CN116936078A - Traditional Chinese medicine pre-inquiry collection management system - Google Patents
Traditional Chinese medicine pre-inquiry collection management system Download PDFInfo
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- G—PHYSICS
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G—PHYSICS
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- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a traditional Chinese medicine pre-consultation collection management system, which belongs to the technical field of medical information and is used for solving the problems of low pre-consultation data collection efficiency, inconvenient information sharing and lack of intelligent assistance in the traditional Chinese medicine pre-consultation, wherein the traditional Chinese medicine pre-consultation collection management system comprises an information acquisition module, a pre-consultation module, a collection feedback module and a management privacy module, and the information acquisition module is used for acquiring basic information of a patient; the pre-consultation module matches an AI model for the patient to pre-consultate the patient; the collection feedback module is used for acquiring data of a pre-consultation department and recommending the department for the patient; the management privacy module is used for privacy protection of the patient pre-consultation data, and the management privacy module is used for helping the patient to complete the pre-consultation by collecting the basic information of the patient and matching AI models for different patients, so that the pre-consultation efficiency is improved, and meanwhile, the manpower resources of a hospital are effectively saved.
Description
Technical Field
The invention belongs to the technical field of medical information, relates to a pre-consultation collection and management technology, and particularly relates to a traditional Chinese medicine pre-consultation collection and management system.
Background
The traditional Chinese medicine pre-inquiry is an important link in the traditional Chinese medicine diagnosis process and is one of the special diagnosis methods of the traditional Chinese medicine, and the traditional Chinese medicine pre-inquiry is to perform the preliminary treatment on the illness state of the patient by collecting the symptom characteristics of the patient so as to assist the doctor of the traditional Chinese medicine to diagnose and treat the patient;
the traditional Chinese medicine pre-inquiry is carried out by the face-to-face communication between doctors and patients, the doctors need to inquire the symptoms of the patients to carry out dialectical treatment so as to achieve the aim of treatment, and the pre-inquiry mode has the problems of inaccurate data collection of the symptoms of the patients, complicated diagnosis process and waste of human resources of hospitals, so that the traditional Chinese medicine pre-inquiry collection and management system is proposed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a traditional Chinese medicine pre-consultation collection management system, which is used for collecting patient symptom information by matching corresponding AI models for different patient symptoms and utilizing the AI models to interact with patients, recommending a specific department for the patients according to the patient symptom information, completing pre-consultation and carrying out privacy management on the pre-consultation information at the same time, thereby solving the problems of inaccurate collection of the hospital pre-consultation information, waste of human resources of the hospital, long registration time of the patients caused by the fact that the patients do not know the departments to which the illness state belongs, difficult privacy protection of the patient pre-consultation and the like.
In order to achieve the above object, the object of the present invention can be achieved by the following technical scheme: the specific working process of each module of the traditional Chinese medicine pre-consultation collection management system is as follows:
the information acquisition module comprises a login verification unit, an interface guiding unit, an information checking unit and an information display unit, and is used for acquiring basic information of a patient and transmitting the basic information to the server;
the patient registers the account number through the user name and the password and enters a login page, the user name and the password are input by the patient, the login verification unit verifies the user name and the password with the user name and the password of the registered account number, and if the verification is correct, the patient is authorized to successfully login; if the verification is incorrect, prompting the patient to reenter the user name and the password;
further, the patient successfully logs in, the interface guiding unit guides the patient to create a patient file through a graphical interface and fills in the patient file, the information filled in by the patient file comprises patient name, age, contact information, academic, AI communication proficiency, gender and home address, and the information collected by the patient file is used as patient basic information;
further, the information checking unit verifies the contact information and the home address in the basic information of the patient, specifically, calls the contact information filled by the patient, and ensures that the patient can normally connect; map inquiry is carried out on home addresses filled by patients, the real existence of the addresses is ensured, distance verification is carried out, and the verification result is utilized to arrange hospital areas for the patients nearby;
further, the information display unit is connected with a control end and a modification end, the control end guides the patient to check the basic information of the patient by using the graphical interface, and if the basic information of the patient is filled in error, the modification end authorizes the patient to modify and store the basic information of the patient; if the basic information of the patient is filled in correctly, the modification end stores the basic information of the patient, and the information acquisition module transmits the basic information of the patient to the server;
the pre-consultation module comprises an AI model selection unit, an AI dialogue unit and a report generation unit, and is used for receiving basic information of a patient through the server, performing pre-consultation on the condition of the patient by matching the AI model for the patient through the basic information of the patient, acquiring a pre-consultation report and transmitting the pre-consultation report to the server;
the AI model selection unit obtains the age N in the patient basic information L X is an academic L AI communication proficiency level C D The method comprises the steps of carrying out a first treatment on the surface of the The AI ac capacity coefficient N is calculated as follows:
N＝(X L +C D )/N L
matching an AI model for the patient according to the AI communication capacity coefficient N;
when N is<N 1 Setting a first AI exchange capacity grade to be matched with a first AI model;
when N is 1 <N<N 2 Setting a second AI exchange capacity grade to be matched with a second AI model;
when N is 2 <N<N 3 Setting a third AI exchange capacity grade to be matched with a third AI model;
when N is 3 <And when N, setting the fourth AI alternating current capacity level to be matched with a fourth AI model.
Further, the AI dialogue unit obtains symptom group information by performing pre-consultation on the patient through the AI model, and the report generation unit generates a pre-consultation report;
the AI model asks the patient well and inquires the name and age information of the patient, and checks the name and age information with the basic information of the patient to ensure that the identity is correct; the AI dialogue unit guides the patient to input symptom characteristics in the dialogue box through the imaging interface, the AI model receives the symptom characteristics, and the symptom characteristics are imported into a symptom group database for searching; if the corresponding symptom group is retrieved in the symptom group database according to the symptom characteristics of the patient, setting the retrieved corresponding symptom group as symptom group information; if the corresponding symptom group cannot be retrieved in the symptom group database according to the symptom characteristics of the patient, the AI model replies to the patient that the corresponding symptom group cannot be retrieved, and the AI dialogue module transmits the symptom group selected by the patient as symptom group information to the report generation module;
further, the report generating unit acquires symptom group information, synthesizes the patient symptom group information of the selected symptom group with the patient basic information, and generates a pre-consultation report; if the AI dialogue model replies that the corresponding symptom group cannot be retrieved, the report generating unit takes the basic information of the patient as a pre-diagnosis report, marks the pre-diagnosis report as a special pre-diagnosis report, and the pre-diagnosis module transmits the pre-diagnosis report to the server.
The collection feedback module comprises a department recommendation unit, a manual service unit and a management feedback unit, receives a pre-consultation report, acquires pre-consultation department data through the pre-consultation report and recommends departments for patients;
the department recommendation unit recommends departments for the patients according to the patient symptom group information in the pre-consultation report, and takes the specific departments recommended for the patients as pre-consultation department data;
further, if the patient pre-consultation report is a special pre-consultation report, the manual service unit is connected with the patient through a contact way in the basic information of the patient, provides pre-consultation service for the patient through a manual customer service inquiry way, and subscribes to register according to the description symptoms of the patient;
further, the management feedback unit manages and transmits the pre-consultation department data to the pre-consultation module, the pre-consultation module feeds back the pre-consultation department data to the patient through the AI dialogue unit, the patient carries out registration and consultation to the corresponding department according to the pre-consultation department data, and the management feedback unit acquires registration and consultation data to generate an outpatient registration record and transmits the outpatient registration record to the management privacy module.
The privacy management module comprises a data desensitization unit, a data encryption unit and a right management unit, and is used for receiving basic information of a patient, a pre-consultation report and pre-consultation department data through a server and carrying out privacy management;
the data desensitization unit performs replacement operation to the name, home address and contact information of the patient in the patient pre-consultation data to finish desensitization treatment, specifically uses the simulation name, simulation address and simulation number randomly generated by the system to replace the real name, the real home address and the real contact information of the patient, and repeatedly compares the replaced simulation name, simulation address and simulation number with the real name, the real home address and the real contact information of the patient, and if the comparison results are consistent, the simulation name, the simulation address and the simulation number are replaced to carry out repeated comparison again; if the results are inconsistent, the desensitization process is complete.
Further, the data encryption unit encrypts the patient pre-consultation data;
generating a byte sequence with the length of 128 bits by using a random number generator as a generating key, wherein the generating key is used for encrypting and decrypting the patient pre-consultation data;
the method comprises the steps of converting patient pre-consultation data into patient pre-consultation data Unicode codes through a Unicode decoder, converting the patient pre-consultation data Unicode into patient pre-consultation data binary codes, dividing the patient pre-consultation data binary codes into data groups with equal lengths, wherein the length of each data group is 128 bits, and a byte sequence key of 128 bits is corresponding to the data groups;
encrypting each data packet by using an AES algorithm and a generated key, specifically, taking the previous data packet of the current data packet as an initial vector, carrying out exclusive-OR operation on the current data packet and the initial vector, encrypting the exclusive-OR operation result by using the generated key, taking the encrypted data as the initial vector of the next data packet, repeating the above processes, taking the last encrypted data as the initial vector of the first data packet, completing encryption on all the data packets, wherein the encrypted data of the data packets are ciphertext;
the patient pre-consultation data is stored and transmitted in a ciphertext mode to finish the management of patient privacy, when the patient pre-consultation data is needed to be used, each ciphertext group is decrypted by using a generating key, exclusive OR operation is carried out on an initial vector, the decrypted data is the initial vector of the next ciphertext group, the first decrypted data is the initial vector of the last data group, the decrypted binary code is used for obtaining the patient pre-consultation data through a Unicode encoder, and the restoration of the patient pre-consultation data is finished.
Further, the authority management unit manages the authority of the access user of the patient pre-consultation data;
the system role setting is carried out according to the identity verified when the system is logged in, the set system roles comprise common users, patients, doctors, nurses and administrators, different authorities are set for different system roles, specifically, the common users are set to have no authority to view and modify the pre-consultation data of the patients, the patients and the doctors are set to have the authority to view and modify the pre-consultation data of the patients, the nurses are set to have the authority to view but not modify the pre-consultation data of the patients, and the administrators are set to be able to set the authority of the system roles.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention utilizes the AI model to collect and record the patient treatment information in an electronic way, so that the patient treatment information is not easy to damage, and is convenient for doctors to review at any time and know the illness state of the patient in time;
2. according to the invention, corresponding AI models are matched for different patients, and the AI models are used for collecting patient symptoms to recommend corresponding departments for the patients, so that the diagnosis and treatment efficiency of a hospital is improved, and meanwhile, the time for the patients to visit is saved;
3. the invention encrypts the patient treatment data by using the AES algorithm and sets different authorities to protect, thereby fully ensuring the privacy safety of the patient.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a diagram of an overall system framework of the present invention;
FIG. 2 is a system frame diagram of an information acquisition module according to the present invention;
FIG. 3 is a block diagram of an AI pre-consultation module system of the present invention;
FIG. 4 is a block diagram of a collection feedback module system according to the present invention;
FIG. 5 is a block diagram of a system for managing privacy modules in accordance with the present invention;
FIG. 6 is a diagram of steps performed in the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the system for collecting and managing the pre-consultation of traditional Chinese medicine includes an information acquisition module, an AI pre-consultation module, a collection feedback module and a privacy management module; the information acquisition module, the AI pre-consultation module, the collection feedback module and the management privacy module are respectively connected with the server.
The information acquisition module acquires basic information of a patient and transmits the basic information to the server.
Referring to fig. 2, the information acquisition module includes a login verification unit, an interface guiding unit, an information checking unit and an information display unit.
The patient registers the account number through the user name and the password and enters a login page, the user name and the password are input by the patient, the login verification unit verifies the user name and the password with the user name and the password of the registered account number, and if the verification is correct, the patient is authorized to successfully login; if the verification is incorrect, prompting the patient to reenter the user name and the password.
The method comprises the steps that a patient successfully logs in, an interface guiding unit guides the patient to create a patient file through a graphical interface and fills in the patient file, information filled in by the patient file comprises patient name, age, contact mode, academic, AI communication proficiency, gender and family address, wherein the academic and AI communication proficiency provides options for the patient to select, the academic provides options comprise primary, middle and high-grade, the AI communication proficiency provides options comprise primary, middle and high-grade, and the information filled in by the patient file is used as basic information of the patient.
The information checking unit verifies the contact information and the home address in the basic information of the patient, specifically, calls the contact information filled by the patient, and ensures that the patient can normally connect; map inquiry is carried out on home addresses filled in by patients, the real existence of the addresses is ensured, distance verification is carried out, and the verification result is utilized to arrange hospital areas for the patients nearby.
The information display unit is connected with a control end and a modification end, the control end guides the patient to check the basic information of the patient by using the graphical interface, and if the basic information of the patient is filled in error, the modification end authorizes the patient to modify and store the basic information of the patient; and if the basic information of the patient is filled in correctly, the modification end stores the basic information of the patient.
When the modification terminal is modified, the modification terminal specifically comprises the following steps:
the modification end is connected with an operation unit, the operation unit is composed of a display and an operation end, a patient observes basic information through the display, inputs correct information through the operation end, and the modification end receives and stores the correct information.
It should be noted that: AI communication proficiency refers to proficiency of a patient in communication with an artificial intelligence AI, and is used to measure the patient's acceptance of the artificial intelligence AI, where the AI communication proficiency in the patient's basic information is selectively filled in by the patient by setting up three options, primary, intermediate and advanced.
The information acquisition module transmits the basic information of the patient to the server.
The pre-consultation module receives basic information of a patient through the server to pre-consultate the condition of the patient, acquires a pre-consultation report and transmits the pre-consultation report to the server.
Referring to fig. 3, the pre-consultation module includes an AI model selection unit, an AI dialogue unit, and a report generation unit.
The AI model selection unit obtains the age N in the patient basic information L X is an academic L AI communication proficiency level C D The AI ac capacity coefficient N is calculated as follows:
N＝(X L +C D )/N L 。
pair X according to the academy in the basic information of the patient L Assigning values a to primary school, middle school and university respectively 1 、a 2 And a 3 And 0 is<a 1 <a 2 <a 3 。
Based on AI communication proficiency in patient profile D Assigning values b corresponding to the primary, intermediate and high-level values respectively 1 、b 2 And b 3 And 0 is<b 1 <b 2 <b 3 。
It can be understood that: when the age values are the same, the higher the academic and the AI communication proficiency assignment are, the higher the AI communication capacity coefficient is, and the faster the AI model communication rhythm can be matched; when the academic and the AI communication proficiency are assigned identically, the larger the age value is, the smaller the AI communication ability coefficient is, and the slower the communication rhythm can be matched with the AI model.
Setting a first AI alternating current capacity grade, a second AI alternating current capacity grade, a third AI alternating current capacity grade and a fourth AI alternating current capacity grade according to the AI alternating current capacity coefficient N, respectively corresponding to the first AI model, the second AI model, the third AI model and the fourth AI model, setting different thresholds to obtain AI alternating current capacity grading data and correspondingly matching the AI alternating current capacity grading data and the AI models.
When N is<N 1 Setting a first AI exchange capacity grade to be matched with a first AI model;
when N is 1 <N<N 2 Setting a second AI exchange capacity grade to be matched with a second AI model;
when N is 2 <N<N 3 Setting a third AI exchange capacity grade to be matched with a third AI model;
when N is 3 <And when N, setting the fourth AI alternating current capacity level to be matched with a fourth AI model.
It should be noted that: wherein N is L Is of age, X L Is the academic course, C D Is the AI communication proficiency, N is the AI communication ability coefficient, N 1 、N 2 And N 3 Standard data of AI exchange capacity coefficient set and 0<N 1 <N 2 <N 3 The method comprises the steps of carrying out a first treatment on the surface of the The AI exchange capacity coefficient corresponding to the first AI exchange capacity level is smaller than the AI exchange capacity coefficient corresponding to the second AI exchange capacity level, the AI exchange capacity coefficient corresponding to the second AI exchange capacity level is smaller than the AI exchange capacity coefficient corresponding to the third AI exchange capacity level, and the AI exchange capacity coefficient corresponding to the third AI exchange capacity level is smaller than the AI exchange capacity coefficient corresponding to the fourth AI exchange capacity level; and setting the human-computer interaction rhythm of the first AI model to be slower than that of the second AI model according to the AI communication capacity grading data, wherein the human-computer interaction rhythm of the second AI model is slower than that of the third AI model, and the human-computer interaction rhythm of the third AI model is slower than that of the fourth AI model.
Further, the human-computer interaction rhythm refers to the communication exhaustive degree of the AI on the pre-consultation of the patient, specifically, when the AI communication capacity coefficient N of the patient is smaller, the understanding capacity is weak, the AI model matched by the AI model selecting unit is slow in communication rhythm with the patient, and the AI model can be interpreted in an exhaustive manner according to the question of the patient and an active guiding manner is adopted in the dialogue with the patient; when the AI communication capacity coefficient N of the patient is large, the understanding capacity is high, the AI model selection unit is fast in communication rhythm of the matched AI model, response time is fast, and pre-consultation efficiency is improved.
The AI dialogue unit carries out pre-consultation by a dialogue interaction mode between an AI model matched with a patient and the patient, and the method is as follows:
the AI model asks the patient well and inquires the name and age information of the patient, and checks the name and age information with the basic information of the patient to ensure that the identity is correct; the AI dialogue unit guides the patient to input symptom characteristics in the dialogue box through the imaging interface, the AI model receives the symptom characteristics, and the symptom characteristics are imported into the symptom group database for searching.
If the corresponding symptom group is retrieved in the symptom group database according to the symptom characteristics of the patient, setting the retrieved corresponding symptom group as symptom group information; if the corresponding symptom group is not retrieved in the symptom group database according to the symptom characteristics of the patient, the AI model replies to the patient that the corresponding symptom group is not retrieved.
It should be noted that:
the symptom group in the symptom group database comprises a first symptom group, a second symptom group, a third symptom group, a fourth symptom group, a fifth symptom group, a sixth symptom group and a seventh symptom group, wherein the first symptom group comprises symptoms: dyspepsia, gastralgia, abdominal distention, constipation, inappetence, fatigue and insomnia; the second symptom group includes symptoms: fracture, joint pain, muscle strain, and lumbago; the third symptom group included symptoms: menoxenia, dysmenorrhea, menorrhagia, infertility and climacteric syndrome; the fourth symptom group included symptoms: infant fever, cough, diarrhea, vomiting, dyspepsia and infantile eczema; the fifth group of symptoms included symptoms: headache, cervical pain, peri-shoulder pain, lumbar pain and lumbago; the sixth symptom group includes symptoms: cervical spondylosis, lumbar muscle strain, shoulder neck pain and fatigue; the seventh symptom group includes symptoms: cold, cough, stomach pain, constipation, insomnia and anxiety.
The AI dialog module communicates the patient selection symptom group as symptom group information to the report generating unit.
The report generating unit acquires symptom group information, and synthesizes the patient symptom group information of the selected symptom group with the basic information of the patient to obtain a pre-consultation report; if the AI dialogue model replies that the corresponding symptom group cannot be retrieved, the report generation unit takes the patient basic information of the AI dialogue model as a pre-consultation report, and the report generation unit marks the pre-consultation report as a special pre-consultation report.
The pre-consultation module transmits the pre-consultation report to the server.
The collection feedback module receives the pre-consultation report through the server, recommends departments for the patients, generates pre-consultation department data and transmits the pre-consultation department data to the server.
Referring to fig. 4, the collection feedback module includes a department recommendation unit, a manual service unit, and a management feedback unit.
The department recommendation unit recommends departments for the patients according to the information of the symptom groups of the patients in the pre-consultation report, specifically, when the information of the symptom groups of the patients is respectively a first symptom group, a second symptom group, a third symptom group, a fourth symptom group, a fifth symptom group, a sixth symptom group and a seventh symptom group, the recommended departments are respectively traditional Chinese medicine internal medicine, traditional Chinese medicine surgery, traditional Chinese medicine gynaecology, traditional Chinese medicine pediatrics, traditional Chinese medicine acupuncture department, traditional Chinese medicine massage department and traditional Chinese medicine department, and the specific departments recommended for the patients are used as pre-consultation department data.
If the patient pre-consultation report is a special pre-consultation report, the manual service unit is connected with the patient through a contact way in the basic information of the patient, and provides pre-consultation service for the patient through a manual customer service inquiry way, specifically, the manual customer service inquires the basic information of the patient and checks the basic information with the information filled in the basic information of the patient, so that the pre-consultation service object is the patient; manual customer service asks the patient for the main symptoms and discomfort and guides the patient to describe specific discomfort symptoms; based on the patient's symptoms, the human customer service further asks the patient's medical history, including past medical history, surgical history, allergic history, and family medical history, to learn about the patient's overall health condition; the manual customer service solves the problem for the patient according to the information provided by the patient and provides advice and guides the doctor to visit, wherein the provided advice comprises advice of self-care and diet conditioning, and the guiding the doctor to visit comprises providing specific positions and contact ways of the hospital and registering the appointment for the patient according to the description symptoms of the patient.
The management feedback unit manages and transmits the pre-consultation department data to the pre-consultation module, the pre-consultation module feeds back the pre-consultation department data to the patient through the AI dialogue unit, the patient carries out registration and consultation to the corresponding department according to the pre-consultation department data, and the management feedback unit acquires registration and consultation data to generate an outpatient registration record and transmits the outpatient registration record to the management privacy module.
The management privacy module receives basic information of a patient, a pre-consultation report and pre-consultation department data through the server and performs privacy management.
Referring to fig. 5, the privacy management module includes a data desensitizing unit, a data encrypting unit and a rights management unit.
The management privacy module sets basic information of a patient, a pre-consultation report and pre-consultation department data as pre-consultation data of the patient, and performs privacy management on the pre-consultation data of the patient.
The data desensitization unit performs replacement operation to the name, home address and contact information of the patient in the patient pre-consultation data to finish desensitization treatment, specifically uses the simulation name, simulation address and simulation number randomly generated by the system to replace the real name, the real home address and the real contact information of the patient, and repeatedly compares the replaced simulation name, simulation address and simulation number with the real name, the real home address and the real contact information of the patient, and if the comparison results are consistent, the simulation name, the simulation address and the simulation number are replaced to carry out repeated comparison again; if the results are inconsistent, the desensitization process is complete.
The data encryption unit processes the patient pre-consultation data, the encryption algorithm selected by the data encryption unit is an AES symmetric encryption algorithm, and the data encryption unit comprises a random number generator, a Unicode encoder and a Unicode decoder, and the specific encryption process is as follows:
a byte sequence with the length of 128 bits is generated by a random number generator as a generation key, and the generation key is used for encrypting and decrypting the patient pre-consultation data.
The method comprises the steps of converting patient pre-consultation data into patient pre-consultation data Unicode codes through a Unicode decoder, converting the patient pre-consultation data Unicode into patient pre-consultation data binary codes, dividing the patient pre-consultation data binary codes into data groups with equal lengths, wherein the length of each data group is 128 bits, and a byte sequence key of 128 bits is corresponding to the data groups; if the patient pre-consultation data binary coding length is not an integer multiple of the data packet length, filling the high order bits of the patient pre-consultation data binary coding with 0 until the patient pre-consultation data binary coding is an integer multiple of the data packet length.
Encrypting each data packet by using an AES algorithm and a generated key, specifically, taking the previous data packet of the current data packet as an initial vector, performing exclusive OR operation (the difference is one and the same is zero) on the current data packet and the initial vector, encrypting the result of the exclusive OR operation by using the generated key, taking the encrypted data as the initial vector of the next data packet, repeating the above processes, taking the last encrypted data as the initial vector of the first data packet, completing the encryption on all the data packets, and taking the encrypted data of the data packets as ciphertext.
The patient pre-consultation data is stored and transmitted in a ciphertext mode to finish the management of patient privacy, when the patient pre-consultation data is needed to be used, each ciphertext group is decrypted by using a generating key, exclusive OR operation is carried out on an initial vector, the decrypted data is the initial vector of the next ciphertext group, the first decrypted data is the initial vector of the last data group, the decrypted binary code is used for obtaining the patient pre-consultation data through a Unicode encoder, and the restoration of the patient pre-consultation data is finished.
The authority management unit performs authority management on access users of the patient pre-consultation data, performs system role setting according to the identity verified when logging in the system, wherein the set system roles comprise common users, patients, doctors, nurses and administrators, different authorities are set for different system roles, specifically, the common users are set to have no authority to view and modify the patient pre-consultation data, the patients and the doctors are set to have the authority to view and modify the patient pre-consultation data, the nurses are set to have the authority to view but not modify the patient pre-consultation data, and the administrators are set to be able to set the authority of the system roles.
It should be noted that:
data desensitization is a data protection technique, and aims to reduce the risk of leakage of sensitive data while maintaining certain usability by processing the sensitive data.
The AES algorithm is a symmetric encryption algorithm that encrypts and decrypts data using the same key, and is one of encryption algorithms widely used at present for protecting the security of data.
Referring to fig. 6, in the present invention, the system for collecting and managing the pre-consultation of traditional Chinese medicine comprises the following steps:
step S1: acquiring basic information of a patient and transmitting the basic information to a server;
step S11: the patient registers the account number through the user name and the password and enters a login page, the user name and the password are input by the patient, and the user name and the password are verified with the user name and the password of the registered account number;
if the verification is correct, the patient is authorized to successfully log in;
if the verification is incorrect, prompting the patient to reenter the user name and the password.
Step S12: the patient successfully logs in, the graphical interface guides the patient to create and fill in a patient file, and the information filled in the patient file comprises the name, age, contact information, academic, AI communication proficiency, sex and home address of the patient, and the information filled in the patient file is used as the basic information of the patient.
Step S13: verifying the contact information and the home address in the basic information of the patient;
making a call on the contact way filled in by the patient, so as to ensure that the patient can normally make a call;
map inquiry is carried out on home addresses filled in by patients, the real existence of the addresses is ensured, distance verification is carried out, and the verification result is utilized to arrange hospital areas for the patients nearby.
Step S14: the control end guides the patient to check the basic information of the patient by using the graphical interface, and if the basic information of the patient is filled with errors, the modification end authorizes the patient to modify and store the basic information of the patient; and if the basic information of the patient is filled in correctly, the modification end stores the basic information of the patient.
Step S2: the method comprises the steps that basic information of a patient is received through a server to conduct pre-consultation on the condition of the patient, and a pre-consultation report is obtained and is transmitted to the server;
step S21: the AI model selection unit obtains the age N in the patient basic information L X is an academic L AI communication proficiency level C D The AI ac capacity coefficient N is calculated as follows:
N＝(X L +C D )/N L
pair X according to the academy in the basic information of the patient L Assigning values a to primary school, middle school and university respectively 1 、a 2 And a 3 And 0 is<a 1 <a 2 <a 3 ；
Based on AI communication proficiency in patient profile D Assigning values b corresponding to the primary, intermediate and high-level values respectively 1 、b 2 And b 3 And 0 is<b 1 <b 2 <b 3 。
Setting a first AI alternating current capacity grade, a second AI alternating current capacity grade, a third AI alternating current capacity grade and a fourth AI alternating current capacity grade according to the AI alternating current capacity coefficient N, respectively corresponding to the first AI model, the second AI model, the third AI model and the fourth AI model, setting different thresholds to obtain AI alternating current capacity grading data and correspondingly matching the AI alternating current capacity grading data and the AI models.
When N is<N 1 Setting a first AI exchange capacity grade to be matched with a first AI model;
when N is 1 <N<N 2 Setting a second AI exchange capacity grade to be matched with a second AI model;
when N is 2 <N<N 3 Setting a third AI exchange capacity grade to be matched with a third AI model;
when N is 3 <And when N, setting the fourth AI alternating current capacity level to be matched with a fourth AI model.
Step S22: the patient-matched AI model and the patient are pre-diagnosed in a dialogue interaction mode, and the method is as follows: the AI model asks the patient well and inquires the name and age information of the patient, and checks the name and age information with the basic information of the patient to ensure that the identity is correct; guiding a patient to input symptom characteristics in a dialog box through an imaging interface, receiving the symptom characteristics by an AI model, and guiding the symptom characteristics into a symptom group database for retrieval; if the corresponding symptom group is retrieved in the symptom group database according to the symptom characteristics of the patient, setting the retrieved corresponding symptom group as symptom group information; if the corresponding symptom group cannot be retrieved in the symptom group database according to the symptom characteristics of the patient, the AI model replies to the patient that the corresponding symptom group cannot be retrieved;
the AI dialog module communicates the patient selection symptom group as symptom group information to the report generating unit.
Step S23: acquiring symptom group information, and integrating the patient symptom group information of the selected symptom group with the basic patient information to generate a pre-consultation report; if the AI dialogue model replies that the corresponding symptom group cannot be retrieved, the report generating unit takes the basic information of the patient as a pre-consultation report, and the report generating unit marks the pre-consultation report as a special pre-consultation report; the pre-consultation module transmits the pre-consultation report to the server.
Step S3: receiving a pre-consultation report through a server, recommending departments for patients, generating pre-consultation department data, and transmitting the pre-consultation department data to the server;
step S31: recommending departments for the patients according to the patient symptom group information in the pre-consultation report, and taking the specific departments recommended for the patients as pre-consultation department data.
Step S32: if the patient pre-consultation report is a special pre-consultation report, the patient is contacted with the patient through a contact way in the basic information of the patient, and pre-consultation service is provided for the patient through a manual customer service inquiry way.
Step S33: the method comprises the steps that pre-consultation department data are managed and transmitted to a pre-consultation module, the pre-consultation module feeds back the pre-consultation department data to a patient through an AI dialogue unit, the patient carries out registration and diagnosis on the corresponding department according to the pre-consultation department data, and a management feedback unit obtains registration and diagnosis data to generate an outpatient registration record and transmits the outpatient registration record to a management privacy module.
Step S4: receiving basic information of a patient, a pre-consultation report and pre-consultation department data through a server and performing privacy management;
step S41: setting basic information of a patient, a pre-consultation report and pre-consultation department data as pre-consultation data of the patient, and carrying out privacy management on the pre-consultation data of the patient; the data desensitization unit performs replacement operation to the name, home address and contact information of the patient in the patient pre-consultation data to finish desensitization treatment, specifically uses the simulation name, simulation address and simulation number randomly generated by the system to replace the real name, the real home address and the real contact information of the patient, and repeatedly compares the replaced simulation name, simulation address and simulation number with the real name, the real home address and the real contact information of the patient, and if the comparison results are consistent, the simulation name, the simulation address and the simulation number are replaced to carry out repeated comparison again; if the results are inconsistent, the desensitization process is complete.
Step S42: the patient pre-consultation data is processed, the encryption algorithm selected by the method is an AES symmetric encryption algorithm, and the data encryption unit comprises a random number generator, a Unicode encoder and a Unicode decoder, and the specific encryption process is as follows:
(1) A byte sequence with the length of 128 bits is generated by a random number generator as a generation key, and the generation key is used for encrypting and decrypting the patient pre-consultation data.
(2) The method comprises the steps of converting patient pre-consultation data into patient pre-consultation data Unicode codes through a Unicode decoder, converting the patient pre-consultation data Unicode into patient pre-consultation data binary codes, dividing the patient pre-consultation data binary codes into data groups with equal lengths, wherein the length of each data group is 128 bits, and a byte sequence key of 128 bits is corresponding to the data groups; if the patient pre-consultation data binary coding length is not an integer multiple of the data packet length, filling the high order bits of the patient pre-consultation data binary coding with 0 until the patient pre-consultation data binary coding is an integer multiple of the data packet length.
(3) Encrypting each data packet by using an AES algorithm and a generated key, specifically, taking the previous data packet of the current data packet as an initial vector, performing exclusive OR operation (the difference is one and the same is zero) on the current data packet and the initial vector, encrypting the result of the exclusive OR operation by using the generated key, taking the encrypted data as the initial vector of the next data packet, repeating the above processes, taking the last encrypted data as the initial vector of the first data packet, completing the encryption on all the data packets, and taking the encrypted data of the data packets as ciphertext.
(4) The patient pre-consultation data is stored and transmitted in a ciphertext mode to finish the management of patient privacy, when the patient pre-consultation data is needed to be used, each ciphertext group is decrypted by using a generating key, exclusive OR operation is carried out on an initial vector, the decrypted data is the initial vector of the next ciphertext group, the first decrypted data is the initial vector of the last data group, the decrypted binary code is used for obtaining the patient pre-consultation data through a Unicode encoder, and the restoration of the patient pre-consultation data is finished.
Step S42: performing authority management on access users of the patient pre-consultation data, performing system role setting according to the identity verified when logging in the system, wherein the set system roles comprise common users, patients, doctors, nurses and administrators, different authorities are set for different system roles, specifically, the common users are set to have no authority to view and modify the patient pre-consultation data, the patients and the doctors are set to have the authority to view and modify the patient pre-consultation data, the nurses are set to have the authority to view but not modify the patient pre-consultation data, and the administrators are set to have the authority to set the system roles.
The calculation formulas are all the coefficients of weight coefficient, proportion coefficient and the like which are calculated by removing dimensions and taking values, the set size is a result value obtained by quantizing each parameter, and the proportional relation between the weight coefficient and the proportion coefficient is not influenced.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The traditional Chinese medicine pre-consultation collection management system is characterized by comprising an information acquisition module, a pre-consultation module, a collection feedback module, a privacy management module and a server;
and the information acquisition module is used for: the system is used for acquiring basic information of a patient and transmitting the basic information to a server;
a pre-consultation module: the patient basic information is used for matching an AI model for the patient to carry out pre-consultation on the patient condition, and a pre-consultation report is obtained and is transmitted to a server;
and a collecting feedback module: receiving a pre-consultation report, acquiring pre-consultation department data through the pre-consultation report, and recommending departments for patients;
and (3) managing a privacy module: the system is used for receiving basic information of a patient, a pre-consultation report and pre-consultation department data through a server and carrying out privacy management;
the server is respectively connected with the information acquisition module, the pre-consultation module, the collection feedback module and the privacy management module.
2. The system according to claim 1, wherein the information acquisition module comprises an interface guiding unit, an information checking unit and an information displaying unit;
the interface guiding unit guides a patient to create a patient file through a graphical interface and fills in the patient file, wherein the information filled in the patient file comprises the name, age, contact information, academic, AI communication proficiency, sex and home address of the patient, and the information collected by the patient file is used as basic information of the patient;
the information checking unit verifies the contact information and the home address in the basic information of the patient;
the information display unit is connected with a control end and a modification end, the control end guides the patient to check the basic information of the patient by using the graphical interface, and if the basic information of the patient is filled in error, the modification end authorizes the patient to modify and store the basic information of the patient; if the basic information of the patient is filled in correctly, the modification end stores the basic information of the patient;
the information acquisition module transmits the basic information of the patient to the server.
3. The system according to claim 1, wherein the pre-consultation module comprises an AI model selection unit, an AI dialogue unit and a report generation unit, and is configured to receive basic information of a patient through a server, and obtain a pre-consultation report, specifically as follows:
the AI model selection unit obtains the age N in the patient basic information L X is an academic L AI communication proficiency level C D The method comprises the steps of carrying out a first treatment on the surface of the The AI ac capacity coefficient N is calculated as follows:
N＝(X L +C D )/N L
matching an AI model for the patient according to the AI communication capacity coefficient N;
when N is<N 1 Setting a first AI exchange capacity grade to be matched with a first AI model;
when N is 1 <N<N 2 Setting a second AI exchange capacity grade to be matched with a second AI model;
when N is 2 <N<N 3 Setting a third AI exchange capacity grade to be matched with a third AI model;
when N is 3 <And when N, setting the fourth AI alternating current capacity level to be matched with a fourth AI model.
4. The system according to claim 3, wherein the AI dialogue unit obtains symptom group information by performing a pre-consultation on the patient by the AI model, and the report generating unit generates a pre-consultation report, specifically as follows:
the AI model asks the patient well and inquires the name and age information of the patient, and checks the name and age information with the basic information of the patient to ensure that the identity is correct; the AI dialogue unit guides the patient to input symptom characteristics in the dialogue box through the imaging interface, the AI model receives the symptom characteristics, and the symptom characteristics are imported into a symptom group database for searching; if the corresponding symptom group is retrieved in the symptom group database according to the symptom characteristics of the patient, setting the retrieved corresponding symptom group as symptom group information; if the corresponding symptom group cannot be retrieved in the symptom group database according to the symptom characteristics of the patient, the AI model replies to the patient that the corresponding symptom group cannot be retrieved;
the AI dialog module communicates the patient selection symptom group as symptom group information to the report generation module;
the report generating unit acquires symptom group information, synthesizes the patient symptom group information of the selected symptom group with the patient basic information, and generates a pre-consultation report; if the AI dialogue model replies that the corresponding symptom group cannot be retrieved, the report generating unit takes the basic information of the patient as a pre-consultation report, and marks the pre-consultation report as a special pre-consultation report;
the pre-consultation module transmits the pre-consultation report to the server.
5. The system according to claim 1, wherein the collection feedback module comprises a department recommendation unit, a manual service unit and a management feedback unit, and is used for recommending departments for patients, and the system is characterized in that:
the department recommendation unit recommends departments for the patients according to the patient symptom group information in the pre-consultation report, and takes the specific departments recommended for the patients as pre-consultation department data;
if the patient pre-consultation report is a special pre-consultation report, the manual service unit is connected with the patient through a contact way in basic information of the patient, provides pre-consultation service for the patient through a manual customer service inquiry way, and registers the appointment for the patient according to the description symptoms of the patient;
the management feedback unit manages and transmits the pre-consultation department data to the pre-consultation module, the pre-consultation module feeds back the pre-consultation department data to the patient through the AI dialogue unit, the patient carries out registration and consultation to the corresponding department according to the pre-consultation department data, and the management feedback unit acquires registration and consultation data to generate an outpatient registration record and transmits the outpatient registration record to the management privacy module.
6. The system according to claim 1, wherein the privacy management module comprises a data desensitizing unit, a data encrypting unit and a right management unit, wherein the data desensitizing unit desensitizes the patient pre-consultation data as follows;
the data desensitization unit performs replacement operation to the name, home address and contact information of the patient in the patient pre-consultation data to finish desensitization treatment, specifically uses the simulation name, simulation address and simulation number randomly generated by the system to replace the real name, the real home address and the real contact information of the patient, and repeatedly compares the replaced simulation name, simulation address and simulation number with the real name, the real home address and the real contact information of the patient, and if the comparison results are consistent, the simulation name, the simulation address and the simulation number are replaced to carry out repeated comparison again; if the results are inconsistent, the desensitization process is complete.
7. The system according to claim 6, wherein the data encryption unit encrypts the patient pre-consultation data, specifically as follows:
generating a byte sequence with the length of 128 bits by using a random number generator as a generating key, wherein the generating key is used for encrypting and decrypting the patient pre-consultation data;
the method comprises the steps of converting patient pre-consultation data into patient pre-consultation data Unicode codes through a Unicode decoder, converting the patient pre-consultation data Unicode into patient pre-consultation data binary codes, dividing the patient pre-consultation data binary codes into data groups with equal lengths, wherein the length of each data group is 128 bits, and a byte sequence key of 128 bits is corresponding to the data groups;
encrypting each data packet by using an AES algorithm and a generated key, specifically, taking the previous data packet of the current data packet as an initial vector, carrying out exclusive-OR operation on the current data packet and the initial vector, encrypting the exclusive-OR operation result by using the generated key, taking the encrypted data as the initial vector of the next data packet, repeating the above processes, taking the last encrypted data as the initial vector of the first data packet, completing encryption on all the data packets, wherein the encrypted data of the data packets are ciphertext;
the patient pre-consultation data is stored and transmitted in a ciphertext mode to finish the management of patient privacy, when the patient pre-consultation data is needed to be used, each ciphertext group is decrypted by using a generating key, exclusive OR operation is carried out on an initial vector, the decrypted data is the initial vector of the next ciphertext group, the first decrypted data is the initial vector of the last data group, the decrypted binary code is used for obtaining the patient pre-consultation data through a Unicode encoder, and the restoration of the patient pre-consultation data is finished.
8. The system according to claim 6, wherein the authority management unit manages the authority of the access user of the patient pre-consultation data, specifically as follows:
the system role setting is carried out according to the identity verified when the system is logged in, the set system roles comprise common users, patients, doctors, nurses and administrators, different authorities are set for different system roles, specifically, the common users are set to have no authority to view and modify the pre-consultation data of the patients, the patients and the doctors are set to have the authority to view and modify the pre-consultation data of the patients, the nurses are set to have the authority to view but not modify the pre-consultation data of the patients, and the administrators are set to be able to set the authority of the system roles.
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