Medical system with layered architecture for coprocessing data

Abstract

The invention relates to a medical system for coprocessing data with a layered architecture, and the system comprises a mobile terminal which comprises an identity recognition module for collecting the identity information of a patient, a doctor's advice association module for doctor's advice data, and a time collection module for time nodes, and comprises an offline caching module and a network recovery supplementary transmission module; the edge node is used as a deployed medical co-body local server and comprises a data verification module, a logic error correction module and a temporary storage module; and the cloud comprises a real-time analysis module for analyzing the first-aid data, a time axis generation module for generating a time axis report, an early warning pushing module for warning, a cross-mechanism sharing module for supporting data sharing and a dynamic authority management module. A hierarchical collaborative architecture of the mobile terminal, the edge node and the cloud is adopted, the mobile terminal serves as a data acquisition entrance, the edge computing node serves as a temporary cache, the cloud serves as an analysis center, breakthrough is conveniently achieved in timeliness, collaboration, operation cost and data quality, and technical support is provided for county medical co-medical first aid.

Description

Technical Field

[0001] The present invention relates to the technical field of medical systems, and in particular to a medical system with a layered architecture for collaborative data processing. Background Art

[0002] In the county-level medical community emergency system, the treatment of critical illnesses such as chest pain, stroke, and trauma is highly dependent on timeliness and multi-institutional collaboration. In the traditional emergency process, patient identification, medical order association, time node recording and other links mostly rely on manual operations, which have problems such as low efficiency, high error rate, and delay in cross-institutional data communication, which directly affect the utilization efficiency of the "golden treatment period". Existing technologies mainly try to optimize the emergency process through the following two types of solutions, but there are still significant defects: The emergency triage module expanded from the traditional HIS system requires the deployment of dedicated equipment, which has high procurement and maintenance costs for primary medical institutions, is difficult to popularize, and is highly dependent on hardware; identity recognition and time recording require switching devices, which can easily lead to data omissions; the average error in manually recording time nodes exceeds 30 seconds, especially at night when emergency staff are insufficient; it only supports data storage within a single hospital, and cross-institutional access requires manual export, with delays of more than 2 hours.

[0003] Regional emergency time management cloud platform: Relies on manual PC-based input of patient information and cannot directly link to medical orders by scanning codes on mobile terminals, increasing operation time by 40%; Not compatible with devices such as smart watches, unable to quickly trigger time collection when doctors' hands are occupied in emergency scenarios; Cloud-based analysis relies on batch uploads, taking 15-30 minutes from collection to report generation, and unable to provide real-time warnings of process bottlenecks; Patient identity and medical orders must be manually bound, and dialects or verbal errors lead to data matching errors. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a medical system for collaboratively processing data using a layered architecture to solve one or more problems in the prior art.

[0005] To achieve the above object, the technical solution of the present invention is as follows: A medical system for collaboratively processing data using a layered architecture, the system comprising: The mobile terminal includes an identity recognition module for collecting patient identity information, a medical order association module for medical order data, and a time collection module for time nodes. It also includes an offline cache module and a network recovery and retransmission module for use in the event of network disconnection; The edge node, as the deployed local server of the medical community, includes a data verification module, a logic error correction module, and a temporary storage module; The cloud includes a real-time analysis module for analyzing emergency data, a timeline generation module for generating timeline reports, an early warning push module for warnings, a cross-institutional sharing module that supports data sharing, and a dynamic authority management module.

[0006] Furthermore, the data verification module acts on the identity recognition module, the medical order association module and the time collection module to cooperate with the logic error correction module to detect the logic conflicts of the time nodes in real time.

[0007] Furthermore, the temporary storage module acts on the offline cache module and the network recovery and retransmission module to achieve uploading of data before and after network interruption.

[0008] Furthermore, the network recovery and retransmission module realizes uploading based on the breakpoint resume transmission protocol FTP.

[0009] Furthermore, the real-time analysis module is based on Spark streaming and acts on the data of the data verification module and the temporary storage module to achieve real-time computing and analysis.

[0010] Furthermore, the real-time analysis module acts on the timeline generation module and the cross-institutional sharing module respectively.

[0011] Furthermore, the timeline generation module acts on the warning push module, which is implemented based on the AI ​​warning model, predicts process bottlenecks based on LSTM and pushes warnings in advance.

[0012] Furthermore, the warning push module, the cross-institutional sharing module and the dynamic authority management module jointly serve medical institutions or medical staff.

[0013] Compared with the prior art, the beneficial technical effects of the present invention are as follows: The present invention adopts a hierarchical collaborative architecture of mobile terminals, edge nodes and cloud, with the mobile terminal as the data collection entrance, the edge computing node as the temporary cache, and the cloud as the analysis center. It has achieved breakthroughs in timeliness, collaboration, operating costs and data quality, and provided technical support for emergency medical care in county-level medical communities. Specifically, in terms of timeliness, the time acquisition module on the mobile terminal reduces the error in acquisition, and the early warning push module on the cloud predicts process bottlenecks in advance; in terms of collaboration, the cross-institutional sharing module on the cloud realizes real-time sharing of data, and the pre-processing of the edge node reduces the cloud computing load on the cloud and improves analysis efficiency; in terms of operational portability and cost control, the identity recognition module and the medical order association module on the mobile terminal improve the correctness of binding, and the offline cache module and the network recovery and retransmission module facilitate data entry; in terms of data quality, the encrypted identity recognition module on the mobile terminal and the dynamic authority management module on the cloud realize quality control of patient medical data to ensure security and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the hierarchical architecture of a medical system for collaborative data processing using a layered architecture according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following is a further detailed description of a medical system for collaborative data processing with a layered architecture proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, please refer to the drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no technical significance. Any modification of the structure, change in the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0016] Please refer to Figure 1 A medical system for collaborative data processing with a layered architecture is characterized in that the system includes three layers: mobile terminal, edge node and cloud. The mobile terminal serves as the data acquisition stage, the edge node serves as the data preprocessing stage, and the remote terminal serves as the data analysis and collaboration stage. Medical data processing is achieved through division of labor and collaboration at different levels.

[0017] Furthermore, the mobile terminal includes an identity recognition module for collecting the patient's identity information. Preferably, the identity recognition module can scan the ID card or medical insurance card through OCR, read the electronic health code or face recognition through NFC, and then automatically fill in the patient information and link it to the cloud. In addition, the transmission of identity information adopts the national secret SM4 encryption, which effectively protects the patient's personal privacy.

[0018] The mobile terminal also includes a medical order association module for identifying medical order data, which obtains patient ID, treatment steps, etc. by scanning the medical order QR code, and automatically binds the identity information. Preferably, the medical order association module also supports a voice error correction engine for manual re-recording, supports adaptation to various local dialects, and the adaptation rate is ≥95%.

[0019] The mobile terminal also includes a time acquisition module for acquiring time nodes. This module is configured to collect and modify information via a mobile phone, record time information in real time via a smartwatch, or trigger time recording via voice commands. This module uses Beidou / GPS dual-mode timing to calibrate time, effectively controlling the time accuracy to ≤0.3s.

[0020] The mobile terminal also includes an offline caching module and a network recovery and retransmission module for use in the event of a network outage. When there's no network connection, data is temporarily stored in the offline caching module, a SQLite database, and supports 72 hours of offline data collection. When the network is restored, the network recovery and retransmission module automatically retransmits the data to the edge node. This module uses FTP, a resumable upload protocol, to achieve a retransmission success rate of ≥99.9%.

[0021] Furthermore, for the edge node, as a deployed local server of the medical community, it includes a data verification module, a logic error correction module and a temporary storage module to pre-process the mobile terminal data.

[0022] The data verification module operates on the identity recognition module, medical order association module, and time acquisition module, cooperating with the logic error correction module to detect logical conflicts at time nodes in real time, such as when "thrombolysis time" is earlier than "admission time," and to prevent logical conflicts completely. Preferably, within the data verification module, a visual rule configurator allows administrators to customize time nodes, such as adding "trauma hemostasis time."

[0023] The logic error correction module is used to clear the data intercepted by the data verification module to reduce the computing load on the cloud.

[0024] The temporary storage module acts on the offline cache module and the network recovery and retransmission module to achieve data upload before and after network interruption. Preferably, the temporary storage module uses Redis cache to support fast reading and writing.

[0025] Furthermore, the cloud includes a real-time analysis module for analyzing emergency data. The real-time analysis module is based on Spark streaming processing and acts on the data of the data verification module and the temporary storage module to achieve real-time computing and analysis and process time series data streams.

[0026] The cloud also includes a timeline generation module that generates timeline reports. The real-time analysis module operates on this module to dynamically generate emergency timelines, such as door-to-needle time and door-to-balloon time. This module dynamically displays the time nodes of each treatment phase and can also mark timeouts. When a patient is transferred to another hospital, the emergency timeline generated by the timeline generation module can be automatically linked to the new institution's HIS system.

[0027] The cloud also includes an early warning push module. The above-mentioned timeline generation module acts on the early warning push module. The early warning push module is implemented based on the AI ​​early warning model and predicts process bottlenecks based on LSTM and pushes early warnings in advance. If a township referral delay occurs, the early warning can be pushed to the responsible department 10 to 15 minutes in advance. The cloud also includes a cross-institutional sharing module that supports data sharing, and the aforementioned real-time analysis module also operates within this cross-institutional sharing module. The data sandbox enables secure data sharing within the medical community, effectively reducing delays such as when township health centers access medical records from county-level hospitals. Once township health centers upload data, county-level hospitals can access it in real time and pre-configure catheterization labs, operating rooms, and other facilities.

[0028] The dynamic permission management module allocates field-level permissions based on the role-based access control RBAC model, such as opening only medication records.

[0029] Furthermore, the early warning push module, the cross-institutional sharing module and the dynamic authority management module jointly serve medical institutions or medical staff to facilitate institutions or personnel to obtain relevant patient information and connect with patients or their families.

[0030] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0031] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A medical system for collaborative data processing using a layered architecture, characterized by: The system comprises: The mobile terminal includes an identity recognition module for collecting patient identity information, a medical order association module for medical order data, and a time collection module for time nodes. It also includes an offline cache module and a network recovery and retransmission module for use in the event of network disconnection; The edge node, as the deployed local server of the medical community, includes a data verification module, a logic error correction module, and a temporary storage module; The cloud includes a real-time analysis module for analyzing emergency data, a timeline generation module for generating timeline reports, an early warning push module for warnings, a cross-institutional sharing module that supports data sharing, and a dynamic authority management module.

2. The medical system for collaborative data processing using a layered architecture as claimed in claim 1, wherein: The data verification module acts on the identity recognition module, the medical order association module and the time acquisition module to cooperate with the logic error correction module to detect the logic conflicts of the time nodes in real time.

3. The medical system for collaborative data processing using a layered architecture as claimed in claim 2, wherein: The temporary storage module acts on the offline cache module and the network recovery and retransmission module to achieve data upload before and after network interruption.

4. The medical system for collaborative data processing using a layered architecture as claimed in claim 3, wherein: The network recovery and retransmission module realizes uploading based on the breakpoint resume transmission protocol FTP.

5. The medical system for collaborative data processing using a layered architecture as claimed in claim 4, wherein: The real-time analysis module is based on Spark streaming and acts on the data of the data verification module and the temporary storage module to achieve real-time computing and analysis.

6. The medical system for collaborative data processing using a layered architecture as claimed in claim 5, wherein: The real-time analysis module acts on the timeline generation module and the cross-institutional sharing module respectively.

7. The medical system for collaborative data processing using a layered architecture as claimed in claim 6, wherein: The timeline generation module acts on the warning push module, which is implemented based on the AI ​​warning model, predicts process bottlenecks based on LSTM and pushes warnings in advance.

8. The medical system for collaborative data processing using a layered architecture as claimed in claim 7, wherein: The early warning push module, the cross-institutional sharing module and the dynamic authority management module jointly serve medical institutions or medical staff.

Images