Chronic disease screening return visit method and system

Abstract

The invention discloses a chronic disease screening return visit method and system, and relates to the technical field of health management, and the method comprises the steps: obtaining the basic information of a to-be-screened person, and constructing a screening file; generating a screening voucher containing an identity label; parallel data acquisition posts are set, personnel hold vouchers to autonomously select posts, and the posts scan identification associated archives and input multi-dimensional data to a central database in real time to form an associated data set; when the data set contains a preset basic data item, calling a risk scoring model to calculate a chronic disease risk score; and generating a differential detection strategy based on a comparison result of the score and a preset threshold value. According to the method, through parallel post setting and data real-time integration, the problems of post congestion and low efficiency of data integration in a traditional screening process are solved, intelligent risk assessment and detection strategy distribution of to-be-screened personnel are realized, the chronic disease screening efficiency and the resource utilization rate are improved, and a systematized data management scheme is provided for early prevention and control of chronic diseases.

Description

Technical Field

[0001] The present invention relates to the field of health management technology, and in particular to a method and system for chronic disease screening and follow-up. Background Art

[0002] Chronic diseases (such as diabetes and hypertension) have become a major public health problem threatening public health. Early screening is a key link in achieving chronic disease prevention and control. Currently, traditional screening processes mostly use sequential job settings. Residents need to complete information entry, indicator measurement, questionnaire filling and other steps according to a fixed process. This is prone to congestion in a single job, resulting in overall low efficiency. At the same time, data from each job relies on manual recording and aggregation, which is time-consuming to integrate and makes it difficult to achieve real-time risk assessment and diversion of people to be screened. Summary of the Invention

[0003] In order to solve the technical problems in the existing technology of low screening efficiency and difficulty in achieving real-time risk assessment and triage of people to be screened, the present invention provides a chronic disease screening follow-up method and system.

[0004] The technical solution adopted in the present invention is:

[0005] The first aspect of the present application provides a chronic disease screening follow-up method, comprising the following steps:

[0006] Step 1: Obtain basic information of the person to be screened and construct a screening file based on the basic information.

[0007] Step 2: Generate a screening credential containing the identity identifier of the person to be screened based on the screening file; the identity identifier forms a data binding relationship with the screening file.

[0008] Step 3: Set up multiple data collection posts to run in parallel, and the screening personnel can choose to visit any data collection post with the screening certificate.

[0009] In step 4, the data collection post scans the identification and associates the screening file, enters the multi-dimensional screening data of the person to be screened in real time and synchronizes it to the central database to form a related data set.

[0010] Step 5: When the associated data set contains preset basic data items, the risk scoring model is called to calculate the chronic disease risk score of the person to be screened through the preset basic data items.

[0011] Step 6: Compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

[0012] Preferably, the step 1 includes the following:

[0013] The identity data in the basic information is obtained by optical character recognition technology of the terminal device.

[0014] The identity data is automatically filled into a preset electronic form, and integrity verification is performed on the preset electronic form to identify missing information fields.

[0015] If the verification passes, it is confirmed as the screening archive; if the verification fails, supplementary prompt information is generated and displayed to the input interface.

[0016] The missing information input by the human through the input interface is received and the verification is re-executed.

[0017] Preferably, the step 2 includes the following contents:

[0018] Based on the unique identification field in the screening archive, a corresponding two-dimensional code image is generated.

[0019] The two-dimensional code image is synthesized with a preset voucher template to generate an electronic screening voucher.

[0020] The entity card form of the electronic screening voucher is output by a printing device, or the electronic screening voucher is displayed through a mobile terminal.

[0021] Preferably, the step 3 includes the following contents:

[0022] An independent data collection area is divided, and multiple parallel data collection posts including a basic measurement post, a diabetes questionnaire post, a hypertension post, a blood glucose measurement post, and other special disease questionnaire posts are set.

[0023] The person to be screened selects any collection post according to self-will to collect data, and the collection order of each collection post is not limited.

[0024] Preferably, the step 4 includes the following contents:

[0025] The data collection post obtains a screening archive index by scanning the identity mark, and establishes a data channel with the central database.

[0026] Based on the type of the data collection post, a corresponding preset data collection form is called.

[0027] Through the preset data collection form, multi-dimensional screening data of the person to be screened is input.

[0028] The multi-dimensional screening data is synchronized to the central database in real time through the data channel, and is associated to the corresponding screening archive to form an associated data set.

[0029] Preferably, the step 5 includes the following contents:

[0030] The associated data set is checked for integrity to determine whether it contains preset basic data items; the basic data items include age, gender, systolic blood pressure, weight, height, waist circumference, and whether there is a family history of diabetes.

[0031] When the integrity check passes, the basic data items are extracted from the associated data set and input into a preset risk scoring model; wherein the risk scoring model includes a diabetes risk scoring sub-model.

[0032] The BMI value of the person to be screened is calculated based on the weight and height data, and the diabetes risk scoring sub-model assigns points to the basic data items and the BMI value to output a diabetes risk score.

[0033] Preferably, step 6 includes the following contents: comparing the diabetes risk score with a preset risk threshold; if the diabetes risk score is greater than or equal to the risk threshold, generating an invasive testing recommendation; if the diabetes risk score is less than the risk threshold, generating routine health advice and providing an autonomous testing option; wherein the invasive testing recommendation includes a blood glucose testing recommendation.

[0034] Preferably, the step 6 and the following steps are further included:

[0035] Step 7: Determine whether a return visit urgency value has been generated for the person to be return visited. If no return visit urgency value has been generated, create a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be return visited.

[0036] Step 8: Obtain the test result data obtained by the differentiated detection strategy of the person to be revisited; extract the blood sugar, blood pressure, BMI, medication status and previous medical history of the person to be revisited from the test result data of the person to be revisited and the associated data set of the person to be revisited.

[0037] Step 9: Make an emergency status determination on the blood sugar and blood pressure data. If they meet the preset numerical range of extremely high blood sugar, extremely high blood pressure or hypoglycemia, a corresponding fixed score is accumulated and an emergency status label is added to the current status list of the persons to be revisited.

[0038] Step 10: For abnormal blood sugar or substandard blood pressure, based on whether there is a previous medical history, distinguish between newly discovered chronic diseases and uncontrolled chronic diseases, accumulate different preset scores, and add the new / out-of-control status label to the current status list of the people to be revisited.

[0039] Step 11: For patients with known diabetes or hypertension, if no medication records are collected, they are determined to be in an untreated state and a preset score is accumulated. At the same time, the treatment compliance status label is added to the current status list of the patients to be revisited.

[0040] Step 12, based on the diabetes risk score, BMI value range, blood sugar and blood pressure classification, respectively, perform diabetes high risk, obesity classification, blood sugar and blood pressure classification assessment, accumulate scores according to different risk levels and add risk factor status labels to the current status list of the people to be revisited.

[0041] Step 13: Summarize the total urgency values ​​of the return visits accumulated from steps 9 to 12 and the status labels in the status list of the persons to be return visited, and generate an evaluation result including the total urgency values ​​of the return visits and the status list of the persons to be return visited.

[0042] A second aspect of the present application provides a chronic disease screening and follow-up system, which applies any of the above-mentioned chronic disease screening and follow-up methods, including:

[0043] A screening file construction module is configured to obtain basic information of the person to be screened and construct a screening file based on the basic information.

[0044] A screening credential generation module is configured to generate a screening credential containing an identity identifier of the person to be screened based on the screening file; the identity identifier forms a data binding relationship with the screening file.

[0045] The data collection scheduling module is configured to set up multiple data collection posts to run in parallel, and the screening personnel can choose to visit any data collection post by holding the screening certificate.

[0046] The multi-dimensional data entry module is configured to input the multi-dimensional screening data of the persons to be screened in real time and synchronize it to the central database to form an associated data set by scanning the identity identification associated screening file at the data collection post.

[0047] The risk score calculation module is configured to call the risk score model when the associated data set contains preset basic data items and calculate the chronic disease risk score of the person to be screened based on the preset basic data items.

[0048] A differentiated detection strategy generation module is configured to compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

[0049] Preferably, it also includes: a return visit initialization module, which is configured to determine whether the person to be return visited has generated a return visit urgency value. If not, it creates a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be return visited.

[0050] A data extraction module configured to extract blood glucose, blood pressure, BMI, medication and medical history from the detection result data and the associated data set.

[0051] An emergency state evaluation module configured to determine the emergency state of blood glucose and blood pressure data, and add a fixed score and an emergency state label to the to-be-visited personnel status list if the preset value range of extremely high blood glucose, extremely high blood pressure or hypoglycemia is met.

[0052] A new / loss of control evaluation module configured to distinguish between newly discovered and uncontrolled chronic diseases according to the presence or absence of medical history, and add different preset scores and a new / loss of control state label to the to-be-visited personnel status list.

[0053] A treatment compliance evaluation module configured to determine the treatment state of known diabetic or hypertensive patients as non-treatment if no medication record is collected, add a preset score to the to-be-visited personnel status list, and add a treatment compliance state label to the to-be-visited personnel status list.

[0054] A risk factor evaluation module configured to evaluate diabetes risk score, BMI value interval and blood glucose and blood pressure classification, and add risk factor state labels to the to-be-visited personnel status list according to different risk levels.

[0055] An evaluation result generation module configured to generate an evaluation result including the to-be-visited personnel status list and the total sum of the emergency value of the to-be-visited personnel.

[0056] The beneficial effects of the present application are at least one of the following:

[0057] By setting multiple parallel data collection posts, the to-be-screened personnel can independently choose the access path, breaking the fixed process limit of traditional sequential posts, reducing the overall waiting time caused by single post congestion; each post synchronously collects data and synchronously uploads to the central database in real time, avoiding the time-consuming process of manual recording and summarizing, and significantly improving the screening efficiency.

[0058] Each data collection post associates the screening archives by scanning the identity, synchronizes the multi-dimensional screening data to the central database in real time, forms an associated data set, solves the problem of scattered data and time-consuming integration in traditional processes, and provides a real-time and complete data basis for subsequent risk assessment.

[0059] After the central database obtains the preset basic data, it calls the risk scoring model to calculate the risk score and automatically triggers the differentiated detection strategy based on the score threshold to achieve intelligent allocation of screening resources, avoid waste of medical resources, and improve the efficiency of early detection of high-risk groups.

[0060] From screening file construction and data collection to risk assessment and detection strategy generation, each step realizes dynamic association and flow of data through unique identity identification and central database, ensuring the consistency and traceability of data during the screening process, and providing systematic data support for the full-cycle management of chronic disease prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the process of the chronic disease screening method according to the first embodiment of the present invention.

[0062] Figure 2 This is a flow chart of a specific implementation method of the chronic disease screening method in Example 1 of the present invention.

[0063] Figure 3 This is a flow chart of the revisit method in the first embodiment of the present invention.

[0064] Figure 4 This is a structural block diagram of the chronic disease screening system according to the second embodiment of the present invention.

[0065] Figure 5 This is a structural block diagram of a chronic disease screening and follow-up system with a follow-up function according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0067] Considering that early screening is a key step in chronic disease prevention and control, the current traditional screening process often uses a sequential workstation setup, requiring residents to complete information entry, indicator measurement, and questionnaire completion according to a fixed process. This can lead to overall inefficiency due to congestion in a single workstation. Furthermore, data from each workstation relies on manual recording and aggregation, which is time-consuming and hinders real-time risk assessment and triage of those awaiting screening.

[0068] In order to solve the above technical problems, the first embodiment provides a chronic disease screening and follow-up method, such as Figure 1 As shown, the following steps are included:

[0069] Step 1: Obtain basic information of the person to be screened and construct a screening file based on the basic information.

[0070] In a possible implementation, step 1 includes the following:

[0071] The terminal device uses optical character recognition technology to parse the resident ID card information and obtain the identity data from the basic information. This identity data is automatically populated into a pre-set electronic form, which is then checked for integrity and missing fields are identified. If the check passes, the profile is confirmed as the screening profile; if the check fails, supplementary information is generated and displayed on the entry interface. Manually entered missing information is received through the entry interface and re-checked.

[0072] For example, in a specific application process, staff conducts screening for residents offline, such as Figure 2 As shown, upon arrival at the screening site, staff will direct residents to the "Basic Information Entry" area, where they will scan their ID cards using a device equipped with optical character recognition (OCR) technology (e.g., a mobile phone, ID card scanner, or tablet). The system automatically interprets the ID card's name, gender, age, ID number, and other personal data, and enters it into a pre-set electronic form.

[0073] The system performs field integrity checks on electronic forms, for example, checking whether non-ID card auto-fill fields such as "Contact Number" and "Residential Address" are missing. If the check passes, the initial screening file is directly generated; if there are missing fields, the system highlights the missing items on the input interface and generates supplementary prompts (such as "Please enter the contact number")

[0074] Staff members will guide residents to fill in the missing information according to prompts. After the information is filled in, the system will re-check until all required fields are complete and finally generate a formal screening file.

[0075] In this embodiment, most of the information is automatically filled in quickly and accurately by residents scanning their ID cards to improve efficiency and reduce errors.

[0076] Step 2: Generate a screening credential containing the identity identifier of the person to be screened based on the screening file; the identity identifier forms a data binding relationship with the screening file.

[0077] In a possible implementation, step 2 includes the following:

[0078] Based on the unique identification field in the screening file, a corresponding QR code image is generated. The QR code image is combined with a preset voucher template to generate an electronic screening voucher. The electronic screening voucher is output as a physical card via a printing device or displayed via a mobile terminal.

[0079] For example, in a specific application process, Figure 2As shown, the system extracts a unique identifier (such as UUID) from the screening file and encodes it into a QR code image through a QR code generation algorithm (such as QR Code). The QR code contains the file index address and can be recognized by the scanning equipment at each data collection post.

[0080] The system combines the QR code image with a pre-set credential template to create an electronic screening credential. Depending on actual needs, this can be printed as a physical card, photographed, or sent to residents' mobile devices (such as their phones) via text message or WeChat official account to create an electronic credential.

[0081] Step 3: Set up multiple data collection posts to run in parallel, and the screening personnel can choose to visit any data collection post with the screening certificate.

[0082] In a possible implementation, step 3 includes the following:

[0083] Independent data collection areas are divided, and multiple parallel data collection posts are set up, including basic measurement posts, diabetes questionnaire posts, hypertension posts, blood sugar measurement posts, and other special disease questionnaire posts; the people to be screened can choose any collection post for data collection according to their own wishes, and the collection order of each collection post is not limited.

[0084] For example, in a specific application process, Figure 2 As shown, the screening site is divided into independent areas and the following parallel positions are set up:

[0085] Basic measurement post: equipped with height and weight scales to collect physiological indicators such as height, weight, and waist circumference.

[0086] Diabetes Questionnaire Post: Staff guide residents to fill out standardized questionnaires to collect information on risk factors such as diet, exercise habits, and family history of diabetes.

[0087] Hypertension post: Staff guide residents to fill out standardized questionnaires, collect information such as past medical history of hypertension and medication status, and are equipped with blood pressure monitors to collect blood pressure and pulse data.

[0088] Blood glucose measurement station: equipped with a blood glucose tester to collect blood glucose values ​​(mmol / L).

[0089] Other specialized disease questionnaire posts: Set up special questionnaire collection posts for diseases such as chronic obstructive pulmonary disease and atrial fibrillation.

[0090] Residents can access the system through a self-directed flow mechanism: Residents can select any station to begin collecting data with their screening credentials, without having to follow a fixed order. Each station displays the real-time queue number on an electronic screen, allowing residents to dynamically adjust their access path based on the queue situation, for example, prioritizing basic measurement stations with no queues.

[0091] In step 4, the data collection post scans the identification and associates the screening file, enters the multi-dimensional screening data of the person to be screened in real time and synchronizes it to the central database to form a related data set.

[0092] In a possible implementation, step 4 includes the following:

[0093] The data collection post obtains the screening file index by scanning the identity identifier and establishes a data channel with the central database; based on the type of the data collection post, the corresponding preset data collection form is called, and the multi-dimensional screening data of the person to be screened is entered through the preset data collection form; the multi-dimensional screening data is synchronized to the central database in real time through the data channel, and associated with the corresponding screening file to form a related data set.

[0094] For example, in a specific application process, Figure 2 As shown, residents scan the QR code of the screening certificate at the post terminal. The terminal retrieves the corresponding screening file from the central database through the unique identifier, establishes a data channel, and each post automatically calls the corresponding data collection form according to the type:

[0095] The basic measurement post calls the "Physiological Indicators Form" to enter height (unit: cm), weight (kg), and waist circumference (cm).

[0096] The diabetes questionnaire post calls the "Diabetes Risk Form" to enter family history (yes / no), weekly exercise frequency (times / week), etc.

[0097] The hypertension post uses the "Hypertension Questionnaire Form" to enter information such as systolic / diastolic blood pressure (mmHg), pulse (beats / minute), history of hypertension (yes / no), and use of antihypertensive drugs (yes / no).

[0098] The blood glucose measurement station calls the "Blood Glucose Measurement Data Form" and enters the blood glucose value (mmol / L).

[0099] When entering data, the system automatically performs format verification (for example, blood pressure values ​​must be numbers) to avoid invalid data. The collected data is synchronized to the central database in real time through a secure channel. The system associates the data with the unique identifier of the screening file to form a "linked data set" containing data from multiple positions, such as merging basic measurement data with questionnaire data for storage.

[0100] Step 5: When the associated data set contains preset basic data items, the risk scoring model is called to calculate the chronic disease risk score of the person to be screened through the preset basic data items.

[0101] In a possible implementation, step 5 includes the following:

[0102] The associated dataset is integrity checked to determine whether it contains pre-set basic data items, including age, gender, systolic blood pressure, weight, height, waist circumference, and whether there is a family history of diabetes. If the integrity check passes, the basic data items are extracted from the associated dataset and input into a pre-set risk scoring model, which includes a diabetes risk scoring sub-model. The BMI value of the individual to be screened is calculated based on the weight and height data. The diabetes risk scoring sub-model assigns scores to the basic data items and BMI value, outputting a diabetes risk score.

[0103] For example, in a specific application process, Figure 2 As shown, the system checks whether the associated dataset contains the preset basic data items (age, gender, systolic blood pressure, weight, height, waist circumference, and family history of diabetes). For example, if the "waist circumference" data is missing, the risk assessment process cannot be triggered until the data is supplemented.

[0104] The system extracts weight (kg) and height (m) from the associated dataset and calculates the body mass index using the formula BMI = weight / height². It also extracts basic data items such as age and gender as input parameters for the risk scoring model.

[0105] The built-in diabetes risk scoring model is called to assign scores to the input parameters. The diabetes risk scoring model is an innovative diabetes risk assessment tool that is suitable for the characteristics of the Chinese population. Its core advantage is that it does not require blood tests. It can quickly and non-invasively quantify an individual's risk of developing type 2 diabetes in the future through simple and easy-to-obtain physiological and behavioral indicators such as age, gender, height, weight, waist circumference, systolic blood pressure, and whether there is a family history of diabetes. The diabetes risk scoring model ultimately outputs a total score. The higher the score, the greater the risk of future illness. In the screening scenario, this score is the key basis for determining whether residents need to undergo the next blood sugar test.

[0106] For example, the various indicators required for calculation are extracted and verified from the residents' records to see if they are complete, including: age, gender, systolic blood pressure, weight, height, waist circumference, and whether there is a family history of diabetes.

[0107] Calculate scores for each item: The following are the ranges and corresponding scores for each indicator.

[0108] Age score:

[0109] If the resident is ≤24 years old, the age score is 0.

[0110] If the resident is between 25 and 34 years old, the age score is 4 points.

[0111] If the resident is between 35 and 39 years old, the age score is 8 points.

[0112] If the resident is between 40 and 44 years old, the age score is 11 points.

[0113] If the resident is between 45 and 49 years old, the age score is 12 points.

[0114] If the resident is between 50 and 54 years old, the age score is 13 points.

[0115] If the resident is between 55 and 59 years old, the age score is 15 points.

[0116] If the resident is between 60 and 64 years old, the age score is 16 points.

[0117] If the resident is ≥65 years old, the age score is 18 points.

[0118] Systolic blood pressure score:

[0119] If the resident's systolic blood pressure is <110 mmHg, the resident's systolic blood pressure score is 0 points.

[0120] If a resident's systolic blood pressure is between 110-119 mmHg, the resident's systolic blood pressure score is 1 point.

[0121] If a resident's systolic blood pressure is between 120-129 mmHg, the resident's systolic blood pressure score is 3 points.

[0122] If a resident's systolic blood pressure is between 130-139 mmHg, the resident's systolic blood pressure score is 6 points.

[0123] If a resident's systolic blood pressure is between 140-149 mmHg, the resident's systolic blood pressure score is 7 points.

[0124] If a resident's systolic blood pressure is between 150-159 mmHg, the resident's systolic blood pressure score is 8 points.

[0125] If the resident's systolic blood pressure is ≥160 mmHg, the resident's systolic blood pressure score is 10 points.

[0126] Body Mass Index (BMI) score (BMI = weight in kg / height in m²):

[0127] If the resident's body mass index (BMI) is <22.0, the resident's body mass index (BMI) score is 0.

[0128] If the resident's body mass index (BMI) is between 22.0 and 23.9, the resident's body mass index (BMI) score is 1 point.

[0129] If the resident's body mass index (BMI) is between 24.0 and 29.9, the resident's body mass index (BMI) score is 3 points.

[0130] If the resident's body mass index (BMI) is ≥ 30.0, the resident's body mass index (BMI) score is 5 points.

[0131] Waist circumference score:

[0132] Male:

[0133] If the male's waist circumference is < 75 cm, the male's waist circumference score is 0 points.

[0134] If the male's waist circumference is 75-79.9 cm, the male's waist circumference score is 3 points.

[0135] If the male's waist circumference is 80-84.9 cm, the male's waist circumference score is 5 points.

[0136] If the male's waist circumference is 85-89.9 cm, the male's waist circumference score is 7 points.

[0137] If the male's waist circumference is 90-94.9 cm, the male's waist circumference score is 8 points.

[0138] If the male's waist circumference is ≥ 95 cm, the male's waist circumference score is 10 points.

[0139] Female:

[0140] If the female's waist circumference is < 70 cm, the female's waist circumference score is 0 points.

[0141] If the female's waist circumference is 70-74.9 cm, the female's waist circumference score is 3 points.

[0142] If the female's waist circumference is 75-79.9 cm, the female's waist circumference score is 5 points.

[0143] If the female's waist circumference is 80-84.9 cm, the female's waist circumference score is 7 points.

[0144] If the female's waist circumference is 85-89.9 cm, the female's waist circumference score is 8 points.

[0145] If the female's waist circumference is ≥ 90 cm, the female's waist circumference score is 10 points.

[0146] Family history of diabetes score: If the resident has a family history of diabetes, the resident's family history of diabetes score is 6 points; if the resident does not have a family history of diabetes, the resident's family history of diabetes score is 0 points.

[0147] Gender score: If the resident is male, the resident's gender score is 2 points; if the resident is female, the resident's gender score is 0 points.

[0148] Total score:

[0149] All the sub-scores calculated above are added up to obtain a final total diabetes risk score.

[0150] Output results: The calculated total score is used as the final output of the diabetes risk scoring model.

[0151] Step 6: Compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

[0152] In one possible embodiment, step 6 includes the following: comparing the diabetes risk score with a preset risk threshold; if the diabetes risk score is greater than or equal to the risk threshold, generating an invasive testing recommendation; if the diabetes risk score is less than the risk threshold, generating routine health advice and providing an autonomous testing option; wherein the invasive testing recommendation includes a blood glucose testing recommendation.

[0153] For example, in a specific application process, Figure 2 As shown, the system compares the diabetes risk score with a preset threshold (such as 25 points): if the score is ≥25 points, it is judged as high risk; if the score is <25 points, it is judged as low risk.

[0154] Testing strategy generation: Generate "invasive testing recommendations" for high-risk groups, such as "fasting blood glucose testing (venous blood sampling)" and push them to the post terminal or residents' mobile devices;

[0155] For low-risk groups, “routine health advice” is generated, such as “maintain regular exercise and have annual checkups”, while providing self-testing options (such as “whether to voluntarily undergo blood sugar testing”).

[0156] In summary, steps 1 through 6 maximize screening efficiency and quality within limited resources by optimizing personnel division of labor, streamlining information entry, and introducing intelligent risk assessment. By implementing a "general screening first, then detailed screening" approach, which involves conducting preliminary risk assessments on a large number of people using non-invasive or low-cost methods, and then conducting necessary, cost-effective in-depth screening (such as blood sugar testing) on ​​high-risk individuals based on the assessment results, screening costs can be effectively controlled and resource utilization improved.

[0157] Considering that after the screening is completed, the staff needs to visit these people again, it is very important to scientifically determine the priority of the return visit among the many patients who need attention. In a possible implementation method, Figure 3 As shown, the step 6 and the following steps are also included:

[0158] Step 7: Determine whether a return visit urgency value has been generated for the person to be return visited. If no return visit urgency value has been generated, create a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be return visited.

[0159] For example, in a specific implementation process, a pre-check is performed: check whether a return visit emergency value has been generated, and if so, the process is terminated. If not, the process is initialized, creating "return visit emergency value sum" = 0, and "list of current status of personnel to be returned" = [empty].

[0160] Step 8: Obtain the test result data obtained by the differentiated detection strategy of the person to be revisited; extract the blood sugar, blood pressure, BMI, medication status and previous medical history of the person to be revisited from the test result data of the person to be revisited and the associated data set of the person to be revisited.

[0161] Step 9: Make an emergency status determination on the blood sugar and blood pressure data. If they meet the preset numerical range of extremely high blood sugar, extremely high blood pressure or hypoglycemia, a corresponding fixed score is accumulated and an emergency status label is added to the current status list of the persons to be revisited.

[0162] For example, an emergency status determination is performed on blood sugar and blood pressure data. If the data falls within the preset value range of extremely high blood sugar, extremely high blood pressure, or low blood sugar, a fixed score is accumulated accordingly and an emergency status tag is added to the current status list of the person to be revisited:

[0163] If fasting blood glucose is ≥13.9 or random blood glucose is ≥16.7, it is considered extremely hyperglycemic. The total score is +30, and "extremely hyperglycemic" is added to the status list of the person to be revisited.

[0164] If the systolic blood pressure is ≥180 and the diastolic blood pressure is ≥110, it is judged as extremely high blood pressure, the total score is +30, and "extremely high blood pressure" is added to the current status list of the person to be revisited.

[0165] If the blood sugar level is <4.4, it is considered hypoglycemia, the total score is +30, and 'hypoglycemia' is added to the current status list of the person to be revisited.

[0166] Step 10: For abnormal blood sugar or substandard blood pressure, based on whether there is a previous medical history, distinguish between newly discovered chronic diseases and uncontrolled chronic diseases, accumulate different preset scores, and add the new / out-of-control status label to the current status list of the people to be revisited.

[0167] Exemplary, emerging and uncontrolled assessment (assessing newly discovered or ineffectively controlled chronic diseases):

[0168] Abnormal blood sugar (random blood sugar ≥11.1 or fasting blood sugar ≥7):

[0169] If the history of diabetes is unknown, it is judged as newly discovered diabetes: the total score is increased by 20, and "newly discovered diabetes" is added to the current status list of the person to be revisited.

[0170] If a history of diabetes is known, the patient is judged to have uncontrolled diabetes: the total score is increased by 10, and 'uncontrolled diabetes' is added to the current status list of the person to be revisited.

[0171] Blood pressure is not up to standard (systolic blood pressure ≥140 or diastolic blood pressure ≥90):

[0172] If the history of hypertension is unknown, it is judged as newly discovered hypertension: the total score is increased by 20, and "newly discovered hypertension" is added to the current status list of the person to be revisited.

[0173] If a history of hypertension is known, the patient is judged to have uncontrolled hypertension: the total score is increased by 10, and 'uncontrolled hypertension' is added to the current status list of the patient to be revisited.

[0174] Step 11: For patients with known diabetes or hypertension, if no medication records are collected, they are determined to be in an untreated state and a preset score is accumulated. At the same time, the treatment compliance status label is added to the current status list of the patients to be revisited.

[0175] Exemplary, Treatment Adherence Assessment:

[0176] If the patient is known to have diabetes but is not taking hypoglycemic medication, the patient is considered to have untreated diabetes: the total score is increased by 10, and 'untreated diabetes' is added to the status list of the patient to be revisited.

[0177] If the patient is known to have hypertension but is not taking antihypertensive drugs, the patient is considered to have untreated hypertension: the total score is increased by 10, and 'untreated hypertension' is added to the current status list of the patient to be revisited.

[0178] Step 12, based on the diabetes risk score, BMI value range, blood sugar and blood pressure classification, respectively, perform diabetes high risk, obesity classification, blood sugar and blood pressure classification assessment, accumulate scores according to different risk levels and add risk factor status labels to the current status list of the people to be revisited.

[0179] Exemplary, other risk factors assessed:

[0180] High risk of diabetes: If the risk score is ≥25 and there is no history of diabetes, the total score is increased by 1, and the "high-risk group for diabetes" is added to the current status list of the person to be revisited.

[0181] Obesity classification:

[0182] If BMI ≥ 32.5, it is judged as severe obesity, the total score is +3, and 'severe obesity' is added to the current status list of the person to be revisited.

[0183] If the BMI is 28-32.4, it is considered obese, the total score is +2, and "obesity" is added to the current status list of the person to be revisited.

[0184] If the BMI is 24-27.9, the individual is considered overweight, the total score is increased by 1, and "overweight" is added to the current status list of the person to be revisited.

[0185] Blood sugar and blood pressure classification:

[0186] If the fasting blood sugar is 5.6-6.9 or the random blood sugar is 7.8-11.0 (no history of diabetes, etc.), then early diabetes is suspected: total score +3, and "suspected early diabetes" will be added to the current status list of the person to be revisited.

[0187] If the fasting blood sugar is 7-9.9 or the random blood sugar is 11.1-13.8, it is judged as mild to moderate hyperglycemia: the total score is increased by 2, and "mild to moderate hyperglycemia" is added to the current status list of the person to be revisited.

[0188] If the fasting blood sugar is 10-13.8 or the random blood sugar is 13.9-16.6, it is marked hyperglycemia: total score +3, and "marked hyperglycemia" will be added to the current status list of the person to be revisited.

[0189] If the blood pressure is 140 / 90-159 / 99, it is mild hypertension: the total score is +2, and "mild hypertension" is added to the current status list of the person to be revisited.

[0190] If the blood pressure is 160 / 100-179 / 109, it is moderate hypertension: the total score is +3, and "moderate hypertension" is added to the current status list of the person to be revisited.

[0191] Step 13: Summarize the total of the return visit urgency values ​​accumulated from steps 9 to 12 and the status labels in the status list of the persons to be return visited, and generate an evaluation result including the return visit urgency value and the status list of the persons to be return visited.

[0192] Summarize scores and status: This step concludes the final calculation. All scores and labels have been accumulated in the previous steps. Output: The final "sum of urgency values ​​for follow-up visits" and "list of current status of people awaiting follow-up visits" are output by the algorithm.

[0193] In summary, steps 7-13 generate a "Follow-up Urgency Value" and a "List of Patients Awaiting Follow-up" label by comprehensively analyzing multiple dimensions of patient data, including blood sugar, blood pressure, BMI, medication use, and medical history. A higher Follow-up Urgency Value indicates a greater health risk for the patient, a more urgent situation, and a higher priority for the medical team to arrange a follow-up visit and intervention. For example, a Follow-up Urgency Value of 10 indicates high priority, a Follow-up Urgency Value of 20 indicates very high priority, and a Follow-up Urgency Value of 30 indicates urgent.

[0194] The current status label intuitively describes the current core issues of the person to be revisited (such as "newly discovered diabetes", "uncontrolled hypertension", etc.), providing a clear communication focus for the revisit work.

[0195] In the implementation process, after the medical staff logs in the system, the emergency follow-up task is pushed to the work terminal in priority. In the telephone follow-up interface, the patient's basic information, core test data (such as the random blood glucose of XX is 11.9 mmol / L, blood pressure is 180 / 114 mmHg), and "very high blood pressure" "newly found diabetes" and other status labels can be quickly browsed, combined with the follow-up suggestions automatically generated by the interface (such as going to the health service center in a certain district for treatment), and the "dial phone" is clicked to initiate communication.

[0196] In the call, the medical staff interprets the health risks around the test data and risk labels, inquires about the symptoms, medication history and other information, and temporarily records the remarks; after the end, according to the patient's feedback, select the corresponding option in the "follow-up result" area - if agree to the hospital, the system automatically assigns a doctor; if refuse, not connected or consider, the task is queued according to the rules.

[0197] After confirming the result, click "complete follow-up, access the next one", the system saves the record and jumps to the next task, realizes the efficient flow from risk assessment to follow-up closed loop, and guarantees that the health risks of high-risk patients are tracked and intervened in time.

[0198] Example two provides a chronic disease screening follow-up system, which applies the chronic disease screening follow-up method of example one, as shown in Figure 4 , which comprises:

[0199] A screening file construction module, which is configured to obtain the basic information of the to-be-screened personnel, and construct a screening file based on the basic information.

[0200] A screening voucher generation module, which is configured to generate a screening voucher containing the identity of the to-be-screened personnel based on the screening file; the identity and the screening file form a data binding relationship.

[0201] A data collection and scheduling module, which is configured to set multiple data collection posts to run in parallel, and the to-be-screened personnel selects any data collection post by holding the screening voucher.

[0202] A multi-dimensional data entry module, the data collection post associates the screening file by scanning the identity, and the multi-dimensional data entry module is configured to enter the multi-dimensional screening data of the to-be-screened personnel in real time and synchronize to the central database, forming an associated data set.

[0203] A risk score calculation module, when the associated data set contains a preset basic data item, the risk score calculation module is configured to call a risk score model to calculate the chronic disease risk score of the to-be-screened personnel through the preset basic data item.

[0204] A differentiated detection strategy generation module is configured to compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

[0205] In one possible implementation, Figure 5 It also includes: a return visit initialization module, which is configured to determine whether the return visit urgency value has been generated for the person to be return visited. If not, a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be return visited are created.

[0206] The data extraction module is configured to obtain the test result data obtained by the differentiated detection strategy of the person to be revisited, and extract blood sugar, blood pressure, BMI, medication status and previous medical history from the test result data and the associated data set.

[0207] The emergency status assessment module is configured to perform emergency status judgment on blood sugar and blood pressure data. If the preset numerical range of extremely high blood sugar, extremely high blood pressure or hypoglycemia is met, a corresponding fixed score is accumulated and an emergency status label is added to the current status list of the persons to be revisited.

[0208] The new / out-of-control assessment module is configured to differentiate between newly discovered chronic diseases and uncontrolled chronic diseases based on whether there is a previous medical history, targeting abnormal blood sugar or substandard blood pressure, and accumulate different preset scores and add the new / out-of-control status label to the current status list of the people to be revisited.

[0209] The treatment compliance assessment module is configured to determine that a patient with known diabetes or hypertension is in an untreated state and accumulate a preset score if no medication record is collected, and at the same time add a treatment compliance status label to the current status list of the person to be revisited.

[0210] The risk factor assessment module is configured to perform high-risk diabetes, obesity grading, and blood sugar and blood pressure grading assessments based on the diabetes risk score, BMI value range, and blood sugar and blood pressure grading, accumulate scores according to different risk levels, and add risk factor status labels to the current status list of the people to be revisited.

[0211] The evaluation result generation module is configured to summarize the total of the return visit urgency values ​​accumulated by each evaluation module and the status labels in the current status list of the personnel to be returned, and generate an evaluation result including the return visit urgency value and the current status list of the personnel to be returned.

[0212] The above-described embodiments merely represent specific implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A chronic disease screening and follow-up method, characterized in that: The following steps are involved: Step 1: Obtain basic information of the person to be screened and construct a screening file based on the basic information; Step 2: generating a screening voucher containing the identity identifier of the person to be screened based on the screening file; the identity identifier and the screening file form a data binding relationship; Step 3: Set up multiple data collection posts to run in parallel, and the screening personnel can choose to access any data collection post with the screening credentials; Step 4: The data collection post scans the identification and associated screening files, enters the multi-dimensional screening data of the person to be screened in real time, and synchronizes it to the central database to form a related data set; Step 5: When the associated data set contains preset basic data items, the risk scoring model is called to calculate the chronic disease risk score of the person to be screened based on the preset basic data items; Step 6: Compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

2. A chronic disease screening and follow-up method according to claim 1, characterized in that: The step 1 includes the following contents: Parsing resident ID card information through optical character recognition technology of the terminal device to obtain identity data in the basic information; Automatically filling the identity data into a preset electronic form, performing an integrity check on the preset electronic form to identify missing information fields; If the verification passes, it is confirmed as the screening file; If the verification fails, additional prompt information will be generated and displayed on the input interface; The manually entered missing information is received through the entry interface and verification is re-executed.

3. A chronic disease screening and follow-up method according to claim 1, characterized in that: The step 2 includes the following contents: Based on the unique identification field in the screening file, generating a corresponding QR code image; Combining the QR code image with a preset voucher template to generate an electronic screening voucher; The electronic screening certificate is output in the form of a physical card through a printing device, or the electronic screening certificate is displayed through a mobile terminal.

4. A chronic disease screening and follow-up method according to claim 1, characterized in that: The step 3 includes the following contents: Divide the data collection area into independent areas and set up multiple parallel data collection posts, including basic measurement posts, diabetes questionnaire posts, hypertension posts, blood sugar measurement posts, and other specialized disease questionnaire posts; The candidates to be screened can choose any collection post to collect data according to their own wishes. There is no restriction on the collection order of each collection post.

5. A chronic disease screening and follow-up method according to claim 4, characterized in that: The step 4 includes the following contents: The data collection post obtains the screening file index by scanning the identity identifier and establishes a data channel with the central database; Based on the type of data collection position, call the corresponding preset data collection form, Enter the multi-dimensional screening data of the people to be screened through the preset data collection form; The multi-dimensional screening data is synchronized to the central database in real time through the data channel and associated with the corresponding screening files to form an associated data set.

6. A chronic disease screening and follow-up method according to claim 5, characterized in that: The step 5 includes the following contents: Performing integrity check on the associated data set to determine whether it contains preset basic data items; the basic data items include age, gender, systolic blood pressure, weight, height, waist circumference, and whether there is a family history of diabetes; When the integrity check passes, extracting the basic data items from the associated data set and inputting them into a preset risk scoring model; wherein the risk scoring model includes a diabetes risk scoring sub-model; The BMI value of the person to be screened is calculated based on the weight and height data, and the diabetes risk scoring sub-model assigns points to the basic data items and the BMI value to output a diabetes risk score.

7. A chronic disease screening and follow-up method according to claim 6, characterized in that: The step 6 includes the following contents: The diabetes risk score is compared with a preset risk threshold. If the diabetes risk score is greater than or equal to the risk threshold, an invasive testing recommendation is generated; if the diabetes risk score is less than the risk threshold, conventional health advice is generated and an autonomous testing option is provided; wherein, the invasive testing recommendation includes a blood glucose testing recommendation.

8. A chronic disease screening and follow-up method according to claim 7, characterized in that: The step 6 and the following steps are also included: Step 7: Determine whether a return visit urgency value has been generated for the person to be returned. If no return visit urgency value has been generated, create a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be returned. Step 8: Obtain the test result data of the person to be revisited obtained through the differentiated testing strategy; extract the blood sugar, blood pressure, BMI, medication status and previous medical history of the person to be revisited from the test result data of the person to be revisited and the associated data set of the person to be revisited; Step 9: Perform emergency status determination on the blood sugar and blood pressure data. If they meet the preset value ranges of extremely high blood sugar, extremely high blood pressure, or low blood sugar, a corresponding fixed score is accumulated and an emergency status tag is added to the current status list of the person to be revisited. Step 10: For abnormal blood sugar or substandard blood pressure, based on whether there is a previous medical history, distinguish between newly discovered chronic diseases and uncontrolled chronic diseases, accumulate different preset scores for each, and add the new / out-of-control status label to the current status list of the person to be revisited; Step 11: For patients with known diabetes or hypertension, if there is no medication record, they are determined to be in an untreated state and a preset score is accumulated. At the same time, a treatment compliance status label is added to the status list of patients to be revisited; Step 12: Based on the diabetes risk score, BMI value range, blood sugar and blood pressure classification, respectively, perform diabetes high risk, obesity classification, and blood sugar and blood pressure classification assessments, accumulate scores according to different risk levels, and add risk factor status labels to the current status list of the persons to be revisited; Step 13: Summarize the total urgency values ​​of the return visits accumulated from steps 9 to 12 and the status labels in the status list of the persons to be return visited, and generate an evaluation result including the total urgency values ​​of the return visits and the status list of the persons to be return visited.

9. A chronic disease screening and follow-up system, characterized in that: A method for screening and following up on a chronic disease according to any one of claims 1 to 8 is applied, comprising: a screening profile building module configured to obtain basic information of the person to be screened and to build a screening profile based on the basic information; A screening credential generation module, wherein the screening credential generation module is configured to generate a screening credential containing an identity identifier of the person to be screened based on the screening file; the identity identifier forms a data binding relationship with the screening file; A data collection and scheduling module is configured to set up multiple data collection posts to run in parallel, and the person to be screened can choose to access any data collection post by holding the screening certificate; A multi-dimensional data entry module, wherein the data collection post associates the screening file by scanning the identity identifier, and the multi-dimensional data entry module is configured to enter the multi-dimensional screening data of the person to be screened in real time and synchronize it to the central database to form a related data set; A risk score calculation module, when the associated data set includes preset basic data items, the risk score calculation module is configured to call a risk score model and calculate the chronic disease risk score of the person to be screened based on the preset basic data items; A differentiated detection strategy generation module is configured to compare the chronic disease risk score with a preset threshold to obtain a comparison result, and generate a differentiated detection strategy based on the comparison result.

10. The chronic disease screening and follow-up system according to claim 9, characterized in that: Also includes: A return visit initialization module is configured to determine whether a return visit urgency value has been generated for the person to be returned. If not, a return visit urgency value sum with an initial value of 0 and an empty current status list of the person to be returned is created. A data extraction module is configured to obtain the test result data obtained by the differentiated testing strategy of the person to be revisited, and extract blood sugar, blood pressure, BMI, medication status and previous medical history from the test result data and the associated data set; An emergency status assessment module is configured to perform an emergency status assessment on blood sugar and blood pressure data. If the blood sugar and blood pressure data meet the preset value ranges of extremely high blood sugar, extremely high blood pressure, or low blood sugar, a fixed score is accumulated accordingly and an emergency status tag is added to the current status list of the person to be revisited; A new / out-of-control assessment module is configured to differentiate between newly discovered chronic diseases and uncontrolled chronic diseases based on whether there is a history of abnormal blood sugar or substandard blood pressure, accumulate different preset scores for each condition, and add a new / out-of-control status label to the current status list of the person to be revisited; A treatment compliance assessment module configured to determine that a patient with known diabetes or hypertension is in an untreated state if no medication record is collected, accumulate a preset score, and add a treatment compliance status tag to the current status list of patients to be revisited; A risk factor assessment module is configured to perform diabetes high risk, obesity classification, and blood sugar and blood pressure classification assessments based on the diabetes risk score, BMI value range, and blood sugar and blood pressure classification, accumulate scores according to different risk levels, and add risk factor status labels to the current status list of the persons to be revisited; The evaluation result generation module is configured to summarize the total return visit urgency values ​​accumulated by each evaluation module and the status labels in the current status list of the persons to be returned, and generate an evaluation result including the total return visit urgency values ​​and the current status list of the persons to be returned.

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