Preoperative information input system based on Internet hospital

Abstract

The invention relates to the technical field of internet hospitals, in particular to an internet hospital-based preoperative information input system, which comprises a patient end information acquisition module, a cloud data processing module and a hospital end management system. The patient end realizes multi-dimensional information input and classification, the cloud end performs intelligent analysis and structured storage by using OCR and natural language processing technologies, and the hospital end provides risk assessment, medicine taking reminding and state tracking functions. The system is seamlessly connected with a hospital system through a medical history docking interface, so that the information acquisition efficiency, the automatic processing capability and the preoperative management intelligent level are remarkably improved, a scientific basis is provided for a doctor to make an operation plan, and the patient experience is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of medical information technology, and specifically relates to a preoperative information entry system based on an Internet hospital. Background Art

[0002] Patent document with publication number CN114999453B discloses a preoperative visit system based on speech recognition and a corresponding speech recognition method. This technical solution realizes the speech collection and processing of patients' preoperative information through components such as image acquisition device, sound acquisition device, speech recognition module, and visit question database, and supports manual entry as a supplement.

[0003] Existing technical solutions for preoperative information entry still have certain shortcomings when applied in internet hospitals. These include limited intelligent collection and classification of multi-dimensional information, inadequate integration of existing patient medical data, insufficient automated processing of complex information, and a lack of integration with hospital systems. Therefore, there is an urgent need to develop a preoperative information entry system for internet hospitals to meet the demands of modern healthcare for efficient and intelligent preoperative information management. Summary of the Invention

[0004] The purpose of the present invention is to provide a preoperative information entry system based on an Internet hospital to solve the problems mentioned in the above background technology, such as insufficient intelligent collection and classification capabilities of multi-dimensional information, failure to fully integrate patients' existing medical data, limited automated processing capabilities for complex information, and lack of docking functions with hospital systems.

[0005] The technical solution of the present invention is: The patient-side information collection module collects and uploads multi-dimensional preoperative information through a mobile terminal. The multi-dimensional information includes at least basic patient information, complications, surgical history, implant status, medication status, and medical examination reports. Cloud data processing module, including: Image recognition unit, which uses OCR technology to parse medical report images uploaded by patients and extract key information; Natural language processing unit, which performs semantic analysis on text content and extracts structured fields; Data classification unit, which classifies multi-dimensional information into structured data tables according to preset rules; Medical history docking interface, which connects to the hospital medical history system through standardized protocols and automatically imports patients' historical medical data; Hospital management system, including: Risk assessment unit, which generates comprehensive surgical risk assessment reports based on structured data tables and historical medical data; Medication reminder unit, which generates medication reminders based on medication status and pushes them to the patient; Patient status tracking unit, which monitors and visually displays changes in patient health status in real time; The patient-side information collection module, cloud-side data processing module and hospital-side management system realize data interaction through the API interface, forming a closed-loop preoperative information management process.

[0006] The basic information entry unit of the patient-side information collection module supports patients to fill in basic information such as gender, age, height, weight, and abdominal circumference, and complete the entry operation through a drop-down menu or input box. The comorbidity selection unit adopts an open-ended multiple-choice question design, allowing patients to check diabetes, hypertension, heart disease, chronic kidney disease or other options. The surgical history recording unit is divided into two parts. The first part is the selection of surgical type, including cesarean section, appendix, gallbladder, major abdominal surgery, laryngeal surgery, orthopedic surgery, etc.; the second part is the selection of surgical method, covering open surgery, laparoscopic surgery, endoscopic surgery, etc. The implant status confirmation unit also adopts the form of open-ended multiple-choice questions, listing options such as steel nails, steel plates, and stents for patients to choose. The medication status collection unit supports patients to upload medication instructions or prescriptions by taking photos. The image recognition unit uses OCR technology to extract text content and perform structured processing. At the same time, according to the discontinuation of medication, options such as not taking, simple discontinuation days, discontinuation and bridging days are set. The existing report upload unit allows patients to take photos of endoscopy, imaging examination and other reports, and the image recognition unit captures key information and stores it in the cloud database.

[0007] The present invention provides an improved preoperative information entry system based on an Internet hospital. Compared with the prior art, the present invention has the following improvements and advantages: By utilizing both a patient-side information collection module and a cloud-based data processing module, the latter comprehensively captures a patient's basic information, comorbidities, surgical history, implant status, and more, supporting multiple data entry methods, including manual entry, photo upload, and multiple-choice questions. The cloud-based data processing module utilizes OCR and natural language processing technologies to intelligently analyze uploaded data, enabling structured storage and classification, significantly improving the efficiency and accuracy of information collection.

[0008] The medical history integration interface and risk assessment unit seamlessly connect to the hospital's medical history system through standardized protocols, automatically importing the patient's medical history and examination results, eliminating omissions or errors that may occur due to manual entry. The risk assessment unit generates a comprehensive risk assessment report based on the patient's uploaded information and imported medical history data, providing a scientific basis for doctors to formulate preoperative plans.

[0009] The system utilizes a medication reminder unit and a patient status tracking unit. The unit automatically generates reminder messages based on the patient's reported medication status and sends them to the patient via the internet hospital platform, helping them take their medication on time or adjust their medication regimen. The patient status tracking unit provides real-time updates on the patient's diet, bowel movements, mobility, and other information, presenting it to the doctor via visual charts, allowing them to promptly understand the patient's overall health and make appropriate adjustments.

[0010] In summary, the present invention solves the shortcomings of existing preoperative information entry systems in intelligent collection, automated processing and system docking through innovative technical means, and provides an efficient and intelligent solution for preoperative information management in the Internet hospital scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a system architecture diagram of the present invention.

[0012] Figure 2 Schematic diagram of the functional units of the patient-side information acquisition module.

[0013] Figure 3 This is the workflow diagram of the cloud data processing module.

[0014] Figure 4 This is a functional unit distribution diagram of the hospital management system.

[0015] Figure 5 This is a schematic diagram of the simulation environment of the present invention. DETAILED DESCRIPTION

[0016] The present invention provides a preoperative information entry system based on an Internet hospital, and its specific implementation is as follows. Figure 1 The system architecture diagram shown here shows the entire system consisting of a patient-side information collection module, a cloud-based data processing module, and a hospital-side management system. These three modules interact via a network connection. The patient-side information collection module is responsible for collecting and uploading patient information, while the cloud-based data processing module performs intelligent analysis and classification on the uploaded data. The hospital-side management system allows doctors to view patient information and generate relevant reminders.

[0017] The patient information collection module includes a basic information entry unit, a comorbidity selection unit, a surgical history record unit, an implant status confirmation unit, a medication status collection unit, and an existing report upload unit. These units connect to the cloud-based data processing module via an API interface and adopt a RESTful architecture design that supports JSON-formatted data transmission. The basic information entry unit allows patients to manually enter basic information such as gender, age, height, weight, and waist circumference through drop-down menus or input boxes. The comorbidity selection unit uses a multiple-choice format, allowing patients to select diabetes, hypertension, heart disease, chronic kidney disease, or other options. The surgical history record unit is divided into two parts. The first part selects the type of surgery, including cesarean section, appendectomy, gallbladder surgery, major abdominal surgery, laryngeal surgery, and orthopedic surgery; the second part selects the surgical method, including open surgery, laparoscopic surgery, and endoscopic surgery. The implant status confirmation unit also uses a multiple-choice format, listing options such as steel nails, steel plates, and stents for patients to choose from. The medication status collection unit allows patients to upload medication instructions or prescriptions by taking a photo. The image recognition unit uses OCR technology to extract and structure text content, and also sets options such as no medication, simple medication withdrawal days, and medication withdrawal with bridging days based on medication withdrawal status. The existing report upload unit allows patients to take photos of endoscopy, imaging, and other reports. The image recognition unit captures key information and stores it in a cloud database.

[0018] The cloud-based data processing module includes an image recognition unit, a natural language processing unit, a data classification unit, and a medical history docking interface. The image recognition unit uses a deep learning algorithm to extract key information from the imaging reports uploaded by the patient, such as the location of the lesion, the diagnosis conclusion, and the recommended treatment plan. The natural language processing unit performs semantic analysis on the text content uploaded by the patient, extracts key fields, and stores them in categories. The data classification unit categorizes the patient's basic information, comorbidities, surgical history, implant status, medication status, and other information according to preset rules to form a structured data table. The medical history docking interface communicates with the hospital's medical history system through the HL7 standard protocol and automatically imports the patient's past medical history, examination results, and treatment records. The image recognition unit and the natural language processing unit achieve efficient processing through a distributed computing framework, specifically using TensorFlow and PyTorch as the core algorithm library.

[0019] The hospital-side management system includes a risk assessment unit, a medication reminder unit, a surgery reminder unit, and a patient status tracking unit. The risk assessment unit generates a comprehensive risk assessment report based on the information uploaded by the patient and imported medical history data. This report includes the patient's general condition score, surgical risk level, and potential complication warnings. The medication reminder unit automatically generates reminder messages based on the patient's reported medication status and sends them to the patient through the Internet hospital platform. The surgery reminder unit pushes precautions and preoperative preparation checklists in advance for high-risk surgeries such as laryngeal surgery and major surgery reported by the patient. The patient status tracking unit updates the patient's diet, bowel movements, mobility, and other information in real time, and displays it to the doctor through visual charts, allowing the doctor to understand the patient's overall health status. The various units of the hospital-side management system achieve asynchronous communication through internal message queues, reducing system latency and improving response speed.

[0020] In order to verify the performance of this system in actual application, simulation tests and clinical trials were designed. Figure 5 As shown in the schematic diagram of the simulation environment, the data interaction process between the patient-side information collection module, the cloud-side data processing module, and the hospital-side management system under the virtual cloud platform technology monitors the operating status of each module through a logging tool to ensure the integrity and consistency of data transmission. The patient-side information collection module generates data upload requests for different types of patients through the simulator. After receiving the request, the cloud-side data processing module calls the image recognition unit and the natural language processing unit to complete the data parsing and classification tasks, and finally transmits the processing results to the hospital-side management system. The clinical trial selected several hospitals as pilot units and invited patients to be admitted to the hospital to complete the preoperative information entry using this system through the Internet hospital platform, collect feedback from doctors and patients, and optimize the system.

[0021] Each unit of the patient information collection module connects to the cloud-based data processing module via an API interface. The API interface adopts a RESTful architecture and supports data transmission in JSON format. The image recognition unit and natural language processing unit of the cloud-based data processing module achieve efficient processing through a distributed computing framework, specifically using TensorFlow and PyTorch as core algorithm libraries. The medical history docking interface communicates with the hospital medical history system via the HL7 standard protocol, ensuring the security and compatibility of data exchange. The various units of the hospital-based management system achieve asynchronous communication through internal message queues, reducing system latency and improving response speed.

[0022] In a specific application scenario, patients access the patient-side information collection module through a mobile terminal device. First, they fill in basic information such as gender, age, height, weight, and abdominal circumference in the basic information entry unit. Then, they check the relevant disease options in the comorbidity selection unit, select the type of surgery and surgical method in the surgical history recording unit, select the type of implant in the implant status confirmation unit, upload the medication instructions or prescription in the medication status collection unit, and upload the examination report in the existing report upload unit. The patient-side information collection module transmits the uploaded data to the cloud data processing module through the API interface. The cloud data processing module calls the image recognition unit and natural language processing unit to parse and classify the data. The image recognition unit extracts key information from the imaging report, and the natural language processing unit performs semantic analysis on the text content and classifies it for storage. The data classification unit classifies patient information according to preset rules to form a structured data table. The medical history docking interface communicates with the hospital medical history system via the HL7 standard protocol, automatically importing the patient's medical history, examination results, and treatment records. The processed data is transmitted to the hospital management system. The doctor generates a comprehensive risk assessment report through the risk assessment unit, generates reminder messages through the medication reminder unit and the surgery reminder unit, and updates the patient status in real time through the patient status tracking unit and displays it to the doctor through visual charts.

[0023] It can be seen from the above specific implementation methods that the present invention solves the problems of the existing technology in insufficient intelligent collection and classification of multi-dimensional information, failure to fully integrate patients' existing medical data, limited automatic processing capabilities of complex information, and lack of docking functions with hospital systems by improving the preoperative information entry system based on the Internet hospital provided here.

[0024] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the implementation principle of the present invention is further explained in detail below in combination with specific application scenarios.

[0025] When using this system, patients first access the patient information collection module through a mobile terminal device. After entering the basic information entry unit, the patient fills in basic information such as gender, age, height, and weight, and completes the operation through the drop-down menu or input box. Subsequently, the patient enters the comorbidity selection unit and checks relevant options such as diabetes, hypertension, and heart disease. At this point, the comorbidity selection unit uses the design of open-ended multiple-choice questions to allow patients to flexibly select multiple disease options based on their own circumstances, and transmits the data in JSON format to the cloud data processing module.

[0026] When a patient enters the surgical history recording unit, the system provides two options: the first is the type of surgery, including cesarean section, appendectomy, gallbladder surgery, etc.; the second is the surgical method, including open surgery, laparoscopic surgery, etc. After the patient completes the selection, the relevant data is uploaded to the data classification unit of the cloud data processing module, and the patient's surgical history information is structured and stored according to preset rules. The implant status confirmation unit adopts a similar design, listing options such as steel nails, steel plates, and stents for patients to choose from. The data is also transmitted to the cloud via the API interface.

[0027] The medication status collection unit supports patients to take photos and upload medication instructions or prescriptions. The image recognition unit uses OCR technology to extract key information such as drug name, dosage and usage, and converts it into structured data. At the same time, the unit sets options such as "not taken", "simple drug discontinuation days", and "drug discontinuation and bridging days" according to the drug discontinuation situation to facilitate subsequent analysis by doctors. The existing report upload unit allows patients to take photos of endoscopy, imaging examination and other reports. The image recognition unit combines deep learning algorithms to extract key fields such as lesion location, diagnostic conclusions and recommended treatment plans, and stores them in the cloud database.

[0028] The natural language processing unit in the cloud data processing module performs semantic analysis on the text content uploaded by the patient, extracts key fields, and stores them in categories. For example, the medical record text uploaded by the patient may contain "a five-year history of hypertension." The natural language processing unit can identify "hypertension" as a comorbidity and "five years" as time information, and classify these fields into the corresponding data table. The data classification unit further integrates the patient's basic information, comorbidities, surgical history, implant status, medication status, and other information according to preset rules to form a complete structured data table.

[0029] The medical history interface communicates with the hospital's medical history system via the HL7 standard protocol, automatically importing the patient's medical history, test results, and treatment records. This process ensures the integrity and accuracy of the patient's existing medical data, avoiding omissions or errors that may arise from manual entry. The image recognition unit and natural language processing unit achieve efficient processing through a distributed computing framework, specifically using TensorFlow and PyTorch as core algorithm libraries to ensure real-time and stable large-scale data processing.

[0030] The risk assessment unit of the hospital-side management system generates a comprehensive risk assessment report based on the information uploaded by the patient and the imported medical history data. The report includes the patient's general condition score, surgical risk level, and potential complication warnings. For example, if the patient has the comorbidity of "hypertension" and a recent history of "major abdominal surgery", the risk assessment unit will calculate a higher surgical risk level based on the preset algorithm and issue an early warning to the doctor. The medication reminder unit automatically generates a reminder message based on the patient's reported medication status and sends it to the patient through the Internet hospital platform. For example, if the patient needs to stop taking the medicine for one week before surgery, the medication reminder unit will push a reminder at the appropriate time to ensure that the patient adjusts the medication plan on time.

[0031] The surgical reminder unit pushes precautions and preoperative preparation lists in advance for high-risk surgical types, such as laryngeal surgery or major abdominal surgery. For example, for patients undergoing laryngeal surgery, the surgical reminder unit will push a reminder message to "fast for six hours before surgery." The patient status tracking unit updates the patient's diet, bowel movement status, mobility and other information in real time, and displays it to the doctor through visual charts, so that the doctor can fully understand the patient's overall health status. For example, if the patient's diet has decreased and their mobility has decreased recently, the doctor can use the visual charts to promptly detect abnormalities and take appropriate measures.

[0032] Throughout the entire process, data exchange between the patient information collection module, the cloud-based data processing module, and the hospital management system is supported by virtual cloud platform technology. Logging tools monitor the operational status of each module to ensure the integrity and consistency of data transmission. For example, when the patient information collection module uploads an imaging report, the logging tool records information such as the upload time, file size, and processing status, ensuring that any anomalies can be traced.

[0033] In summary, the present invention achieves efficient operation of the preoperative information entry system based on the Internet hospital through the above steps. The patient-side information collection module is responsible for the comprehensive collection of multi-dimensional information, the cloud-side data processing module implements data analysis and classification through intelligent algorithms, and the hospital-side management system provides doctors with scientific basis and real-time reminder functions. Each link ensures the security, accuracy and efficiency of the data through standardized protocols and technical means, thus solving the shortcomings of existing technologies in intelligent collection, automated processing and system docking.

[0034] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0035] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The preoperative information entry system based on the Internet hospital is characterized by: include The patient-side information collection module collects and uploads multi-dimensional preoperative information through a mobile terminal. The multi-dimensional information includes at least basic patient information, complications, surgical history, implant status, medication status, and medical examination reports. Cloud data processing module, including: Image recognition unit, which uses OCR technology to parse medical report images uploaded by patients and extract key information; Natural language processing unit, which performs semantic analysis on text content and extracts structured fields; Data classification unit, which classifies multi-dimensional information into structured data tables according to preset rules; Medical history docking interface, which connects to the hospital medical history system through standardized protocols and automatically imports patients' historical medical data; Hospital management system, including: Risk assessment unit, which generates comprehensive surgical risk assessment reports based on structured data tables and historical medical data; Medication reminder unit, which generates medication reminders based on medication status and pushes them to the patient; Patient status tracking unit, which monitors and visually displays changes in patient health status in real time; The patient-side information collection module, cloud-side data processing module and hospital-side management system realize data interaction through the API interface, forming a closed-loop preoperative information management process.

2. The preoperative information entry system based on the Internet hospital according to claim 1 is characterized by: The patient-side information collection module includes a basic information entry unit, a comorbidity selection unit, an operation history recording unit, an implant status confirmation unit, a medication status collection unit, and an existing report uploading unit.

3. The preoperative information entry system based on the Internet hospital according to claim 2 is characterized in that: The basic information entry unit supports patients to fill in gender, age, height, weight, and waist circumference and complete the entry operation through a drop-down menu or input box. The comorbidity selection unit uses an open-ended multiple-choice question design for patients to check diabetes, hypertension, heart disease, chronic kidney disease or other options.

4. The preoperative information entry system based on the Internet hospital according to claim 2 is characterized in that: The surgical history recording unit is divided into a surgical type selection part and a surgical method selection part. The surgical type selection part includes cesarean section, appendectomy, gallbladder, major abdominal surgery, laryngeal surgery, and orthopedic surgery options. The surgical method selection part includes open surgery, laparoscopic surgery, and endoscopic surgery options.

5. The preoperative information entry system based on the Internet hospital according to claim 2 is characterized in that: The medication status acquisition unit supports patients to upload medication instructions or prescriptions by taking photos, and the image recognition unit in the cloud data processing module uses OCR technology to extract text content and perform structured processing.

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