Hospital management system and method based on hospital management data

Abstract

The present invention belongs to the field of hospital management technology, and specifically relates to a hospital management system and method based on hospital management data, which obtains data from various hospitals and current patient data, retrieves a historical medical database, extracts the case with the best disease progression among similar cases as a reference case, and extracts the treatment plan from the reference case; retrieves academic achievement data, extracts the content related to the current patient, and determines whether the pre-treatment plan has an improved treatment method; the doctor of the current patient obtains the reference case and the pre-treatment plan through a doctor module to replace or adjust; transmits the verified replacement or adjusted treatment plan to a data acquisition module, stores it in a historical medical database, updates the historical medical database, obtains the current medical data of the current patient, and compares the current medical data of the current patient with the patient data at a specified time to determine the disease. The above scheme improves the work efficiency of doctors while reducing the error rate.

Description

Technical Field

[0001] The present invention belongs to the technical field of hospital management, and in particular relates to a hospital management system and method based on hospital management data. Background Art

[0002] Hospital management involves the planning, organization, implementation and control of the hospital operation process. It is a general term for various core resource management tasks that are closely related to the creation of medical services. Existing hospital management generally only operates and manages a certain hospital itself, and each hospital is independent of each other. Therefore, the current hospital management does not involve unified operation and management between hospitals, resulting in the separation of management between hospitals. Since the doctors in each department of each hospital are relatively fixed, the communication and learning of relevant medical technology for doctors in a certain department of the hospital is limited to their own department. The improvement of the medical capabilities of doctors in various departments is very limited, resulting in uneven capabilities between the same departments of each hospital, and it is also impossible to promote the development and improvement of medical technology capabilities, resulting in patients gathering in large hospitals, and some small hospitals have only very few patients. And because of the heavy medical pressure in hospitals, a doctor may need to diagnose dozens or even hundreds of patients a day. Assuming that a doctor works 8 hours a day and needs to diagnose and treat 100 patients, including reading various examination reports, on average, the doctor's diagnosis and treatment plan for a patient only takes four or five minutes. In four or five minutes, the doctor may not be able to understand the patient's condition in detail, give the best treatment plan or choose the best medication. Even in such a short time, the doctor may make a diagnosis error or medication error.

[0003] Therefore, this application hopes to build a hospital management system to achieve unified operation and management among all hospitals, and to achieve communication and learning between the same departments of each hospital. On the one hand, it promotes the development of medical technology in related disciplines between the same departments of each hospital and promotes the improvement of the medical technology capabilities of doctors in each hospital. On the other hand, it assists doctors in diagnosing patients' conditions and choosing medications, while improving doctors' work efficiency and reducing their work error rate. Summary of the invention

[0004] The purpose of the present invention is to provide a hospital management system and method based on hospital management data, which is used to realize unified operation and management among all hospitals, and to realize communication and learning among the same departments of each hospital. On the one hand, it promotes the development of medical technology of related disciplines between the same departments of each hospital and promotes the improvement of medical technology capabilities of doctors in each hospital. On the other hand, it assists doctors in diagnosing patients' conditions and selecting medications, while improving doctors' work efficiency and reducing doctors' work error rate.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, a hospital management method based on hospital management data is provided, comprising the following steps:

[0007] S1: The data acquisition module acquires the medical data and academic achievement data of each hospital based on big data. The medical data includes patient data and diagnosis and treatment data and disease progression data corresponding to the patient data. The academic achievement data includes papers, projects, patents, and works, and constructs a historical medical database and a historical academic achievement database.

[0008] S2: The data acquisition module acquires the current patient data, retrieves the historical medical database, and retrieves similar cases in the historical medical database based on the patient data, and extracts the case with the best disease progression among the similar cases as a reference case, and extracts the treatment plan in the reference case as the pre-treatment plan for the current patient;

[0009] S3: Retrieve the academic achievement data, extract the content related to the current patient, and determine whether the pre-treatment plan has an improved treatment plan based on the extracted academic achievements related to the current patient. If not, transmit it to the doctor module. If so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module.

[0010] S4: The doctor of the current patient obtains the reference case and the pre-treatment plan through the doctor module. The doctor replaces or adjusts the pre-treatment plan based on the difference between the current patient data and the patient data in the reference case, and transmits the replaced or adjusted treatment plan to the plan verification module for plan verification;

[0011] S5: transmitting the verified replacement or adjusted treatment plan to the data acquisition module, storing it in the historical medical database, and updating the data in the historical medical database;

[0012] S6: After the specified time, the current medical data of the current patient is obtained, and based on the current medical data of the current patient, it is compared with the patient data in step S2 to determine whether the relevant indicators of the patient are improved after the specified time of treatment. If so, continue the current treatment plan, if not, loop through steps S2-S6.

[0013] Preferably, if there is a CT image in the current patient data, the specific process in step S2 is as follows:

[0014] S21: extracting the first lesion image data in the current patient data and the second lesion image data in the historical medical database, dividing the lesion areas in the CT images of the two into micro-regions according to the specified size, and extracting the pixel points of each micro-region;

[0015] S22: extracting the color feature value of each micro-region pixel point of the CT images of the first lesion image data and the second lesion image data, and comparing the color feature value of each micro-region pixel point of the CT image in the first lesion image data with the color feature value of each micro-region pixel point of the CT image in the second lesion image data;

[0016] S23: Calculate the difference between the color characteristic value of each micro-region pixel point of the CT image in the first lesion image data and the color characteristic value of each micro-region pixel point of the CT image in the second lesion image data;

[0017] S24: Count the number of micro-areas whose difference values ​​in step S23 are within a specified range, and take the cases whose counted number of micro-areas is greater than a preset threshold as similar cases.

[0018] Preferably, the pixel point of a certain micro-area of ​​the CT image of the first lesion image data in step S22 is recorded as ( x 1, y 1), the color feature value is c ( x 1, y 1); let the pixel point of a certain micro-area of ​​the CT image of the second lesion image data be ( x 2, y 2), the color feature value is c ( x 2, y 2) Calculate the color feature value of a certain micro-region pixel point of the CT image in the first lesion image data c ( x 1, y 1) Color feature value of a certain micro-region pixel point of the CT image of the second lesion image data c ( x 2, y 2) The formula for the difference is:

[0019] △ c 1 = w [ c ( x 2, y 2)- c ( x 1, y 1)];

[0020] c ( x 1, y 1)=( b 1, g 1, r 1);

[0021] c( x 2, y 2)=( b 2, g 2, r 2);

[0022] Among them, △ c 1 is a pixel ( x 1, y 1) and pixel points ( x 2, y 2) The difference in color feature values, w is a pixel ( x 1, y 1) is the contribution of micro-region Ω, b 1, g 1, r 1 respectively indicates that the pixel is ( x 1, y 1) the blue, green and red component values, b 2, g 2, r 2 respectively represent the pixel points as ( x 2, y 2) the blue, green and red component values, b 1. g 1. r 1 、b 2. g 2. r The value range of 2 is an integer from 0 to 255, indicating the intensity of the color.

[0023] Preferably, the process of extracting the case with the best disease progression from similar cases as a reference case in step S2 is:

[0024] Extracting the lesion image data of the first time and the second time of the CT image of each case in the similar cases, dividing the lesion area in the CT images of the two cases into micro-areas according to the specified size, and extracting the pixel points of each micro-area;

[0025] Extracting the color characteristic value of each micro-region pixel point of the CT image of the lesion image data at the first time and the second time, and comparing the color characteristic value of each micro-region pixel point of the CT image in the lesion image data at the first time with the color characteristic value of each micro-region pixel point of the CT image in the lesion image data at the second time;

[0026] Calculate the difference between the color characteristic value of each micro-area pixel point of the CT image in the lesion image data at the first time and the color characteristic value of each micro-area pixel point of the CT image in the lesion image data at the second time, and the calculation process is the same as the calculation process in step S22; and sum the differences of all pixels, and take the case corresponding to the minimum value of the sum of the differences of all pixels of the lesion image data at the first time and the second time in similar cases as the reference case.

[0027] Preferably, the process of determining whether there is an improved treatment plan for the pre-treatment plan in step S3 is: determining whether there are drugs or chemotherapeutic methods that have a promoting therapeutic effect on the corresponding lesions in the academic achievements.

[0028] Preferably, the replacement or adjustment of the pretreatment regimen in step S4 includes replacement or adjustment of medication categories and adjustment of medication dosages.

[0029] Preferably, if there is no CT image in the current patient data, the various indicator data in the current patient data are extracted, and the historical medical database is retrieved, and the various indicator data in the current patient data are compared with the corresponding indicator data in the cases in the historical medical database, and the difference between the various indicator data in the current patient data and the various indicators in the cases in the historical medical database is calculated, and the number of indicators whose difference values ​​of various indicators are within the corresponding preset range is counted, and the top three cases in terms of the number of indicators counted are taken as similar cases, and the indicator changes of the three cases before and after a certain period are calculated, and the number of indicators whose direction of indicator change represents improvement of the condition is counted, and the case with the largest number of indicators representing improvement of the condition is taken as the case with the best disease development, and is used as a reference case.

[0030] Preferably, it also includes a doctor management method, in which the doctor registers his identity in the system, and then logs in and authenticates his identity based on the registered identity. After the identity authentication is passed, the designated hospital is selected according to the hospital module. After the corresponding management personnel review and approve it, the doctor enters the designated department in the department module of the designated hospital, obtains the case data of relevant patients based on the patient module, or obtains relevant academic achievements based on the doctor module.

[0031] In a second aspect, a hospital management system based on hospital management data is provided, which is used to implement any one of the hospital management methods based on hospital management data, and is characterized in that it includes a hospital module, a department module, a doctor module, a patient module, a data acquisition module, a data extraction module, a feature extraction module, a data analysis module and a management center, and the hospital module, department module, doctor module, patient module, data acquisition module, data extraction module and feature extraction module are all connected to the data analysis module and the management center;

[0032] The hospital module is used to display various hospitals and related data;

[0033] The department module is used to display the department data of each hospital;

[0034] The doctor module is used to display the doctor data of each department;

[0035] The patient module is used to display the patient data of each doctor's subordinates;

[0036] The data acquisition module is used to acquire the medical data and academic achievement data of each hospital based on big data, wherein the medical data includes patient data and diagnosis and treatment data and disease progression data corresponding to the patient data, and the academic achievement data includes papers, projects, patents, and works, and to construct a historical medical database and a historical academic achievement database;

[0037] The data extraction module is used to retrieve the historical medical database, and retrieve similar cases in the historical medical database based on the analysis results of the data analysis module, and extract the case with the best disease progression among the similar cases, and extract the treatment plan in the reference case;

[0038] The feature extraction module is used to extract the patient data lesion image data and the color feature value of each micro-area pixel;

[0039] The data analysis module is used to analyze and process the medical data and academic achievement data, calculate the difference between the color characteristic value of each micro-area pixel point of the CT image in the first lesion image data and the color characteristic value of each micro-area pixel point of the CT image in the second lesion image data, count the number of micro-areas whose color characteristic value difference is within a specified range, and determine whether the pre-treatment plan has an improved treatment plan, if not, transmit it to the doctor module, and if so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module;

[0040] The management center is used to obtain the hospital data, department data, doctor data and patient data in the hospital module, department module, doctor module and patient module, and to uniformly manage each hospital, department, doctor and patient based on the hospital module, department module, doctor module and patient module.

[0041] The beneficial effects of the present invention include:

[0042] The hospital management system and method based on hospital management data provided by the present invention obtain data from various hospitals, obtain current patient data, call up a historical medical database, extract the case with the best disease progression among similar cases as a reference case, and extract the treatment plan from the reference case; call up academic achievement data, extract the content related to the current patient, and judge whether there is any improved treatment in the pre-treatment plan; the doctor of the current patient obtains the reference case and the pre-treatment plan through the doctor module to replace or adjust; the verified replaced or adjusted treatment plan is transmitted to the data acquisition module, stored in the historical medical database, and the historical medical database is updated; the current medical data of the current patient is obtained after a specified time, and the patient's condition is judged based on the comparison between the current medical data of the current patient and the patient data before the specified time.

[0043] First, by obtaining the medical data and academic achievement data of each hospital, the historical medical database is retrieved after obtaining the current patient data, and similar cases in the historical medical database are retrieved based on the patient data, and the case with the best disease progression among the similar cases is extracted as a reference case, and the treatment plan in the reference case is extracted as the pre-treatment plan for the current patient. Since the time a doctor spends on each patient in his daily work is limited, the medical data of each hospital is used, and the cases similar to the current patient are extracted by comparison, and the treatment plan of the case with the best disease recovery is extracted as the current pre-treatment plan. The doctor can check and adjust based on the pre-treatment plan, which can greatly reduce the doctor's diagnosis time and improve work efficiency on the one hand, and reduce the misdiagnosis rate and improve the doctor-patient relationship on the other hand.

[0044] Second, similar cases are obtained by calculating the difference between the color feature values ​​of each micro-region pixel point of the CT image in the lesion image data of the current patient and the color feature values ​​of each micro-region pixel point of the CT image in the lesion image data of the historical case, and the difference between the color feature values ​​of the similar cases before and after the specified time period is calculated, and the case with the best disease progression is used as a reference case; or if the current patient has no CT image data, the difference between the various indicator data in the current patient data and the various indicators of the cases in the historical medical database is calculated through the corresponding indicator data to obtain similar cases, and then the case with the largest number of indicators representing the improvement of the disease before and after the specified time period in the similar cases is used as a reference case. Provide an accurate data basis for the given pre-treatment plan to ensure the accuracy of the pre-treatment plan.

[0045] Third, by retrieving the latest academic achievement data, extracting the content related to the current patient, and judging whether there is any improved treatment plan for the pre-treatment plan based on the extracted academic achievements related to the current patient, if there is a way that the latest academic achievements are beneficial to the development of the disease, the pre-treatment plan can be further improved to further promote the development of the current patient's disease;

[0046] Fourth, the doctor of the current patient obtains the reference case and pre-treatment plan through the doctor module. The doctor replaces or adjusts the pre-treatment plan based on the difference between the current patient data and the patient data in the reference case, and transmits the replaced or adjusted treatment plan to the plan verification module for plan verification. The doctor manually controls the pre-treatment plan to improve the accuracy of the treatment plan.

[0047] Fifth, the verified replacement or adjusted treatment plan is transmitted to the data acquisition module, stored in the historical medical database, and the historical medical database is updated to ensure that the historical medical database is iteratively updated with the passage of time and the development of medical technology, and to ensure that subsequent pre-treatment plans are also based on the latest medical technology.

[0048] Sixth, after the specified time, the current medical data of the current patient is obtained, and based on the comparison between the current medical data of the current patient and the patient data before the specified time, it is determined whether the relevant indicators of the patient are improved after the specified time of treatment, so as to adjust the treatment plan later.

[0049] Seventh, by setting up hospital modules, department modules, doctor modules, and patient modules, each hospital is included in the system for unified management, which facilitates learning and communication between doctors in the same departments of each hospital, improves medical skills, and promotes medical development in all directions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flowchart of the hospital management method based on hospital management data of the present invention.

[0051] Figure 2 It is a flowchart of selecting similar cases for the hospital management method based on hospital management data of the present invention.

[0052] Figure 3 It is a schematic diagram of the architecture of the hospital management system based on hospital management data of the present invention. DETAILED DESCRIPTION

[0053] The following is combined with Figure 1~Figure 3 The present invention is further described in detail:

[0054] Example 1

[0055] See attached Figure 1 As shown, the hospital management method based on hospital management data includes the following steps:

[0056] S1: The data acquisition module acquires the medical data and academic achievement data of each hospital based on big data. The medical data includes patient data and the diagnosis and treatment data and disease progression data corresponding to the patient data. The academic achievement data includes papers, projects, patents, and works, and constructs a historical medical database and a historical academic achievement database. In the medical field, except for some rare diseases that have been available for a relatively short time, most diseases have been studied for a long time and basically have relatively mature treatment plans. Therefore, by acquiring the medical data and academic achievement data of each hospital, the long-term treatment plans for each disease can be obtained as the data basis for providing subsequent pre-treatment plans.

[0057] S2: The data acquisition module acquires the current patient data, retrieves the historical medical database, and retrieves similar cases in the historical medical database based on the patient data, and extracts the case with the best disease progression among the similar cases as a reference case, and extracts the treatment plan in the reference case as the pre-treatment plan for the current patient.

[0058] By obtaining the medical data and academic achievement data of each hospital, the historical medical database is retrieved after obtaining the current patient data, and similar cases in the historical medical database are retrieved based on the patient data, and the case with the best disease progression among the similar cases is extracted as a reference case, and the treatment plan in the reference case is extracted as the pre-treatment plan for the current patient. Since the time a doctor spends on each patient in his daily work is limited, the medical data of each hospital is used, and the cases similar to the current patient are extracted by comparison, and then the treatment plan of the case with the best disease recovery is extracted as the current pre-treatment plan. The doctor can check and adjust based on the pre-treatment plan, which can greatly reduce the doctor's diagnosis time and improve work efficiency on the one hand, and reduce the misdiagnosis rate and improve the doctor-patient relationship on the other hand.

[0059] S3: Retrieve the academic achievement data, extract the content related to the current patient, and judge whether there is any improved treatment plan for the pre-treatment plan based on the extracted academic achievements related to the current patient. If not, transmit it to the doctor module. If so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module. By retrieving the latest academic achievement data, extracting the content related to the current patient, and judging whether there is any improved treatment plan for the pre-treatment plan based on the extracted academic achievements related to the current patient, if there is a way that the latest academic achievements are beneficial to the development of the disease, the pre-treatment plan can be further improved to further promote the development of the current patient's disease.

[0060] S4: The doctor of the current patient obtains the reference case and pre-treatment plan through the doctor module. The doctor replaces or adjusts the pre-treatment plan based on the difference between the current patient data and the patient data in the reference case, and transmits the replaced or adjusted treatment plan to the plan verification module for plan verification. The doctor of the current patient obtains the reference case and pre-treatment plan through the doctor module. The doctor replaces or adjusts the pre-treatment plan based on the difference between the current patient data and the patient data in the reference case, and transmits the replaced or adjusted treatment plan to the plan verification module for plan verification, and further manually controls the pre-treatment plan to ensure the accuracy of the treatment plan.

[0061] S5: The verified replacement or adjusted treatment plan is transmitted to the data acquisition module, stored in the historical medical database, and the historical medical database is updated. The verified replacement or adjusted treatment plan is transmitted to the data acquisition module, stored in the historical medical database, and the historical medical database is updated to ensure that the historical medical database is iteratively updated with the passage of time and the development of medical technology, and that the subsequent pre-treatment plan is also based on the latest medical technology. After the specified time, the current medical data of the current patient is obtained, and based on the current medical data of the current patient and the patient data before the specified time, it is determined whether the relevant indicators of the patient have improved after the specified time of treatment, so as to adjust the treatment plan later.

[0062] S6: After the specified time, the current medical data of the current patient is obtained, and the current medical data of the current patient is compared with the patient data in step S2 to determine whether the relevant indicators of the patient have improved after the specified time of treatment. If so, continue the current treatment plan, if not, loop through steps S2-S6. After the specified time, the current medical data of the current patient is obtained, and the current medical data of the current patient is compared with the patient data before the specified time to determine whether the relevant indicators of the patient have improved after the specified time of treatment, so as to adjust the treatment plan later.

[0063] Example 2

[0064] See also Figure 2 Based on Example 1, if there is a CT image in the current patient data, the specific process in step S2 is as follows:

[0065] S21: extract the first lesion image data in the current patient data and the second lesion image data in the historical medical database, and divide the lesion area in the CT images of the two into micro-areas according to the specified size, and extract the pixel points of each micro-area. The reason for extracting the lesion image data in the current patient data and the historical medical database is that the image data of the lesion, such as the CT image data, is the most intuitive embodiment of the disease. Therefore, if the patient has a corresponding lesion CT image, by comparing and analyzing the current patient data and the lesion image data in the historical medical database, the case closest to the current patient's condition can be found.

[0066] S22: extracting the color feature value of each micro-region pixel point of the CT image of the first lesion image data and the second lesion image data, and comparing the color feature value of each micro-region pixel point of the CT image in the first lesion image data with the color feature value of each micro-region pixel point of the CT image in the second lesion image data. Since the color of the lesion area of ​​the CT image is different from the color of the normal area, the color feature value at any pixel point in the image can be characterized by the intensity of the three primary colors of red, green and blue, and then the color feature value of the color of the lesion area can be used to extract the characteristics of the lesion.

[0067] S23: Calculate the difference between the color feature value of each micro-area pixel point of the CT image in the first lesion image data and the color feature value of each micro-area pixel point of the CT image in the second lesion image data, wherein the difference reflects the difference between the color feature value at the pixel point of the current patient's lesion and the color feature value of the pixel point of the lesion in the case in the historical medical database. The smaller the difference, the closer the color feature values ​​at the two pixel points are.

[0068] S24: Count the number of micro-areas whose differences in step S23 are within the specified range, and take the cases whose counted number of micro-areas is greater than a preset threshold as similar cases, because the more micro-areas whose differences are within the specified range, the smaller the difference between the color feature values ​​at the pixel points of the current patient's lesions and the color feature values ​​of the pixel points of the lesions in the cases in the historical medical database, and the closer the conditions are.

[0069] Note that a certain micro-region pixel point of the CT image of the first lesion image data in step S22 is ( x 1, y 1), the color feature value is c ( x 1, y 1); let the pixel point of a certain micro-area of ​​the CT image of the second lesion image data be ( x 2, y 2), the color feature value is c ( x 2, y2) Calculate the color feature value of a certain micro-region pixel point of the CT image in the first lesion image data c ( x 1, y 1) Color feature value of a certain micro-region pixel point of the CT image of the second lesion image data c ( x 2, y 2) The formula for the difference is:

[0070] △ c 1 = w [ c ( x 2, y 2)- c ( x 1, y 1)];

[0071] c ( x 1, y 1)=( b 1, g 1, r 1);

[0072] c ( x 2, y 2)=( b 2, g 2, r 2);

[0073] Among them, △ c 1 is a pixel ( x 1, y 1) and pixel points ( x 2, y 2) The difference in color feature values, w is a pixel ( x 1, y 1) is the contribution of micro-region Ω, b 1, g 1, r 1 respectively indicates that the pixel is ( x 1, y 1) the blue, green and red component values, b 2, g 2, r 2 respectively represent the pixel points as ( x 2, y 2) the blue, green and red component values, b 1. g 1. r 1 、b 2. g 2. rThe value range of 2 is an integer from 0 to 255, indicating the intensity of the color.

[0074] Example 3

[0075] Based on Example 1 or Example 2, the process of extracting the case with the best disease progression among similar cases as a reference case in step S2 is:

[0076] Extract the lesion image data of the first time and the second time of the CT image of each case in the similar cases, and divide the lesion area in the CT images of the two cases into micro-areas according to the specified size, and extract 1 pixel in each micro-area, because the number of pixels in a picture is very large, if the color feature value is calculated or extracted for each pixel, the workload is too large and the time consumed is particularly large. Extract the color feature value of each micro-area pixel of the CT image of the lesion image data of the first time and the second time, and compare the color feature value of each micro-area pixel of the CT image of the lesion image data of the first time with the color feature value of each micro-area pixel of the CT image of the lesion image data of the second time. Calculate the difference between the color feature value of each micro-area pixel of the CT image of the lesion image data of the first time and the color feature value of each micro-area pixel of the CT image of the lesion image data of the second time, and the calculation process is the same as the calculation process in step S22; and sum the difference of all pixels, and take the case corresponding to the minimum value of the sum of the difference of all pixels of the lesion image data of the first time and the second time in the similar case as the reference case.

[0077] If some diseases do not involve CT images, if there are B-ultrasound or other image data, they will be processed in the above manner.

[0078] The process of determining whether there is an improved treatment plan for the pre-treatment plan in step S3 is to determine whether there are drugs or other treatment methods such as radiotherapy and chemotherapy that have a therapeutic effect on the corresponding lesions in the academic results. The replacement or adjustment of the pre-treatment plan in S4 includes the replacement or adjustment of the medication category and the adjustment of the medication dosage.

[0079] Since some diseases may not have image data, for diseases without image data, the corresponding indicator analysis method is adopted to extract similar cases and select reference cases. The specific process is as follows:

[0080] If there is no CT image in the current patient data, then extract the various indicator data in the current patient data, call the historical medical database, and compare the various indicator data in the current patient data with the corresponding indicator data in the cases in the historical medical database, and calculate the difference between the various indicator data in the current patient data and the various indicators of the cases in the historical medical database, and count the number of indicators whose differences are within the corresponding preset range, and take the top three cases in terms of the number of indicators counted as similar cases, calculate the indicator changes of the three cases before and after a certain period, and count the number of indicators whose indicator change directions represent the improvement of the condition, and take the case with the largest number of indicators representing the improvement of the condition as the case with the best disease development, and use it as a reference case.

[0081] The hospital management method based on hospital management data also includes a doctor management method. The doctor registers his identity in the system, then logs in and authenticates his identity based on the registered identity. After the identity authentication is passed, the doctor selects a designated hospital according to the hospital module. After the corresponding management personnel review and approve it, the doctor enters the designated department in the department module of the designated hospital, obtains the case data of the relevant patient based on the patient module, or obtains the relevant academic achievements based on the doctor module. By pushing similar cases and reference medical records through the system, doctors can also obtain a large number of excellent cases in their daily work, and by viewing these cases, they can improve their own medical skills. Over time, the level of departments between hospitals continues to develop upward, thereby continuously narrowing the gap between hospitals, so that doctors in small hospitals can also have a higher level, thereby reducing the degree of resource and patient differentiation between large hospitals and small hospitals or local hospitals.

[0082] See also Figure 3 , a hospital management system based on hospital management data, is used to implement any one of the hospital management methods based on hospital management data, and is characterized in that it includes a hospital module, a department module, a doctor module, a patient module, a data acquisition module, a data extraction module, a feature extraction module, a data analysis module and a management center, and the hospital module, department module, doctor module, patient module, data acquisition module, data extraction module, feature extraction module are all connected to the data analysis module and the management center.

[0083] The hospital module is used to display various hospitals and related data; the department module is used to display the department data of each hospital; the doctor module is used to display the doctor data of each department; the patient module is used to display the patient data of each doctor; the data acquisition module is used to acquire the medical data and academic achievement data of each hospital based on big data, the medical data includes patient data and diagnosis and treatment data and disease progression data corresponding to the patient data, the academic achievement data includes papers, projects, patents, and works, and constructs a historical medical database and a historical academic achievement database; the data extraction module is used to retrieve the historical medical database, and based on the analysis results of the data analysis module, retrieve similar cases in the historical medical database, extract cases with the best disease progression among similar cases, and extract treatment plans from the reference cases. The feature extraction module is used to extract the patient data lesion image data and the color feature value of each micro-area pixel; the data analysis module is used to analyze and process the medical data and academic achievement data, calculate the difference between the color feature value of each micro-area pixel of the CT image in the first lesion image data and the color feature value of each micro-area pixel of the CT image in the second lesion image data, count the number of micro-areas whose color feature value difference is within a specified range, and determine whether the pre-treatment plan has an improved treatment plan. If not, transmit it to the doctor module. If so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module.

[0084] The management center is used to obtain the hospital data, department data, doctor data and patient data in the hospital module, department module, doctor module and patient module, and to uniformly manage each hospital, department, doctor and patient based on the hospital module, department module, doctor module and patient module.

[0085] In summary, the hospital management system and method based on hospital management data provided by the present invention obtains the medical data and academic achievement data of each hospital, obtains the current patient data, retrieves the historical medical database, and retrieves similar cases in the historical medical database based on the patient data, and extracts the case with the best disease progression from the similar cases as a reference case, and extracts the treatment plan from the reference case as the pre-treatment plan for the current patient. Since the time a doctor spends on each patient in his daily work is limited, the medical data of each hospital is used, and the cases similar to the current patient are extracted by comparison, and then the treatment plan of the case with the best disease recovery is extracted as the current pre-treatment plan. The doctor can check and adjust based on the pre-treatment plan, which greatly reduces the doctor's diagnosis time, improves work efficiency, reduces the misdiagnosis rate, and improves the doctor-patient relationship.

[0086] By calculating the difference between the color feature value of each micro-region pixel point of the CT image in the lesion image data of the current patient and the color feature value of each micro-region pixel point of the CT image of the lesion image data of the historical case, similar cases are obtained, and the difference between the color feature values ​​of the similar cases before and after the specified time period is calculated, and the case with the best disease development is used as a reference case; or if the current patient has no CT image data, the difference between the various indicator data in the current patient data and the various indicators of the cases in the historical medical database is calculated through the corresponding indicator data to obtain similar cases, and then the case with the largest number of indicators representing the improvement of the disease before and after the specified time period in the similar cases is used as a reference case. Provide an accurate data basis for the given pre-treatment plan to ensure the accuracy and adaptability of the pre-treatment plan. Transmit the verified replacement or adjusted treatment plan to the data acquisition module, store it in the historical medical database, update the data of the historical medical database, ensure that the historical medical database is iteratively updated with the passage of time and the development of medical technology, and ensure that the subsequent pre-treatment plan is also based on the latest medical technology. After the specified time, the current medical data of the current patient is obtained, and based on the current medical data of the current patient and the patient data before the specified time, it is determined whether the relevant indicators of the patient have improved after the specified time of treatment, so as to adjust the treatment plan later. By setting up hospital modules, department modules, doctor modules, and patient modules, each hospital is included in the system for unified management, which facilitates the learning and communication between doctors in the same department of each hospital, improves the level of medical skills, and promotes the development of medicine in all directions.

Claims

1. A hospital management method based on hospital management data, characterized in that: The following steps are involved: S1: The data acquisition module acquires the medical data and academic achievement data of each hospital based on big data. The medical data includes patient data and diagnosis and treatment data and disease progression data corresponding to the patient data. The academic achievement data includes papers, projects, patents, and works, and constructs a historical medical database and a historical academic achievement database. S2: The data acquisition module acquires the current patient data, retrieves the historical medical database, and retrieves similar cases in the historical medical database based on the patient data, and extracts the case with the best disease progression among the similar cases as a reference case, and extracts the treatment plan in the reference case as the pre-treatment plan for the current patient; S3: Retrieve the academic achievement data, extract the content related to the current patient, and determine whether the pre-treatment plan has an improved treatment plan based on the extracted academic achievements related to the current patient. If not, transmit it to the doctor module. If so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module. S4: The doctor of the current patient obtains the reference case and the pre-treatment plan through the doctor module. The doctor replaces or adjusts the pre-treatment plan based on the difference between the current patient data and the patient data in the reference case, and transmits the replaced or adjusted treatment plan to the plan verification module for plan verification; S5: transmitting the verified replacement or adjusted treatment plan to the data acquisition module, storing it in the historical medical database, and updating the data in the historical medical database; S6: after the specified time, obtain the current medical data of the current patient, and compare the current medical data of the current patient with the patient data in step S2 to determine whether the relevant indicators of the patient are improved after the specified time of treatment. If so, continue the current treatment plan; if not, loop through steps S2-S6; If there is a CT image in the current patient data, the specific process in step S2 is as follows: S21: extracting the first lesion image data in the current patient data and the second lesion image data in the historical medical database, dividing the lesion areas in the CT images of the two into micro-regions according to the specified size, and extracting the pixel points of each micro-region; S22: extracting the color feature value of each micro-region pixel point of the CT images of the first lesion image data and the second lesion image data, and comparing the color feature value of each micro-region pixel point of the CT image in the first lesion image data with the color feature value of each micro-region pixel point of the CT image in the second lesion image data; S23: Calculate the difference between the color characteristic value of each micro-region pixel point of the CT image in the first lesion image data and the color characteristic value of each micro-region pixel point of the CT image in the second lesion image data; S24: Counting the number of micro-areas whose difference values ​​in step S23 are within a specified range, and taking the cases whose number of micro-areas counted is greater than a preset threshold as similar cases; Note that a certain micro-region pixel point of the CT image of the first lesion image data in step S22 is ( x 1, y 1), the color feature value is c ( x 1, y 1); let the pixel point of a certain micro-area of ​​the CT image of the second lesion image data be ( x 2, y 2), the color feature value is c ( x 2, y 2) Calculate the color feature value of a certain micro-region pixel point of the CT image in the first lesion image data c ( x 1, y 1) Color feature value of a certain micro-region pixel point of the CT image of the second lesion image data c ( x 2, y 2) The formula for the difference is: △ c 1 = w [ c ( x 2， y 2)- c ( x 1， y 1)]； c ( x 1， y 1)=( b 1， g 1， r 1)； c ( x 2， y 2)=( b 2， g 2， r 2)； Among them, △ c 1 is a pixel ( x 1, y 1) and pixel points ( x 2, y 2) The difference in color feature values, w is a pixel ( x 1, y 1) is the contribution of micro-region Ω, b 1, g 1, r 1 respectively indicates that the pixel is ( x 1, y 1) the blue, green and red component values, b 2, g 2, r 2 respectively represent the pixel points as ( x 2, y 2) the blue, green and red component values, b 1. g 1. r 1 、b 2. g 2. r The value range of 2 is an integer from 0 to 255, indicating the intensity of the color; If there is no CT image in the current patient data, extract the various indicator data in the current patient data, call the historical medical database, compare the various indicator data in the current patient data with the corresponding indicator data in the cases in the historical medical database, calculate the difference between the various indicator data in the current patient data and the various indicators in the cases in the historical medical database, count the number of indicators whose difference is within the corresponding preset range, take the top three cases in terms of the number of indicators counted as similar cases, calculate the indicator changes of the three cases before and after a fixed period, and count the number of indicators whose indicator change direction represents the improvement of the condition, take the case with the largest number of indicators representing the improvement of the condition as the case with the best disease development, and use it as a reference case; It also includes a doctor management method, in which the doctor registers his identity in the system, then logs in and authenticates his identity based on the registered identity. After the identity authentication is passed, the designated hospital is selected according to the hospital module. After the corresponding management personnel review and approve it, the doctor enters the designated department in the department module of the designated hospital, obtains the case data of relevant patients based on the patient module, or obtains relevant academic achievements based on the doctor module.

2. The hospital management method based on hospital management data according to claim 1, characterized in that: The process of extracting the case with the best disease progression from similar cases as a reference case in step S2 is as follows: Extracting the lesion image data of the first time and the second time of the CT image of each case in the similar cases, dividing the lesion area in the CT images of the two cases into micro-areas according to the specified size, and extracting the pixel points of each micro-area; Extracting the color characteristic value of each micro-region pixel point of the CT image of the lesion image data at the first time and the second time, and comparing the color characteristic value of each micro-region pixel point of the CT image in the lesion image data at the first time with the color characteristic value of each micro-region pixel point of the CT image in the lesion image data at the second time; Calculate the difference between the color characteristic value of each micro-area pixel point of the CT image in the lesion image data at the first time and the color characteristic value of each micro-area pixel point of the CT image in the lesion image data at the second time, and the calculation process is the same as the calculation process in step S22; and sum the differences of all pixels, and take the case corresponding to the minimum value of the sum of the differences of all pixels of the lesion image data at the first time and the second time in similar cases as the reference case.

3. The hospital management method based on hospital management data according to claim 1, characterized in that: The process of judging whether there is any improved treatment plan for the pre-treatment plan in step S3 is: judging whether there is any drug or radiotherapy or chemotherapy method that can promote the treatment of the corresponding lesion in the academic results.

4. The hospital management method based on hospital management data according to claim 1, characterized in that: The replacement or adjustment of the pre-treatment regimen in step S4 includes replacement or adjustment of medication categories and adjustment of medication dosages.

5. A hospital management system based on hospital management data, used to implement the hospital management method based on hospital management data according to any one of claims 1 to 4, characterized in that: It includes a hospital module, a department module, a doctor module, a patient module, a data acquisition module, a data extraction module, a feature extraction module, a data analysis module and a management center, wherein the hospital module, the department module, the doctor module, the patient module, the data acquisition module, the data extraction module and the feature extraction module are all connected to the data analysis module and the management center; The hospital module is used to display various hospitals and related data; The department module is used to display the department data of each hospital; The doctor module is used to display the doctor data of each department; The patient module is used to display the patient data of each doctor's subordinates; The data acquisition module is used to acquire the medical data and academic achievement data of each hospital based on big data, wherein the medical data includes patient data and diagnosis and treatment data and disease progression data corresponding to the patient data, and the academic achievement data includes papers, projects, patents, and works, and to construct a historical medical database and a historical academic achievement database; The data extraction module is used to retrieve the historical medical database, and retrieve similar cases in the historical medical database based on the analysis results of the data analysis module, and extract the case with the best disease progression among the similar cases as a reference case, and extract the treatment plan in the reference case; The feature extraction module is used to extract the patient data lesion image data and the color feature value of each micro-area pixel; The data analysis module is used to analyze and process the medical data and academic achievement data, calculate the difference between the color characteristic value of each micro-area pixel point of the CT image in the first lesion image data and the color characteristic value of each micro-area pixel point of the CT image in the second lesion image data, count the number of micro-areas whose color characteristic value difference is within a specified range, and determine whether there is an improved treatment plan for the pre-treatment plan. If not, transmit it to the doctor module. If so, adjust the pre-treatment plan and transmit the adjusted pre-treatment plan to the doctor module; The management center is used to obtain the hospital data, department data, doctor data and patient data in the hospital module, department module, doctor module and patient module, and to uniformly manage each hospital, department, doctor and patient based on the hospital module, department module, doctor module and patient module.

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