A follow-up content processing method, system, storage medium and electronic device

Abstract

The application discloses a follow-up content processing method, system, storage medium and electronic equipment. The hospital data of a patient is obtained through a preset follow-up mode, the hospital data and the hospital data obtained in advance are integrated through a preset integration mode, multi-dimensional patient health data is obtained, the multi-dimensional patient health data is used for representing multi-dimensional patient health data in a real world environment in a complete follow-up cycle, and a corresponding scientific research report is generated according to the multi-dimensional patient health data. Based on the above, the follow-up mode of online consultation dialogue, intelligent follow-up, telephone follow-up, incentive follow-up and the like is supported in the follow-up process of the patient who leaves the hospital, the doctor and the patient are facilitated to communicate, the electronic medical records, scientific research follow-up records, home equipment data and the like of the patient outside the hospital are integrated to form complete real world data and generate a scientific research report, the doctor is provided with scientific research and clinical decision support, the clinical data management efficiency is improved, and the scientific research implementation cost is reduced.

Description

Technical Field

[0001] This application relates to the field of medical and health technology, and more specifically, to a follow-up content processing method, system, storage medium, and electronic device. Background Technology

[0002] Real-world studies (RWS; Real-world research, RWR) involve collecting real-world data (RWD) related to patients in real-world settings for post-discharge follow-up. Through follow-up analysis, real-world clinical evidence (RWE) is obtained regarding the use value and potential benefits or risks of medical products.

[0003] Traditional post-discharge follow-up methods may not be able to complete the data collection at the corresponding nodes on time and in the required quantity. Even if patients are willing to cooperate with the follow-up, the required actions may be frequent or complex, and patients may not be able to persist due to the excessive time required or forget to provide feedback, resulting in omissions and missing data.

[0004] Therefore, issues such as patient loss to follow-up and incomplete data can lead to defects in the quality of clinical data, affecting the application of clinical data in real-world research, reducing the efficiency of clinical data management, and increasing the cost of scientific research implementation. Summary of the Invention

[0005] In view of this, this application discloses a follow-up content processing method, system, storage medium, and electronic device, aiming to improve the efficiency of clinical data management and reduce the cost of scientific research implementation.

[0006] To achieve the above objectives, the disclosed technical solution is as follows:

[0007] The first aspect of this application discloses a method for processing follow-up content, the method comprising:

[0008] Patient outpatient data is obtained through a preset follow-up method; the outpatient data consists of real-world case data of patients discharged from the hospital at different stages of the follow-up period; the outpatient data includes at least outpatient patients' consultation records, outpatient patients' home medication records, and outpatient patients' real-time home device data.

[0009] By integrating out-of-hospital data and pre-acquired in-hospital data through a preset integration method, multidimensional patient health data is obtained; the in-hospital data refers to the patient's medical records within the hospital; the multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period.

[0010] Based on the multidimensional patient health data, a corresponding research report is generated.

[0011] Preferably, the preset follow-up method includes at least one of the following: online chat, intelligent follow-up, telephone follow-up, and incentive-based follow-up; obtaining the patient's out-of-hospital data through the preset follow-up method includes:

[0012] Patient outpatient data can be obtained through at least one of the following methods: online chat, intelligent follow-up, telephone follow-up, and incentivized follow-up.

[0013] Preferably, the process of obtaining patient outpatient data through incentivized follow-up includes:

[0014] During the follow-up of patients, a follow-up task plan is generated; the follow-up task plan includes the follow-up task plan for different stages within the follow-up period.

[0015] Incentive methods are set up, and complete outpatient data of patients within the follow-up period is obtained through the incentive methods and the follow-up task plan.

[0016] Preferably, the process of integrating outpatient data and pre-acquired inpatient data using a preset integration method to obtain multidimensional patient health data includes:

[0017] Construct a preset dataset architecture; the preset dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information;

[0018] Through the aforementioned preset dataset architecture, external data and pre-acquired internal data are subjected to unified data standardization operations;

[0019] By integrating the standardized data from outside the hospital with the data from inside the hospital, multidimensional patient health data is obtained.

[0020] Preferred options also include:

[0021] A virtual patient identifier is constructed; the virtual patient identifier is used for patient identification.

[0022] The virtual patient identifier interacts with the multidimensional patient health data to generate tagged patient data and basic health indicator data; the tagged patient data includes at least gender tags, age range tags, and health status tags.

[0023] Preferred options also include:

[0024] The multidimensional patient health data is formatted to form a disease-specific database.

[0025] A second aspect of this application discloses a follow-up content processing system, the system comprising:

[0026] The acquisition unit is used to acquire patients' outpatient data through a preset follow-up method; the outpatient data is the case data of patients discharged from the hospital in a real-world environment at different stages within the follow-up period; the outpatient data includes at least the outpatient patient's consultation record, the outpatient patient's home medication record, and the outpatient patient's real-time home device data.

[0027] The integration unit is used to integrate outpatient data and pre-acquired inpatient data through a preset integration method to obtain multidimensional patient health data; the inpatient data refers to the patient's medical records within the hospital; the multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period.

[0028] The generation unit is used to generate a corresponding research report based on the multidimensional patient health data.

[0029] Preferably, the acquisition unit is specifically used for:

[0030] Patient outpatient data can be obtained through at least one of the following methods: online chat, intelligent follow-up, telephone follow-up, and incentivized follow-up.

[0031] A third aspect of this application discloses a storage medium including storage instructions, wherein, when the instructions are executed, the device in which the storage medium is located is controlled to perform the follow-up content processing method as described in any one of the first aspects.

[0032] The fourth aspect of this application discloses an electronic device including a memory and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors using the follow-up content processing method as described in any of the first aspects.

[0033] As can be seen from the above technical solution, this application discloses a follow-up content processing method, system, storage medium, and electronic device. It acquires patients' out-of-hospital data through a preset follow-up method. The out-of-hospital data consists of real-world case data of discharged patients at different stages of the follow-up period. The out-of-hospital data includes at least the out-of-hospital patient's consultation records, home medication records, and real-time home device data. The out-of-hospital data is integrated with the pre-acquired in-hospital data through a preset integration method to obtain multidimensional patient health data. The in-hospital data consists of the patient's case data within the hospital. The multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment throughout the complete follow-up period. Based on the multidimensional patient health data, a corresponding research report is generated. The above-mentioned solution supports various follow-up methods during the follow-up of discharged patients, including online consultations, intelligent follow-up, telephone follow-up, and incentive-based follow-up. This facilitates communication between doctors and patients. Furthermore, by integrating in-hospital data with electronic medical records, research follow-up records, and home device data from out-of-hospital patients, it forms complete real-world data and generates research reports. This provides doctors with decision support for research and clinical practice, improves the efficiency of clinical data management, and reduces the cost of research implementation. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 This is an architecture diagram of the internet healthcare platform disclosed in the embodiments of this application;

[0036] Figure 2 This is a flowchart illustrating a follow-up content processing method disclosed in an embodiment of this application;

[0037] Figure 3 This is a schematic diagram of ventilator data disclosed in an embodiment of this application;

[0038] Figure 4 This is a schematic diagram illustrating follow-up outreach as disclosed in an embodiment of this application;

[0039] Figure 5 This is a schematic diagram of the follow-up rule configuration disclosed in the embodiments of this application;

[0040] Figure 6 This is an example diagram illustrating the online follow-up task and incentives disclosed in the embodiments of this application;

[0041] Figure 7This is a schematic diagram illustrating the data interaction between the patient's identity model and online multi-source data disclosed in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram of the encoding dictionary model for the health indicator dataset disclosed in an embodiment of this application;

[0043] Figure 9 This is a schematic diagram of the structure of a follow-up content processing system disclosed in an embodiment of this application;

[0044] Figure 10 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] As the background technology indicates, traditional post-discharge follow-up methods may not be able to complete the data collection at the required time and in the required quantity. Even if patients are willing to cooperate with follow-up, the required actions may be frequent or complex, and patients may not be able to persist due to excessive time commitment or forget to provide feedback, leading to omissions and missing data. Therefore, problems such as patient loss to follow-up and incomplete data can lead to defects in the quality of clinical data, affecting the application of clinical data in real-world research, reducing the efficiency of clinical data management, and increasing the cost of research implementation.

[0048] To address the aforementioned issues, this application discloses a method, system, storage medium, and electronic device for processing follow-up content. During the follow-up of discharged patients, it supports various follow-up methods such as online consultations, intelligent follow-up, telephone follow-up, and incentivized follow-up, facilitating communication between doctors and patients. Furthermore, by integrating in-hospital data and combining it with electronic medical records, research follow-up records, and home device data from out-of-hospital patients, it generates complete real-world data and research reports, providing doctors with decision support for research and clinical practice, improving the efficiency of clinical data management, and reducing research implementation costs. Specific implementation methods are illustrated in the following embodiments.

[0049] This disclosure assumes that entities responsible for collecting, analyzing, disclosing, transmitting, storing, or otherwise using such personal information data will comply with established privacy policies and / or privacy practices. Specifically, such entities should implement and adhere to privacy policies and practices that are generally recognized as meeting or exceeding industry or requirements for maintaining the privacy and security of personal information data. Such policies should be easily accessible to users and should be updated as data collection and / or use change. Personal information from users should be collected for the entity's lawful and reasonable purposes and not shared or sold outside of these lawful uses. Furthermore, such collection / sharing should be conducted only after obtaining informed consent from users. In addition, such entities should consider taking any necessary steps to protect and safeguard access to such personal information data and ensure that others with access to such personal information data comply with their privacy policies and processes. Additionally, such entities may be subject to third-party evaluations to demonstrate their compliance with widely accepted privacy policies and practices. Furthermore, policies and practices should be adapted to the specific types of personal information data collected and / or accessed, and to applicable laws and standards, including specific considerations regarding jurisdiction. For example, in the United States, the collection or acquisition of certain health data may be governed by federal and / or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); while in other countries, health data may be subject to other regulations and policies and should be handled accordingly. Therefore, different privacy practices should be maintained for different types of personal data in each country.

[0050] Regardless of the foregoing, this disclosure also contemplates implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure contemplates providing hardware and / or software components to prevent or block access to such personal information data. For example, with regard to accessing or storing health information, the technology of this invention can be configured to allow users to opt-in or opt-out to participate in the collection of personal information data during or at any time after registering for the service. In another example, users can choose not to provide specific types of health-related information. In yet another example, users can choose to limit the duration of maintaining health-related information or completely prohibit the storage of health-related information. In addition to providing "opt-in" and "opt-out" options, this disclosure envisions providing notifications related to access to or use of personal information. For example, users can be notified when downloading an application that their personal information data will be accessed, and then reminded again just before the application accesses the personal information data.

[0051] Furthermore, the purpose of this disclosure is to manage and process personal information data to minimize the risk of unintentional or unauthorized access or use. Once data is no longer needed, this risk can be minimized by limiting data collection and deleting data. Additionally, and where applicable, including in certain health-related applications, data deidentification can be used to protect user privacy. Deidentification can be facilitated, where appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than the address level), controlling how data is stored (e.g., aggregating data among users), and / or other methods.

[0052] The following section first introduces a follow-up content processing method and the architecture diagram of the internet healthcare platform to which this system is applicable. For details, please refer to... Figure 1 As shown, the internet healthcare platform includes an in-hospital data collection device 11, an out-of-hospital real-world research follow-up platform 12, an internet-based diagnosis and treatment service platform 13, a patient home device data collection device 14, and a medical research big data center 15.

[0053] The in-hospital data acquisition device 11 includes a Hospital Information System (HIS), an Electronic Medical Record (EMR), a Picture Archiving and Communication System (PACS), a Laboratory Information Management System (LIS), a Clinical Data Repository (CDR), and a data interaction platform.

[0054] The data exchange platform interacts with the Medical Research Big Data Center 15 through standards such as Health Level 7 (HL7), Clinical Document Architecture (CDA) standard, and interoperability standard.

[0055] The off-campus real-world research follow-up platform 12 includes functions such as queue entry, follow-up execution, task triggering, outbound telephone calling, self-evaluation / peer evaluation, data auditing, progress management, and display of pending tasks.

[0056] The Internet-based medical service platform 13 includes functions such as patient check-in, online consultation, follow-up plan, plan execution, treatment plan, and online outreach.

[0057] The Medical Research Big Data Center 15 is equipped with a data platform, data governance device, RWD data access and expansion device, and patient record device.

[0058] The data platform includes general meta-point configuration and general disease model.

[0059] The data governance device includes data standardization, data quality control, data normalization, and data integration functions.

[0060] The RWD data access expansion device involves IoT data, public health data, physical examination data, hospital data, follow-up data, patient medication purchase data, etc.

[0061] The patient record device includes basic information, IoT data, electronic medical record data, etc.

[0062] The patient home equipment data acquisition device 14 includes devices such as a blood pressure monitor, ventilator, oxygen concentrator, pulmonary function test kit, wristband, and pulse oximeter.

[0063] The specific data interaction process between the in-hospital data acquisition device 11, the out-of-hospital real-world scientific research follow-up platform 12, the internet-based medical service platform 13, the patient's home-based device data acquisition device 14, and the medical research big data center 15 is as follows:

[0064] The patient home device data acquisition device 14 collects real-time data of the patient's home devices outside the hospital; the real-time data of the home devices includes blood pressure monitor data, ventilator data, oxygen concentrator data, lung function data, wristband data, pulse oximeter data, etc.

[0065] The off-site real-world research follow-up platform 12 acquires patients' off-site data through preset follow-up methods; the off-site data consists of case data of patients discharged from the hospital in real-world environments at different stages within the follow-up period; the off-site data includes at least the off-site patient's consultation records, off-site patient's home medication records, and off-site patient's real-time home device data.

[0066] The preset follow-up methods include at least one of the following: online chat, intelligent follow-up, telephone follow-up, and incentive-based follow-up.

[0067] Patient outpatient data can be obtained through at least one of the following methods: online chat, intelligent follow-up, telephone follow-up, and incentivized follow-up.

[0068] When the preset follow-up method is online conversation, the patient's out-of-hospital data is obtained through the Internet medical service platform 13.

[0069] The specific process by which the off-site real-world research follow-up platform 12 obtains patients' off-site data through a preset follow-up method is as follows:

[0070] During the follow-up of patients, the follow-up task plan content is generated through the off-site real-world scientific research follow-up platform 12; the follow-up task plan content consists of the follow-up task plan content at different stages within the follow-up period.

[0071] The off-site real-world scientific research follow-up platform 12 sets up incentive mechanisms and obtains complete off-site patient data within the follow-up period through these incentive mechanisms and the content of the follow-up task plan.

[0072] The follow-up task plan content at different stages sets incentive forms for each stage, and by setting incentive forms for each stage of the follow-up task plan content, patients are motivated to complete the follow-up tasks throughout the entire follow-up cycle, so that the off-site real-world scientific research follow-up platform 12 can obtain complete off-site data of patients within the follow-up cycle.

[0073] The in-hospital data acquisition device 11 acquires in-hospital data; the in-hospital data is the patient's medical record data within the hospital.

[0074] The Medical Research Big Data Center 15 integrates out-of-hospital and in-hospital data through a pre-defined integration method to obtain multidimensional patient health data; the multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period.

[0075] Specifically, the Medical Research Big Data Center 15 integrates external and internal hospital data through a pre-defined integration method to obtain multidimensional patient health data as follows:

[0076] The Medical Research Big Data Center 15 constructs a pre-defined dataset architecture; the pre-defined dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information.

[0077] The Medical Research Big Data Center 15 uses a pre-defined dataset architecture to standardize the data processing of external data and pre-acquired internal data.

[0078] The Medical Research Big Data Center 15 integrates external and internal hospital data after standardizing data operation to obtain multidimensional patient health data.

[0079] The Medical Research Big Data Center generates corresponding research reports based on multidimensional patient health data.

[0080] In this embodiment, the follow-up process for discharged patients supports various follow-up methods such as online consultation, intelligent follow-up, telephone follow-up, and incentive-based follow-up, facilitating communication between doctors and patients. Furthermore, by integrating in-hospital data with electronic medical records, research follow-up records, and home device data from out-of-hospital patients, complete real-world data is generated and research reports are produced, providing doctors with decision support for research and clinical practice, improving the efficiency of clinical data management, and reducing the cost of research implementation.

[0081] refer to Figure 2 The diagram shown is a flowchart illustrating a follow-up content processing method disclosed in an embodiment of this application. This follow-up content processing method mainly includes the following steps:

[0082] S201: Obtain patients' outpatient data through a preset follow-up method; outpatient data consists of real-world patient case data from different stages of the follow-up period; outpatient data includes at least outpatient patients' consultation records, outpatient patients' home medication records, and outpatient patients' real-time home device data.

[0083] The preset follow-up methods include at least one of the following: online chat, intelligent follow-up, telephone follow-up, and incentive-based follow-up.

[0084] Patient outpatient data can be obtained through at least one of the following methods: online chat, intelligent follow-up, telephone follow-up, and incentivized follow-up.

[0085] Real-time data from home devices includes blood pressure monitor data, ventilator data, oxygen concentrator data, lung function data, wristband data, pulse oximeter data, etc.

[0086] Real-time data of home devices used by specific outpatients combined with Figure 3 To explain, Figure 3 This diagram illustrates the data for home-use ventilators. Figure 3 This is just an example image.

[0087] Figure 3 In this context, ventilator data includes tidal volume, leakage volume, respiratory rate, minute ventilation, average number of apneas and hypopneas per hour during sleep (AHI), blood oxygen saturation, ventilator parameters, trend graphs, and number of on / off cycles.

[0088] The parameters of a ventilator include treatment mode, initial expiratory pressure, and inspiratory pressure.

[0089] The trend chart of ventilator data includes time parameters such as usage duration, number of days used, total usage duration, longest daily usage, and shortest daily usage.

[0090] The AHI index refers to the average number of apneas and hypoventilations per hour during sleep. The AHI index is the most objective data for measuring the severity of sleep apnea and is also an important indicator for evaluating the effectiveness of sleep apnea devices.

[0091] The richness of outpatient data is crucial for real-world data analysis (RWS), and data quality is equally important. Follow-up management is a key method for outpatient data collection; the completion rate of follow-ups, the completeness and accuracy of data collection all affect data quality. Since patients outside the hospital are no longer subject to face-to-face intervention by medical personnel, data collection involves significant uncertainty. Therefore, it is necessary to integrate inpatient data with outpatient electronic medical records, research follow-up records, and home device data from internet healthcare platforms to form a complete real-world data set. This data, after formatting, forms a disease-specific database, providing physicians with decision support for research and clinical practice, improving clinical data management efficiency, and reducing research implementation costs.

[0092] The specific process of obtaining patients' out-of-hospital data through pre-defined follow-up methods, combined with Figure 4 To explain, Figure 4 A schematic diagram of follow-up outreach is shown.

[0093] Figure 4 In China, doctors can collect follow-up information from patients through various methods such as online chat, human telephone, intelligent follow-up, AI telephone follow-up, instant messaging (IM), and public accounts.

[0094] Among them, online conversations rely on the online consultation qualifications of internet hospitals. During the follow-up process, online consultations can be conducted to facilitate communication between doctors and patients. Online conversations can also be used to assign follow-up tasks, allow patients to complete them, and provide feedback. Further management such as diagnosis and prescription can be carried out based on the follow-up.

[0095] The patient-side features include follow-up visit reminders, task incentives, online consultations, medication purchases, follow-up summary functions, paid purchases, treatment plan reception, self-assessment, receiving feedback from others, and patient education and science popularization functions.

[0096] The doctor's side includes functions such as task push, pending task prompts, follow-up data collection, IoT device alerts, follow-up data analysis, online communication, prescription issuance, follow-up summary, and follow-up service configuration.

[0097] The intelligent follow-up system supports customizable category tags, such as age range, gender range, geographical range, and preferences. After patients are imported into the platform, the system extracts relevant tag value ranges based on tag annotation capabilities, supports visual analysis of population characteristic distribution, supports multi-dimensional combination for population feature screening and segmentation, and supports one-click intelligent grouping, quickly helping researchers establish ideal population groups. Simultaneously, the system supports researchers in defining inclusion and exclusion rules during the patient enrollment process, filtering populations based on these rules to make patient enrollment more accurate.

[0098] AI-powered telephone follow-up utilizes technologies such as intelligent semantic logic setting, script template import, audience targeting, and dynamic updates to achieve intelligent outbound calling. During the human-like dialogue, it enables emotion recognition and intelligent question answering, supporting the intelligent delivery of simple follow-up tasks to large groups, significantly reducing repetitive work and improving research efficiency. Simultaneously, the system's telephone follow-up capabilities support two-way communication between the caller and receiver, providing call recording playback tailored to the follow-up task.

[0099] Building upon intelligent patient segmentation and grouping, the system will support multi-path follow-up capabilities, including automated follow-up. It allows for the setting of contextual keywords and the mapping of relevant follow-up templates based on these keywords. Through natural language processing, keywords from the dialogue process will be extracted to trigger automated follow-up. The system supports setting follow-up stages based on multiple baseline nodes and periodically distributing materials such as Countermeasure Request Forms (CRFs), scales, and health education articles. Relying on open and scalable form integration capabilities, it supports the rapid import of professional scales and questionnaires to meet diverse research needs.

[0100] To facilitate understanding of the process of supporting automated follow-up based on intelligent patient identification and grouping, combined with... Figure 5 To explain, Figure 5 A schematic diagram of follow-up rule configuration is shown. Figure 5 This is just an example image.

[0101] Figure 5 In the process, automatic follow-up is performed by creating a new automatic follow-up rule. The new automatic follow-up rule is as follows:

[0102] During the automated follow-up process, the follow-up reach can be configured with rules such as name, trigger scenario, diagnosed disease, department, whether to enable, task items (follow-up plan, consultation form, patient education article), and adding task items.

[0103] Leveraging the online consultation qualifications of internet hospitals, the system supports online consultation dialogues during follow-up visits, facilitating communication between doctors and patients. It also supports the distribution of follow-up tasks, patient execution, and information feedback within online conversations, enabling further management such as diagnosis and prescription based on the follow-up experience. This direct dialogue capability facilitates in-depth patient tracking. Furthermore, it allows for the automatic delivery of follow-up questionnaires, health education materials, and return-to-visit notifications to patients at scheduled times via public accounts and apps.

[0104] The specific process of obtaining patients' out-of-hospital data through incentive-based follow-up is as follows:

[0105] First, during the follow-up of patients, follow-up task plan content is generated; the follow-up task plan content consists of follow-up task plan content for different stages within the follow-up period.

[0106] To facilitate understanding of the follow-up task plan, combined with Figure 6 To explain, Figure 6 An example diagram showing the online follow-up task and incentives is provided.

[0107] After receiving the follow-up assignment, patients are required to provide feedback on their physical condition. If home testing is required for related diseases, patients can bind their devices on the platform, and the system will automatically collect real-time information from the home testing devices, which will facilitate doctors to track and guide patients' conditions in a timely manner.

[0108] The follow-up task plan includes incentive-based follow-up methods. These methods (such as coupons, free online consultations, etc.) encourage patients to complete the entire follow-up cycle, thereby improving patient compliance and preventing patients from dropping out of the follow-up task.

[0109] Then, incentive methods are set, and complete outpatient data of patients within the follow-up period are obtained through the incentive methods and the content of the follow-up task plan.

[0110] Incentives for each stage of the follow-up task plan are set in the content of the follow-up task plan. By setting incentives for each stage of the follow-up task plan, patients are motivated to complete the follow-up tasks throughout the entire follow-up cycle in order to obtain complete outpatient data of patients during the follow-up cycle.

[0111] S202: By integrating out-of-hospital data and pre-acquired in-hospital data through a preset integration method, multidimensional patient health data is obtained; in-hospital data refers to the patient's medical records within the hospital; multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period.

[0112] Specifically, the process of integrating outpatient data and pre-acquired inpatient data through a preset integration method to obtain multidimensional patient health data is shown in A1-A3.

[0113] A1: Construct a pre-defined dataset architecture; the pre-defined dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information.

[0114] A2: By using a pre-defined dataset architecture, external data and pre-acquired internal data are subjected to unified data standardization operations.

[0115] A3: Integrate the out-of-hospital and in-hospital data after standardizing data operations to obtain multidimensional patient health data.

[0116] One advantage of the RWS platform based on internet healthcare scenarios proposed in this solution is its diverse and structured online data. This data is easily standardized and integrated to form multi-dimensional patient health data. Through the aforementioned methods, online follow-up data, online consultation data, outreach data, and case data from online consultations are collected. This enables multi-dimensional integration of patient data, simplifying data cleaning and processing workloads.

[0117] The proposed research follow-up solution is a patient-centered solution. It establishes a patient identity model and designs consultation data collection processes, case collection processes, and follow-up data collection processes based on this model, enabling data interaction on a multi-source online internet platform.

[0118] To facilitate understanding the data interaction process under multiple data sources on internet platforms, combined with Figure 7 Please provide an explanation. Figure 7 This diagram illustrates the data interaction between the patient's identity model and online multi-source data.

[0119] Figure 7 In China, online data includes electronic medical records, medical records, IoT monitoring records, follow-up assessment records, etc.

[0120] Construct a virtual patient identifier (PID); the virtual patient identifier (PID) is used for patient identification.

[0121] Among these measures, sensitive social attribute information of patients will be stored separately in encrypted form.

[0122] Sensitive social attribute information recorded in encrypted files includes patient contact phone number, patient ID information, and patient address information.

[0123] By interacting with virtual patient identifiers and multidimensional patient health data, tagged patient data (de-identified social label attribute information) and basic health indicator data are generated; the tagged patient data includes at least gender labels, age range labels, and health status labels; the basic health indicator data includes at least allergy history data, surgical history data, and blood pressure data.

[0124] De-identified social labeling attributes include gender labels, age range labels, and health status.

[0125] Basic health indicators include patient allergy history, surgical history, and blood pressure data.

[0126] The preset dataset architecture is the health indicator dataset encoding dictionary model.

[0127] To facilitate the integration and processing of various data types, a data specification for health indicators was established, supporting a two-tier dataset architecture. The underlying general field set provides a standardized dictionary specification, ensuring uniformity and standardization in information processing. Based on the general field set, a disease-specific field set is constructed to meet the diverse research needs and supports custom disease-specific models. The field set has a two-sided structure: a module layer supports the definition of health indicator classification information, and based on each module, secondary field codes can be designed to standardize the specific codes for indicators. In this way, various types of multi-dimensional data will use this coding as a standard for data production and interaction, thus maintaining the interoperability and integration of multi-dimensional data within the platform.

[0128] The specific structure of the health indicator dataset encoding dictionary model is as follows: Figure 8 As shown.

[0129] Figure 8 In the health indicator dataset, the encoding dictionary model includes a general field set, a module dictionary, a field dictionary, a gastric cancer-specific field set, a glaucoma-specific field set, etc.

[0130] The module dictionary includes demographic information, medical visit information, medical record information, diagnosis, test results, medical orders, and other data.

[0131] The field dictionary includes blood type, gender, date of birth, smoking history, drinking history, and past medical conditions.

[0132] S203: Generate corresponding research reports based on multidimensional patient health data.

[0133] Multidimensional patient health data is formatted to form a disease-specific database.

[0134] This application combines internet hospitals with outpatient consultation records, home medication records, and real-time data uploads from home devices on internet medical platforms. It integrates in-hospital and out-of-hospital data, and by establishing a unified standard at the data model level, it achieves the integration and fusion of diverse data to form complete real-world data, providing doctors with decision support for scientific research and clinical practice.

[0135] In this embodiment, the follow-up process for discharged patients supports various follow-up methods such as online consultation, intelligent follow-up, telephone follow-up, and incentive-based follow-up, facilitating communication between doctors and patients. Furthermore, by integrating in-hospital data with electronic medical records, research follow-up records, and home device data from out-of-hospital patients, complete real-world data is generated and research reports are produced, providing doctors with decision support for research and clinical practice, improving the efficiency of clinical data management, and reducing the cost of research implementation.

[0136] Based on the above embodiments Figure 2 The present application discloses a method for processing follow-up content, and also discloses a system for processing follow-up content. Figure 9 As shown, the follow-up content processing system includes an acquisition unit 901, an integration unit 902, and a generation unit 903.

[0137] The acquisition unit 901 is used to acquire patients' outpatient data through a preset follow-up method; the outpatient data is the case data of patients discharged from the hospital in a real-world environment at different stages of the follow-up period; the outpatient data includes at least the outpatient patient's consultation record, the outpatient patient's home medication record, and the outpatient patient's home device real-time data.

[0138] Integration unit 902 is used to integrate outpatient data and pre-acquired inpatient data through a preset integration method to obtain multidimensional patient health data; inpatient data refers to the patient's medical records in the hospital; multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period.

[0139] The generation unit 903 is used to generate corresponding research reports based on multidimensional patient health data.

[0140] Furthermore, the acquisition unit 901 is specifically used to acquire the patient's outpatient data through at least one of the following methods: online conversation, intelligent follow-up, telephone follow-up, and incentive follow-up.

[0141] Furthermore, the acquisition unit 901 includes a generation module and a setting module.

[0142] The generation module is used to generate follow-up task plan content during the follow-up of patients; the follow-up task plan content includes follow-up task plan content for different stages within the follow-up period.

[0143] The settings module is used to set the incentive format and obtain complete outpatient data for the follow-up period through the incentive format and follow-up task plan content.

[0144] Furthermore, the integration unit 902 includes a building module, an operation module, and an integration module.

[0145] The building module is used to construct a preset dataset architecture; the preset dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information.

[0146] The operation module is used to perform unified data standardization operations on external data and pre-acquired internal data through a preset dataset architecture.

[0147] The integration module is used to integrate external and internal data after standardized data processing to obtain multidimensional patient health data.

[0148] Furthermore, the follow-up content processing system also includes building units and interaction units.

[0149] The building unit is used to construct virtual patient identifiers; the virtual patient identifiers are used for patient identification.

[0150] The interaction unit is used to interact with multidimensional patient health data through virtual patient identifiers to generate tagged patient data and basic health indicator data; the tagged patient data includes at least gender tags, age range tags, and health status tags.

[0151] Furthermore, the follow-up content processing system also includes a processing unit.

[0152] The processing unit is used to format multidimensional patient health data to form a disease-specific database.

[0153] In this embodiment, the follow-up process for discharged patients supports various follow-up methods such as online consultation, intelligent follow-up, telephone follow-up, and incentive-based follow-up, facilitating communication between doctors and patients. Furthermore, by integrating in-hospital data with electronic medical records, research follow-up records, and home device data from out-of-hospital patients, complete real-world data is generated and research reports are produced, providing doctors with decision support for research and clinical practice, improving the efficiency of clinical data management, and reducing the cost of research implementation.

[0154] This application embodiment also provides a storage medium, which includes stored instructions, wherein, when the instructions are executed, the device where the storage medium is located is controlled to perform the above-described follow-up content processing method.

[0155] This invention also provides an electronic device, the structural schematic of which is shown below. Figure 10 As shown, it specifically includes a memory 1001 and one or more instructions 1002, wherein one or more instructions 1002 are stored in the memory 1001 and configured to be executed by one or more processors 1003 to perform the above-mentioned follow-up content processing method.

[0156] The specific implementation processes and derivative methods of the above embodiments are all within the protection scope of this invention.

[0157] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0158] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0159] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0160] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing follow-up content, characterized in that, The method includes: Patient outpatient data is acquired through a preset follow-up method. The outpatient data consists of real-world case data of discharged patients at different stages of the follow-up period. The outpatient data includes at least the outpatient patient's consultation records, home medication records, and real-time home device data. In the process of acquiring outpatient data through the preset follow-up method, based on intelligent patient segmentation and grouping, multi-path follow-up capabilities and automatic follow-up are supported. Keywords from doctor-patient dialogue are extracted through natural language processing to trigger automatic follow-up. Follow-up stages are set based on multiple baseline nodes, and case report content is periodically distributed. By integrating out-of-hospital data and pre-acquired in-hospital data through a preset integration method, multidimensional patient health data is obtained; the in-hospital data refers to the patient's medical records within the hospital; the multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period. Based on the multidimensional patient health data, a corresponding research report is generated; The process involves integrating outpatient data and pre-acquired inpatient data using a pre-defined integration method to obtain multidimensional patient health data, including: A pre-defined dataset architecture is constructed; the pre-defined dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information; based on each module, a secondary field code is designed to standardize the specific code of the indicator; various types of multi-dimensional data use the secondary field code as the standard to produce and interact with the data, maintaining the interoperability and integration of multi-dimensional data under the platform; Through the aforementioned preset dataset architecture, external data and pre-acquired internal data are subjected to unified data standardization operations; By integrating the standardized data from outside the hospital with the data from inside the hospital, multidimensional patient health data is obtained.

2. The method according to claim 1, characterized in that, The preset follow-up methods include at least one of the following: online doctor-patient conversation, intelligent follow-up, telephone follow-up, and incentive-based follow-up; The method of obtaining patients' outpatient data through a preset follow-up approach includes: Patient outpatient data can be obtained through at least one of the following methods: online doctor-patient conversations, intelligent follow-up, telephone follow-up, and incentivized follow-up.

3. The method according to claim 2, characterized in that, The process of obtaining patient outpatient data through incentivized follow-up includes: During the follow-up of patients, a follow-up task plan is generated; the follow-up task plan includes the follow-up task plan for different stages within the follow-up period. Incentive methods are set up, and complete outpatient data of patients within the follow-up period is obtained through the incentive methods and the follow-up task plan.

4. The method according to claim 1, characterized in that, Also includes: Construct virtual patient identifiers; The virtual patient identifier is used for patient identification. The virtual patient identifier interacts with the multidimensional patient health data to generate tagged patient data and basic health indicator data; the tagged patient data includes at least gender tags, age range tags, and health status tags.

5. The method according to claim 1, characterized in that, Also includes: The multidimensional patient health data is formatted to form a disease-specific database.

6. A follow-up content processing system, characterized in that, The system includes: The acquisition unit is used to acquire patients' outpatient data through a preset follow-up method. The outpatient data consists of real-world case data of discharged patients at different stages within the follow-up period. The outpatient data includes at least the outpatient patient's consultation records, outpatient patient's home medication records, and real-time data of the outpatient patient's home devices. In the process of acquiring patients' outpatient data through the preset follow-up method, based on intelligent patient segmentation and grouping, it supports multi-path follow-up capabilities and automatic follow-up. Through natural language processing, it extracts keywords from the doctor-patient dialogue process to trigger automatic follow-up. It sets follow-up stages based on multiple baseline nodes and periodically distributes case report content. The integration unit is used to integrate outpatient data and pre-acquired inpatient data through a preset integration method to obtain multidimensional patient health data; the inpatient data refers to the patient's medical records within the hospital; the multidimensional patient health data is used to characterize multidimensional patient health data in a real-world environment during a complete follow-up period. The generation unit is used to generate a corresponding research report based on the multidimensional patient health data. The integration unit includes: A construction module is used to build a preset dataset architecture; the preset dataset architecture includes at least a bottom layer and a module layer; the bottom layer is used to provide standardized dictionary specifications through a common field set to ensure the uniformity and standardization of information processing; the module layer is used to support the definition of health indicator classification information; based on each module, a secondary field code is designed to standardize the specific code of the indicator; various types of multi-dimensional data use the secondary field code as the standard to produce and interact with data, maintaining the interoperability and integration of multi-dimensional data under the platform; The operation module is used to perform unified data standardization operations on external data and pre-acquired internal data through the preset dataset architecture; The integration module is used to integrate the outpatient data after standardized data processing with the inpatient data to obtain multidimensional patient health data.

7. The system according to claim 6, characterized in that, The acquisition unit is specifically used for: Patient outpatient data can be obtained through at least one of the following methods: online doctor-patient conversations, intelligent follow-up, telephone follow-up, and incentivized follow-up.

8. A storage medium, characterized in that, The storage medium includes stored instructions, wherein, when the instructions are executed, the device containing the storage medium is controlled to perform the follow-up content processing method as described in any one of claims 1 to 5.

9. An electronic device, characterized in that, It includes a memory, and one or more instructions, wherein one or more instructions are stored in the memory and configured to be executed by one or more processors as described in any one of claims 1 to 5.

Images