High-risk anesthesia warning system and method based on distributed network

Abstract

The invention discloses a high-risk anesthesia warning system and method based on a distributed network, and belongs to the field of medical information security. The system comprises a central database server which stores a high-risk patient list including a malignant hyperpyrexia history, a difficult airway history, a serious adverse drug reaction history and an abuse drug history based on a non-relational database; the security authentication gateway is used for realizing security access based on an OAuth 2.0 and a TLS protocol; the data access interface is used for providing RESTful API (Application Program Interface) to receive the patient identifier subjected to hash processing; the data processing and comparing module is used for executing low-latency query; and the warning information generating and pushing module is used for pushing the warning information in the JSON format to the target terminal through the message queue. Through a series of technical means, information islands among medical institutions are effectively broken, safe, real-time and efficient data interaction and warning are achieved, serious anesthesia complications can be remarkably avoided from the technical level, and medical safety in the perioperative period is improved.

Description

Technical Field

[0001] This invention belongs to the field of medical information technology and clinical anesthesia safety technology. Specifically, it relates to an intelligent system and method for proactively identifying and alerting patients with a high-risk anesthesia history during the perioperative period, based on distributed computing, real-time data security interaction protocol and asynchronous message communication technology. Background Technology

[0002] Anesthesia safety is central to perioperative management, but some patients face extremely high risks due to specific medical histories, including: 1. History of malignant hyperthermia (MH): A rare, autosomal dominant inherited muscle disease triggered by inhaled anesthetics and depolarizing muscle relaxants, which can lead to fatal hyperthermia and hypermetabolism with an extremely high mortality rate.

[0003] 2. History of difficult airway: A history of difficult intubation or ventilation clearly recorded in previous anesthesia records significantly increases the risk of recurrence of accidents.

[0004] 3. History of serious adverse reactions to specific drugs: such as prolonged phase II blockade of succinylcholine, allergy to local anesthetics, pseudocholinesterase deficiency, etc.

[0005] 4. History of drug abuse: Long-term abuse of stimulants and other drugs can lead to drug tolerance, dependence and complex pharmacological interactions, which can greatly affect the determination of anesthetic drug regimens and dosages, and may induce withdrawal reactions.

[0006] 5. History of other high-risk anesthetic events: such as intraoperative awareness, severe drug allergic reaction (under reasonable drug use), etc.

[0007] Currently, the transmission of such critical information relies heavily on patient self-reporting and reviewing previous paper medical records or local electronic medical record systems. However, patients may be unable to provide accurate information due to impaired consciousness, forgetfulness, or deliberate concealment. Furthermore, accessing historical medical records across hospitals is cumbersome and time-consuming, making it difficult to complete in the short time before surgery. This information barrier allows anesthesiologists to unknowingly misuse triggers (such as sevoflurane for MH patients) or underestimate the difficulty of anesthesia management, thus placing patients in extremely dangerous situations. Simultaneously, the health information systems (HIS) and electronic medical records (EMR) of different medical institutions form information silos, lacking cross-institutional technical interoperability. The fundamental technical deficiency lies in: 1. Data heterogeneity: Different systems have different data models and interface protocols, making direct communication impossible.

[0008] 2. Lack of secure transmission mechanisms: There is a lack of standardized security authentication and encrypted data transmission schemes that comply with the Cybersecurity Law and the Personal Information Protection Law.

[0009] 3. Insufficient real-time capability: The existing process cannot complete automated identity verification and information push within the critical preoperative time window.

[0010] Therefore, there is an urgent need in this field for an innovative technical solution to address the above problems at the system architecture level, and to achieve secure, real-time, and efficient cross-institutional data interaction through specific technical means, so as to directly improve medical safety. Summary of the Invention

[0011] (a) Purpose of the invention This invention aims to overcome the shortcomings of existing technologies and provide a high-risk anesthesia warning system and method. Through a series of technical means such as distributed network architecture, standardized data interface, strong security authentication protocol and asynchronous message push mechanism, it breaks down information silos and realizes proactive, real-time and safe identification and warning of high-risk patients. It aims to proactively and timely alert anesthesiologists to the potential high risks of patients and avoid the occurrence of serious anesthesia complications.

[0012] (II) Technical Solution To achieve the above objectives, the present invention adopts the following technical solution: A high-risk anesthesia alert system based on a distributed network, characterized in that it includes: Centralized distributed database server: A cluster of non-relational databases (such as MongoDB and Cassandra) is used to store the list of high-risk patients. The data uses the hash value (such as SHA-256) of the patient's unique identifier as the primary key and JSON documents containing fields such as high-risk category code, source of evidence, and update timestamp as the value, supporting high-concurrency and low-latency queries.

[0013] Security Authentication Gateway: As the sole entry point to the system, it integrates the OAuth 2.0 client credential authorization process and the TLS 1.3+ encryption protocol to perform identity authentication, authorization, and transmission encryption on all access requests, ensuring controllable access and data security.

[0014] Standardized data access interface: Provides an application programming interface (API) based on RESTful architecture, defines standardized request / response message formats (the request body contains a hash identifier, and the response body is a JSON format warning message), and solves interoperability issues between systems.

[0015] Real-time data processing and comparison engine: It adopts a key-value query algorithm based on hash tables. After receiving a successfully authenticated query request, it performs millisecond-level index matching in the distributed database.

[0016] Asynchronous message push module: After successful comparison, a standardized JSON message object is generated and pushed to the target terminal through a message queue (such as RabbitMQ, Kafka) or WebSocket long connection to ensure the reliability and real-time performance of the message.

[0017] Target terminals: Client applications deployed on anesthesia workstations, operating rooms, and mobile devices, which receive and render warning information (including graphical pop-ups and sound alarms) by subscribing to message queues or maintaining WebSocket connections.

[0018] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are: 1. Breaking down information silos through technical architecture: By adopting standardized RESTful APIs and distributed databases, the problem of data interoperability across systems and organizations is fundamentally solved.

[0019] 2. Secure data exchange through security protocols: Integrating OAuth 2.0 and TLS enables end-to-end authentication, authorization, and encryption, fully complying with medical data privacy regulations.

[0020] 3. Improve efficiency and real-time performance through algorithms and communication mechanisms: Low-latency queries based on hash indexes and asynchronous message queues ensure that warning information is delivered within seconds at critical moments before surgery.

[0021] 4. Directly improves clinical medical safety: It fundamentally avoids serious anesthesia accidents caused by the incorrect use of triggers or insufficient preparation due to lack of information.

[0022] 5. Standardized management: It helps to establish a regional and even national high-risk patient registration and warning network, and promotes the homogenization of anesthesia quality. Attached Figure Description

[0023] Figure 1 This is a system structure block diagram of an embodiment of the present invention.

[0024] Figure 2 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0025] See Figure 1 This system is deployed on a cloud platform. When a patient checks in for hospitalization or pre-operative check-in, the hospital's HIS system automatically calls the system's RESTful API, and the request message contains the patient's ID number (de-identified) processed by SHA-256 hashing.

[0026] The request first arrives at the security authentication gateway, which then verifies the client credentials of the HIS system before allowing it to proceed.

[0027] The data processing and comparison engine receives requests, uses hash values ​​as keys, and executes queries within the MongoDB sharded cluster. If a match is found, the risk code (e.g., `MH:1`) is extracted from the document.

[0028] The alert message generation module converts the code into a JSON format alert message (including detailed risk descriptions and handling suggestions, such as: "History of malignant hyperthermia: Discontinue all volatile inhaled anesthetics and succinylcholine. Total intravenous anesthesia is recommended; prepare sufficient dantrolone."), and delivers it to the designated RabbitMQ exchange.

[0029] The anesthesia workstation terminal deployed in the target operating room obtains the message in real time by subscribing to the queue, and after parsing it, renders it on the UI interface as a red pop-up window and a high-frequency alarm sound to remind the anesthesiologist.

[0030] All data access operations are logged for auditing and compliance checks. Database creation, deletion, modification, and querying require authorized administrators to complete the same security authentication process. Database maintenance is performed by qualified anesthesiologists or hospital administrators who have passed security authentication, based on solid medical evidence (such as genetic testing reports and previous anesthesia records).

Claims

1. A high-risk anesthesia warning system and method based on a distributed network, characterized in that, include: The central database server stores and manages a database of high-risk patient lists based on a non-relational database model. The security authentication gateway is used to authenticate and authorize all access requests based on the OAuth 2.0 protocol and enforce TLS encrypted transmission. The data access interface provides an application programming interface based on RESTful architecture for external systems to receive hashed patient identification information. The data processing and comparison module is connected to the security authentication gateway and the data access interface, and is used to perform low-latency index queries; The warning message generation and push module is connected to the data processing and comparison module and is used to generate standardized warning messages and push them through an asynchronous message mechanism. The target terminal is used to receive and visualize the warning information.

2. The system according to claim 1, characterized in that, The patient identification information received by the data access interface is a hash value processed by the SHA-256 algorithm.

3. The system according to claim 1, characterized in that, The asynchronous messaging mechanism is either a publish-subscribe model based on message queues (RabbitMQ / Kafka) or a WebSocket long-connection protocol.

4. The system according to claim 1, characterized in that, The standardized warning message generated by the warning information generation and push module is a JSON format document, which contains high-risk category codes and plaintext warning suggestions.

5. The system according to claim 1, characterized in that, The visual display of the target terminal includes modal pop-ups on the graphical user interface and system-level sound alarms.

6. A high-risk anesthesia warning method based on a distributed network, characterized in that, The method, employing the system as described in any one of claims 1-5, comprises: The system receives securely authenticated query requests from external systems via a RESTful API interface. These requests contain hashed patient identification information. Perform low-latency index queries for comparison in a distributed database; If the comparison is successful, a standardized alert message in JSON format will be generated; The alert message is pushed to the subscribed target terminal via an asynchronous message queue or WebSocket connection; The warning message is parsed and rendered on the target terminal to complete the visual warning.

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