Patient disease data management system

Abstract

The invention discloses a patient disease data management system, and relates to the technical field of medical information, the patient disease data management system comprises a data management module, a patient symptom module and a communication sharing module, the data management module is in communication connection with the patient symptom module, and the patient symptom module is in communication connection with the communication sharing module; the data management module is used for managing a database and completing normal database operation, the patient symptom module is used for storing and updating symptom data in a patient database, and the communication sharing module is used for managing a mode of communicating a treatment scheme and a conditioning means for a patient. The data management module comprises a database transmission module and a patient self-recording module, the patient self-recording module is in communication connection with the database transmission module, and the patient file module is in communication connection with the symptom filing module; and the database transmission module is used for registering a newly registered patient in the database in the medical system. The system and the method have the characteristic of accurate matching.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and particularly to a patient disease data management system. Background Art

[0002] A patient disease data management system is a comprehensive tool designed for medical institutions, aiming to efficiently collect, store, manage, and analyze patients' disease-related data, enhancing medical staff's ability to monitor patients' health conditions and manage diseases.

[0003] Existing patient data management systems only consider the relationship between doctors and patients, without considering the interaction between patients. Patients need to interact to find appropriate treatment plans and conditioning methods. Existing interaction methods only enable patients with the same disease to communicate. However, the symptoms of many diseases are significantly different in different patients. If interactions are based on disease types, the matching will be inaccurate. Therefore, it is necessary to design a patient disease data management system with accurate matching. Summary of the Invention

[0004] The purpose of the present invention is to provide a patient disease data management system to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A patient disease data management system includes a data management module, a patient symptom module, and a communication and sharing module. The data management module is communicatively connected to the patient symptom module, and the patient symptom module is communicatively connected to the communication and sharing module; the data management module is used to manage the database and complete normal database operations, the patient symptom module is used to store and update symptom data in the patient database, and the communication and sharing module is used to manage the way patients communicate treatment plans and conditioning methods.

[0006] According to the above technical solution, the data management module includes a database transmission module, a patient self-record module, and a patient file module. The patient self-record module is communicatively connected to the database transmission module, and the patient file module is communicatively connected to the symptom filing module; The database transmission module is used to register newly registered patients in the database in the medical system. The patient self-record module is used to allow patients to fill in relevant symptom information of the patients in the database. The patient file module is used to cancel the usage rights of original patients and grant usage rights to newly arrived patients for seeing a doctor; The patient symptom module includes a symptom filing module, a symptom library generation module, a symptom addition module, and an upgrade and update module. The symptom filing module is communicatively connected to the symptom library generation module, and the symptom addition module is communicatively connected to the upgrade and update module; The symptom filing module is used for patients to save their symptom data in the database. The symptom database generation module is used to generate a symptom database based on the patient information filled in by the patients collected. The symptom addition module is used to upgrade the symptom database in the database when new symptoms appear in the patient. The upgrade and update module is used to send the upgraded symptom data in the database to the medical system; The communication and sharing module includes a matching calculation module, a follow-up feedback module, a patient communication module, a display screen, and a weight adjustment module. The follow-up feedback module is communicatively connected to the information analysis module. The display screen is communicatively connected to the weight adjustment module and the patient communication module; The matching calculation module is used to calculate the matching between the symptoms of each patient. The follow-up feedback module is used to select the patients recommended subsequently according to the feedback of the patient. The patient communication module is used to allow patients to share information among patients to find appropriate treatment plans and conditioning methods. The display screen is used to display the relevant symptom information of the patients to be seen in the database. The weight adjustment module is used to adjust the weights of various indicators during recommendation.

[0007] According to the above technical solution, the working method of the system includes: S1. The patient registers in the database of the medical system. When requesting the big data matching service, the patient fills in and enters the patient symptom information through the patient self-record module. The medical system summarizes and stores the patient information filled in by the patient during the visit through the symptom filing module; S2. The system, according to the patient information entered by the patient, uses the symptom database generation module to count the patient symptom information of the patients in the entire medical system and generates a patient symptom database. The system updates the database through the symptom addition module according to the real-time updated patient information; S3. The system, according to the patient symptom information entered by the patient, uses the matching calculation module to perform a matching analysis on the same disease patient symptoms entered by other patients on the medical system; S4. The system recommends and sorts other patients according to the matching between patients. If the patient is satisfied with the recommendation result, they communicate with each other about the treatment plan and conditioning method; S5. When the patient is not satisfied with the default recommendation result, the system uses the follow-up feedback module to select and sort the patients recommended subsequently, so that the patient can find the patients meeting the requirements faster.

[0008] According to the above technical solution, in S1, the method for entering patient symptom information is: The patient enters according to his own symptoms. n is the number of symptoms of all patients in a certain disease. , is the symptom of patient one. When patient one selects a symptom, enter to value. When the patient has a certain symptom, enter , otherwise, enter ; is the symptom of Patient 2. When the patient of Patient 2 selects a symptom, enter to value. When the patient has a certain symptom, enter , otherwise, enter ; Among them, the symptom is one of the direct manifestation symptoms and the instrument examination symptoms, and they are respectively classified into two independent number sequences for separate statistics.

[0009] According to the above technical solution, in S3, the specific method for performing matching analysis is as follows: The calculation method of the matching X between Patient 1 and Patient 2 is as follows:

[0010] When the matching X is higher, it means that the symptoms of Patient 1 and Patient 2 are more similar.

[0011] According to the above technical solution, in S4, the specific method for performing recommended sorting is as follows: The calculation result of the matching X is divided into two aspects, namely the matching of the direct manifestation symptoms and the matching of the instrument examination symptoms . If the patient wants to share the conditioning experience and emotional support with other patients more, then recommend the patients with high matching of the direct manifestation symptoms and low matching of the instrument examination symptoms . When the patients want to discuss the treatment plan, then recommend the patients with low matching of the direct manifestation symptoms and high matching of the instrument examination symptoms .

[0012] According to the above technical solution, in S5, the specific method for selecting and sorting the subsequently recommended patients is as follows: At the initial recommendation, calculate the disease severity of the patients, and let the patients rate the severity of each of their own symptoms to obtain , and enter the duration of each symptom to obtain , and obtain the comprehensive disease severity , where is the severity coefficient, is the duration coefficient. While the matching X is high, select the one that is closest for matching, and let is the comprehensive disease severity of Patient 1, is the comprehensive disease severity of Patient 2, Closest means the smallest.

[0013] According to the above technical solution, in S5, when performing multiple matches, when matches are performed, record the comprehensive disease severity of a certain patient at the last recommendation as , and compare it with the comprehensive disease severity of a certain patient at the penultimate recommendation. If the patient's disease gradually deteriorates, recommend patients with more similar comprehensive disease severities to communicate, and reduce the requirement for matching X. If the patient's disease gradually improves, recommend patients with a higher matching X to communicate, and reduce the requirement for the proximity of the comprehensive disease severity . The specific formula for the communication weight between two patients is:

[0014] where is the weight coefficient of the comprehensive severity of the two patients during communication, is the weight coefficient of the matching of the two patients during communication. Calculate for all patients in the system and select the two patients with the largest value to communicate.

[0015] Calculate and compare the product of the matching X and the comprehensive disease severity of two patients. Let be the comprehensive disease severity of Patient 1, be the comprehensive disease severity of Patient 2, be the conversion coefficient. When , it means that the matching of the two patients is greater than the severity, then arrange the consultations of the two patients for the same doctor. When , then arrange the consultations of the two patients for different doctors.

[0016] Compared with the prior art, the beneficial effects achieved by the present invention are: By recording the symptoms of patients, the present invention enables the exchange of treatment plans and conditioning means between patients with more similar symptoms, rather than just matching according to the disease type, which can take more into account the patient's own feelings. And as the patient's disease severity changes, when the disease worsens, the patient can be more matched with critically ill patients to encourage each other, and when the disease alleviates, the patient can be matched with patients with more similar symptoms to better assist in the conditioning of the disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and form a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 It is a schematic diagram of the overall module structure of the present invention. Detailed implementation manners

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1 , the present invention provides a technical solution: a patient disease data management system, including a data management module, a patient symptom module, and a communication and sharing module. The data management module is communicatively connected to the patient symptom module, and the patient symptom module is communicatively connected to the communication and sharing module; the data management module is used to manage the database and complete normal database operations, the patient symptom module is used to store and update the symptom data in the patient database, and the communication and sharing module is used to manage the ways for patients to communicate treatment plans and conditioning means; The data management module includes a database transmission module, a patient self-record module, and a patient file module. The patient self-record module is communicatively connected to the database transmission module. The database transmission module is used to register newly registered patients in the database in the medical system. The patient self-record module is used to allow patients to fill in relevant symptom information of the patients in the database. The patient file module is used to cancel the usage rights of original patients and grant usage rights to new patients coming to see a doctor; The patient symptom module includes a symptom filing module, a symptom library generation module, a symptom addition module, and an upgrade and update module. The symptom filing module is communicatively connected to the symptom library generation module, and the symptom addition module is communicatively connected to the upgrade and update module; The symptom filing module is used for patients to save the symptom data of the patients in the database. The symptom library generation module is used to generate a symptom database according to the patient information filled in by the patients collected. The symptom addition module is used to upgrade the symptom database in the database when new symptoms appear in the patients. The upgrade and update module is used to send the upgraded symptom data in the database to the medical system; The communication and sharing module includes a matching calculation module, a follow-up feedback module, a patient communication module, a display screen, and a weight adjustment module. The follow-up feedback module is communicatively connected to the information analysis module, and the display screen is communicatively connected to the weight adjustment module and the patient communication module; The matching calculation module is used to calculate the matching between various patient symptoms. The subsequent feedback module is used to select the subsequent recommended patients based on the patient's feedback. The patient communication module is used to allow patients to share information among patients to find appropriate treatment plans and conditioning methods. The display screen is used to display the relevant symptom information of patients waiting to see a doctor on the database. The weight adjustment module is used to adjust the weights of various indicators during recommendation; The working method of this system includes: S1. The patient registers in the database of the medical system. When requesting the big data matching service, the patient fills in and enters the patient symptom information through the patient self-record module. The medical system summarizes and stores the patient information filled in by the patient during the visit through the symptom filing module; S2. According to the patient-entered patient information, the system uses the symptom library generation module to count the patient symptom information of patients in the entire medical system and generates a patient symptom database. The system updates the database through the symptom addition module according to the real-time updated patient information; S3. According to the patient symptom information entered by the patient, the system uses the matching calculation module to perform a matching analysis of the same disease patient symptoms entered by other patients on the medical system; S4. The system recommends and sorts other patients according to the matching between patients. If the patient is satisfied with the recommended result, they communicate with each other about the treatment plan and conditioning method; S5. When the patient is not satisfied with the default recommended result, the system uses the subsequent feedback module to select and sort the subsequent recommended patients, so that the patient can find the patients meeting the requirements faster; In S1, the method for entering patient symptom information is: The patient enters according to their own symptoms. n is the number of symptoms of all patients in a certain disease. , is the symptom of patient one. When the patient of patient one selects the symptom, enter the value from to When patient one has a certain symptom, enter , otherwise, enter ; is the symptom of patient two. When the patient of patient two selects the symptom, enter the value from to When patient two has a certain symptom, enter , otherwise, enter ; Among them, the symptom is one of the direct manifestation symptoms and instrument examination symptoms, and they are respectively classified into two independent sequences for separate statistics; In S3, the specific method for performing the matching analysis is: S3, the specific method for performing the matching analysis is: The calculation method of the matching degree X between Patient 1 and Patient 2 is as follows:

[0020] When the matching degree X is higher, it means that the symptoms of Patient 1 and Patient 2 are more similar. More information sharing and communication opportunities are given to two patients with a high matching degree X.

[0021] If both patients meet a certain symptom category, the result is 1; if at least one of the two patients does not meet a certain symptom category, the result is recorded as 0. Finally, the total result is calculated. If two patients have most of the symptoms, the matching degree will be very high. If two patients have fewer symptoms, due to the principle that multiplying by 0 gives 0, the matching degree will be greatly reduced. In the extreme case, the symptoms that the two patients meet are completely non-overlapping, resulting in a total matching degree of 0. This calculation method will amplify the matching degree of patients with multiple complications and reduce the matching degree of patients without complications. Since the physical conditions and disease incentives of patients with multiple complications are more similar, more communication is needed.

[0022] In S4, the specific method for performing the recommendation ranking is as follows: The calculation result of the matching degree X is divided into two aspects, namely the matching degree of directly manifested symptoms and the matching degree of symptoms detected by instruments . If a patient wants to share the conditioning experience and emotional support with other patients more, then recommend patients with a high matching degree of directly manifested symptoms and a low matching degree of symptoms detected by instruments . When patients want to discuss the treatment plan, then recommend patients with a low matching degree of directly manifested symptoms and a high matching degree of symptoms detected by instruments ; In S5, the specific method for selecting and ranking the subsequently recommended patients is as follows: At the initial recommendation, calculate the disease severity of the patients, rate the severity of each symptom of the patients to obtain , and enter the duration of each symptom to obtain , and obtain the comprehensive disease severity , where is the severity coefficient, is the duration coefficient. While the matching degree X is high, select the closest one for matching. Let be the comprehensive disease severity of Patient 1, be the comprehensive disease severity of Patient 2, the closest means is the smallest; In S5, during multiple matches, when For the second match, record the overall severity of a patient's disease at the last recommendation as , and compare it with the overall severity of the patient's disease at the penultimate recommendation . If the patient's disease is deteriorating, recommend patients with more similar overall severity of the disease to communicate, and reduce the requirement for matching X. If the patient's disease is improving, recommend patients with a higher matching X to communicate, and reduce the requirement for the proximity of the overall severity of the disease . The communication weight between two patients is calculated as follows:

[0023] where is the weight coefficient of the overall severity of the two patients during communication, is the weight coefficient of the matching of the two patients during communication. Calculate for all patients in the system and select the two patients with the largest value to communicate. This method takes into account the subsequent dynamic communication weight mechanism as the patient's condition develops. When the patient's disease is still getting worse despite receiving treatment, it means that the treatment process is becoming more and more uncontrollable. Communicating with critically ill patients with the closest overall severity of the disease can make them understand and encourage each other more, and pay more attention to whether better mental state construction can be achieved. At this time, whether the diseases are similar becomes less important, but the similarity of the feelings of the two patients is more considered.

[0024] Calculate and compare the product of the matching X and the overall severity of the disease of two patients. Let be the overall severity of the disease of patient one, be the overall severity of the disease of patient two, be the conversion coefficient. When , it means that the matching of the two patients is greater than the severity, then arrange the visits of the two patients to the same doctor. When , then arrange the visits of the two patients to different doctors. The advantage is that when the matching degree between the two is higher than the severity, assigning them to the same doctor because of the similarity helps to improve the efficiency of the visit, and even if the doctor makes a mistake when it is not serious, it will not delay the treatment process too much. When the severity is relatively high compared to the matching degree, assigning them to different doctors helps to avoid the deviation of a doctor's judgment.

[0025] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0026] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A patient disease data management system, characterized in that: The working method of the system includes: S1. The patient registers in the database of the medical system. When requesting the big data matching service, the patient fills in and enters the patient's symptom information through the patient's self-record module. The medical system summarizes and stores the patient information filled in by the patient during the visit through the symptom filing module. S2. According to the patient information entered by the patient, the system uses the symptom database generation module to count the patient's symptom information of all patients in the medical system and generates a patient symptom database. The system uses the symptom addition module to update the database according to the real-time updated patient information. S3. According to the patient's symptom information entered by the patient, the system uses the matching calculation module to perform a matching analysis on the same disease's patient symptoms entered by other patients on the medical system. S4. The system ranks and recommends other patients according to the matching between patients. If the patient is satisfied with the recommended result, they communicate with each other about the treatment plan and conditioning measures. S5. When the patient is not satisfied with the default recommended result, the system uses the subsequent feedback module to select and rank the subsequently recommended patients, so that the patient can find the patients meeting the requirements faster.

2. A patient disease data management system according to claim 1, wherein: in S1, the method for entering the patient's symptom information is: Patients enter information according to their own symptoms. n is the number of symptoms of all patients in a certain disease. , are the symptoms of Patient 1. When Patient 1 selects symptoms, enter the to value. When Patient 1 has a certain symptom, enter , otherwise, enter ; For the symptoms of Patient 2, when the patient of Patient 2 selects symptoms, enter into the value. When Patient 2 has a certain symptom, enter , otherwise, enter ; Wherein the symptom is one of the direct manifestation symptoms and the instrument examination symptoms, and they are respectively classified into two independent sequences for separate statistics.

3. A patient disease data management system according to claim 2, wherein: in S3, the specific method for performing the matching analysis is: The calculation method of the matching X between patient one and patient two is: ； When the matching X is higher, it means that the symptoms of patient one and patient two are more similar.

4. A patient disease data management system according to claim 3, wherein: in S5, the specific method for selecting and sorting the subsequently recommended patients is as follows: at the initial recommendation, calculate the disease severity of the patients, and let the patients rate the severity of each of their symptoms to obtain , and enter the duration of each symptom to obtain , and obtain the comprehensive disease severity , where is the severity coefficient, is the duration coefficient. While the matching X is high, select the closest one for matching, and let be the comprehensive disease severity of patient one, be the comprehensive disease severity of patient two, the closest means is the smallest.

5. The patient disease data management system according to claim 4, wherein: in S5, during multiple matches, when matches are made, record the comprehensive disease severity of a certain patient at the last recommendation as , and compare it with the comprehensive disease severity of the same patient at the penultimate recommendation. If the patient's disease deteriorates gradually, recommend patients with a more similar comprehensive disease severity for communication, and reduce the requirement for matching X. If the patient's disease improves gradually, recommend patients with a higher matching X for communication, and reduce the requirement for the proximity of the comprehensive disease severity . The specific formula for the communication weight between two patients is: ； Among them is the weight coefficient of the comprehensive severity of two patients during communication, is the weight coefficient of the matching degree of two patients during communication. Calculate for all patients in the system and select the two patients with the largest value for communication.

6. A patient disease data management system according to claim 5, characterized in that: the product of the matching degree X of two patients and the comprehensive disease severity is calculated and compared, is a conversion coefficient. When , it means that the matching degree of the two patients is greater than the severity, then the visits of the two patients are arranged for the same doctor. When , the visits of the two patients are arranged for different doctors.

7. A patient disease data management system according to claim 6, wherein: in S4, the specific method for performing recommendation ranking is as follows: the calculation result of the matching degree X is divided into two aspects, namely the matching degree of directly manifested symptoms and the matching degree of symptoms detected by instruments . If the patient prefers to share conditioning experiences and emotional support with other patients, then the matching degree of directly manifested symptoms is recommended Patients with high matching degree and low matching degree of symptoms detected by instruments . When patients want to discuss treatment plans, then the directly manifested symptoms are recommended Patients with low matching degree and high matching degree of symptoms detected by instruments are recommended.

8. A patient disease data management system according to claim 7, wherein: it includes a data management module, a patient symptom module, and a communication and sharing module. The data management module is communicatively connected to the patient symptom module, and the patient symptom module is communicatively connected to the communication and sharing module; the data management module is used to manage the database and complete normal database operations. The patient symptom module is used to store and update the symptom data in the patient database. The communication and sharing module is used to manage the ways for patients to communicate treatment plans and conditioning measures. The data management module includes a database transmission module, a patient self-record module, and a patient file module. The patient symptom module includes a symptom filing module, a symptom database generation module, a symptom addition module, and an upgrade and update module. The communication and sharing module includes a matching calculation module, a subsequent feedback module, a patient communication module, a display screen, and a weight adjustment module.

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