diseases.

[0003] In the existing technology, different hospitals generally use their own intranets, and there are information barriers between different hospitals. The data generated when the same patient undergoes examinations and treatments in different hospitals cannot be communicated, resulting in incomplete medical information for the patient. When the medical data of these patients is needed as reference data for medical activities, the reference value provided by these medical data will be greatly reduced.

[0004] At the same time, when patients visit the hospital for treatment, the current diagnosis results are often relatively mild. However, after one or two years after the patient returns home, the original small lesion develops into a very serious disease. In this process, the medical data of other patients stored in the hospital, which should have been of important reference value, did not play any role in the deterioration of the patient's condition. Summary of the invention

[0005] The purpose of the present invention is to provide a patient data integration and management method and system to solve the problems raised in the above background technology.

[0006] In the first aspect, the present application provides a method for integrating and managing patient data, which adopts the following technical solutions:

[0007] Obtaining the basic information of the patient, searching the historical medical data of the patient in the historical data of multiple hospitals based on the basic information, and integrating the historical medical data to obtain historical disease data;

[0008] Obtain the patient's current initial medical consultation data, combine it with the historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data;

[0009] Based on the initial disease data, the disease type of the patient is obtained, and based on the disease type, similar disease data is searched in the disease data of multiple hospitals, and the similar disease data is integrated to obtain disease integrated data;

[0010] The patient's behavior data is obtained, and the patient's condition change is predicted by combining the behavior data, the disease integration data and the initial disease data, and a prediction result is generated.

[0011] By adopting the above technical solution, the basic information of the patient is obtained, and the patient's historical medical data is searched in different hospitals based on the patient's basic information, and the historical medical data is integrated to generate historical disease data, and the patient's current medical data is obtained. The patient's initial disease data is judged based on the historical disease data. The patient's disease type is judged based on the initial disease data, and the same disease data with the same disease type as the patient is found in multiple hospitals, and the data is integrated to obtain disease integration data, and the patient's behavior data is obtained. According to the patient's behavior data, the patient's condition changes are predicted in combination with the disease integration data and the initial disease data to obtain the prediction results. The reference of the patient's disease data and the disease data of the same type as the patient's and the comprehensiveness of the prediction of the patient's condition are improved.

[0012] Preferably, the step of obtaining the basic information of the patient, searching the historical medical data of the patient in the historical data of multiple hospitals based on the basic information, and integrating the historical medical data to obtain the historical disease data includes:

[0013] Obtaining basic information of the patient and establishing a data connection relationship, wherein the data connection relationship is connected with multiple hospitals;

[0014] Identify historical data in multiple hospitals based on the basic information, and obtain historical segment medical data of the identified patients;

[0015] Based on the acquired multiple historical medical data, obtain the medical time of each historical medical data, and concatenate the historical medical data based on the medical time to generate historical medical data;

[0016] The historical medical treatment data is analyzed to obtain the patient's disease type, cause of disease and treatment plan, and the disease is classified according to the disease type to generate historical disease data.

[0017] By adopting the above technical solution, the basic information of the patient is obtained, a data connection relationship is established, and multiple hospitals are connected. Based on the basic information of the patient, the data in the hospital is identified, and the historical medical data related to the patient is captured, the time of each historical medical data is determined, and multiple historical medical data are connected in series according to the time to generate historical medical data, analyze the historical medical data, determine the type of disease, cause of disease and treatment plan of the patient, and classify the historical medical data to generate historical disease data. The comprehensiveness of the acquisition and analysis of the patient's historical medical data is improved.

[0018] Preferably, the step of obtaining the patient's current initial medical consultation data, combining the historical disease data, making a preliminary diagnosis of the disease, and obtaining the initial disease data includes:

[0019] Acquire the patient's current initial medical consultation data, perform data cleaning and data division on the initial medical consultation data, and acquire initial examination data;

[0020] Performing data analysis on the initial inspection data to obtain abnormal data in the initial inspection data;

[0021] Based on the abnormal data, combined with the historical disease data, the degree of data consistency between the abnormal data and the historical disease data is obtained, and the data consistency is compared with a preset threshold value;

[0022] If the data consistency is less than a preset threshold, based on the abnormal data, the abnormal data is integrated to generate initial disease data;

[0023] If the data consistency is greater than or equal to a preset threshold, historical examination data and historical treatment plans are obtained based on historical disease data, and initial disease data is generated based on the historical examination data and historical treatment plans combined with abnormal data.

[0024] By adopting the above technical solution, the patient's current initial medical data is obtained, and the initial medical data is processed and divided, the initial examination data of different examination items is obtained, and the initial examination data is judged, the abnormal data therein is obtained, and the abnormal data is judged for data consistency with the historical disease data. If the data consistency is lower than the threshold, the abnormal data is integrated and the initial disease data is generated. If the data consistency is higher than the threshold, the historical examination data and historical treatment plan are obtained based on the historical disease data, and the initial disease data is generated in combination with the abnormal data. The accuracy of the analysis and judgment of the initial disease data is improved.

[0025] Preferably, the step of obtaining the patient's disease type based on the initial disease data, searching for similar disease data in multiple hospitals based on the disease type, and integrating the similar disease data to obtain disease integrated data includes:

[0026] Determine the disease type of the patient based on the initial disease data, and generate a disease data screen based on the disease type;

[0027] Based on the disease data screen, the disease data in multiple hospitals are screened to obtain multiple pieces of similar disease data;

[0028] Jointly analyzing the plurality of pieces of data on the same disease to obtain similar points in the plurality of pieces of data on the same disease, and extracting the similar points;

[0029] Based on the similarities, multiple pieces of similar disease data are data-associated to obtain difference data among the multiple pieces of similar disease data, and the difference data are data-integrated to generate disease integration data.

[0030] By adopting the above technical solution, the patient's disease type is determined based on the initial disease data, and a data screen is generated. The disease data in multiple hospitals are screened based on the data screen to obtain the same disease data as the patient's disease type, and the similarities in multiple pieces of similar disease data are determined, and the similarities are extracted. Based on the similarities, multiple pieces of similar disease data are associated with each other, and different difference data are determined, and the difference data are integrated to generate disease integrated data. The utilization rate of disease data in different hospitals and the integrity of data processing are improved.

[0031] Preferably, after the step of obtaining the patient's disease type based on the initial disease data, searching for similar disease data in multiple hospitals based on the disease type, and integrating the similar disease data to obtain disease integration data, the method further includes:

[0032] Obtain historical behavior data of multiple patients corresponding to multiple pieces of similar disease data, and obtain historical disease progression of similar disease data;

[0033] Generate a historical behavior curve and a historical disease condition curve based on the historical behavior data and the historical disease condition development respectively;

[0034] Determine the change trend of the historical condition curve, and generate a deterioration segment according to the first curve segment where the condition changes sharply on the historical condition curve;

[0035] Matching the historical behavior curve with the historical condition curve, obtaining a second curve segment of the historical behavior curve corresponding to the deterioration segment, and marking it as a deterioration behavior segment;

[0036] The multiple deteriorating behavior segments are integrated and analyzed to determine the behavior-condition relationship between the patient's condition changes and the behavior data.

[0037] By adopting the above technical solution, the historical behavior data of multiple patients corresponding to multiple pieces of similar disease data are obtained, and the historical disease progression of the patients is obtained. The historical behavior curve and the historical disease progression are generated respectively according to the historical behavior data and the historical disease progression, and the change trend of the historical disease progression curve is determined. The first curve segment with a sharp change in the historical disease progression curve is determined and marked as a deterioration segment, and the second curve segment on the historical behavior curve corresponding to the deterioration segment is obtained and marked as a deterioration behavior segment, and the behavior-disease relationship between the patient's disease progression and the behavior data is determined. The accuracy and scope of identifying the impact of the patient's behavior data on the disease progression are improved.

[0038] Preferably, the step of acquiring the patient's behavioral data, predicting the patient's condition change based on the disease integrated data and the initial disease data, and generating a prediction result includes:

[0039] Acquiring behavioral data of the patient, performing curve analysis on the behavioral data, and generating a behavioral curve of the patient;

[0040] Generate a curve starting point based on the initial disease data, and generate an initial disease curve with the curve starting point based on the disease integrated data;

[0041] Matching the behavior curve with the initial disease curve, and obtaining the curve influence of the behavior data on the initial disease curve based on the behavior-disease relationship;

[0042] Based on the influence of the curve, the initial disease curve is adjusted to generate the patient's disease curve, and the change trend of the disease curve is obtained. The patient's condition is predicted according to the change trend to obtain the prediction result.

[0043] By adopting the above technical solution, the patient's behavior data is obtained and the patient's behavior curve is generated. According to the initial disease data, the starting point of the curve is generated, and combined with the disease integration data, the initial disease curve is generated according to the starting point of the curve. The behavior curve is matched with the initial disease curve, and the behavior-disease relationship is used as a trimmer to correct the initial disease curve, generate the patient's disease curve, and obtain the change trend of the disease curve, predict the patient's condition, and obtain the prediction result. The accuracy of the prediction of the patient's condition is improved.

[0044] Preferably, after the step of acquiring the patient's behavioral data, predicting the patient's condition change based on the disease integrated data and the initial disease data, and generating a prediction result, the method further includes:

[0045] Obtain the patient's treatment plan and doctor's orders, and determine whether the patient's behavior data complies with the treatment plan and doctor's orders based on the patient's behavior data;

[0046] If not, then based on the prediction results, generate the possible adverse consequences of the disease change, and generate a behavior adjustment plan based on the patient's behavior data;

[0047] Based on the adverse consequences and the behavior adjustment plan, generating a first warning notification, and sending the first warning notification to the patient end;

[0048] If it is in compliance, then based on the prediction result, it is determined whether the change in the condition is benign. If it is not benign, a second warning notification is generated and sent to the patient to remind the patient to undergo a re-examination.

[0049] By adopting the above technical solution, the patient's treatment plan and doctor's orders are obtained, and based on the patient's behavior data, it is judged whether the patient complies with the treatment plan and doctor's orders. If not, the possible adverse consequences are judged based on the patient's current behavior data, and a behavior adjustment plan is generated based on the patient's behavior data. The first warning notification is generated based on the behavior adjustment plan and the adverse consequences and sent to the patient. If it complies, it is judged whether the trend of the disease change is benign. If not, a second warning notification is generated to remind the patient to re-examine. The timeliness of patient behavior supervision and the predictability of disease changes are improved.

[0050] In a second aspect, the present application provides a patient data integration and management system, comprising:

[0051] A basic information analysis module is used to obtain the basic information of the patient, and based on the basic information, search for the patient's historical medical data in the historical data of multiple hospitals, and integrate the historical medical data to obtain historical disease data;

[0052] The disease initial analysis module is used to obtain the patient's current initial medical data, combine the historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data;

[0053] A disease data integration module, used to obtain the patient's disease type based on the initial disease data, and to search for similar disease data in the disease data of multiple hospitals based on the disease type, and to integrate the similar disease data to obtain disease integration data;

[0054] The condition change prediction module is used to obtain the patient's behavioral data, combine the behavioral data, the disease integrated data and the initial disease data to predict the patient's condition change, and generate a prediction result.

[0055] Preferably, the disease data integration module includes an analysis integration unit and a disease progression analysis unit;

[0056] The analysis and integration unit is used to determine the disease type of the patient based on the initial disease data, and screen the disease data in multiple hospitals based on the disease type to obtain multiple pieces of similar disease data;

[0057] Jointly analyzing the plurality of similar disease data to obtain similar points in the plurality of similar disease data, extracting the similar points, and correlating the plurality of similar disease data according to the similar points to obtain difference data, and integrating the difference data to generate disease integration data;

[0058] The disease progression analysis unit is used to generate a historical behavior curve and a historical disease progression curve based on the historical behavior data and the historical disease progression, determine the change trend of the historical disease progression curve, and generate a deterioration segment based on the first curve segment where the disease progression changes sharply on the historical disease progression curve;

[0059] Matching the historical behavior curve with the historical condition curve, obtaining a second curve segment of the historical behavior curve corresponding to the deterioration segment, and marking it as a deterioration behavior segment;

[0060] The multiple deteriorating behavior segments are integrated and analyzed to determine the behavior-condition relationship between the patient's condition changes and the behavior data.

[0061] Preferably, the disease change prediction module includes a disease curve analysis unit and a disease prediction unit;

[0062] The disease curve analysis unit is used to obtain the patient's behavior data, perform curve analysis on the behavior data, and generate the patient's behavior curve;

[0063] Generate a curve starting point based on the initial disease data, and generate an initial disease curve with the curve starting point based on the disease integrated data;

[0064] Matching the behavior curve with the initial disease curve, and obtaining the curve influence of the behavior data on the initial disease curve based on the behavior-disease relationship;

[0065] The disease prediction unit is used to adjust the initial disease curve based on the influence of the curve, generate the patient's disease curve, obtain the change trend of the disease curve, predict the patient's condition according to the change trend, and obtain the prediction result.

[0066] In summary, the present application includes at least one of the following beneficial technical effects:

[0067] 1. Obtain the patient's basic information, and search for the patient's historical medical data in different hospitals based on the patient's basic information, and integrate the historical medical data to generate historical disease data, obtain the patient's current medical data, and determine the patient's initial disease data based on the historical disease data. Based on the initial disease data, determine the patient's disease type, and search for similar disease data with the same disease type as the patient in multiple hospitals, and integrate the data to obtain disease integration data, obtain the patient's behavioral data, and predict the patient's condition changes based on the patient's behavioral data, combined with the disease integration data and the initial disease data, and obtain the prediction results. This improves the reference value of the patient's disease data and the disease data of the same type as the patient, as well as the comprehensiveness of the patient's condition prediction.

[0068] 2. Obtain the historical behavior data of multiple patients corresponding to multiple pieces of similar disease data, and obtain the historical disease progression of the patients, generate the historical behavior curve and the historical disease progression curve respectively according to the historical behavior data and the historical disease progression, and judge the change trend of the historical disease progression curve, judge the first curve segment of the sharp change in the historical disease progression curve, mark it as the deterioration segment, and obtain the second curve segment on the historical behavior curve corresponding to the deterioration segment, and mark it as the deterioration behavior segment, and judge the behavior-disease relationship between the patient's disease progression and the behavior data. Improve the accuracy and scope of identifying the impact of the patient's behavior data on the disease progression.

[0069] 3. Obtain the patient's behavior data and generate the patient's behavior curve. Generate the starting point of the curve based on the initial disease data, and generate the initial disease curve based on the starting point of the curve in combination with the disease integration data. Match the behavior curve with the initial disease curve, use the behavior-disease relationship as a trimmer, modify the initial disease curve, generate the patient's disease curve, and obtain the change trend of the disease curve, predict the patient's condition, and obtain the prediction result. This improves the accuracy of the patient's condition prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flow chart of the steps of a patient data integration and management method of the present invention;

[0071] Figure 2 is a flow chart of sub-steps of step S100 in a patient data integration and management method of the present invention;

[0072] Figure 3 is a sub-step flow chart of step S200 in a patient data integration and management method of the present invention;

[0073] Figure 4 is a flow chart of sub-steps of step S300 in a method for integrating and managing patient data of the present invention;

[0074] Figure 5 is a flow chart of sub-steps after step S300 in a patient data integration and management method of the present invention;

[0075] Figure 6 is a flow chart of sub-steps of step S400 in a method for integrating and managing patient data of the present invention;

[0076] Figure 7 is a flow chart of sub-steps after step S400 in a patient data integration and management method of the present invention;

[0077] Figure 8 It is a module block diagram of a patient data integration and management system of the present invention.

[0078] Explanation of the accompanying drawings: 1. Basic information analysis module; 2. Initial disease analysis module; 3. Disease data integration module; 4. Disease change prediction module. DETAILED DESCRIPTION

[0079] The following is combined with Figure 1-8 The present application is further described in detail, but the embodiments of the present invention are not limited thereto.

[0080] In the embodiments of the present application, a patient data integration and management method and system are disclosed.

[0081] In this embodiment, refer to Figure 1 , a patient data integration and management method, comprising the following steps:

[0082] S100: Obtaining basic information of the patient, searching for the patient's historical medical data in historical data of multiple hospitals based on the basic information, and integrating the historical medical data to obtain historical disease data;

[0083] S200: Obtain the patient's current initial medical consultation data, combine it with historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data;

[0084] S300: Based on the initial disease data, the disease type of the patient is obtained, and based on the disease type, similar disease data is searched in the disease data of multiple hospitals, and the similar disease data is integrated to obtain disease integrated data;

[0085] S400: Obtaining the patient's behavioral data, and predicting the patient's condition change by combining the behavioral data, disease integration data, and initial disease data, and generating a prediction result.

[0086] It should be pointed out that the above steps are only a preferred implementation order. In the specific implementation process, some steps can be replaced without affecting the overall implementation effect.

[0087] Reference Figure 2 In step S100, the basic information of the patient is obtained, and based on the basic information, the historical medical data of the patient is searched in the historical data of multiple hospitals, and the historical medical data is integrated to obtain the historical disease data, including:

[0088] S101: Obtain basic information of the patient and establish a data connection relationship, where the data connection relationship is connected with multiple hospitals;

[0089] S102: Identify historical data in multiple hospitals based on basic information, and obtain historical segment medical data of the identified patients;

[0090] S103: based on the acquired multiple historical medical data segments, acquiring the medical time of each historical medical data segment, and concatenating the historical medical data segments based on the medical time to generate historical medical data;

[0091] S104: Analyze historical medical treatment data to obtain the patient's disease type, cause of disease and treatment plan, and classify them according to the disease type to generate historical disease data.

[0092] During application, if a patient has been treated in three hospitals, the patient's basic information, including name, gender and other basic information, is obtained. The patient's previous historical medical data is searched in the database based on the patient's basic information. At the same time, it is determined that the treatment times in these three hospitals are 2017, 2018, and 2019 respectively. The three historical medical data are connected according to the chronological order to generate a complete historical medical data, and the patient's disease type, cause of the disease and treatment plan are determined, and classified at the same time to obtain the patient's historical disease data.

[0093] Reference Figure 2 In step S200, the patient's current initial medical data is obtained, and the disease is preliminarily judged in combination with the historical disease data. The step of obtaining the initial disease data includes:

[0094] S201: Acquire the patient's current initial medical consultation data, clean and divide the initial medical consultation data, and acquire initial examination data;

[0095] S202: Analyze the initial inspection data to obtain abnormal data in the initial inspection data;

[0096] S203: Based on the abnormal data and in combination with the historical disease data, the degree of data consistency between the abnormal data and the historical disease data is obtained, and the degree of data consistency is compared with a preset threshold value;

[0097] S204: If the data consistency is less than a preset threshold, based on the abnormal data, the abnormal data is integrated to generate initial disease data;

[0098] S205: If the data consistency is greater than or equal to a preset threshold, historical examination data and historical treatment plans are obtained based on historical disease data, and initial disease data is generated based on the historical examination data and historical treatment plans combined with abnormal data.

[0099] During use, a patient goes to a hospital for medical treatment, the patient's current medical treatment data in the hospital is obtained, the current medical treatment data is cleaned and divided, and the initial examination data is obtained. If the initial examination data includes blood tests and X-ray examinations, the initial examination data is analyzed and it is found that the content of a certain substance in the relevant data of the patient's blood test is too high, and the patient's historical disease data also shows that the content of the substance in the blood is too high, then it is judged that the data consistency between the abnormal data and the historical disease data exceeds the threshold, and the initial disease data of the patient is generated based on the historical disease data and the abnormal data. If it is found that there is no problem with the relevant data of the patient's blood test, and shadows are found in the patient's lungs in the X-ray examination items, and this situation has not appeared in the historical disease data, then the X-ray detection data is integrated to generate the patient's initial disease data.

[0100] Reference Figure 3 In step S300, based on the initial disease data, the disease type of the patient is obtained, and based on the disease type, similar disease data is searched in the disease data of multiple hospitals, and the similar disease data is integrated to obtain the integrated disease data, including:

[0101] S301: Determine the disease type of the patient based on the initial disease data, and generate a disease data screen based on the disease type;

[0102] S302: Screening disease data in multiple hospitals based on the disease data screening network to obtain multiple pieces of similar disease data;

[0103] S303: jointly analyzing multiple pieces of similar disease data to obtain similar points in the multiple pieces of similar disease data, and extracting the similar points;

[0104] S304: Based on the similarities, multiple pieces of similar disease data are associated with each other to obtain difference data among the multiple pieces of similar disease data, and the difference data are integrated to generate disease integration data.

[0105] During application, the initial disease data of a patient is obtained, and the disease type of the patient is determined to be tuberculosis. Then, a disease data screen for tuberculosis is generated according to the disease type of tuberculosis. The disease data screen is used to screen data in the databases of multiple hospitals to obtain similar disease data of the same type of tuberculosis. These data are jointly analyzed and it is found that there are three similarities A, B, and C in these data strings. Then, similarities are extracted from A, B, and C, and data in different places are associated. At the same time, data integration is performed, and disease integration data is generated by combining the three similarities A, B, and C.

[0106] Reference Figure 3 After step S300, based on the initial disease data, the disease type of the patient is obtained, and based on the disease type, similar disease data is searched in the disease data of multiple hospitals, and the similar disease data is integrated. After the step of obtaining the disease integration data, the following is also included:

[0107] S311: Obtain historical behavior data of multiple patients corresponding to multiple pieces of similar disease data, and obtain historical disease progression of the similar disease data;

[0108] S312: Generate a historical behavior curve and a historical disease condition curve based on the historical behavior data and the historical disease condition development respectively;

[0109] S313: determining the change trend of the historical condition curve, and generating a deterioration segment according to the first curve segment where the condition changes sharply on the historical condition curve;

[0110] S314: Matching the historical behavior curve with the historical condition curve, obtaining a second curve segment of the historical behavior curve corresponding to the deterioration segment, and marking it as the deterioration behavior segment;

[0111] S315: Integrate and analyze multiple deteriorating behavior segments to determine the behavior-disease relationship between the patient's condition changes and the behavioral data.

[0112] During application, a patient is diagnosed with tuberculosis, and similar disease data of tuberculosis are obtained from multiple hospitals. Based on the similar disease data, the historical behavior data of each patient corresponding to the similar disease data and their historical disease progression are obtained, and the historical behavior data and historical disease progression of these patients are respectively curve-formed, and the changing trend of the historical disease curve is judged. If it is found that there are three curves with sharp changes among multiple historical disease curves, the sharply changing segments on these three curves are marked as deterioration segments, and the curve segments of the historical behavior curves corresponding to these three curves are obtained and marked as deterioration behavior segments. If it is judged that the three deterioration behavior segments all contain behaviors that are unfavorable to the tuberculosis condition, the three deterioration behavior segments are analyzed to determine the behavior-disease relationship in tuberculosis.

[0113] Reference Figure 4 In step S400, the patient's behavior data is obtained, and the patient's condition change is predicted by combining the behavior data, disease integration data and initial disease data, and the prediction result is generated, including:

[0114] S401: Obtaining the patient's behavior data, performing curve analysis on the behavior data, and generating a behavior curve of the patient;

[0115] S402: Generate a curve starting point based on the initial disease data, and generate an initial disease curve based on the curve starting point based on the disease integration data;

[0116] S403: Matching the behavior curve with the initial disease curve, and obtaining the curve influence of the behavior data on the initial disease curve based on the behavior-disease relationship;

[0117] S404: Based on the influence of the curve, the initial disease curve is adjusted to generate the patient's disease curve, and the change trend of the disease curve is obtained. The patient's condition is predicted according to the change trend to obtain the prediction result.

[0118] During application, a patient suffers from tuberculosis. The patient's behavioral data is obtained and a behavioral curve is generated. At the same time, the starting point of the curve is generated based on the obtained initial disease curve, and the initial disease curve is generated based on the disease integration data. When analyzing the patient's behavioral data, it is found that the patient once had serious habits of smoking and drinking, but in the current behavioral data, these bad habits have disappeared. Then, there is an error in the initial disease curve generated based on the disease integration data. The initial disease curve is adjusted based on the current patient's behavioral curve and the behavior-disease relationship. Since the current patient's behavioral habits are good and the trend of the behavioral curve is good, when the patient's initial disease curve is adjusted, it is found that the initial disease curve is trending well, and a disease curve is generated. At the same time, it is predicted that the patient's disease development will be benign.

[0119] Reference Figure 4 After step S400, the method further includes: obtaining the patient's behavior data, predicting the patient's condition change based on the disease integration data and the initial disease data, and generating a prediction result;

[0120] S411: Obtain the patient's treatment plan and doctor's orders, and determine whether the behavior data complies with the treatment plan and doctor's orders based on the patient's behavior data;

[0121] S412: If not, then based on the prediction results, generate the possible adverse consequences of the disease change, and generate a behavior adjustment plan based on the patient's behavior data;

[0122] S413: generating a first warning notification based on the adverse consequences and the behavior adjustment plan, and sending the first warning notification to the patient end;

[0123] S414: If it is in compliance, then based on the prediction result, it is determined whether the change in the condition is benign. If it is not benign, a second warning notification is generated and sent to the patient to remind the patient to undergo a re-examination.

[0124] During application, a patient suffers from tuberculosis. It is found from the patient's medical advice and treatment plan that the doctor recommends the patient to quit smoking and drinking. If it is found from the patient's behavioral data that the patient still retains the habit of smoking and drinking frequently, and the predicted result for the patient is an adverse development, a first warning notification is generated of the adverse consequences of the patient's smoking and drinking and the recommendation to quit smoking and drinking, and the patient is reminded and warned. If it is found from the patient's behavioral data that the patient complies with the doctor's advice and treatment plan to quit smoking and drinking, and the behavioral data is good, but the predicted result of the patient's disease development still shows an adverse development, a second warning notification is issued to the patient, suggesting that the patient go to the hospital for a follow-up visit.

[0125] Reference Figure 5 The embodiment of the present invention provides a patient data integration and management system, using any one of the patient data integration and management methods described above, including:

[0126] Basic information analysis module 1 is used to obtain the basic information of the patient, and based on the basic information, search for the patient's historical medical data in the historical data of multiple hospitals, and integrate the historical medical data to obtain historical disease data;

[0127] The disease initial analysis module 2 is used to obtain the patient's current initial medical treatment data, combine the historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data;

[0128] The disease data integration module 3 is used to obtain the patient's disease type based on the initial disease data, and to search for similar disease data in the disease data of multiple hospitals based on the disease type, and to integrate the similar disease data to obtain disease integration data;

[0129] The condition change prediction module 4 is used to obtain the patient's behavioral data, combine the behavioral data, the disease integrated data and the initial disease data to predict the patient's condition change, and generate a prediction result.

[0130] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for integrating and managing patient data, characterized in that: The following steps are involved: Obtaining the basic information of the patient, searching the historical medical data of the patient in the historical data of multiple hospitals based on the basic information, and integrating the historical medical data to obtain historical disease data; Obtain the patient's current initial medical consultation data, combine it with the historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data; Based on the initial disease data, the disease type of the patient is obtained, and based on the disease type, similar disease data is searched in the disease data of multiple hospitals, and the similar disease data is integrated to obtain disease integrated data; The patient's behavior data is obtained, and the patient's condition change is predicted by combining the behavior data, the disease integrated data, and the initial disease data, and a prediction result is generated.

2. A patient data integration and management method according to claim 1, characterized in that: The step of obtaining the basic information of the patient, searching the historical medical data of the patient in the historical data of multiple hospitals based on the basic information, and integrating the historical medical data to obtain the historical disease data includes: Obtaining basic information of the patient and establishing a data connection relationship, wherein the data connection relationship is connected with multiple hospitals; Identify historical data in multiple hospitals based on the basic information, and obtain historical segment medical data of the identified patients; Based on the acquired multiple historical medical data, obtain the medical time of each historical medical data, and concatenate the historical medical data based on the medical time to generate historical medical data; The historical medical treatment data is analyzed to obtain the patient's disease type, cause of disease and treatment plan, and the disease is classified according to the disease type to generate historical disease data.

3. A patient data integration and management method according to claim 1, characterized in that: The step of obtaining the patient's current initial medical consultation data, combining the historical disease data, making a preliminary diagnosis of the disease, and obtaining the initial disease data includes: Acquire the patient's current initial medical consultation data, perform data cleaning and data segmentation on the initial medical consultation data, and acquire initial examination data; Performing data analysis on the initial inspection data to obtain abnormal data in the initial inspection data; Based on the abnormal data, combined with the historical disease data, the degree of data consistency between the abnormal data and the historical disease data is obtained, and the data consistency is compared with a preset threshold value; If the data consistency is less than a preset threshold, based on the abnormal data, the abnormal data is integrated to generate initial disease data; If the data consistency is greater than or equal to a preset threshold, historical examination data and historical treatment plans are obtained based on historical disease data, and initial disease data is generated based on the historical examination data and historical treatment plans combined with abnormal data.

4. A patient data integration and management method according to claim 1, characterized in that: The step of obtaining the patient's disease type based on the initial disease data, searching for similar disease data in disease data of multiple hospitals based on the disease type, and integrating the similar disease data to obtain disease integration data includes: Determine the disease type of the patient based on the initial disease data, and generate a disease data screen based on the disease type; Based on the disease data screen, the disease data in multiple hospitals are screened to obtain multiple pieces of similar disease data; Jointly analyzing the plurality of pieces of data on the same disease to obtain similar points in the plurality of pieces of data on the same disease, and extracting the similar points; Based on the similarities, multiple pieces of similar disease data are data-associated to obtain difference data among the multiple pieces of similar disease data, and the difference data are data-integrated to generate disease integration data.

5. A patient data integration and management method according to claim 1, characterized in that: After the step of obtaining the patient's disease type based on the initial disease data, searching for similar disease data in the disease data of multiple hospitals based on the disease type, and integrating the similar disease data to obtain disease integration data, the method further includes: Obtain historical behavior data of multiple patients corresponding to multiple pieces of similar disease data, and obtain historical disease progression of similar disease data; Generate a historical behavior curve and a historical disease condition curve based on the historical behavior data and the historical disease condition development respectively; Determine the change trend of the historical condition curve, and generate a deterioration segment according to the first curve segment where the condition changes sharply on the historical condition curve; Matching the historical behavior curve with the historical condition curve, obtaining a second curve segment of the historical behavior curve corresponding to the deterioration segment, and marking it as a deterioration behavior segment; The multiple deteriorating behavior segments are integrated and analyzed to determine the behavior-condition relationship between the patient's condition changes and the behavior data.

6. A patient data integration and management method according to claim 1, characterized in that: The step of obtaining the patient's behavior data, predicting the patient's condition change by combining the behavior data, the disease integrated data and the initial disease data, and generating a prediction result includes: Acquiring behavioral data of the patient, performing curve analysis on the behavioral data, and generating a behavioral curve of the patient; Generate a curve starting point based on the initial disease data, and generate an initial disease curve with the curve starting point based on the disease integrated data; Matching the behavior curve with the initial disease curve, and obtaining the curve influence of the behavior data on the initial disease curve based on the behavior-disease relationship; Based on the influence of the curve, the initial disease curve is adjusted to generate the patient's disease curve, and the change trend of the disease curve is obtained. The patient's condition is predicted according to the change trend to obtain the prediction result.

7. A patient data integration and management method according to claim 1, characterized in that: After the step of acquiring the patient's behavior data, predicting the patient's condition change based on the disease integrated data and the initial disease data, and generating a prediction result, the method further includes: Obtain the patient's treatment plan and doctor's orders, and determine whether the patient's behavior data complies with the treatment plan and doctor's orders based on the patient's behavior data; If not, then based on the prediction results, generate the possible adverse consequences of the disease change, and generate a behavior adjustment plan based on the patient's behavior data; Based on the adverse consequences and the behavior adjustment plan, generating a first warning notification, and sending the first warning notification to the patient end; If it is in compliance, then based on the prediction result, it is determined whether the change in the condition is benign. If it is not benign, a second warning notification is generated and sent to the patient to remind the patient to undergo a re-examination.

8. A patient data integration and management system, using any one of the patient data integration and management methods as claimed in claims 1 to 7, characterized in that: include: A basic information analysis module (1) is used to obtain the basic information of the patient, and based on the basic information, search for the patient's historical medical data in the historical data of multiple hospitals, and integrate the historical medical data to obtain historical disease data; The disease initial analysis module (2) is used to obtain the patient's current initial medical consultation data, combine the historical disease data, make a preliminary diagnosis of the disease, and obtain initial disease data; A disease data integration module (3) is used to obtain the patient's disease type based on the initial disease data, and to search for similar disease data in the disease data of multiple hospitals based on the disease type, and to integrate the similar disease data to obtain disease integration data; The disease condition change prediction module (4) is used to obtain the patient's behavioral data, combine the behavioral data, the disease integrated data and the initial disease data to predict the patient's disease condition change, and generate a prediction result.

9. A patient data integration and management system according to claim 8, characterized in that: The disease data integration module (3) comprises an analysis integration unit and a disease progression analysis unit; The analysis and integration unit is used to determine the disease type of the patient based on the initial disease data, and screen the disease data in multiple hospitals based on the disease type to obtain multiple pieces of similar disease data; Jointly analyzing the plurality of similar disease data to obtain similar points in the plurality of similar disease data, extracting the similar points, and correlating the plurality of similar disease data according to the similar points to obtain difference data, and integrating the difference data to generate disease integration data; The disease progression analysis unit is used to generate a historical behavior curve and a historical disease progression curve based on the historical behavior data and the historical disease progression, determine the change trend of the historical disease progression curve, and generate a deterioration segment based on the first curve segment where the disease progression changes sharply on the historical disease progression curve; Matching the historical behavior curve with the historical condition curve, obtaining a second curve segment of the historical behavior curve corresponding to the deterioration segment, and marking it as a deterioration behavior segment; The multiple deteriorating behavior segments are integrated and analyzed to determine the behavior-condition relationship between the patient's condition changes and the behavior data.

10. A patient data integration and management system according to claim 8, characterized in that: The disease change prediction module (4) comprises a disease curve analysis unit and a disease prediction unit; The disease curve analysis unit is used to obtain the patient's behavior data, perform curve analysis on the behavior data, and generate the patient's behavior curve; Generate a curve starting point based on the initial disease data, and generate an initial disease curve with the curve starting point based on the disease integrated data; Matching the behavior curve with the initial disease curve, and obtaining the curve influence of the behavior data on the initial disease curve based on the behavior-disease relationship; The disease prediction unit is used to adjust the initial disease curve based on the influence of the curve, generate the patient's disease curve, obtain the change trend of the disease curve, predict the patient's condition according to the change trend, and obtain the prediction result.

Images