Hospital endoscope diagnosis and treatment information query system based on smart cloud management

Abstract

The invention relates to the technical field of hospital information inquiry, in particular to a hospital endoscope diagnosis and treatment information inquiry system based on smart cloud management, which comprises a pre-diagnosis module, an in-diagnosis module, a post-diagnosis module and an information inquiry module, and is characterized in that the pre-diagnosis module comprises a smart cloud reservation unit, a smart cloud customer service unit and a pre-diagnosis information push unit; the smart cloud reservation unit supports four modes of telephone reservation, doctor reservation, center reservation and official account reservation, and adopts a reservation resource allocation algorithm to optimize medical resource allocation. According to the hospital endoscopic diagnosis and treatment information query system based on smart cloud management, a patient can complete operations such as appointment, payment and query through one mobile phone, queuing conditions are reduced, easy medical treatment is realized, and medical treatment efficiency is improved through process reconstruction and innovation. By adopting the functions of multi-channel reservation, information reminding, automatic sign-in, dynamic state query, self-service printing, science popularization pushing, satisfaction investigation and the like, digitally-driven intelligent diagnosis and treatment are realized, and the management efficiency of an endoscope center is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hospital information query, and in particular to a hospital endoscopic diagnosis and treatment information query system based on smart cloud management. Background Art

[0002] Gastrointestinal endoscopy is a necessary means and the best method for diagnosing and treating most digestive tract diseases. With the popularization of endoscopic diagnosis and treatment and people's increasing attention to digestive system physical examinations, the risks associated with endoscopic care and management have also increased in recent years with the diversification and refinement of endoscopic diagnosis and treatment projects.

[0003] Endoscopic diagnosis and treatment has the characteristics of less trauma, quick recovery and good effect, and is very popular among patients. However, the management services of traditional endoscopy centers are often limited to a single link in the examination and treatment process, the medical process is not smooth, the information sharing between patients and their families is insufficient, the service quality is difficult to guarantee, the patient experience is poor, and data management is difficult. There is a lack of comprehensive management and service support for the entire cycle of patient treatment, which has led to some phenomena that are not conducive to the health and safety of patients, the medical experience and the efficiency of medical services. These problems are not conducive to improving the efficiency and quality of endoscopic examination and treatment, and also restrict the rational use of medical resources and the improvement of patients' medical experience. Summary of the invention

[0004] The purpose of the present invention is to provide a hospital endoscopic diagnosis and treatment information query system based on intelligent cloud management to solve the problems existing in the traditional endoscopy center management services, realize the intelligent cloud management of multiple scenarios before, during and after endoscopic diagnosis and treatment, and improve the efficiency and quality of medical services as well as the patient's medical experience.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a hospital endoscopy diagnosis and treatment information query system based on smart cloud management, comprising a pre-diagnosis module, a diagnosis module, a post-diagnosis module and an information query module, wherein the pre-diagnosis module comprises a smart cloud reservation unit, a smart cloud customer service unit and a pre-diagnosis information push unit; The smart cloud appointment unit supports four methods: telephone appointment, doctor appointment, center appointment, and public account appointment, and uses an appointment resource allocation algorithm to optimize medical resource allocation; The smart cloud customer service unit provides 24-hour online consulting services; The pre-diagnosis information push unit pushes endoscopic diagnosis and treatment related knowledge and examination preparation information; The diagnosis module includes a smart cloud guidance unit, a smart cloud accompanying unit and a smart cloud consultation unit; The smart cloud guidance unit pushes appointment information, parking guidance information and sign-in reminders at different time points before the examination, and uses a guidance time optimization algorithm to plan reminder time; The smart cloud accompanying unit realizes unaccompanied nursing, family binding and progress information push; The smart cloud medical consultation unit supports pre-deposit recharge and one-person-one-code full-process real-time clock-in record; The post-diagnosis module includes a smart cloud management unit, a smart cloud archive unit and a smart cloud evaluation unit; The smart cloud management unit pushes medical consultation reminders, medical treatment information, and test result reminders; The smart cloud archive unit stores diagnosis and treatment data and provides health management solutions using health risk assessment algorithms; The smart cloud evaluation unit pushes a satisfaction survey questionnaire; The information query function supports patients and authorized family members to query information related to endoscopic diagnosis and treatment, including appointment records, examination reports, test results, diagnosis and treatment cost details, and health management recommendations.

[0006] Preferably, the smart cloud reservation unit introduces a reservation resource allocation algorithm to optimize the allocation and scheduling of medical resources. Assuming that the total number of equipment available for endoscopic diagnosis and treatment in the hospital is N, the time occupied by the equipment for different types of endoscopic examinations is respectively , the number of appointments required for each examination is , the optimal device allocation scheme is calculated by the following algorithm formula: .

[0007] Preferably, the smart cloud customer service unit provides 24-hour continuous service with the help of Internet technology. When patients have needs, they can initiate online consultation, and the endoscopy specialist nursing team will answer questions related to intestinal preparation and drug administration methods online.

[0008] Preferably, the smart platform pushes popular science tweets and videos on endoscopic diagnosis and treatment-related knowledge, examination preparation instructions and other precautions to patients, provides patients with home health guidance, and helps patients correctly complete preoperative preparations.

[0009] Preferably, the day before the examination by the smart cloud guidance unit, the hospital smart platform pushes appointment information details to the patient, reminding the appointment time and examination items. Two hours before the examination, the system automatically pushes parking guidance information, and the patient can view the parking locations around the hospital, real-time remaining space information, and obtain navigation services to quickly reach the hospital and solve the parking problem. One hour before the examination, the smart in-hospital navigation obtains the patient's location and reminds the patient to sign in automatically. In order to optimize the guidance time planning, the guidance time optimization algorithm is adopted. Assuming that the patient's estimated travel time from the departure point to the hospital is The average parking time around the hospital is , the walking time from the parking spot to the endoscopy center is , then the best reminder time The calculation formula is: ,in, It is the scheduled inspection time, and ΔT is the reserved buffer time.

[0010] Preferably, in the smart cloud medical treatment unit, patients can make pre-deposit and recharge for medical treatment through the smart platform, generate an identity identification QR code after signing in, realize one-person-one-code during endoscopic diagnosis and treatment, and record the whole process in real time. It can complete real-time monitoring of barcode printing, anesthesia consultation, intravenous injection, waiting and other links, deeply integrate medical services with Internet technology, innovate service models, and optimize service processes.

[0011] Preferably, in the smart cloud management unit, the system identifies intelligent scenarios and automatically pushes appointment reminders the day before the appointment, hospital appointment instructions, treatment information to accompanying personnel, waiting information, post-appointment test result reminders, and post-appointment follow-up reminders to provide patients with butler-style services and improve the service level and management efficiency of medical institutions.

[0012] Preferably, the smart cloud archive reminds the post-diagnosis test results, helps patients monitor and manage their health, stores endoscopic diagnosis and treatment data in the cloud archive, realizes accurate reminders, pushes professional popular science information, and intelligently follows up continuously, introduces a health risk assessment algorithm, and calculates the health risk index R according to the patient's endoscopic diagnosis and treatment data (such as lesion condition L, pathological result P), medical history H, and living habits (such as smoking frequency S, drinking volume A, exercise frequency E) through the following formula: ,in, is the weight coefficient of each factor, β is the constant term. According to the calculated health risk index, we manage personal health data for patients, design personalized health management plans, and provide customized health advice.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The hospital endoscopy diagnosis and treatment information inquiry system based on smart cloud management has effectively improved the "three longs and one short" problem of the hospital. Patients can complete appointments, payments, inquiries and other operations through a mobile phone, reducing multiple queues and long queues, and achieving easy medical treatment. Through process reengineering and innovation, it adopts multi-channel appointments, information reminders, automatic sign-in, dynamic query status, self-service printing, popular science push, satisfaction surveys and other functions, realizing digital-driven smart diagnosis and treatment and improving the management efficiency of the endoscopy center.

[0014] 2. The hospital endoscopy diagnosis and treatment information query system based on smart cloud management has built a new model of cloud medical service, providing patients with comprehensive and personalized services at all stages before, during and after diagnosis, improving service quality and patient satisfaction, promoting the comprehensive development of medical services, significantly improving the diagnosis and treatment experience of endoscopic patients, realizing the deep integration of medical services and Internet technologies, and promoting the construction of smart hospitals and hospital information standardization with the "three-in-one" of smart medical care, smart services and smart management, which is in line with the development trend of intelligent, information-based, remote, personalized, cross-border integrated and secure privacy-oriented cloud medical management services throughout the entire cycle of the endoscopy center. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0016] Figure 1 This is a structural diagram of the endoscopic diagnosis and treatment information query system of the present invention. DETAILED DESCRIPTION

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

[0018] See also Figure 1 , the present invention provides a technical solution: A hospital endoscopy diagnosis and treatment information query system based on smart cloud management, including a pre-diagnosis module, a diagnosis module, a post-diagnosis module and an information query module. The pre-diagnosis module includes a smart cloud reservation unit, a smart cloud customer service unit and a pre-diagnosis information push unit. The smart cloud appointment unit supports four methods: telephone appointment, doctor appointment, center appointment, and public account appointment, and uses an appointment resource allocation algorithm to optimize the allocation of medical resources; The Smart Cloud customer service unit provides 24-hour online consulting services; The pre-diagnosis information push unit pushes endoscopic diagnosis and treatment related knowledge and examination preparation information; The diagnosis module includes the smart cloud guidance unit, the smart cloud accompanying unit and the smart cloud consultation unit; The smart cloud guidance unit pushes appointment information, parking guidance information and sign-in reminders at different time points before the examination, and uses the guidance time optimization algorithm to plan the reminder time; The smart cloud accompanying unit realizes unaccompanied nursing, family binding and progress information push; The smart cloud medical consultation unit supports pre-deposit recharge, one person one code and real-time clock-in record throughout the whole process; The post-diagnosis module includes the smart cloud management unit, the smart cloud archive unit and the smart cloud evaluation unit; The smart cloud management unit pushes medical reminders, diagnosis and treatment information, and test result reminders; The smart cloud archive unit stores diagnosis and treatment data and uses health risk assessment algorithms to provide health management solutions; The Smart Cloud Evaluation Unit pushes out a satisfaction survey questionnaire; The information query function supports patients and authorized family members to query information related to endoscopic diagnosis and treatment, including appointment records, examination reports, test results, diagnosis and treatment cost details, and health management recommendations.

[0019] Data is collected from multiple sources such as hospital information systems (HIS), endoscopic diagnosis and treatment equipment, and patient mobile terminals. By connecting to the HIS system interface, basic patient information (such as name, age, gender, medical record number, etc.), past medical history, test results, etc. are obtained; endoscopic examination images, videos, and examination parameters (such as examination time, examination site, operating doctor, etc.) are collected from endoscopic diagnosis and treatment equipment; and patient mobile terminals collect patient self-filled information (such as symptom description, family medical history, etc.) and health monitoring data (such as physical examination data).

[0020] Combination of real-time and scheduled data collection: Real-time data collection of key data in the endoscopic diagnosis and treatment process (such as equipment operation status, real-time inspection data) to ensure the timeliness and accuracy of the data. For example, through the built-in sensors and data transmission modules of the equipment, endoscopic image data is collected at a frequency of [X] times per second, and transmitted to the data processing center in real time through the network. For some relatively stable data (such as basic patient information and regular physical examination data), a scheduled collection strategy is adopted, such as synchronously updating data from the relevant database at 2 a.m. every day.

[0021] Data cleaning and preliminary verification: During the collection process, the data is preliminarily cleaned and verified. Duplicate data is removed through preset rules. Regular expressions are used to verify the format of input data such as phone numbers and ID numbers. If the format is incorrect, it will prompt you to re-enter or make corrections. A reasonable range is set for numerical data (such as age, blood pressure, etc.), and data outside the range is marked and verified.

[0022] Database selection and architecture design: A combination of relational databases (such as MySQL) and non-relational databases (such as MongoDB) is used to store data. Relational databases store structured data, such as basic patient information and medical records. Patient tables, medical records tables, and doctor tables are created and associated through foreign keys. Non-relational databases store unstructured data, such as endoscopic examination images and videos, in the form of documents for easy and fast storage and retrieval.

[0023] Data sharding and redundant backup: To improve storage performance and data security, data is stored in shards. Data is stored in multiple storage nodes in a distributed manner according to the hash modulus of the patient ID. For example, the patient ID is modulo 10, and the data with the result of 0-2 is stored in node 1, and the data with the result of 3-5 is stored in node 2, etc. At the same time, a redundant backup strategy is adopted to regularly back up data to an off-site storage center. The backup frequency is full backup once a day and off-site storage once a week.

[0024] Data storage optimization: compress the stored data. For text data, the GZIP compression algorithm is used to compress the data size. For image data, the JPEG-XR compression standard is used to reduce storage space while ensuring image quality. Create indexes to speed up data query, such as indexing commonly used query fields such as patient ID and diagnosis and treatment time.

[0025] Data integration and association analysis: Integrate data from different data sources, use the patient ID as the unique identifier, and associate patient basic information, medical records, examination images and other data. Use data mining algorithms, such as the Apriori algorithm, to analyze the association rules between patient history, symptoms and endoscopic examination results. For example, set the support threshold to 0.2 and the confidence threshold to 0.6 to mine association rules such as "patients with gastric ulcers and long-term smoking have a higher probability of finding gastric lesions during endoscopic examinations" to provide a reference for clinical diagnosis.

[0026] Data analysis and visualization: Use statistical analysis methods to perform statistical analysis on the data. Calculate the disease detection rate of endoscopic examinations for patients of different age groups and genders, draw bar charts or line graphs for visualization, and help doctors intuitively understand the distribution of diseases.

[0027] The system adopts a role-based access control (RBAC) model to assign different permissions to different roles such as doctors, nurses, and administrators. Doctors can view and modify the diagnosis and treatment data of the patients they are responsible for; nurses can view the basic information and nursing records of patients; administrators have the highest permissions and can perform operations such as system configuration and data management. Fine-grained permission control is achieved through access control lists (ACLs), such as restricting doctors to access patient data in specific departments only during working hours.

[0028] Data encryption: During the data transmission process, the SSL / TLS encryption protocol is used to encrypt the transmitted data to prevent the data from being stolen and tampered with. When storing data, sensitive data (such as patient ID number, medical insurance card number, medical record content, etc.) is encrypted and stored using the AES encryption algorithm. The key length is 256 bits, and the key is changed regularly to ensure the confidentiality of the data.

[0029] The AES (Advanced Encryption Standard) algorithm is used to encrypt patient information. AES is a symmetric encryption algorithm that uses the same key for encryption and decryption. Its security is based on splitting data into fixed-size blocks (usually 128 bits) and then encrypting these blocks through multiple rounds of complex mathematical transformations. When encrypting patient information, a 256-bit key length is used. Compared with 128-bit and 192-bit keys, 256-bit keys can provide higher security and greatly increase the difficulty of brute force cracking.

[0030] Key generation: Use a professional key generation tool to generate a 256-bit AES encryption key. This key must be kept strictly confidential as it is the key to encryption and decryption. The generated key can be stored in a dedicated key management system (KMS), which ensures key security through access control, encrypted storage, and other methods.

[0031] Data grouping: The patient information to be stored is grouped into 128-bit groups. If the last group is less than 128 bits, a specific padding method (such as PKCS7 padding) is used to make it 128 bits.

[0032] Encryption processing: Each data packet is encrypted using the AES encryption algorithm and the generated 256-bit key. The AES algorithm includes multiple rounds of operations such as byte replacement, row shift, column confusion, and round key addition. After multiple rounds of processing, the original data is converted into ciphertext.

[0033] Storing ciphertext: The encrypted ciphertext is stored in a database or other storage device. When storing, the ciphertext can be stored together with related metadata (such as encryption algorithm identifier, key version, etc.) to facilitate subsequent decryption.

[0034] Establishing a secure channel: Before transmitting patient information, both parties use the TLS (Transport Layer Security) protocol to establish a secure channel. The TLS protocol negotiates encryption algorithms and exchanges key materials through a handshake process to ensure communication security. For example, the client sends a list of supported TLS versions and encryption algorithms to the server, and the server selects the appropriate algorithm and returns a confirmation message, and the two parties establish a secure connection based on this.

[0035] Encrypted data: After the secure channel is established, the sender uses the previously generated AES key to encrypt the patient information. The process is the same as when it is encrypted for storage.

[0036] Transmitting ciphertext: Transmit the encrypted ciphertext over the network. Since the data is encrypted, even if it is intercepted during transmission, it is difficult for attackers to obtain the original information.

[0037] Decrypted data: After receiving the ciphertext, the receiver uses the same AES key and the corresponding decryption algorithm to decrypt the ciphertext and restore the original patient information. The decryption process is the reverse operation of the encryption process, which performs operations such as round key addition, column confusion, row shift, and byte replacement in sequence.

[0038] Use machine learning algorithms, such as logistic regression models, to predict patients' health risks. Using the patient's age, medical history, examination indicators, etc. as features, the model is trained to predict the patient's probability of developing digestive tract diseases in the future.

[0039] Incorporate patient age directly into the model as a continuous feature, e.g. Indicates age.

[0040] Code the medical history. If there is a specific medical history related to digestive tract diseases (such as a history of gastric ulcer), it can be set as , if not ; If there are multiple medical histories, one-hot encoding can be used, such as using Indicates the presence or absence of different medical histories.

[0041] Select key examination indicators, such as the size of polyps found during gastroscopy , levels of certain tumor markers in the blood Etc. If there are many indicators, you may need to perform feature selection first to remove indicators with high correlation or small contribution to prediction to avoid overfitting of the model.

[0042] Logistic regression model formula: Linear combination: The model first performs a linear combination of the input features, the formula is: ,in, is the intercept term, is the coefficient corresponding to each feature, are the above-extracted features such as age, medical history, and examination indicators. These coefficients reflect the influence of each feature on the prediction result. For example, if If it is positive and large, it means that the older you are, the more likely you are to suffer from digestive tract diseases.

[0043] The result z of the linear combination is converted into a probability value through a logistic function. The formula of the logistic function is: , the function maps the z value to the (0,1) interval, and the result σ(z) is the predicted probability of the patient suffering from a digestive tract disease. For example, when σ(z)=0.7, it means that the model predicts that the probability of the patient suffering from a digestive tract disease is 70%.

[0044] Use maximum likelihood estimation to determine the parameters of the logistic regression model (i.e. ), assuming that the data set has m samples, the true label of the i-th sample (1 for those with digestive tract diseases and 0 for those without) is , the predicted probability is , then the likelihood function is: ; For ease of calculation, the logarithm of the likelihood function is usually taken to obtain the log-likelihood function: ; The goal of model training is to maximize the log-likelihood function .

[0045] Gradient ascent method or stochastic gradient ascent method is often used to solve the parameters that maximize the log-likelihood function. Taking the gradient ascent method as an example, the parameter update formula is: ,in, is the learning rate, which controls the step size of each parameter update; is the log-likelihood function with respect to the parameter In each iteration, the gradient is calculated and the parameters are updated according to the learning rate until the log-likelihood function converges, that is, the parameters no longer change significantly.

[0046] After training is completed, the test set is used to evaluate the model performance. Common evaluation indicators include accuracy, recall, F1 value, etc.

[0047] Accuracy: reflects the proportion of samples predicted correctly by the model to the total number of samples. The formula is: ; Recall rate: measures the model's ability to correctly predict positive samples (samples with digestive tract diseases). The formula is: ; F1 value: Comprehensively consider the accuracy and recall rate, the formula is: . Through these evaluation indicators, we can judge whether the model is effective in predicting the probability of patients suffering from digestive tract diseases. The smart cloud reservation unit introduces a reservation resource allocation algorithm to optimize the allocation and scheduling of medical resources. Assuming that the total number of equipment available for endoscopic diagnosis and treatment in the hospital is N, the time occupied by different types of endoscopic examinations is respectively, and the number of reservations required for each examination is , the optimal device allocation scheme is calculated by the following algorithm formula: .

[0048] The Smart Cloud Customer Service Unit provides 24-hour continuous service with the help of Internet technology. When patients have needs, they can initiate online consultations, and the endoscopy specialist nursing team will answer questions related to intestinal preparation and medication methods online.

[0049] The smart platform pushes popular science tweets and videos about endoscopic diagnosis and treatment, as well as precautions for examination preparation, to patients, providing them with home health guidance and helping them to correctly complete preoperative preparations.

[0050] The day before the examination of the smart cloud guidance unit, the hospital's smart platform pushes appointment information details to patients, reminding them of the appointment time and examination items. Two hours before the examination, the system automatically pushes parking guidance information. Patients can view parking locations around the hospital, real-time remaining space information, and obtain navigation services to quickly reach the hospital and solve the parking problem. One hour before the examination, the smart in-hospital navigation obtains the patient's location and reminds the patient to sign in automatically. In order to optimize the guidance time planning, the guidance time optimization algorithm is used. Assuming that the patient's estimated travel time from the departure point to the hospital is The average parking time around the hospital is , the walking time from the parking spot to the endoscopy center is , then the best reminder time The calculation formula is: ,in, is the scheduled inspection time, and ΔT is the reserved buffer time. In the smart cloud medical treatment unit, patients can make pre-deposit and recharge for medical treatment through the smart platform, generate an identity identification QR code after signing in, and realize one-person-one-code during the endoscopic diagnosis and treatment process, with real-time clock-in records for the entire process. It can also complete real-time monitoring of barcode printing, anesthesia consultation, intravenous injection, waiting time and other links, deeply integrate medical services with Internet technology, innovate service models, and optimize service processes.

[0051] In the smart cloud management unit, the system identifies intelligent scenarios and automatically pushes reminders for medical consultations the day before the consultation, instructions for hospital consultations, medical treatment information to accompanying personnel, waiting information, reminders for post-consultation test results, and reminders for follow-up consultations, providing patients with butler-style services and improving the service level and management efficiency of medical institutions.

[0052] The smart cloud archive reminds patients of the test results after diagnosis, helps patients monitor and manage their health, stores endoscopic diagnosis and treatment data in the cloud archive, realizes accurate reminders, pushes professional popular science information, and intelligently follows up continuously. The health risk assessment algorithm is introduced to calculate the health risk index R according to the patient's endoscopic diagnosis and treatment data (such as lesion condition L, pathological result P), medical history H and living habits (such as smoking frequency S, drinking amount A, exercise frequency E) through the following formula: ,in, is the weight coefficient of each factor, β is the constant term. According to the calculated health risk index, we manage personal health data for patients, design personalized health management plans, and provide customized health advice.

[0053] Step 1: Demand research and analysis: Through the investigation of the existing management service system of the endoscopy center, we can understand its existing problems and needs, and lay the foundation for building a new cloud medical intelligent management service system.

[0054] Step 2: Technical architecture design: Based on the results of the demand analysis, design the technical architecture of the cloud medical consultation intelligent management service system that meets the needs of the endoscopy center, including the overall design of the system and the relationship between each module.

[0055] Step 3: Functional module design: According to the technical architecture design, the system is divided into different functional modules, including appointment registration, examination management, report query, remote consultation and other functions, laying the foundation for subsequent development work.

[0056] Step 4: System development and integration: Based on the functional module design, develop each functional module of the system and perform integration testing to ensure that all parts of the system can work in a coordinated manner.

[0057] Step 5: Data management and security: Design and implement a systematic data management strategy, including data collection, storage, processing, and security protection, to ensure the security and privacy of patient information.

[0058] Step 6: User interface design: Design a user-friendly interface, including mobile applications and web terminals, so that patients and medical staff can easily use the system for appointments, inquiries and management.

[0059] Step 7: System testing and optimization: Conduct comprehensive testing on the system, including functional testing, performance testing, and security testing, and optimize and improve the system based on the test results.

[0060] Step 8: Implementation and promotion: Deploy the system to endoscopy centers, carry out implementation and promotion work, train relevant personnel, and ensure that the system operates normally and is effectively utilized.

[0061] Step 9: System evaluation and improvement: Evaluate the system regularly, collect user feedback, improve and optimize the system based on the evaluation results, and continuously improve system performance and user experience.

[0062] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0063] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hospital endoscopy diagnosis and treatment information query system based on smart cloud management, including a pre-diagnosis module, a diagnosis module, a post-diagnosis module and an information query module, characterized in that: The pre-diagnosis module includes a smart cloud appointment unit, a smart cloud customer service unit and a pre-diagnosis information push unit; The smart cloud appointment unit supports four methods: telephone appointment, doctor appointment, center appointment, and public account appointment, and uses an appointment resource allocation algorithm to optimize medical resource allocation; The smart cloud customer service unit provides 24-hour online consulting services; The pre-diagnosis information push unit pushes endoscopic diagnosis and treatment related knowledge and examination preparation information; The diagnosis module includes a smart cloud guidance unit, a smart cloud accompanying unit and a smart cloud consultation unit; The smart cloud guidance unit pushes appointment information, parking guidance information and sign-in reminders at different time points before the examination, and uses a guidance time optimization algorithm to plan reminder time; The smart cloud accompanying unit realizes unaccompanied nursing, family binding and progress information push; The smart cloud medical consultation unit supports pre-deposit recharge and one-person-one-code full-process real-time clock-in record; The post-diagnosis module includes a smart cloud management unit, a smart cloud archive unit and a smart cloud evaluation unit; The smart cloud management unit pushes medical consultation reminders, medical treatment information, and test result reminders; The smart cloud archive unit stores diagnosis and treatment data and provides health management solutions using health risk assessment algorithms; The smart cloud evaluation unit pushes a satisfaction survey questionnaire; The information query function supports patients and authorized family members to query information related to endoscopic diagnosis and treatment, including appointment records, examination reports, test results, diagnosis and treatment cost details, and health management recommendations.

2. According to claim 1, a hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management is characterized by: The smart cloud reservation unit introduces a reservation resource allocation algorithm to optimize the allocation and scheduling of medical resources. Assuming that the total number of equipment available for endoscopic diagnosis and treatment in the hospital is N, the time occupied by different types of endoscopic examinations is respectively, and the number of reservations required for each examination is, the optimal equipment allocation plan is calculated by the following algorithm formula: 。 3. According to the hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management according to claim 1, it is characterized by: The smart cloud customer service unit uses Internet technology to provide 24-hour continuous service. When patients need it, they can initiate online consultation, and the endoscopy specialist nursing team will answer questions related to intestinal preparation and medication methods online.

4. According to claim 1, a hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management is characterized by: The smart platform pushes popular science tweets and videos about endoscopic diagnosis and treatment, as well as precautions for examination preparation, to patients, providing them with home health guidance and helping them to correctly complete preoperative preparations.

5. According to the hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management according to claim 1, it is characterized by: The day before the examination of the smart cloud guidance unit, the hospital's smart platform pushes appointment information details to the patient, reminding the appointment time and examination items. Two hours before the examination, the system automatically pushes parking guidance information. Patients can view the parking locations around the hospital, real-time remaining space information, and obtain navigation services to quickly reach the hospital and solve the parking problem. One hour before the examination, the smart in-hospital navigation obtains the patient's location and reminds the patient to sign in automatically. In order to optimize the guidance time planning, the guidance time optimization algorithm is adopted. Assuming that the patient's estimated travel time from the departure place to the hospital is The average parking time around the hospital is , the walking time from the parking spot to the endoscopy center is , then the best reminder time The calculation formula is: ,in, It is the scheduled inspection time, and ΔT is the reserved buffer time.

6. According to claim 1, a hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management is characterized by: In the smart cloud medical treatment unit, patients can make pre-deposit and recharge for medical treatment through the smart platform, generate an identity identification QR code after signing in, and realize one-person-one-code during endoscopic diagnosis and treatment, with real-time clock-in records for the entire process, and complete real-time monitoring of barcode printing, anesthesia consultation, intravenous injection, waiting time and other links, deeply integrating medical services with Internet technology, innovating service models, and optimizing service processes.

7. According to claim 1, a hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management is characterized by: In the smart cloud management unit, the system identifies intelligent scenarios and automatically pushes reminders for medical consultations the day before the consultation, instructions for hospital consultations, medical treatment information to accompanying personnel, waiting information, reminders for post-consultation test results, and reminders for follow-up consultations, providing patients with butler-style services and improving the service level and management efficiency of medical institutions.

8. According to claim 1, a hospital endoscopy diagnosis and treatment information query system based on intelligent cloud management is characterized by: The smart cloud archive reminds patients of post-diagnosis test results, helps patients monitor and manage their health, stores endoscopic diagnosis and treatment data in the cloud archive, realizes accurate reminders, pushes professional science information, and intelligently continues follow-up, introduces health risk assessment algorithms, calculates health risk indexes, manages personal health data for patients, designs personalized health management plans, and provides customized health advice.

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