A data conversion method, system, terminal and storage medium based on patient portrait

Abstract

The present invention relates to the field of data conversion technology, and discloses a data conversion method, system, terminal and storage medium based on patient portraits, wherein the method comprises obtaining patient-related information of multiple patients, performing conversion processing to obtain a patient standard data set, and constructing a patient portrait label library; obtaining the labels of patients to be converted of the target department, performing matching processing to obtain a list of patients to be converted, and sending a message reminder; obtaining the target patient, and performing label configuration, and sending a message reminder to patients with business labels that meet the business labels; if there are patients to be converted or patients with business labels who have completed the diagnostic project, the labels are updated to obtain a first update result and a second update result respectively; and the corresponding first conversion result information and the corresponding second conversion result information are displayed. The present invention provides patients with an accurate closed-loop service model based on multi-dimensional patient portrait data, thereby accurately converting the patient's relevant data and improving the data conversion rate.

Description

Technical Field

[0001] The present invention relates to the field of data conversion technology, and in particular to a data conversion method, system, terminal and computer-readable storage medium based on patient portraits. Background Art

[0002] As the times progress, patients' expectations for a better medical experience are also rising. It is necessary to meet patients' diverse and multi-level medical service needs and improve the level of intelligent medical services. Especially in the field of patient rehabilitation, the hospital's patient service level will directly affect patient compliance and patient satisfaction, and thus affect the effectiveness of rehabilitation diagnosis and treatment.

[0003] At present, patient services in the field of rehabilitation diagnosis and treatment are still in a manual service state, such as making phone calls, recording on paper forms, etc., and in terms of informatization, they are still limited to single diagnosis and treatment links such as registration reminders, payment reminders, and appointment reminders. A continuous, full-cycle closed-loop service model for personalized rehabilitation diagnosis and treatment has not been formed, and effective information about patients cannot be obtained. In addition, the current rehabilitation patient service mode lacks the support of a medical disease knowledge base, and does not fully integrate the patient's multi-dimensional personalized patient portrait data (for example, screening reports, assessment reports and other dimensional data), so the patient's relevant data cannot be accurately converted, resulting in a low corresponding conversion rate, resulting in low stickiness between patients and relevant departments, and an inability to provide patients with better patient services.

[0004] Therefore, the existing technology still needs to be improved and developed. Summary of the Invention

[0005] The main purpose of the present invention is to provide a data conversion method, system, terminal and storage medium based on patient portraits, aiming to solve the problem that the existing technology has not formed a closed-loop service model for medical services for patients, and is unable to obtain effective patient information and is not fully combined with the patient portrait data, thereby being unable to accurately convert the patient's relevant data, resulting in a low corresponding conversion rate, resulting in low stickiness between patients and relevant departments, and an inability to provide patients with better patient services.

[0006] To achieve the above object, the present invention provides a data conversion method based on patient portraits, which comprises the following steps:

[0007] Obtaining patient-related information of multiple patients, converting all of the patient-related information to obtain a patient standard data set, and building a patient portrait label library based on the patient standard data set;

[0008] Obtain the labels of patients to be converted in the target department, match the labels of patients to be converted with the patient portrait label library to obtain a list of patients to be converted, and send message reminders to all patients to be converted in the list of patients to be converted;

[0009] Obtain all target patients who have been prescribed a project in the target department, configure labels for all target patients according to the patient portrait label library, obtain multiple business label patients, and send message reminders to business label patients who meet the business labels;

[0010] If there is a patient to be converted or a patient with a service tag who completes the corresponding diagnostic item according to the message reminder, it is determined that the patient to be converted or the patient with the service tag has completed the conversion, and the tag information of the patient to be converted and the patient with the service tag are updated respectively to obtain a first update result and a second update result;

[0011] First conversion result information is obtained according to the first update result, second conversion result information is obtained according to the second update result, and the first conversion result information and the second conversion result information are displayed.

[0012] Optionally, the patient portrait-based data conversion method, wherein obtaining patient-related information of multiple patients and converting all of the patient-related information to obtain a patient standard data set, specifically includes:

[0013] Obtaining patient-related information of multiple patients in different systems, performing comparison processing on all fields of the patient-related information to obtain comparison results, and configuring a standard field table based on the comparison results, wherein the patient-related information includes patient basic information, health and status information, and medical behavior information;

[0014] According to the standard field table, all the patient basic information, all the health and status information and all the medical behavior information are standardized and converted to obtain a first standard data set, a second standard data set and a third standard data set, and a patient standard data set is obtained based on the first standard data set, the second standard data set and the third standard data set.

[0015] Optionally, the patient portrait-based data conversion method, wherein the step of constructing a patient portrait label library based on the patient standard dataset, specifically includes:

[0016] Obtaining the label type of the patient standard data set, wherein the label type includes a patient portrait basic class, a patient data statistics class, and a patient prediction analysis class;

[0017] Perform rule configuration based on the patient portrait basic class to obtain a first label generation rule, perform rule configuration based on the patient data statistics class to obtain a second label generation rule, and perform rule configuration based on the patient prediction analysis class to obtain a third label generation rule;

[0018] Extracting data from the patient standard data set to obtain target data, and generating labels for the target data according to the first label generation rule to obtain patient portrait basic class labels;

[0019] Generate labels for the target data according to the second label generation rule to obtain patient data statistical labels;

[0020] Generate labels for the target data according to the third label generation rule to obtain patient prediction analysis labels;

[0021] A patient portrait label library is constructed based on the patient portrait basic class labels, the patient data statistics class labels and the patient prediction analysis class labels.

[0022] Optionally, the patient portrait-based data conversion method, wherein the step of generating labels for the target data according to the third label generation rule to obtain patient prediction analysis labels, specifically includes:

[0023] Performing a database call based on the target data to obtain a target knowledge base, and performing matching processing on the target data based on the target knowledge base to obtain a matching result;

[0024] A predictive analysis is performed based on the matching result to obtain an analysis result, and based on the analysis result, it is determined that the patient needs to be identified as a patient corresponding to the third label generation rule, and a label is generated for the patient according to the third label generation rule to obtain a patient predictive analysis class label.

[0025] Optionally, in the patient portrait-based data conversion method, the service tags include unstarted tags, triggered tags, interrupted tags, and follow-up tags;

[0026] The step of obtaining all target patients who have been prescribed a project in the target department, configuring labels for all target patients according to the patient portrait label library, obtaining multiple service label patients, and sending message reminders to service label patients who meet the service labels specifically includes:

[0027] Obtain all target patients who have been prescribed projects in the target department, extract project data of all target patients according to the patient standard data set, perform label matching on all project data according to the patient portrait label library to obtain label matching results, and configure labels for all target patients according to the label matching results to obtain multiple business label patients;

[0028] If there is project data that is an unstarted project, a label is defined for the unstarted project according to the patient portrait label library to obtain the unstarted label and the corresponding unstarted label generation rule, and all patients with business labels are screened according to the unstarted label and the unstarted label generation rule to obtain a first screening result, and a message reminder is sent regularly to the patients with business labels in the first screening result;

[0029] If there is project data that is a trigger-type project, the trigger-type project is labeled according to the patient portrait label library to obtain the trigger-type label and the corresponding trigger-type label generation rule, and all patients with business labels are screened according to the trigger-type label and the trigger-type label generation rule to obtain a second screening result, and message reminders are sent regularly to patients with business labels in the second screening result;

[0030] If there is project data that is an interruption-type project, a label is defined for the interruption-type project according to the patient portrait label library to obtain the interruption-type label and the corresponding interruption-type label generation rule, and all patients with service labels are screened according to the interruption-type label and the interruption-type label generation rule to obtain a third screening result, and a message reminder is sent regularly to the patients with service labels in the third screening result;

[0031] If there is project data that is a follow-up project, the follow-up project is labeled according to the patient portrait label library, and the follow-up label and the corresponding follow-up label generation rule are obtained. According to the follow-up label and the follow-up label generation rule, all patients with business labels are screened to obtain the fourth screening result, and message reminders are sent regularly to the patients with business labels in the fourth screening result.

[0032] Optionally, the patient portrait-based data conversion method, wherein the sending of a message reminder to the target patient who meets the service tag further includes:

[0033] If there are patients to be converted or patients with business tags who have not completed the corresponding diagnostic items according to the message reminder, it is determined that the patients to be converted or the patients with business tags have not completed the conversion, and a corresponding conversion strategy is formulated. The patients to be converted or the patients with business tags are converted according to the conversion strategy.

[0034] Optionally, the patient portrait-based data conversion method, wherein obtaining first conversion result information according to the first update result and obtaining second conversion result information according to the second update result, specifically includes:

[0035] Acquire the number of converted patients in the list of patients to be converted according to the first update result, and calculate the conversion rate based on the number of converted patients and the total number of patients in the list of patients to be converted to obtain first conversion result information;

[0036] The number of converted target patients is obtained according to the second update result, and the conversion rate is calculated based on the number of converted target patients and the total number of target patients meeting the service tag to obtain second conversion result information.

[0037] In addition, to achieve the above-mentioned purpose, the present invention further provides a data conversion system based on patient portraits, wherein the data conversion system based on patient portraits comprises:

[0038] A label library construction module obtains patient-related information of multiple patients, converts all the patient-related information to obtain a patient standard data set, and constructs a patient portrait label library based on the patient standard data set;

[0039] The patient conversion module obtains the labels of patients to be converted in the target department, matches the labels of patients to be converted with the patient portrait label library, obtains a list of patients to be converted, and sends message reminders to all patients to be converted in the list of patients to be converted;

[0040] The patient association module obtains all target patients who have been prescribed projects in the target department, configures labels for all the target patients according to the patient portrait label library, obtains multiple business label patients, and sends message reminders to the business label patients who meet the business labels;

[0041] a label updating module, which determines that the patient to be converted or the patient with the service label has completed the conversion if there is a patient to be converted or a patient with the service label who completes the corresponding diagnostic item according to the message reminder, and updates the label information of the patient to be converted and the patient with the service label respectively, to obtain a first update result and a second update result;

[0042] The conversion rate display module obtains first conversion result information according to the first update result, obtains second conversion result information according to the second update result, and displays the first conversion result information and the second conversion result information.

[0043] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a patient portrait-based data conversion program stored on the memory and runnable on the processor, and when the patient portrait-based data conversion program is executed by the processor, the steps of the patient portrait-based data conversion method described above are implemented.

[0044] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data conversion program based on patient portraits, and when the data conversion program based on patient portraits is executed by a processor, the steps of the data conversion method based on patient portraits as described above are implemented.

[0045] In the present invention, patient-related information of multiple patients is obtained, all the patient-related information is converted to obtain a patient standard data set, and a patient portrait label library is constructed based on the patient standard data set; the patient label to be converted of the target department is obtained, the patient label to be converted is matched with the patient portrait label library to obtain a list of patients to be converted, and message reminders are sent to all patients to be converted in the patient list to be converted; all target patients who have been prescribed projects in the target department are obtained, all the target patients are labeled according to the patient portrait label library to obtain multiple business label patients, and message reminders are sent to business label patients who meet the business labels; if there is a patient to be converted or a business label patient who completes the corresponding diagnostic project according to the message reminder, it is determined that the patient to be converted or the business label patient has completed the conversion, and the label information of the patient to be converted and the business label patient are updated respectively to obtain a first update result and a second update result; first conversion result information is obtained according to the first update result, and second conversion result information is obtained according to the second update result, and the first conversion result information and the second conversion result information are displayed. The present invention provides patients with an accurate closed-loop service model based on multi-dimensional patient portrait data, thereby accurately converting the patient's relevant data, improving the data conversion rate, and increasing the stickiness between patients and relevant departments, thereby providing patients with better patient services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flow chart of a preferred embodiment of the data conversion method based on patient portraits of the present invention;

[0047] Figure 2 It is a structural diagram of a preferred embodiment of the data conversion system based on patient portraits of the present invention;

[0048] Figure 3 FIG. 4 is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0051] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features specified as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] The data conversion method based on patient portrait described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the data conversion method based on patient portraits includes the following steps:

[0053] Step S10: Obtain patient-related information of multiple patients, convert all the patient-related information to obtain a patient standard data set, and build a patient portrait label library based on the patient standard data set.

[0054] Specifically, in an embodiment of the present invention, before obtaining a patient standard data set, it is necessary to obtain RWD (Real World Data) data of multiple patients, including basic patient information, health and status information, and medical behavior information. Specifically, the RWD data is collected from the hospital's HIS (Hospital Information System), LIS (Laboratory Information System), PACS (Picture Archiving and Communication System), HEMS (Health Examination Management System), and other systems by using an ETL (Extract Transform Load) data acquisition tool (e.g., Kettle). Since the data information standards of each system are different, the same field information has multiple forms in different systems. Therefore, it is necessary to compare the fields in different systems to form a unique standard, that is, configure a standard field table based on the comparison results. Subsequently, the fields of each system are standardized and converted using the configured standard field table to form a patient standard data set, providing data support for the subsequent generation of patient portrait labels. The purpose of the standardization conversion is to establish a unique data standard system.

[0055] It should be noted that the information (including but not limited to patient-related information, etc.) and data (including but not limited to patient standard data sets, etc.) involved in this application are all information and data authorized by the patients or fully authorized by all parties, and the collection, use and processing of relevant information and data comply with the laws, regulations and standards of relevant countries and regions.

[0056] For the conversion of patient basic information, the patient basic information is obtained from the hospital HIS system according to the patient ID, including patient ID, patient name, patient gender, date of birth, home address, contact information, etc.; and the obtained patient basic information is standardized according to the configured standard field table to convert the standard data set defined in this system to obtain the first standard data set; in the first standard data set, "PatientID" means "patient ID", "PatientName" means "patient name", "PatientSex" means "patient gender", "DateOfBirth" means "date of birth", "HomeAddress" means "home address", "MaritalStatus" means "marital status", and "ContactInfo" means "contact information".

[0057] For the conversion of health and status information, health and status information is obtained from the hospital HIS system and HEMS system according to the patient ID, including blood type, height, weight, body mass index, allergy history, chronic disease history, smoking status, drinking status, etc.; and the obtained health and status information is standardized according to the configured standard field table to obtain a second standard data set. In the second standard data set, "BloodType" represents "blood type", "Height" represents "height", "Weight" represents "weight", "BMI" represents "body mass index", "AllergyHistory" represents "allergy history", "ChronicDiseaseHistory" represents "chronic disease history", "SmokingStatus" represents "smoking status", and "DrinkingStatus" represents "drinking status".

[0058] For the conversion of medical behavior information, medical behavior information data is obtained from the hospital HIS, LIS, EMR and other systems according to the patient ID, including medical record number, visit number, visit department, visit type, visit time, diagnosis type, diagnosis code, diagnosis name, screening report, assessment report, project code, project name, etc.; and the obtained medical behavior information is standardized according to the configured standard field table to obtain a third standard data set. In the third standard data set, "MedicalRecordNo" represents "medical record number", "VisitID" represents "visit number", "Department" represents "visit department", "VisitType" represents "visit type", "VisitTime" represents "visit time", "DiagnosisType" represents "diagnosis type", "DiagnosisCode" represents "diagnosis code", "DiagnosisName" represents "diagnosis name", "ScreeningReport" represents "screening report", "AssessmentReport" represents "assessment report", "itemCode" represents "project code", and "itemName" represents "project name".

[0059] A patient standard dataset is obtained according to the first standard dataset, the second standard dataset, and the third standard dataset.

[0060] Afterwards, a corresponding patient portrait label library is constructed based on the patient standard data set, specifically, the label type of the patient standard data set is obtained, wherein the label type includes a patient portrait basic class, a patient data statistics class, and a patient prediction analysis class; rule configuration is performed according to the patient portrait basic class to obtain a first label generation rule, rule configuration is performed according to the patient data statistics class to obtain a second label generation rule, and rule configuration is performed according to the patient prediction analysis class to obtain a third label generation rule. Data extraction is performed on the patient standard data set to obtain target data (for example, the patient's medical data, diagnostic information, and diagnostic report, etc.). Corresponding labels need to be generated for the target data according to the first label generation rule, the second label generation rule, and the third label generation rule respectively. For the target data, labels are generated according to the first label generation rule (for example, the patient's age is calculated by obtaining the patient's date of birth) to obtain the patient's basic portrait class label. In an embodiment of the present invention, the patient portrait basic class label can be exemplified by RWD data to set the patient's age group label. The patient's age group label is ageTag (0 represents 0-1 years old, 1 represents 1-6 years old, 2 represents 6-18 years old, 3 represents 18-60 years old, and 4 represents over 60 years old). The patient's age can be calculated by obtaining the patient's date of birth, and the corresponding age group label is generated for the patient according to the calculated age. For example, the Json format (JavaScript Object Notation, a lightweight data exchange format) data corresponding to the patient A information is: {"PatientID":"P202506120001","PatientName":"A","PatientSex":"Male","Date":" "Of Birth": "2021-07-15", "HomeAddress": "City A", "MaritalStatus": "Single", "ContactInfo": "1XXXXXX"}, the obtained "Date Of Birth" is "2021-07-15", and the calculated age of the patient is 3 years old. Therefore, the age group label of patient A is 1.

[0061] Generate labels for the target data according to the second label generation rule (for example, the interruption judgment rule) to obtain patient data statistical labels; as an example, generate a label for whether the patient's musculoskeletal rehabilitation treatment program has been interrupted (0: not interrupted, 1: there is a risk of interruption, 2: interrupted, 3: the program has not been done), by configuring the interruption judgment rule (the project has an unfinished number of times, and the most recent treatment time exceeds 30 days for interruption, between 20 and 30 days for interruption risk, and within 20 days for uninterrupted). For example, the Json format data corresponding to patient B's diagnosis and treatment behavior data is: {"PatientID":"P202506120002","PatientName":"B","itemCode":"001","itemName":"Musculoskeletal Rehabilitation Treatment","executeTime":"2025-05-20 10:02:06","executeDeptName":"Postpartum and Pelvic Floor Rehabilitation Department","total":"10","executedTimes":"8"}; After obtaining the relevant data from patient B to the most recent musculoskeletal rehabilitation treatment, by calculating the total number of times and the number of times executed, the remaining number is 2. By obtaining the most recent execution time and the current time (for example, 2025-06-12), the interval is calculated to be 23 days. According to the interruption judgment rule, it can be determined that the interruption treatment label of patient B's musculoskeletal rehabilitation treatment item is 1, indicating that there is an interruption risk.

[0062] The patient prediction analysis class label is generated by analyzing the patient's multidimensional data using a large model. Specifically, a database call is performed based on the target data to obtain a target knowledge base, and the target data is matched according to the target knowledge base to obtain a matching result; a prediction analysis is performed based on the matching result to obtain an analysis result, and based on the analysis result, it is determined that the patient needs to be identified as a patient corresponding to the third label generation rule, and then a label is generated for the patient according to the third label generation rule to obtain a patient prediction analysis class label; as an example, a patient bone rehabilitation label is generated, and the patient bone rehabilitation label is mskRehabTag (0: No, 1: Yes). The diagnosis information of patient C can be used to determine whether it needs to be identified as a bone rehabilitation patient. For example, the Json format data corresponding to the diagnosis information of patient C is: {"PatientID":"P202506120003","PatientN ame": "C", "PatientSex": "Male", "DateOfBirth": "2021-07-15", "clinicLis": "Clubfoot", "reportList": [{"reportName": "Children's Musculoskeletal Screening"}, {"reportName": "Muscle Tone Assessment"}]}; After assembling the patient C's diagnostic information, diagnostic report and other data, the specialized disease knowledge base established by the department is called, and the assembled data is analyzed and compared by AI to find the knowledge base data with the highest matching degree. According to the type defined in this knowledge base data, the proprietary knowledge base is combined with AI to determine whether patient C is a skeletal rehabilitation label. The diagnostic information of patient C contains clubfoot, which can be used to determine that patient C is a skeletal rehabilitation patient, and the mskRehabTag of patient C is marked as 1.

[0063] Afterwards, a patient portrait label library is constructed based on the patient portrait basic class labels, the patient data statistics class labels and the patient prediction analysis class labels.

[0064] Step S20: Obtain the labels of patients to be converted in the target department, match the labels of patients to be converted with the patient portrait label library to obtain a list of patients to be converted, and send message reminders to all patients to be converted in the list of patients to be converted.

[0065] Specifically, in an embodiment of the present invention, relying on the constructed patient portrait label library, the target department (for example, the pediatric department) selects the labels that need to be converted into undergraduate patients (because the generation of labels comes from all the data in the hospital, the department needs to obtain a certain type of patients that meet the department conditions based on the labels), filters out all patients with matching labels through labels, and pushes messages to convert them into patients of the target department. Specifically, the labels of patients to be converted in the target department are obtained, the labels of patients to be converted are matched with the patient portrait label library to obtain a list of patients to be converted, and message reminders are sent to all patients to be converted in the list of patients to be converted through scheduled tasks. For example, patient A is reminded that he may have bone problems and can go to a certain department of a certain hospital for further examination, and the diagnosis and treatment report and diagnosis information of patient A are attached. For example, the pediatric department needs to screen out patients under 6 years old who have bone rehabilitation needs (i.e., potential patients for the pediatric musculoskeletal rehabilitation treatment program), and selects the patient tags to be converted, which are ageTag (0, 1), mskRehabTag (1), and itemInterruptionTag001 (3), where mskRehabTag represents the bone rehabilitation tag and itemInterruptionTag001 represents the item 1 interruption tag; obtain a list of all patients who meet the conditions of the patient tags to be converted (i.e., the patient list to be converted). For example, there are 3 patients to be converted who meet the conditions of the patient tags to be converted in the patient list, and the corresponding Json format data are, respectively, Patient A: {"PatientID":"P202506120001","PatientName":"A","tagList (tag list)":[{"tag Patient B: {"PatientID":"P202506120002","PatientName":"B","tagList":[{"tagName":"ageTag","tagValue":"3"},{"tagName":"mskRehabTag","tagValue":"1"},{"tagName":"itemInterruptionTag001","tagValue":"3"}]};Patient C: {"PatientID":"P202506120003","PatientName":"C","tagList":[{"tagName":"ageTag","tagValue":"0"},{"tagName":"mskRehabTag","tagValue":"0"},{"tagName":"itemInterruptionTag001","tagValue":"3"}]}; According to the patient tags to be converted, it can be judged that patient A's ageTag(1), mskRehabTag(1), itemInterruptionTag001(3) simultaneously meet the requirements of ageTag(0,1), mskRehabTag(1), itemInterruption Tag001 (3) condition, which meets the potential patient group of the children's musculoskeletal rehabilitation treatment program; patient B's ageTag (3), mskRehabTag (1), itemInterruptionTag001 (3) meet the mskRehabTag (1), itemInterruptionTag001 (3) conditions, but do not meet the ageTag (0, 1) condition, and do not meet the potential patient group of the children's musculoskeletal rehabilitation treatment program; patient C's ageTag (0), mskRehabTag (0), itemInterruptionTag001 (3) meet the ageTag (0, 1), itemInterruptionTag001 (3) conditions at the same time, but do not meet the mskRehabTag (0), and do not meet the potential patient group of the children's musculoskeletal rehabilitation treatment program. Therefore, it can be determined that patient A meets the conditions, and through the scheduled task, a message that further examination of bone problems may be required is pushed to patient A at regular intervals.

[0066] Step S30: Obtain all target patients who have been prescribed projects in the target department, configure labels for all target patients according to the patient portrait label library, obtain multiple business label patients, and send message reminders to business label patients who meet the business labels.

[0067] Specifically, in an embodiment of the present invention, relying on the patient portrait tag library, the target department selects different tag sets according to each link (for example, the link before treatment, the link during treatment, and the link after treatment) from the diagnosis and treatment items generated by the patient in the department during the full process of rehabilitation diagnosis and treatment, and generates the corresponding patient group increase and department relevance based on the tag set. After the doctor prescribes a project for the patient in the department based on the patient's diagnostic information (that is, the diagnosis content given by the doctor based on the patient's symptoms), the rehabilitation diagnosis and treatment service of the entire process cycle is carried out, from the unstarted business operation for patients who have not received treatment, to the triggered business operation during treatment, the interrupted business operation during treatment, and the follow-up business operation after treatment. Specifically, all target patients who have been prescribed projects in the target department are obtained, the project data of all the target patients are extracted according to the patient standard data set, all the project data are labeled according to the patient portrait label library to obtain label matching results, and all the target patients are labeled according to the label matching results to obtain multiple business label patients, wherein the business labels include unstarted labels, trigger labels, interruption labels and follow-up labels; thereafter, the project data of all the target patients in the target patient list are extracted according to the patient standard data set.

[0068] If there is project data that is an unstarted project, the unstarted project is labeled and defined according to the patient portrait label library to obtain the unstarted label and the corresponding unstarted label generation rule, and all patients with business labels are screened according to the unstarted label and the unstarted label generation rule to obtain a first screening result, and message reminders are sent regularly to patients with business labels in the first screening result (for example, by pushing information through SMS and other means to remind target patients to come to the department for rehabilitation treatment in time), that is, by performing statistical analysis on the project data of patients with this business label, it is generated whether the patient with this business label has a label that has not been started for the project; for example, the pediatric department needs to screen out patients who have not started musculoskeletal rehabilitation treatment (i.e., children For patients who have not started the pediatric musculoskeletal rehabilitation treatment project), select the unstarted tag: ageTag (0, 1), notStartedTag001 (1), where "notStartedTag001" means "Project 1 unstarted tag". The corresponding unstarted tag generation rule is that the patient is under 6 years old, has been prescribed a pediatric musculoskeletal rehabilitation treatment project, and has not started a treatment for more than 30 days. The tag value of the patient who meets the rule is 1, and the tag value of the patient who does not meet the rule is 0; obtain all business tag patients who meet the above tag conditions, and then push messages to all business tag patients through scheduled tasks, reminding all business tag patients that a certain project has been started for x days, please come to the hospital for rehabilitation treatment in time.

[0069] If there is project data that is a trigger-type project, the trigger-type project is labeled and defined according to the patient portrait label library to obtain the trigger-type label and the corresponding trigger-type label generation rule. All business-labeled patients are screened according to the trigger-type label and the trigger-type label generation rule to obtain a second screening result, and message reminders are sent regularly to the business-labeled patients in the second screening result, that is, by performing statistical analysis on the project data of the business-labeled patient, it is generated whether the business-labeled patient is a trigger-type label for the project. If it is determined that the business-labeled patient is a trigger-type label patient for this project, information is pushed through text messages or the like to remind the business-labeled patient of home precautions and subsequent treatment-related information; for example, the pediatric department needs to screen out patients who are triggered by musculoskeletal rehabilitation treatment. For patients with the disease-related treatment (i.e., the group of patients triggered by the children's musculoskeletal rehabilitation treatment project), the trigger class tags are selected: ageTag (0, 1), triggerTag001 (1), where "triggerTag001" means that the trigger class tag generation rule corresponding to "Project 1 trigger tag" is that the patient is under 6 years old, the children's musculoskeletal rehabilitation treatment project is prescribed, and the project is pushed on the same day after treatment; patients who meet the above conditions and receive the project treatment on the same day are marked as (1), and those who do not receive the project treatment are marked as (0); all business tag patients who meet the above tag conditions are obtained, and then messages are pushed to all business tag patients through scheduled tasks, reminding all business tag patients that they received rehabilitation treatment for project x on the same day, home precautions, follow-up visit time and other information.

[0070] If there is project data that is an interruption-type project, the interruption-type project is labeled and defined according to the patient portrait label library to obtain the interruption-type label (a label indicating that the patient receiving treatment in the department may have his treatment interrupted due to other reasons) and the corresponding interruption-type label generation rule. According to the interruption-type label and the interruption-type label generation rule, all patients with business labels are screened to obtain a third screening result, and message reminders are sent regularly to patients with business labels in the third screening result. For example, the pediatric department needs to screen out patients whose musculoskeletal rehabilitation treatment is interrupted (i.e., the group of patients whose pediatric musculoskeletal rehabilitation treatment is interrupted), so the interruption-type label is selected: ag eTag (0, 1), itemInterruptionTag001 (2), the corresponding interruption class tag generation rule is: age less than 6 years old, prescribed children's musculoskeletal rehabilitation treatment project, the project has unfinished times, the most recent treatment time exceeds 30 days for interruption, between 20 and 30 days for interruption risk, and within 20 days for uninterrupted (0: uninterrupted, 1: interruption risk, 2: interrupted, 3: not done the project); obtain all business tag patients that meet the above tag conditions, and then send messages to all business tag patients through scheduled tasks, reminding all business tag patients that a certain project has been interrupted for x days and please come to the hospital for rehabilitation treatment in time.

[0071] If there is project data that is a follow-up project, the follow-up project is labeled and defined according to the patient portrait label library to obtain the follow-up label (referring to the label that the patient who received treatment in the department needs to have a follow-up visit after a period of time) and the corresponding follow-up label generation rule. According to the follow-up label and the follow-up label generation rule, all patients with business labels are screened to obtain the fourth screening result, and message reminders are sent regularly to the patients with business labels in the fourth screening result; for example, the pediatric department needs to screen out patients who receive follow-up treatment for musculoskeletal rehabilitation treatment (that is, the group of patients who receive follow-up treatment for the pediatric musculoskeletal rehabilitation treatment project), then the follow-up label is selected. Signature: ageTag (0, 1), followupVisitTag001 (1), where "followupVisitTag001" represents "Project 1 Follow-up Label", and the corresponding follow-up label generation rule is: age less than 6 years old, prescribed children's musculoskeletal rehabilitation treatment project, the project has completed treatment, and a follow-up visit is required after 30 days (the treatment patients who meet the above conditions are marked as 1, and those who do not meet the conditions are marked as 0); obtain all business label patients who meet the above label conditions, and then send messages to all business label patients through a scheduled task, reminding all business label patients that a certain project has been x days since the last treatment ended, and please come to the hospital for a follow-up visit in time.

[0072] For example, there are several target patients' label data in Json format as follows:

[0073] Patient D: {"PatientID": "P202506120004", "PatientName": "D", "tagList": [{"tagName": "ageTag", "tagValue": "1"}, {"tagName": "mskRehabTag", "tagValue": "1"}, {"tagName": "notStartedTag001", "tagValue": "0"}, {"tagName": "triggerTag001", "tagValue": "0"}, {"tagName": "itemInterruptionTag001", "tagValue": "2"}, {"tagName": "followupVisitTag001", "tagValue": "0"}]};

[0074] Patient E: {"PatientID": "P202506120005", "PatientName": "E", "tagList": [{"tagName": "ageTag", "tagValue": "3"}, {"tagName": "mskRehabTag", "tagValue": "1"}, {"tagName": "notStartedTag001", "tagValue": "0"}, {"tagName": "triggerTag001", "tagValue": "0"}, {"tagName": "itemInterruptionTag001", "tagValue": "0"}, {"tagName": "followupVisitTag001", "tagValue": "0"}]};

[0075] Patient F: {"PatientID": "P202506120006", "PatientName": "F", "tagList": [{"tagName": "ageTag", "tagValue": "1"}, {"tagName": "mskRehabTag", "tagValue": "1"}, {"tagName": "notStartedTag001", "tagValue": "1"}, {"tagName": "triggerTag001", "tagValue": "0"}, {"tagName": "itemInterruptionTag001", "tagValue": "0"}, {"tagName": "followupVisitTag001", "tagValue": "0"}]};

[0076] Patient G: {"PatientID": "P202506120007", "PatientName": "G", "tagList": [{"tagName": "ageTag", "tagValue": "1"}, {"tagName": "mskRehabTag", "tagValue": "1"}, {"tagName": "notStartedTag001", "tagValue": "0"}, {"tagName": "triggerTag001", "tagValue": "1"}, {"tagName": "itemInterruptionTag001", "tagValue": "0"}, {"tagName": "followupVisitTag001", "tagValue": "0"}]};

[0077] Patient H: "PatientID": "P202506120008", "PatientName": "H", "tagList": [{"tagName": "ageTag", "tagValue": "1"}, {"tagName": "mskRehabTag", "tagValue": "1"}, {"tagName": "notStartedTag00 1", "tagValue": "0"}, {"tagName": "triggerTag001", "tagValue": "0"}, {"tagName": "itemInterruptionTag001", "tagValue": "0"}, {"tagName": "followupVisitTag001", "tagValue": "1"}]};

[0078] Based on the label data of the patients with the above business labels, it can be judged that patient F meets the conditions of the "not started" label, and thus it can be judged that patient F belongs to the group of patients who have not started the children's musculoskeletal rehabilitation treatment project. Through the scheduled task, relevant messages are pushed to patient F that the project has been started for x days and the patient should come to the hospital for treatment in time; it can be judged that patient G meets the conditions of the "trigger" label, and thus it can be judged that patient G belongs to the group of patients who are triggered for the children's musculoskeletal rehabilitation treatment project, and relevant messages of the "trigger" category are pushed to patient G through the scheduled task; it can be judged that patient D meets the conditions of the "interruption" label, and relevant messages of the "interruption" category are pushed to patient D through the scheduled task; it can be judged that patient H meets the conditions of the "follow-up" label, and relevant messages of the "follow-up" category are pushed to patient H through the scheduled task.

[0079] Step S40: If there is a patient to be converted or a patient with a business tag who completes the corresponding diagnostic item according to the message reminder, it is determined that the patient to be converted or the patient with the business tag has completed the conversion, and the tag information of the patient to be converted and the patient with the business tag is updated respectively to obtain a first update result and a second update result.

[0080] Specifically, if there is a patient to be converted who completes the corresponding diagnostic item according to the message reminder, it is determined that the patient to be converted has completed the conversion, and the label information of the patient to be converted is updated to obtain a first update result. For example, because patient A meets the label conditions of the patient to be converted, after receiving a push message that may require further examination of bone problems, he comes to the hospital department for examination according to the push message and undergoes treatment for related projects. Then the personal label of patient A is updated to: {"PatientID":"P202506120001","PatientName":"A","tagList":[{"tagName":"ageTag","tagValue":"1"},{"tagName":"mskRehabTag","tagValue":"1"},{"tagName":"itemInterruptionTag001","tagValue":"0"}]}. Therefore, the updated patient A does not meet the label of the patient to be converted, and patient A is removed from the patients under 6 years old who need to be screened for bone rehabilitation needs in the pediatric department (i.e., potential patients for the pediatric musculoskeletal rehabilitation treatment project).

[0081] If there is a patient with a business tag who completes the corresponding diagnostic item according to the message reminder, it is determined that the patient with the business tag has completed the conversion, and the tag information of the patient with the business tag is updated to obtain the second update result. For example, because patient E meets the follow-up tag condition, after receiving the push message of the relevant follow-up, he comes to the hospital department for treatment of the relevant project according to the push message, then the personal tag of patient H is updated to: {"PatientID":"P202506120008","PatientName":"H","tagList":[{"tagName":"ageTag","tagValue":"1"},{"tagName":"mskRehabTag"," Since the updated patient E does not meet the conditions of the follow-up label, patient E is removed from the patients who need to be screened for follow-up treatment for musculoskeletal rehabilitation in the pediatric department.

[0082] Furthermore, if there are patients to be converted or patients with business tags who have not completed the corresponding diagnostic items according to the message reminder, it is determined that the patients to be converted or the patients with business tags have not completed the conversion, and a corresponding conversion strategy is formulated (for example, the department can conduct offline visits based on patient-related information), and the patients to be converted or the patients with business tags are converted according to the conversion strategy to improve the corresponding conversion rate.

[0083] Step S50: Obtain first conversion result information according to the first update result, obtain second conversion result information according to the second update result, and display the first conversion result information and the second conversion result information.

[0084] Specifically, in an embodiment of the present invention, the operation data results can be displayed, and an automatic conversion operation query and statistics page can be provided, and a patient treatment compliance improvement operation query and statistics page can be provided for management personnel to use. Specifically, the number of converted patients in the list of patients to be converted is obtained according to the first update result, and the conversion rate is calculated based on the number of converted patients and the total number of patients in the list of patients to be converted to obtain first conversion result information, including an unconverted list, a converted list, a first total list and a first conversion rate, wherein the total list includes the patient name, the first message sending time (messageSentTime1), the first treatment time (treatmentTime1) and the conversion cycle (onversionCycle1); for example, the data of the first total list is:

[0085] Patient A: {"PatientName":"A","messageSentTime1":"2025-05-20 10:20:16","treatmentTime1":"2025-05-30 09:30:26","onversionCycle1":"10 days"};

[0086] Patient B: {"PatientName": "B", "messageSentTime1": "", "treatmentTime1": "", "onversionCycle1": ""};

[0087] Patient C: {"PatientName":"C","messageSentTime1":"2025-04-30 14:30:52","treatmentTime1":"2025-05-26 08:30:43","onversionCycle1":"26 days"};

[0088] It can be calculated that the number of patients who received the information and were treated in the hospital is 2, and the number of patients who have sent the information is 3. The corresponding first conversion rate is about 67%. After that, medical staff can carry out targeted operations based on the unconverted patients in the list.

[0089] A patient diagnosis and treatment compliance improvement operation query and statistics page may also be provided. Specifically, the number of patients with converted business tags is obtained according to the second update result, and the conversion rate is calculated based on the number of patients with converted business tags and the total number of patients with business tags that meet the business tags, to obtain second conversion result information, including a compliance unconverted list, a compliance converted list, a total list, and a second conversion rate. The second total list includes the patient name, the second message sending time (messageSentTime2), the second treatment time (treatmentTime2), and the compliance conversion cycle (onversionCycle2). For example, the data in the second total list is:

[0090] Patient A: {"PatientName": "A", "messageSentTime2": "2025-04-30 14:30:52", "treatmentTime2": "2025-05-22 09:30:13", "onversionCycle2": "22"};

[0091] Patient B: {"PatientName": "B", "messageSentTime2": "2025-04-30 14:30:52", "treatmentTime2": "2025-05-26 08:30:43", "onversionCycle2": "26"};

[0092] Patient C: {"PatientName": "C", "messageSentTime2": "2025-04-30 14:30:52", "treatmentTime2": "2025-05-12 08:20:25", "onversionCycle2": "12"};

[0093] Patient D: {"PatientName": "D", "messageSentTime2": "2025-04-30 14:30:52", "treatmentTime2": "", "onversionCycle2": ""};

[0094] Patient E: {"PatientName": "E", "messageSentTime2": "2025-04-30 14:30:52", "treatmentTime2": "2025-05-21 14:30:26", "onversionCycle2": "21"};

[0095] It can be calculated that the number of patients who received the information and received treatment in the hospital is 4, and the number of patients who have sent the information is 5, so the corresponding second conversion rate is 80%; medical staff can then carry out targeted operations based on the unconverted patients in the list.

[0096] The present invention provides patients with accurate medical services based on multi-dimensional patient portrait data and medical disease knowledge base, and establishes a one-stop, continuous, full-cycle personalized rehabilitation diagnosis and treatment service model for rehabilitation patients, forming a continuous rehabilitation patient operation system, thereby accurately converting the patient's relevant data, improving the data conversion rate, and increasing the stickiness between patients and related departments, thereby providing patients with better patient services.

[0097] Furthermore, if Figure 2 As shown, based on the above-mentioned data conversion method based on patient portraits, the present invention also provides a data conversion system based on patient portraits, wherein the data conversion system based on patient portraits includes:

[0098] The label library construction module 51 obtains patient-related information of multiple patients, converts all the patient-related information to obtain a patient standard data set, and constructs a patient portrait label library based on the patient standard data set;

[0099] The patient conversion module 52 obtains the labels of patients to be converted in the target department, matches the labels of patients to be converted with the patient portrait label library to obtain a list of patients to be converted, and sends message reminders to all patients to be converted in the list of patients to be converted;

[0100] The patient association module 53 obtains all target patients who have been prescribed a project in the target department, configures tags for all the target patients according to the patient portrait tag library, obtains multiple service tag patients, and sends message reminders to service tag patients who meet the service tags;

[0101] The label updating module 54 determines that the patient to be converted or the patient with the service label has completed the conversion if there is a patient to be converted or a patient with the service label who completes the corresponding diagnostic item according to the message reminder, and updates the label information of the patient to be converted and the patient with the service label respectively, obtaining a first update result and a second update result;

[0102] The conversion rate display module 55 obtains first conversion result information according to the first update result, obtains second conversion result information according to the second update result, and displays the first conversion result information and the second conversion result information.

[0103] Furthermore, if Figure 3 As shown, based on the above-mentioned patient portrait-based data conversion method and system, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 3 Only some of the components of the terminal are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0104] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Furthermore, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a data conversion program 40 based on a patient portrait is stored on the memory 20, and the data conversion program 40 based on a patient portrait can be executed by the processor 10, thereby realizing the data conversion method based on a patient portrait in the present application.

[0105] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the patient portrait-based data conversion method.

[0106] In some embodiments, the display 30 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual patient interface. Components 10-30 of the terminal communicate with each other via a system bus.

[0107] In one embodiment, when the processor 10 executes the patient portrait-based data conversion program 40 in the memory 20 , the steps of the patient portrait-based data conversion method described above are implemented.

[0108] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a data conversion program based on a patient portrait, and when the data conversion program based on a patient portrait is executed by a processor, the steps of the data conversion method based on a patient portrait as described above are implemented.

[0109] In summary, the present invention provides a data conversion method, system, terminal and storage medium based on patient portraits, the method including: obtaining patient-related information of multiple patients, converting all the patient-related information to obtain a patient standard data set, and constructing a patient portrait label library based on the patient standard data set; obtaining the patient label to be converted of the target department, matching the patient label to be converted with the patient portrait label library to obtain a list of patients to be converted, and sending message reminders to all patients to be converted in the patient list to be converted; obtaining all target patients who have been prescribed projects in the target department, labeling all the target patients according to the patient portrait label library to obtain multiple business label patients, and sending message reminders to business label patients who meet the business labels; if there is a patient to be converted or a business label patient who completes the corresponding diagnostic project according to the message reminder, it is determined that the patient to be converted or the business label patient has completed the conversion, and the label information of the patient to be converted and the business label patient are updated respectively to obtain a first update result and a second update result; first conversion result information is obtained according to the first update result, second conversion result information is obtained according to the second update result, and the first conversion result information and the second conversion result information are displayed. The present invention provides patients with an accurate closed-loop service model based on multi-dimensional patient portrait data, thereby accurately converting the patient's relevant data and improving the data conversion rate.

[0110] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal. In the absence of further limitations, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, article, or terminal comprising the element.

[0111] Of course, those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium that can be read by a computer. When executed, the program can include the processes in the above-described method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disk, etc.

[0112] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A data conversion method based on patient portraits, characterized in that: The data conversion method based on patient portraits includes: Obtaining patient-related information of multiple patients, converting all of the patient-related information to obtain a patient standard data set, and building a patient portrait label library based on the patient standard data set; The step of constructing a patient portrait label library based on the patient standard dataset specifically includes: Obtaining the label type of the patient standard data set, wherein the label type includes a patient portrait basic class, a patient data statistics class, and a patient prediction analysis class; Perform rule configuration based on the patient portrait basic class to obtain a first label generation rule, perform rule configuration based on the patient data statistics class to obtain a second label generation rule, and perform rule configuration based on the patient prediction analysis class to obtain a third label generation rule; Extracting data from the patient standard data set to obtain target data, and generating labels for the target data according to the first label generation rule to obtain patient portrait basic class labels; Generate a label for the target data according to the second label generation rule to obtain a patient data statistical label; Generate labels for the target data according to the third label generation rule to obtain patient prediction analysis labels; Construct a patient portrait tag library based on the patient portrait basic class tags, the patient data statistics class tags and the patient prediction analysis class tags; Obtain the labels of patients to be converted in the target department, match the labels of patients to be converted with the patient portrait label library to obtain a list of patients to be converted, and send message reminders to all patients to be converted in the list of patients to be converted; Obtain all target patients who have been prescribed a project in the target department, configure labels for all target patients according to the patient portrait label library, obtain multiple business label patients, and send message reminders to business label patients who meet the business labels; If there is a patient to be converted or a patient with a service tag who completes the corresponding diagnostic item according to the message reminder, it is determined that the patient to be converted or the patient with the service tag has completed the conversion, and the tag information of the patient to be converted and the patient with the service tag are updated respectively to obtain a first update result and a second update result; First conversion result information is obtained according to the first update result, second conversion result information is obtained according to the second update result, and the first conversion result information and the second conversion result information are displayed.

2. The data conversion method based on patient portrait according to claim 1, characterized in that: The step of obtaining patient-related information of a plurality of patients and converting all of the patient-related information to obtain a patient standard data set specifically includes: Obtaining patient-related information of multiple patients in different systems, performing comparison processing on all fields of the patient-related information to obtain comparison results, and configuring a standard field table based on the comparison results, wherein the patient-related information includes patient basic information, health and status information, and medical behavior information; According to the standard field table, all the patient basic information, all the health and status information and all the medical behavior information are standardized and converted to obtain a first standard data set, a second standard data set and a third standard data set, and a patient standard data set is obtained based on the first standard data set, the second standard data set and the third standard data set.

3. The data conversion method based on patient portrait according to claim 1 is characterized in that: Generating labels for the target data according to the third label generation rule to obtain patient prediction analysis labels specifically includes: Performing a database call based on the target data to obtain a target knowledge base, and performing matching processing on the target data based on the target knowledge base to obtain a matching result; A predictive analysis is performed based on the matching result to obtain an analysis result, and based on the analysis result, it is determined that the patient needs to be identified as a patient corresponding to the third label generation rule, and a label is generated for the patient according to the third label generation rule to obtain a patient predictive analysis class label.

4. The data conversion method based on patient portrait according to claim 1, characterized in that: The service tags include unstarted tags, triggered tags, interrupted tags and follow-up tags; The step of obtaining all target patients who have been prescribed a project in the target department, configuring labels for all target patients according to the patient portrait label library, obtaining multiple service label patients, and sending message reminders to service label patients who meet the service labels specifically includes: Obtain all target patients who have been prescribed projects in the target department, extract project data of all target patients according to the patient standard data set, perform label matching on all project data according to the patient portrait label library to obtain label matching results, and configure labels for all target patients according to the label matching results to obtain multiple business label patients; If there is project data that is an unstarted project, a label is defined for the unstarted project according to the patient portrait label library to obtain the unstarted label and the corresponding unstarted label generation rule, and all patients with business labels are screened according to the unstarted label and the unstarted label generation rule to obtain a first screening result, and a message reminder is sent regularly to the patients with business labels in the first screening result; If there is project data that is a trigger-type project, the trigger-type project is labeled according to the patient portrait label library to obtain the trigger-type label and the corresponding trigger-type label generation rule, and all patients with business labels are screened according to the trigger-type label and the trigger-type label generation rule to obtain a second screening result, and message reminders are sent regularly to patients with business labels in the second screening result; If there is project data that is an interruption-type project, a label is defined for the interruption-type project according to the patient portrait label library to obtain the interruption-type label and the corresponding interruption-type label generation rule, and all patients with service labels are screened according to the interruption-type label and the interruption-type label generation rule to obtain a third screening result, and a message reminder is sent regularly to the patients with service labels in the third screening result; If there is project data that is a follow-up project, the follow-up project is labeled according to the patient portrait label library, and the follow-up label and the corresponding follow-up label generation rule are obtained. According to the follow-up label and the follow-up label generation rule, all patients with business labels are screened to obtain the fourth screening result, and message reminders are sent regularly to the patients with business labels in the fourth screening result.

5. The data conversion method based on patient portrait according to claim 1 is characterized in that: The sending of a message reminder to the target patient meeting the service tag further includes: If there are patients to be converted or patients with business tags who have not completed the corresponding diagnostic items according to the message reminder, it is determined that the patients to be converted or the patients with business tags have not completed the conversion, and a corresponding conversion strategy is formulated. The patients to be converted or the patients with business tags are converted according to the conversion strategy.

6. The data conversion method based on patient portrait according to claim 1, characterized in that: The obtaining of first conversion result information according to the first update result and obtaining of second conversion result information according to the second update result specifically includes: Acquire the number of converted patients in the list of patients to be converted according to the first update result, and calculate the conversion rate based on the number of converted patients and the total number of patients in the list of patients to be converted to obtain first conversion result information; The number of patients with converted business labels is obtained according to the second update result, and a conversion rate is calculated based on the number of patients with converted business labels and the total number of patients with business labels that meet the business label to obtain second conversion result information.

7. A data conversion system based on patient portraits, characterized in that: The patient portrait-based data conversion system is applied to the patient portrait-based data conversion method according to any one of claims 1 to 6, and the patient portrait-based data conversion system includes: A label library construction module obtains patient-related information of multiple patients, converts all the patient-related information to obtain a patient standard data set, and constructs a patient portrait label library based on the patient standard data set; The patient conversion module obtains the labels of patients to be converted in the target department, matches the labels of patients to be converted with the patient portrait label library, obtains a list of patients to be converted, and sends message reminders to all patients to be converted in the list of patients to be converted; The patient association module obtains all target patients who have been prescribed projects in the target department, configures labels for all the target patients according to the patient portrait label library, obtains multiple business label patients, and sends message reminders to the business label patients who meet the business labels; a label updating module, which determines that the patient to be converted or the patient with the service label has completed the conversion if there is a patient to be converted or a patient with the service label who completes the corresponding diagnostic item according to the message reminder, and updates the label information of the patient to be converted and the patient with the service label respectively, to obtain a first update result and a second update result; The conversion rate display module obtains first conversion result information according to the first update result, obtains second conversion result information according to the second update result, and displays the first conversion result information and the second conversion result information.

8. A terminal, characterized in that: The terminal includes: a memory, a processor, and a patient portrait-based data conversion program stored in the memory and runnable on the processor. When the patient portrait-based data conversion program is executed by the processor, the steps of the patient portrait-based data conversion method as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a data conversion program based on a patient portrait, and when the data conversion program based on a patient portrait is executed by a processor, the steps of the data conversion method based on a patient portrait as described in any one of claims 1 to 6 are implemented.

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