Medical patient appointment registration management system based on Internet technology

Abstract

The invention relates to the technical field of medical informatization, in particular to a medical patient appointment registration management system based on an internet technology, which constructs a decentralized intelligent appointment ecology through deep fusion of a block chain, artificial intelligence, the internet of things and a big data technology. The system comprises a distributed block chain network module, an intelligent distribution module, a cross-institution data sharing module, a personalized service module, a multi-terminal interaction module and a security system, it is ensured that data cannot be tampered through an alliance chain architecture and an intelligent contract, and intelligent distribution of number sources is achieved through an improved ant colony algorithm. A zero-knowledge proof technology is adopted to guarantee credible sharing of cross-mechanism data, and personalized health services are provided in combination with BERT and LSTM algorithms. The problems of low efficiency, potential safety hazards and cooperation barriers of a traditional system are effectively solved, the medical service quality and the resource utilization efficiency are remarkably improved, and the system is suitable for medical institutions at all levels and regional medical cooperation scenes.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and in particular to a medical patient appointment registration management system based on Internet technology. Background Technology

[0002] In the process of developing medical informatization, although traditional medical patient appointment registration management systems have improved the medical treatment process to a certain extent, their limitations have become increasingly prominent with the growth of medical service demand and technological development. For example, there are problems such as data security and privacy protection dilemmas, unreasonable allocation of medical resources, difficulties in cross-institutional information sharing, and lack of personalized and proactive services.

[0003] Among related technologies, mobile internet appointment platforms such as "WeDoctor" and "Health160" have integrated appointment resources from multiple hospitals to achieve online appointment functionality, improving the convenience of appointments to some extent. However, these platforms still essentially adopt a centralized architecture, with patient data stored on platform servers, and data security and privacy protection relying on the platform's management capabilities. Furthermore, the platform's appointment allocation algorithm is relatively simple, generally employing a "fixed-time release + first-come, first-served" model, leading to appointments in popular departments being snapped up within milliseconds (e.g., appointments in the orthopedics department of Peking Union Medical College Hospital sell out in an average of 1.2 seconds), while the vacancy rate for appointments in hospitals in remote areas reaches 40%. Moreover, technical barriers exist between institutions; when patients seek treatment at different hospitals, 78% of examination reports need to be reprinted and resubmitted. Therefore, dynamic optimization of medical resource allocation, secure and reliable sharing of patient data, and accurate delivery of personalized health services are urgently needed. Summary of the Invention

[0004] This application provides a medical patient appointment registration management system based on Internet technology, including:

[0005] The distributed blockchain network module adopts a consortium blockchain architecture as its underlying blockchain layer, connecting at least three types of nodes, including medical institutions, patients, and medical insurance departments, to achieve decentralized storage and multi-party consensus of patient data.

[0006] The intelligent allocation module includes a multi-dimensional data input interface to obtain doctor scheduling data, equipment status data, and patient demand data, and dynamically optimizes appointment allocation based on machine learning algorithms;

[0007] The cross-institutional data sharing module uses zero-knowledge proof (ZKP) technology to achieve reliable interoperability of inspection and testing results;

[0008] The personalized service module, a natural language processing unit for the medical field based on the BERT model, enables symptom analysis and doctor recommendations, and integrates electronic health records to provide intelligent health management.

[0009] In some embodiments, the distributed blockchain network module includes:

[0010] A decentralized identity generation mechanism based on elliptic curve cryptography is used to generate a unique digital identity (DID) for patients.

[0011] A group of smart contracts deployed on a blockchain includes at least a reservation rules contract, a fee settlement contract, and a data authorization contract.

[0012] In some embodiments, the intelligent allocation module includes:

[0013] The ant colony optimization algorithm model uses waiting time, resource utilization, and patient preference as objective functions.

[0014] The real-time scheduling engine dynamically adjusts the appointment queue based on doctor scheduling data, equipment status data, patient demand data, and IoT device data.

[0015] In some embodiments, an ant colony optimization algorithm model is used, and the objective function is:

[0016]

[0017] in For waiting time, To improve equipment utilization, For patient preference matching, These are dynamic weighting coefficients.

[0018] In some embodiments, the cross-agency data sharing module includes:

[0019] The zero-knowledge proof verification unit enables patients to prove the authenticity and completeness of their test reports to medical institutions without disclosing specific data.

[0020] The Verifiable Credential (VC) generation module, in accordance with W3C standards, converts examination reports into encrypted credentials containing biometric hashes, timestamps, and institutional signatures, allowing for fine-grained patient authorization.

[0021] The cross-chain data gateway supports interaction between Hyperledger Fabric and different consortium blockchains such as Ethereum, and achieves atomic synchronization of appointment information and medical insurance settlement data through Hash Locking (HTLC).

[0022] In some embodiments, the cross-institutional data sharing module has a built-in data access auditing mechanism that records the time of data access, the IP address of the accessing node, and the operation type (query / download / authorization) in real time, and stores the audit logs on the blockchain, supporting full-process traceability by the blockchain explorer.

[0023] In some embodiments, the personalization service module includes:

[0024] The intelligent consultation recommendation unit, based on the BERT pre-trained model and fine-tuned with medical corpus, realizes entity recognition and relation extraction of Chinese medical terms, and generates department recommendations and doctor matching lists based on the patient's symptom description.

[0025] The proactive health management system connects to IoT devices such as smart pillboxes and blood pressure monitors to collect health data in real time and generate personalized medication reminders and follow-up visit plans based on LSTM neural networks.

[0026] In some embodiments, the multi-dimensional data of the intelligent allocation module includes doctor data such as consultation time, professional level, and historical consultation efficiency; equipment data such as appointment time, maintenance cycle, and real-time status; and patient data such as disease level, geographical location, and historical medical preferences.

[0027] In some embodiments, the system further includes a multi-terminal interaction module that supports access from devices such as WeChat mini programs, H5 web pages, self-service terminals, and smartwatches. It interacts with the core functional layer through RESTful APIs and provides unified identity authentication and interface adaptation functions.

[0028] In some embodiments, the system further includes a security architecture module, the security architecture module comprising:

[0029] The national cryptographic algorithm encryption module uses SM4 symmetric encryption for patient data transmission, SM2 asymmetric encryption for blockchain transaction signatures, and SM3 algorithm for hash calculation.

[0030] The access control mechanism uses attribute-based encryption (ABE) to achieve "data is available but not visible," allowing patients to customize the validity period and access scope of data sharing. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a block diagram of a system proposed in one embodiment of this application;

[0033] Figure 2 This is a step-by-step diagram of the patient's appointment process;

[0034] Figure 3 This is a hardware layout diagram of a system proposed in an embodiment of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] Traditional medical appointment systems suffer from the following core technological bottlenecks:

[0037] (a) The dual challenges of data security and privacy protection

[0038] Traditional medical appointment systems employ a centralized database architecture, storing patient identity information (ID number, mobile phone number), medical records (prescriptions, examination reports), and payment data (medical insurance card number, out-of-pocket amount) on a single server. According to the 2024 Global Healthcare Data Security Report, the cost of a data breach in the healthcare industry reaches $450 per record, far exceeding the financial industry ($350 per record). Typical cases include:

[0039] In 2023, due to a database permission configuration error, the medical records of 500,000 patients at an internet hospital were publicly accessed, including sensitive diagnostic information of cancer patients.

[0040] In early 2024, a provincial appointment platform was attacked by ransomware. The attackers encrypted patient appointment data and demanded a ransom, resulting in the inability to make appointments and register normally throughout the province for three days.

[0041] In addition, traditional systems rely on username / password authentication, and biometric data (fingerprints, faces) is stored in plaintext or weak encryption. A 2024 inspection by the National Medical Products Administration showed that 60% of medical apps failed the biometric data security test, posing a risk of fingerprint information leakage.

[0042] (II) Root Causes of Inefficiency in Medical Resource Allocation

[0043] The existing appointment allocation mechanism has three major flaws:

[0044] Imbalance in the time dimension: The adoption of the "fixed time release of appointments + first come, first served" model has led to the appointment slots for popular departments being snapped up within milliseconds (e.g., appointment slots for the orthopedics department of Peking Union Medical College Hospital sold out in an average of 1.2 seconds), while the vacancy rate of appointment slots in hospitals in remote areas reaches 40%;

[0045] Mismatch in capabilities: Failure to consider the precise matching of doctors' expertise with patients' needs, such as randomly assigning hypertension patients to cardiovascular surgeons, leads to a 15% increase in the misdiagnosis rate;

[0046] Equipment-related issues: The appointment system for examination equipment is separate from the appointment system. Patients may have to wait 3-7 days after making an appointment for an outpatient visit before they can have a CT scan, which extends the treatment cycle by 30%.

[0047] The "2024 China Medical Resource Utilization White Paper" shows that the average daily patient reception rate of doctors in tertiary hospitals across the country is 18, but this can be increased to 25 per day through reasonable scheduling, and the equipment utilization rate can be increased from 75% to 90%, showing huge potential.

[0048] (iii) Technical barriers to cross-institutional collaboration

[0049] The information systems of different medical institutions adopt heterogeneous technology architectures:

[0050] Tertiary hospitals generally use the HL7V2 and DICOM3.0 standards, while community hospitals mostly use self-built databases;

[0051] The medical insurance system uses the National Medical Security Information Platform (NMIPA) and connects with the hospital system through an API gateway. Data conversion takes 10-30 seconds per transaction.

[0052] Regional healthcare platforms (such as medical alliances) lack a unified identity authentication system, and patients need to register accounts repeatedly when seeking medical treatment at different hospitals (taking an average of 15 minutes per visit).

[0053] These technological barriers lead to:

[0054] When patients seek medical treatment at other hospitals, 78% of test reports need to be reprinted and resubmitted; medical insurance settlement requires manual entry of 12 basic information items, with an error rate of 8%, leading to reimbursement disputes; doctors cannot obtain patients' medication records at other hospitals in real time, resulting in a 20% increase in the risk of duplicate medication.

[0055] (iv) Technological gap in personalized services

[0056] Traditional systems only provide the basic "appointment-visit-discharge" process, lacking proactive services based on data intelligence.

[0057] Chronic disease management: For example, diabetic patients need to manually record their blood glucose levels, and the system cannot automatically associate this with medication dosage to generate adjustment suggestions;

[0058] Follow-up management: For example, the telephone follow-up rate for elderly patients over 60 years old is only 45%, and the follow-up content is all the same (such as "please take your medicine on time"), lacking personalization;

[0059] Health intervention: For example, after an obese patient seeks medical attention, the system is unable to push personalized diet and exercise plans based on indicators such as BMI and body fat percentage.

[0060] To address the aforementioned issues, this application provides a medical patient appointment registration management system that integrates blockchain, artificial intelligence, Internet of Things, and big data technologies. This system, by constructing a decentralized intelligent appointment ecosystem, enables dynamic optimization of medical resource allocation, secure and reliable sharing of patient data, and precise delivery of personalized health services. It is applicable to medical institutions at all levels, regional medical collaboration platforms, medical insurance settlement systems, and health management institutions.

[0061] Reference Figure 1 A medical patient appointment registration management system based on Internet technology includes:

[0062] The distributed blockchain network module adopts a consortium blockchain architecture as its underlying blockchain layer, connecting at least three types of nodes, including medical institutions, patients, and medical insurance departments, to achieve decentralized storage and multi-party consensus of patient data.

[0063] The intelligent allocation module includes a multi-dimensional data input interface to obtain doctor scheduling data, equipment status data, and patient demand data, and dynamically optimizes appointment allocation based on machine learning algorithms;

[0064] The cross-institutional data sharing module uses zero-knowledge proof (ZKP) technology to achieve reliable interoperability of inspection and testing results;

[0065] The personalized service module, a natural language processing unit for the medical field based on the BERT model, enables symptom analysis and doctor recommendations, and integrates electronic health records to provide intelligent health management.

[0066] Specifically, the medical patient appointment registration management system includes:

[0067] 1. Distributed blockchain network module:

[0068] (1) Decentralized Identity Management (DID)

[0069] Identity Generation: When a patient registers for the first time, the system generates a public / private key pair using the Elliptic Curve Cryptography (ECDSA) algorithm, and combines it with biometric (fingerprint + face) hash values ​​to generate a unique DID (format:did:med:1234567890abc).

[0070] Data binding: Appointment records, electronic health records (EHRs), examination reports, and other data are distributed and stored via IPFS. Only the file hash values ​​are stored on the blockchain, and patients control data access permissions through their private keys.

[0071] Cross-institutional recognition: DID complies with W3C standards and supports seamless authentication across different hospitals and medical insurance platforms, eliminating the need for repeated registration.

[0072] (2) Deployment strategy for consortium blockchain nodes

[0073] Node types include:

[0074] Consensus Nodes: Select three top-tier hospitals with strong technical capabilities in the region (such as the Provincial People's Hospital and the Affiliated Hospital of the Medical University), configure high-performance servers (dual Intel Xeon Platinum 8380, 512GB memory) to undertake block packaging and consensus verification;

[0075] Verification nodes: including provincial medical insurance bureaus, health commissions, and drug administrations, deploying lightweight nodes (storing only block headers, with ≥32GB of memory) to supervise data compliance;

[0076] User nodes: Patients and equipment suppliers access the system via a mobile app, using a lightweight client (SPV node) that only synchronizes transaction data relevant to themselves.

[0077] Consensus algorithm optimization: Improve the PBFT algorithm and introduce dynamic node weights (adjusted according to node stability and computing power contribution), which improves consensus efficiency by 30% and shortens fault recovery time from 5 minutes to 2 minutes.

[0078] (3) DID Identity Authentication System

[0079] Biometric encryption: The algorithm of "fingerprint minutiae feature + facial 3D key points" is used to extract 128-dimensional fingerprint feature vector and 256-dimensional facial feature vector. The biometric hash (B_hash) is generated by the SM3 algorithm, with an anti-counterfeiting rate of 99.99%.

[0080] Identity lifecycle management: Patients can apply for DID cancellation through the APP. The system will automatically clear the associated data on the blockchain (using zero-knowledge proof to verify the legality of the cancellation request). Once the data is deleted, it cannot be recovered.

[0081] (4) Engineering implementation of smart contracts

[0082] Appointment Rules Contract: Added "Emergency Appointment Reservation" function, which reserves 20% of appointment slots for patients with a disease level ≥3 (ICD-11 emergency code). The number of patients' historical emergency visits is calculated in real time through on-chain data to prevent abuse.

[0083] Payment settlement contract: Connects to the national medical insurance electronic voucher interface, supports facial recognition payment (calls the biometric authentication interface of the medical insurance bureau), automatically calculates the payment ratio of the pooled fund and the personal account, and the settlement error is ≤0.01 yuan.

[0084] 2. Intelligent Allocation Module:

[0085] (1) Multi-dimensional data fusion technology

[0086] Physician competence modeling: Constructing a three-dimensional competence matrix for physicians (professionalism, efficiency, and satisfaction): Professionalism: based on the coverage of ICD-11 codes for primary diseases (e.g., the number of I20-I25 codes covered by a cardiologist).

[0087] Efficiency value: Historical average consultation time (minutes / person), collected in real time through the HIS system;

[0088] Satisfaction: Patient rating weighted score (1-5 points, weighted based on data from the last 30 days).

[0089] Real-time device status monitoring: Device data is collected via IoT sensors, and noise is removed using a Kalman filter algorithm, achieving a device status recognition accuracy of ≥98%. For example, MRI device status is categorized as follows:

[0090] Available (by appointment)

[0091] Reservation will be released when the current patient's remaining examination time is less than 10 minutes.

[0092] Maintenance (maintenance schedule is automatically synchronized; this time slot is excluded when scheduling).

[0093] (2) Optimization of dynamic allocation algorithm

[0094] Improvements to the ant colony algorithm: The "elite ant strategy" is introduced to enhance the pheromone concentration of historical best solutions by 30%, which improves the convergence speed by 20%; the tabu list is used to avoid repeated access to the assigned source and ensure the uniqueness of the solution.

[0095] The ant colony optimization algorithm model is adopted, and the objective function is:

[0096]

[0097] in For waiting time, To improve equipment utilization, For patient preference matching, These are dynamic weighting coefficients.

[0098] Reinforcement learning model training: Employs ExperienceReplay technology, storing over 100,000 historical assignment cases, updating weight coefficients hourly, and supporting both online learning and offline training modes.

[0099] Real-time queue adjustment: Based on the arrival time prediction using patients' mobile phone GPS data, patients who are more than 15 minutes late are automatically downgraded, and appointment slots are reserved for patients who are about to arrive.

[0100] Cross-departmental collaboration: When a department's appointment slots are full, the system analyzes the physician capability matrix of related departments (such as cardiology and cardiac surgery), recommends alternative options, and seeks the patient's consent.

[0101] Reference Figure 2 For example, the steps for a patient to schedule an MRI examination:

[0102] When a patient selects "MRI examination" in the APP, the system automatically obtains the current time, the hospital, and the disease level (e.g., if lung cancer is suspected, the disease level is 4).

[0103] The intelligent allocation engine queries equipment data: Our hospital's MRI equipment is available from 14:00 to 15:00 today, while the neighboring hospital's MRI equipment is available from 13:30 to 14:30.

[0104] Calculate the objective function:

[0105] Our hospital's plan: waiting time 30 minutes, equipment utilization rate 85%, patient preference matching degree 0.8 (close proximity);

[0106] The neighboring hospital's solution: 15-minute waiting time, 90% equipment utilization rate, and 0.6 patient preference matching score (requires a 10-minute walk).

[0107] With dynamic weights α=0.3, β=0.4, γ=0.3, the target value of our hospital's plan is 0.3×30+0.4×(1-0.85)+0.3×0.8=9.12, and the target value of the neighboring hospital's plan is 0.3×15+0.4×(1-0.90)+0.3×0.6=4.54. Therefore, we choose the neighboring hospital's plan.

[0108] The system sends an appointment suggestion: "We recommend an MRI examination at XX Hospital, at 13:30, which is expected to save 15 minutes of waiting time." The patient confirms and completes the appointment.

[0109] (3) Real-time scheduling engine implementation

[0110] Arrival time prediction model: Based on LSTM neural network, input GPS coordinates, real-time traffic conditions, and historical commuting time, prediction error ≤3 minutes (confidence ≥90%); for patients predicted to be late, an "appointment reservation countdown" is automatically triggered (e.g., reservation is reserved if late for up to 10 minutes, and released if late).

[0111] 3. Cross-organizational data sharing module:

[0112] (1) Zero-knowledge proof engineering implementation

[0113] Data authorization process (taking chest CT report as an example):

[0114] Patient A requested Hospital B to share the chest CT report;

[0115] The system generates a random number r and calculates the proof value π = ZKP(r, CT_hash);

[0116] After hospital B verifies the π, the CT report is confirmed to be authentic and valid, and there is no need to obtain specific image data.

[0117] Specifically, the system uses the Groth16 algorithm for optimization: pre-generating a common reference string (CRS) reduces the proof generation time from 200ms to 50ms and the verification time from 150ms to 30ms, supporting verification of millions of data points.

[0118] Access control: Patients can authorize access to examination reports by content segment (e.g., "share lung window images from CT images, but not mediastinal window images"), using attribute-based encryption (ABE). Patients can also grant access with fine granularity (e.g., "share only blood glucose test data since 2024").

[0119] For example, the steps for sharing pathology reports when transferring patients to other hospitals:

[0120] The patient clicks "Share Pathology Report" on the original hospital's APP, selects the receiving hospital, validity period (7 days), and access permissions (visible only to the chief physician of the pathology department);

[0121] The original hospital generates a VC, which includes report hash, patient DID, and institution signature, and stores it on the blockchain.

[0122] Upon receiving an access request from the chief physician of the hospital's pathology department, the system verifies the physician's professional title (by querying in real time through the hospital's HIS system interface).

[0123] Patients can authenticate and authorize the report by using fingerprint authentication and zero-knowledge proofs to verify its authenticity without transmitting specific pathology images;

[0124] We receive anonymized pathology reports from hospitals (such as "adenocarcinoma, grade II differentiation") to help develop treatment plans.

[0125] (2) Verifiable Credentials (VC) Extension

[0126] Credential lifecycle management: Supports credential updates (such as supplementing new inspection reports), revocation (such as discovering data errors), and extensions (such as extending the sharing validity period), with state transitions executed automatically through smart contracts.

[0127] Cross-chain credential mutual recognition: Following the W3C VerifiableCredentials standard, it enables mutual recognition of credentials between different consortium chains (such as VC interoperability between provincial medical chains and municipal medical insurance chains).

[0128] (3) Cross-chain data gateway design

[0129] Heterogeneous Chain Adaptation Layer: Supports mainstream blockchain platforms such as Hyperledger Fabric, Ethereum, and AntChain, and converts the API interfaces of different chains through the Adapter Pattern.

[0130] Atomicity Interaction Protocol: It adopts two-phase commit (2PC) combined with hash locking (HTLC) to ensure the atomicity of cross-chain transactions. When a transaction fails, it is automatically rolled back with a rollback time of ≤1 second.

[0131] 4. Personalized Service Module:

[0132] (1) Intelligent consultation recommendation system, including:

[0133] Medical NLP technology implementation: Construct a knowledge base containing 3 million medical entities (covering ICD-11 codes, drug names, and symptom descriptions), and use the BERT-wwm-ext pre-trained model to achieve an F1 score of 93% on the symptom parsing task (exceeding the industry benchmark by 8%).

[0134] Dynamic recommendation strategy: By combining the patient's historical medical records (such as "previously allergic to penicillin"), doctors who prescribe related drugs are automatically excluded when recommending doctors, thereby reducing the risk of medication use.

[0135] (2) An active health management system, including:

[0136] Intelligent Follow-up Engine: A follow-up strategy model based on reinforcement learning. Inputting 12 features such as patient age, condition, and compliance, it automatically generates a follow-up plan (e.g., ...).

[0137] Young diabetic patients: Weekly blood glucose record reminders pushed by the app;

[0138] Elderly patients with hypertension: Daily smartwatch vibration reminders for medication, triggering follow-up phone calls upon missed doses. Health intervention knowledge base: Integrating over 10 clinical guidelines, including the "Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes," to build a knowledge base containing over 5,000 intervention rules, supporting automatic matching of suggestions based on patient indicators such as BMI and blood pressure.

[0139] 5. Multi-terminal interaction module:

[0140] (1) Unified identity authentication system

[0141] Biometric authentication fusion: Supports multimodal authentication of fingerprint, face and iris, and adopts dynamic weight fusion algorithm (e.g. fingerprint authentication weight 60% in the morning, face authentication weight 40%; iris authentication weight 70% at night), with a false recognition rate ≤0.001%.

[0142] Cross-terminal status synchronization: After a patient makes an appointment at the self-service machine, the appointment information is synchronized in real time on the mobile APP, supporting "one-click cancellation" and "rescheduling" operations, with a data synchronization delay of ≤2 seconds.

[0143] (2) Intelligent interface adaptation technology

[0144] Responsive design engine: Automatically detects the screen size of the device (e.g., 1.4-inch smartwatch, 21.5-inch self-service machine) and dynamically adjusts interface elements.

[0145] Small-screen devices: Hide secondary functions (such as collapsing the "History" display);

[0146] Large screen devices: Add visual charts (such as a heat map of doctors' consultation times).

[0147] Voice interaction support: Integrates Baidu speech recognition (ASR) and Tencent speech synthesis (TTS), supports multilingual interaction in Mandarin, Cantonese and English, with a speech recognition accuracy of ≥95% (quiet environment).

[0148] 6. Security System:

[0149] (1) Application of national cryptographic algorithms across the entire chain

[0150] Transport layer: Data transmission is encrypted using the SM4 algorithm, the key is negotiated using the SM2 algorithm, and it supports the TLS national cryptographic suite (GMTLS). The encryption throughput is ≥100MB / s.

[0151] Storage layer: IPFS file content is encrypted using AES-256 (equivalent to the national cryptographic standard SM4), and on-chain transaction hashes are calculated using the SM3 algorithm with a hash value length of 256 bits;

[0152] Application layer: Biometric data only stores hash values. The original data is deleted within 10 seconds of collection and cannot be recovered after deletion.

[0153] (2) Access control mechanism

[0154] Attribute-based encryption (ABE): Defines access policies such as "(Hospital = XX City People's Hospital) AND (Title = Chief Physician) AND (Expiration Date > 2025-06-01)", users who do not meet the conditions cannot decrypt the data; supports dynamic updates of policies (such as automatically adjusting the sharing permissions of examination reports when the patient's condition changes).

[0155] Blockchain auditing: All data operations (queries, downloads, authorizations) are recorded on the blockchain, including operation time, user DID, device IP, and operation type. The entire process can be traced through a blockchain explorer, and the audit logs are tamper-proof.

[0156] Based on the above system, the system deployment scheme of this application can be arranged as follows:

[0157] (a) Hardware environment, refer to Figure 3 ,include:

[0158] Blockchain nodes: Deploy N Huawei TaiShan servers (2U rackmount, Kunpeng 920 processor, 128GB memory), using a master-slave backup architecture;

[0159] IoT devices: Bluetooth beacons are deployed in the hospital waiting area to locate patients in real time; NB-IoT modules are added to the examination equipment to enable real-time status reporting;

[0160] Self-service terminals: Capacitive touchscreen + fingerprint + camera modules are deployed in the hospital waiting area for biometric data collection, registration and payment.

[0161] (II) Software Configuration

[0162] Blockchain platform: Hyperledger Fabric 2.5, configured with N consensus nodes (top-tier hospitals), N verification nodes (medical insurance bureaus), and N user nodes.

[0163] AI services: TensorFlow 2.12 is used to build deep learning models, and Kubernetes is used to achieve automated deployment and dynamic scaling of the models.

[0164] Security System: Data encryption is performed using Chinese cryptographic algorithms (SM2 / SM3 / SM4), and a Web Application Firewall (WAF) is deployed to defend against OWASP Top 10 attacks.

[0165] The following results were obtained through testing and verification:

[0166] (a) Functional testing

[0167] Concurrency performance: Simulating 5000 concurrent user reservations using JMeter, the system response time is ≤2 seconds, and the transaction success rate is 100%.

[0168] Data security: Penetration testing using AWVS revealed no vulnerabilities such as SQL injection or XSS. Blockchain data was stored after being hashed using SHA-256, and its collision resistance met the FIPS 180-4 standard. (II) Clinical Pilot

[0169] A provincial-level medical consortium (comprising 3 tertiary hospitals and 5 community hospitals) underwent a 6-month trial operation. Key indicators were compared as follows:

[0170] index Before the pilot program After the pilot program Improvement rate Average appointment time 28min 4.5min 83.9% Average daily number of patients seen by doctors 20 26 32% Inter-hospital mutual recognition rate 32% 89% 178% Equipment idle rate 20% 12% 40%

[0171] Security Enhancement:

[0172] Zero data breaches occurred; blockchain audit log query response time was ≤10 seconds.

[0173] The error rate in medical insurance settlement has dropped from 8% to 0.5%, and the dispute resolution cycle has been shortened from 15 days to 2 days.

[0174] Experience Enhancement:

[0175] Patients with chronic diseases showed improved follow-up adherence from 40% to 75%, and missed doses decreased from 30% to 10%.

[0176] Patient satisfaction with cross-hospital visits increased from 55% to 89%, and the cost of repeated examinations was reduced by an average of 420 yuan per person per year.

[0177] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0178] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0179] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0180] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0183] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0184] The above provides a detailed description of the medical patient appointment registration management system based on Internet technology provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A medical patient appointment registration management system based on Internet technology, characterized in that, include: A distributed blockchain network module is used to achieve decentralized storage and multi-party consensus of patient data; The intelligent allocation module dynamically optimizes the allocation of license plate slots based on machine learning algorithms. The cross-institutional data sharing module uses zero-knowledge proof and verifiable credential technology to achieve trusted data interoperability. The personalized service module integrates electronic health records to provide intelligent health management; The multi-terminal interaction module supports access from multiple devices and provides unified identity authentication.

2. The system according to claim 1, characterized in that, The distributed blockchain network module includes: The underlying network adopts a consortium blockchain architecture, connecting multiple nodes such as medical institutions, patients, medical insurance departments, and equipment suppliers, and realizes data consistency verification based on the PBFT consensus algorithm; The decentralized identity (DID) generation unit generates a unique digital identity for patients by combining elliptic curve cryptography (ECDSA) with biometric hash values ​​(fingerprint + face). A smart contract group, which includes at least a reservation rule contract, a fee settlement contract, and a data authorization contract, is written in Solidity and deployed on the blockchain.

3. The system according to claim 1, characterized in that, The intelligent allocation module includes: Multi-dimensional data collection interface to obtain real-time doctor's consultation time, professional level, historical consultation efficiency (number of patients per unit time), equipment appointment time, maintenance cycle, real-time operating status (collected through IoT sensors), patient's condition level (ICD-11 coding mapping), geographical location (LBS positioning), and historical medical preferences; The dynamic appointment allocation model employs an improved ant colony optimization algorithm to construct a multi-objective function with the goals of minimizing waiting time, maximizing resource utilization, and maximizing patient preference matching. The real-time scheduling engine dynamically adjusts the appointment queue based on doctor scheduling data, equipment status data, patient demand data, and IoT device data.

4. The system according to claim 3, characterized in that, The ant colony optimization algorithm model is adopted, and the objective function is: in For waiting time, To improve equipment utilization, For patient preference matching, These are dynamic weighting coefficients.

5. The system according to claim 1, characterized in that, The cross-agency data sharing module includes: The zero-knowledge proof verification unit enables patients to prove the authenticity and completeness of their test reports to medical institutions without disclosing specific data. The Verifiable Credential (VC) generation module, in accordance with W3C standards, converts examination reports into encrypted credentials containing biometric hashes, timestamps, and institutional signatures, allowing for fine-grained patient authorization. The cross-chain data gateway supports interaction between different consortium blockchains such as Hyperledger Fabric and Ethereum, and achieves atomic synchronization of appointment information and medical insurance settlement data through Hash Locking (HTLC).

6. The system according to claim 5, characterized in that, The cross-institutional data sharing module has a built-in data access auditing mechanism that records the time of data access, access node IP, and operation type (query / download / authorization) in real time, and stores the audit logs on the blockchain, supporting full-process traceability by the blockchain explorer.

7. The system according to claim 1, characterized in that, The personalized service module includes: The intelligent consultation recommendation unit, based on the BERT pre-trained model and fine-tuned with medical corpus, realizes entity recognition and relation extraction of Chinese medical terms, and generates department recommendations and doctor matching lists based on the patient's symptom description; The proactive health management system connects to IoT devices such as smart pillboxes and blood pressure monitors to collect health data in real time and generate personalized medication reminders and follow-up visit plans based on LSTM neural networks.

8. The system according to claim 1, characterized in that, The multi-dimensional data in the intelligent allocation module includes doctor data such as consultation time, professional level, and historical consultation efficiency; equipment data such as appointment time, maintenance cycle, and real-time status; and patient data such as disease level, geographical location, and historical medical preferences.

9. The system according to claim 1, characterized in that, The multi-terminal interaction module supports access from devices such as WeChat mini-programs, H5 web pages, self-service terminals, and smartwatches. It interacts with the core functional layer through RESTful APIs, providing unified identity authentication and interface adaptation functions.

10. The system according to claim 1, characterized in that, The system also includes a security architecture module, which includes: The national cryptographic algorithm encryption module uses SM4 symmetric encryption for patient data transmission, SM2 asymmetric encryption for blockchain transaction signatures, and SM3 algorithm for hash calculation. The access control mechanism uses attribute-based encryption (ABE) to achieve "data is available but not visible," allowing patients to customize the validity period and access scope of data sharing.

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