Digital medical science popularization and patient management coverage system and solving method thereof

Abstract

The invention discloses a digital medical science popularization and patient management coverage system and a solving method thereof, and belongs to the technical field of digital doctors, comprising a medical illness state database based on Internet of Things interaction, a key medical illness state data outline system customized according to the medical illness state database, and an AI large model GPT customization, creating a doctor-patient interaction AI illness state linguistic data model, and constructing a digital human medical knowledge display model. According to the invention, an AI illness state linguistic data model, an AI digital doctor image and a human body display model are established based on AI model customization software, so that the AI illness state linguistic data model and the AI digital doctor image and human body display model are used for the patients to answer the inquiry of medical questions and popular science of the corresponding questions, and the unified illness state linguistic data model and the digital human body medical knowledge display model are established through an AI technology. The system is used for forming different digital doctor models, and identifying, integrating and matching the doctor seeing information of a patient, so that a corresponding digital doctor can be quickly and effectively called out for explanation and science popularization according to a corresponding illness state.

Description

Technical Field

[0001] The present invention relates to the field of digital doctor technology, and in particular to a digital medical science popularization and patient management coverage system and a solution thereof. Background Art

[0002] At present, there are many problems in the medical system, such as uneven distribution of medical resources, high medical costs, and low efficiency of medical services, which seriously restrict the healthy development of my country's medical industry. In this context, digital medicine, as a new medical model, provides innovative solutions to these problems by integrating modern computer technology and information technology. Digital medicine uses advanced technical means to realize the digitization, networking and intelligence of medical information, which can not only improve the efficiency and quality of medical services, but also alleviate the problem of uneven distribution of medical resources, reduce medical costs, and improve patients' medical experience.

[0003] At present, digital medicine mainly stays in the stage of doctor-patient diagnosis and chat, and cannot play a targeted role in popularizing medical knowledge for patients. This makes it impossible to assist doctors in explaining medical knowledge, resulting in inefficient medical service levels, and it is also unable to play a role in the daily management of patients, and cannot effectively improve the quality and efficiency of medical services. Based on this, a digital medical popularization and patient management coverage system and its solution are proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art and to propose a digital medical science popularization and patient management coverage system and its solution.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Digital medical science popularization and patient management coverage system and solutions, including:

[0007] Medical data model unit: including a medical condition database based on IoT interaction, customizing a key medical condition data outline system based on the medical condition database, creating a doctor-patient interaction AI condition language data model and building a digital human medical knowledge display model through AI big model GPT customization;

[0008] Patient information collection unit: including the IoT data terminal of the hospital where the patient is treated and the user terminal. The IoT data terminal of the hospital where the patient is treated obtains the patient's physical data, medical information, and medical service information. The user terminal obtains the medical consultation data filled in by the patient based on the APP or a third-party website;

[0009] Patient medical data matching unit: including information integration module and data matching module. The information integration module searches the data in the patient database according to the keywords of the patient's medical data information, and adjusts the data in the patient database into professional terms according to the search results to obtain the patient's orderly medical information; the data matching module realizes the application of the patient's orderly medical information in the key medical disease data system, and searches and identifies the corresponding AI disease language data model and digital human medical knowledge display model;

[0010] Digital AI doctor construction unit: including data matching interaction module and model integration module. The data matching interaction module re-matches the patient's medical data according to the identified AI disease terminology data model, simplifies professional terminology, and matches popular science content. The model integration module selects a digital human medical knowledge display model according to the patient's requirements or the corresponding disease department, and integrates the AI ​​disease terminology data model with the digital human medical knowledge display model.

[0011] Doctor-patient communication and popular science unit: Using AIGC and digital human technology, the A1 doctor model is used to popularize precision medicine knowledge, answer questions about the condition, and introduce treatment plans to patients;

[0012] Patient full-process coverage management unit: in-hospital treatment management module and subsequent rehabilitation follow-up management. The in-hospital treatment management module pays attention to the patient's dynamics in real time through the in-hospital treatment management module or patient location information, follows up according to patient needs, and jointly controls the corresponding departments for data interaction to achieve human-machine joint management; the subsequent rehabilitation follow-up management is through APP or a third party to provide practical reminders and answer questions according to the doctor's advice.

[0013] Preferably, the digital human medical knowledge display model includes customizing an exclusive digital human image for the doctor based on AI digital human technology, and conducting digital science popularization around the doctor's superior abilities and strengths;

[0014] It also includes building a human body display model based on 3D Slicer software technology for dynamic view display.

[0015] Preferably, the information acquisition method of the IoT data terminal of the hospital includes one or more of the hospital work software such as HIS system, LIS system, PACS system, physical examination software system, electrocardiogram workstation, B-ultrasound workstation or imaging workstation.

[0016] The solution proposed by the digital medical popularization and patient management coverage system includes the following steps:

[0017] S1. Create an AI doctor database: Build a key medical disease data outline system based on the medical disease database, accurately search and establish a corresponding AI disease language database based on the main medical and medical diagnosis categories opened by the hospital, give language descriptions based on the AI ​​disease language database, and establish an AI disease language data model for doctor-patient interaction;

[0018] S2. Build a data display model: Through AI big model GPT customization, based on digital human technology, create an AI digital doctor image, explain science popularization videos based on the AI ​​digital doctor image, and simultaneously generate a text introduction to explain science popularization. Based on 3D Slicer software technology, build a human body display model, and display the patient's condition in a dynamic view;

[0019] S3. Patient information collection: one part uses the identity information provided by the patient to obtain the medical data of the hospital through the Internet of Things, and obtains the patient's physical data, medical information, and medical service information through the IoT data terminal of the hospital system; the other part is obtained on the user side through the patient's inquiry on the APP, mini-program, online consultation platform and other third-party websites, through the AI ​​smart assistant or by filling out the consultation form to obtain the patient's consultation information data, extract keywords from the above data, and obtain the patient's medical category;

[0020] S4. Patient data matching: retrieve and integrate the keywords in the acquired patient information in the patient database, and identify and match the patient's previous consultation data. When the matching rate is highly similar and reaches the predetermined similarity threshold, the patient's consultation data and consultation type can be determined, and the consultation type can be matched with the corresponding professional terminology;

[0021] Import the matched professional terms into the AI ​​doctor database in S1, match the corresponding AI disease language data model for doctor-patient interaction, and simultaneously obtain the AI ​​digital doctor image and human body display model corresponding to the AI ​​disease language data model for doctor-patient interaction;

[0022] S5. Popular science of doctor-patient communication: The AI ​​disease language data model, AI digital doctor image and human body display model of doctor-patient interaction are displayed synchronously on the client to popularize precision medicine knowledge, answer questions about the disease, introduce treatment plans, and provide corresponding voice and text replies according to the patient's questions;

[0023] S6. In-hospital treatment management: By obtaining the location information of the user end, the patient's location in the hospital is known, the patient's movement trajectory is supervised and managed, and the local area network in the hospital is connected. The data of the corresponding treatment department background is obtained through the Internet of Things module. The doctor-patient interaction AI disease language data is updated in real time according to the patient's treatment progress to realize data processing of the patient's treatment process.

[0024] S7. Rehabilitation treatment management: Popularize nursing knowledge according to the patient's recovery cycle to improve the efficiency of popularization, enable patients to understand the prevention, treatment and rehabilitation measures related to their health problems, and promote patients' self-care awareness.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention establishes AI disease language data model, AI digital doctor image and human body display model based on AI model customization software, which is used for answering medical questions asked by patients and popularizing corresponding questions. It can significantly improve patients' acceptance of medical popular science knowledge, enable patients to understand medical treatment, improve patients' medical experience, and reduce doctor-patient conflicts.

[0027] 2. Based on the current disease database, the present invention uses AI technology to establish a unified disease language data model and a digital human medical knowledge display model, which are used to form different digital doctor models. According to the patient's medical information, it can identify, integrate and match the corresponding digital doctor for the corresponding disease quickly and effectively, significantly improve the accuracy and efficiency of medical services, and provide doctors with a more comprehensive and accurate diagnosis basis. This not only improves the accuracy of diagnosis and reduces misdiagnosis and missed diagnosis, but also helps doctors quickly formulate treatment plans and improve treatment efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the digital medical science popularization and patient management coverage system proposed by the present invention;

[0029] Figure 2 This is a flow chart of the digital medical science popularization and patient management coverage system and its solution proposed by the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0032] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0033] Example, see Figure 1 to Figure 2 , digital medical popular science and patient management coverage system and its solutions, including medical data model unit, patient information collection unit, patient medical data matching unit, digital AI doctor construction unit, doctor-patient communication popular science unit and patient full coverage management unit.

[0034] Medical data model unit: including medical condition database based on IoT interaction, including but not limited to Medline, micromedex and other databases. Customize key medical condition data outline system based on medical condition database, accurately search and establish corresponding AI condition language database according to the main medical and medical diagnosis categories opened by the hospital, give language description based on AI condition language database, customize AI condition language data model for doctor-patient interaction through AI big model GPT, and build digital human medical knowledge display model;

[0035] Patient information collection unit: including the IoT data terminal of the hospital where the patient is treated and the user terminal. The IoT data terminal of the hospital where the patient is treated obtains the patient's physical data, medical information, and medical service information. The user terminal obtains the medical consultation data filled out by the patient based on the APP or third-party website;

[0036] The ways in which the IoT data terminal of the hospital obtains information include one or more of the hospital work software such as HIS system, LIS system, PACS system, physical examination software system, ECG workstation, B-ultrasound workstation or imaging workstation.

[0037] Patient medical data matching unit: including information integration module and data matching module. The information integration module searches the data in the patient database according to the keywords of the patient's medical data information, and adjusts the data in the patient database into professional terms according to the search results to obtain the patient's orderly medical information; the data matching module realizes the application of the patient's orderly medical information in the key medical disease data system, and searches and identifies the corresponding AI disease language data model and digital human medical knowledge display model;

[0038] The digital human medical knowledge display model includes customizing exclusive digital human images for doctors based on AI digital human technology, and conducting digital science popularization around the doctors' advantages and strengths; it also includes establishing a human body display model based on 3D Slicer software technology for dynamic view display.

[0039] Digital AI doctor construction unit: including data matching interaction module and model integration module. The data matching interaction module matches the patient's medical data again according to the identified AI disease terminology data model, simplifies professional terminology, and matches popular science content. The model integration module selects the digital human medical knowledge display model according to the patient's requirements or the corresponding disease department, and integrates the AI ​​disease terminology data model with the digital human medical knowledge display model.

[0040] Doctor-patient communication and popular science unit: Using AIGC and digital human technology, the A1 doctor model is used to popularize precision medicine knowledge, answer questions about the condition, and introduce treatment plans to patients;

[0041] Patient full-process coverage management unit: in-hospital treatment management module and subsequent rehabilitation follow-up management. The in-hospital treatment management module pays attention to patient dynamics in real time through the in-hospital treatment management module or patient location information, follows up according to patient needs, and jointly controls the corresponding departments for data interaction to achieve human-machine joint management; subsequent rehabilitation follow-up management, through APP or a third party according to the doctor's advice to provide practical reminders and answer questions.

[0042] The solution proposed by the digital medical popular science and patient management coverage system is to build a key medical disease data outline system based on the medical disease database, accurately search and establish a corresponding AI disease language database based on the main medical and medical diagnosis categories opened by the hospital, give language description based on the AI ​​disease language database, and establish an AI disease language data model for doctor-patient interaction;

[0043] Through AI large model GPT customization, based on digital human technology, AI digital doctor images are created, science popularization videos are explained based on AI digital doctor images, and text introductions for science popularization are generated simultaneously. Based on 3D Slicer software technology, human body display models are established, and dynamic views are displayed for corresponding patient conditions.

[0044] The specific method is selected based on whether the patient has received a hospital diagnosis. One method uses the patient's identity information provided by the patient to obtain the hospital's medical data through the Internet of Things, and obtains the patient's physical data, medical information, and medical service information through the hospital's IoT data terminal. The other method is to obtain the patient's medical information data through AI smart assistants or by filling out medical consultation forms when the patient inquires on the user side through APP, mini-programs, or third-party websites such as online consultation platforms, and extract keywords from the above data to obtain the patient's medical category.

[0045] The keywords in the acquired patient information are retrieved and integrated in the patient database, and the patient's previous consultation data are identified and matched. When the matching rate is highly similar and reaches a predetermined similarity threshold, the patient's consultation data and consultation type can be determined, and the consultation type can be matched with the corresponding professional terminology;

[0046] Import the matched professional terms into the AI ​​doctor database, match the corresponding AI disease language data model for doctor-patient interaction, and simultaneously obtain the AI ​​digital doctor image and human body display model corresponding to the AI ​​disease language data model for doctor-patient interaction;

[0047] Through AI technology, a unified disease language data model and a digital human medical knowledge display model are established to form different digital doctor models. According to the patient's medical information, they can identify, integrate and match the corresponding digital doctor for the corresponding disease quickly and effectively, significantly improving the accuracy and efficiency of medical services.

[0048] The AI ​​disease language data model, AI digital doctor image and human body display model for doctor-patient interaction are displayed synchronously on the client to popularize precision medicine knowledge, answer questions about the disease, and introduce treatment plans to patients. The corresponding voice and text replies are provided according to the patients' questions.

[0049] By obtaining the location information of the user end, the patient's location in the hospital is known, and the patient's movement trajectory is supervised and managed. This system is connected to the local area network in the hospital, and obtains data from the background of the corresponding treatment department through the Internet of Things module. The doctor-patient interaction AI disease language data is updated in real time according to the patient's treatment progress, and data processing of the patient's treatment process is realized; the data doctor model required at different stages is different, which can meet the real-time update requirements.

[0050] Popularize nursing knowledge according to the patient's recovery cycle to improve the efficiency of popularization, enable patients to understand the prevention, treatment and rehabilitation measures related to their health problems, and promote patients' self-care awareness.

[0051] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. Digital medical science popularization and patient management coverage system, characterized by: include: Medical data model unit: including a medical condition database based on IoT interaction, customizing a key medical condition data outline system based on the medical condition database, creating a doctor-patient interaction AI condition language data model and building a digital human medical knowledge display model through AI big model GPT customization; Patient information collection unit: including the IoT data terminal of the hospital where the patient is treated and the user terminal. The IoT data terminal of the hospital where the patient is treated obtains the patient's physical data, medical information, and medical service information. The user terminal obtains the medical consultation data filled in by the patient based on the APP or a third-party website; Patient medical data matching unit: including information integration module and data matching module. The information integration module searches the data in the patient database according to the keywords of the patient's medical data information, and adjusts the data in the patient database into professional terms according to the search results to obtain the patient's orderly medical information; the data matching module realizes the application of the patient's orderly medical information in the key medical disease data system, and searches and identifies the corresponding AI disease language data model and digital human medical knowledge display model; Digital AI doctor construction unit: including data matching interaction module and model integration module. The data matching interaction module re-matches the patient's medical data according to the identified AI disease terminology data model, simplifies professional terminology, and matches popular science content. The model integration module selects a digital human medical knowledge display model according to the patient's requirements or the corresponding disease department, and integrates the AI ​​disease terminology data model with the digital human medical knowledge display model. Doctor-patient communication and popular science unit: Using AIGC and digital human technology, the A1 doctor model is used to popularize precision medicine knowledge, answer questions about the condition, and introduce treatment plans to patients; Patient full-process coverage management unit: in-hospital treatment management module and subsequent rehabilitation follow-up management. The in-hospital treatment management module pays attention to the patient's dynamics in real time through the in-hospital treatment management module or patient location information, follows up according to patient needs, and jointly controls the corresponding departments for data interaction to achieve human-machine joint management; the subsequent rehabilitation follow-up management is through APP or a third party to provide practical reminders and answer questions according to the doctor's advice.

2. The digital medical science popularization and patient management coverage system according to claim 1 is characterized in that: The digital human medical knowledge display model includes customizing a unique digital human image for doctors based on AI digital human technology, and conducting digital science popularization around the doctors’ superior abilities and strengths; It also includes building a human body display model based on 3D Slicer software technology for dynamic view display.

3. The digital medical science popularization and patient management coverage system according to claim 1 is characterized in that: The information acquisition method of the IoT data terminal of the hospital includes one or more of the hospital work software such as HIS system, LIS system, PACS system, physical examination software system, electrocardiogram workstation, B-ultrasound workstation or imaging workstation.

4. The solution proposed by the digital medical science popularization and patient management coverage system according to any one of claims 1 to 3 is characterized in that: The following steps are involved: S1. Create an AI doctor database: Build a key medical disease data outline system based on the medical disease database, accurately search and establish a corresponding AI disease language database based on the main medical and medical diagnosis categories opened by the hospital, give language descriptions based on the AI ​​disease language database, and establish an AI disease language data model for doctor-patient interaction; S2. Build a data display model: Through AI big model GPT customization, based on digital human technology, create an AI digital doctor image, explain science popularization videos based on the AI ​​digital doctor image, and simultaneously generate a text introduction to explain science popularization. Based on 3D Slicer software technology, build a human body display model, and display the patient's condition in a dynamic view; S3. Patient information collection: one part uses the identity information provided by the patient to obtain the medical data of the hospital through the Internet of Things, and obtains the patient's physical data, medical information, and medical service information through the IoT data terminal of the hospital system; the other part is obtained on the user side through the patient's inquiry on the APP, mini-program, online consultation platform and other third-party websites, through the AI ​​smart assistant or by filling out the consultation form to obtain the patient's consultation information data, extract keywords from the above data, and obtain the patient's medical category; S4. Patient data matching: retrieve and integrate the keywords in the acquired patient information in the patient database, and identify and match the patient's previous consultation data. When the matching rate is highly similar and reaches the predetermined similarity threshold, the patient's consultation data and consultation type can be determined, and the consultation type can be matched with the corresponding professional terminology; Import the matched professional terms into the AI ​​doctor database in S1, match the corresponding AI disease language data model for doctor-patient interaction, and simultaneously obtain the AI ​​digital doctor image and human body display model corresponding to the AI ​​disease language data model for doctor-patient interaction; S5. Popular science of doctor-patient communication: The AI ​​disease language data model, AI digital doctor image and human body display model of doctor-patient interaction are displayed synchronously on the client to popularize precision medicine knowledge, answer questions about the disease, introduce treatment plans, and provide corresponding voice and text replies according to the patient's questions; S6. In-hospital treatment management: By obtaining the location information of the user end, the patient's location in the hospital is known, the patient's movement trajectory is supervised and managed, and the local area network in the hospital is connected. The data of the corresponding treatment department background is obtained through the Internet of Things module. The doctor-patient interaction AI disease language data is updated in real time according to the patient's treatment progress to realize data processing of the patient's treatment process. S7. Rehabilitation treatment management: Popularize nursing knowledge according to the patient's recovery cycle to improve the efficiency of popularization, enable patients to understand the prevention, treatment and rehabilitation measures related to their health problems, and promote patients' self-care awareness.

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