Intelligent follow-up method, device, computer equipment and storage medium for clinical trial subjects

Abstract

The application discloses a clinical test subject intelligent follow-up method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a clinical test scheme; generating a subject follow-up table according to the clinical test scheme; sending and updating the subject follow-up table according to the clinical test scheme; and detecting and warning the updated subject follow-up table. The method of the application can optimize the research process, reduce the operation cost, improve the clinical research quality, enhance the internal communication efficiency, track the test progress in real time, quickly identify problems, and thus improve the overall efficiency and quality.

Description

TECHNICAL FIELD

[0001] The present application relates to artificial intelligence, more particularly to a clinical trial subject intelligent follow-up method and device, a computer device and a storage medium. BACKGROUND

[0002] With the deepening of informationization in clinical research, the traditional follow-up method is mainly the self-follow-up method, and the information sharing between different centers is insufficient, which leads to the dispersion of patient data in different systems and causes difficulties in integration and analysis. This data island phenomenon limits the effectiveness and comprehensiveness of clinical research. The traditional manual recording and telephone follow-up method not only consumes time but also is prone to errors, and it is difficult to support large-scale follow-up requirements. This inefficient follow-up method affects the timeliness and accuracy of the research. Due to the single existing follow-up method, the lack of individualization and interactivity, the participation and satisfaction of patients are generally low. This not only affects the quality of patient feedback, but also reduces the reliability of research results. With the increase of follow-up data, it is crucial to protect patient privacy and data security. Advanced encryption technology and security protocols must be used to prevent data leakage and unauthorized access to ensure the security of patient information.

[0003] Therefore, the traditional follow-up method has been unable to meet the growing demand and the expectations of patients for high-quality services.

[0004] Therefore, it is necessary to design a new method to optimize the research process, reduce operating costs, and improve the quality of clinical research, enhance internal communication efficiency, track the progress of the trial in real time, quickly identify problems, and thus improve overall efficiency and quality. SUMMARY

[0005] The present application aims to overcome the defects of the prior art and provide a clinical trial subject intelligent follow-up method, device, computer device and storage medium.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a clinical trial subject intelligent follow-up method, comprising:

[0007] obtaining a clinical trial plan;

[0008] generating a subject follow-up table according to the clinical trial plan;

[0009] sending and updating the subject follow-up table according to the clinical trial plan;

[0010] detecting and warning the updated subject follow-up table.

[0011] The further technical solution is that the subject follow-up table is generated according to the clinical trial plan, comprising:

[0012] Setting of follow-up type for the clinical trial scheme;

[0013] Anchoring a baseline for the set clinical trial scheme and generating a subject follow-up table.

[0014] Further technical solutions thereof are that the follow-up type comprises dynamic follow-up and static follow-up, and when the set follow-up type is dynamic follow-up, a cycle type, a number of times, a dependency relationship between items and an over-window range are specified.

[0015] Further technical solutions thereof are that the anchoring a baseline for the set clinical trial scheme and generating a subject follow-up table comprises:

[0016] Setting a baseline and a corresponding follow-up label for the set clinical trial scheme;

[0017] Replacing numbers with a specified format and performing calculation according to preset rules to obtain a follow-up item name;

[0018] Calculating a planned follow-up date;

[0019] Generating a subject follow-up table according to the baseline, the follow-up label, the follow-up item name and the planned follow-up date.

[0020] Further technical solutions thereof are that the calculating a planned follow-up date comprises:

[0021] Calculating a planned follow-up date according to pre-enrollment and post-enrollment follow-up of the clinical trial scheme.

[0022] Further technical solutions thereof are that the calculating a planned follow-up date according to pre-enrollment and post-enrollment follow-up of the clinical trial scheme comprises:

[0023] Excluding a group without an enrollment label and a hidden follow-up item;

[0024] Expanding a number of times of dynamic follow-up and merging with static follow-up;

[0025] Calculating a planned follow-up date according to a date formula type.

[0026] Further technical solutions thereof are that the detecting and warning of the updated subject follow-up table comprises:

[0027] Obtaining all follow-up item names in the updated subject follow-up table;

[0028] Traversing each follow-up item name to screen a detection item in a usable state from a data dictionary;

[0029] Matching a detection item for each follow-up item name in the data dictionary, and taking a first item when multiple items are matched;

[0030] Generate a detection record and assign a value, wherein the detection record includes a detection name, a standard name, a standard department, and a prompt content;

[0031] According to the detection record, a warning is given.

[0032] The application also provides a clinical trial subject intelligent follow-up device, comprising:

[0033] A scheme acquisition unit is configured to acquire a clinical trial scheme.

[0034] A follow-up table generation unit is configured to generate a subject follow-up table according to the clinical trial scheme.

[0035] A sending and updating unit is configured to send and update the subject follow-up table according to the clinical trial scheme.

[0036] A detection and warning unit is configured to detect and warn the updated subject follow-up table.

[0037] The application also provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0038] The application also provides a storage medium, which stores a computer program, and the computer program is executed by the processor to implement the above method.

[0039] Compared with the prior art, the application has the beneficial effects that: the application generates and sends a subject follow-up table according to a clinical trial scheme, and performs corresponding updating. Then, the updated follow-up table is detected and warned to ensure the accuracy and timeliness of data, optimize the research process, reduce the operation cost, improve the clinical research quality, enhance the internal communication efficiency, track the trial progress in real time, quickly identify problems, and thus improve the overall efficiency and quality.

[0040] The application will be further described below in combination with the drawings and specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 The application provides a clinical trial subject intelligent follow-up method, and a schematic diagram of an application scenario of the clinical trial subject intelligent follow-up method is shown in the figure.

[0043] Figure 2A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0044] Figure 3 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0045] Figure 4 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0046] Figure 5 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0047] Figure 6 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0048] Figure 7 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1.

[0049] Figure 8 A flowchart of an intelligent follow-up method for a clinical trial subject provided by an embodiment of the present application is shown in FIG. 1. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0051] It should be understood that, when used in the specification and the appended claims, the terms “comprise” and “include” indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052] It should also be understood that the terms used in the present application specification are only for the purpose of describing particular embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms “a”, “an” and “the” are intended to include the plural forms as well.

[0053] It should be further understood that the term “and / or” used in the present application specification and the appended claims means one or more of the associated listed items as well as all possible combinations of the items, and includes the combinations.

[0054] Referring to Figure 1 and Figure 2 , Figure 1 The application scenario diagram of the intelligent follow-up method for clinical trial subjects provided by the embodiments of the present application. Figure 2 The schematic flowchart of the intelligent follow-up method for clinical trial subjects provided by the embodiments of the present application. The intelligent follow-up method for clinical trial subjects is applied in a server. The server interacts with the terminal to improve the overall efficiency and quality of the research through systematic process optimization. First, a subject follow-up table is generated based on the clinical trial plan, realizing standardized management and reducing the error rate of manual operation. Second, the setting of dynamic and static follow-up types makes the follow-up process more flexible and can adapt to different research needs, further saving operating costs. Third, through baseline anchoring and accurate calculation of the planned follow-up date, the timeliness of follow-up is ensured and the reliability of data is improved. The updated follow-up table can identify potential problems in real time through comprehensive detection and early warning, ensuring the smooth progress of the trial. This method also promotes internal communication, enabling team members to quickly access key information and improve collaboration efficiency. In summary, intelligent follow-up management significantly optimizes the research process and improves the overall quality of clinical research.

[0055] Figure 2 The flowchart of the intelligent follow-up method for clinical trial subjects provided by the embodiments of the present application. As shown in Figure 2 , the method comprises the following steps S110 to S140.

[0056] S110, acquiring a clinical trial plan.

[0057] In this embodiment, acquiring a clinical trial plan is the first crucial step in clinical research, involving collecting and organizing specific trial design, objectives, methods, subject criteria, data collection processes, and expected results from relevant literature, institutions, or databases to form a clinical trial plan.

[0058] S120, generating a subject follow-up table according to the clinical trial plan.

[0059] In this embodiment, the subject follow-up table is a document used in clinical trials to record and track information and progress of subjects during the research process. This document not only helps researchers systematically collect data, but also ensures the scientificity and effectiveness of the research.

[0060] Specifically, the subject follow-up table is a tool used in clinical trials to record the health status, treatment response, side effects, and other relevant data of each subject during the follow-up period. It usually contains the basic information of the subject, follow-up time, evaluation indicators and results, helping the research team to monitor the safety and effectiveness of the subject

[0061] In an embodiment, please refer to Figure 3 The step S120 described above can include steps S131-S132.

[0062] S131, setting the follow-up type for the clinical trial protocol.

[0063] In the present embodiment, the follow-up type includes dynamic follow-up and static follow-up. When the set follow-up type is dynamic follow-up, the cycle type, number of times, dependency between items, and window range are specified.

[0064] Specifically, the follow-up type setting includes:

[0065] Dynamic follow-up: specifying follow-up during the study according to the status of the subjects or the progress of the study, and flexibly adjusting the follow-up time points.

[0066] The cycle type (e.g., weekly, monthly), number of times (e.g., a total of 5 follow-ups), and dependency between items (e.g., the results of a follow-up affect the time arrangement of subsequent follow-ups) need to be set.

[0067] The window range (e.g., the allowed follow-up time deviation) also needs to be determined.

[0068] Static follow-up: periodic follow-up according to a predetermined schedule, usually fixed time nodes for evaluation (e.g., each follow-up is one month after treatment).

[0069] S132, anchoring the baseline of the set clinical trial protocol and generating the subject follow-up table.

[0070] In an embodiment, please refer to Figure 5 The step S132 described above can include steps S1321-S1324.

[0071] S1321, setting the baseline and corresponding follow-up labels for the set clinical trial protocol.

[0072] In the present embodiment, the baseline refers to the status of the subjects before the intervention begins, usually including initial health status, laboratory test results, etc. This data is used for comparison of subsequent follow-up results.

[0073] Determine the label for each follow-up time point (e.g., 1st follow-up, 2nd follow-up) to facilitate identification and recording.

[0074] Ensure that the baseline data and follow-up labels are accurate and clear to facilitate subsequent data collection and analysis.

[0075] S1322, replace the numbers with specified formats and perform calculations according to the preset rules to obtain the follow-up item name.

[0076] In this embodiment, preset rules and formats are used to convert the numbers that need to be recorded (such as follow-up intervals, times, etc.) into readable follow-up item names (such as "first follow-up", "second follow-up", etc.) to facilitate understanding and recording.

[0077] Specifically, the rules for generating dynamic follow-up item names are as follows:

[0078] {} contains only sn: single-point loop, sn dynamically replaces the self-incrementing number;

[0079] {} contains Integer.valueOf(sn): a single-point loop, sn dynamically replaces the self-incrementing number and participates in the calculation;

[0080] {} contains sn1, sn2, sn3...: group loop, sn dynamically replaces the self-incrementing number;

[0081] It should be noted that in the same plan, the follow-ups containing sn1 are combined into one group, the follow-ups containing sn2 are combined into one group, and so on.

[0082] For example: Cycle ${Integer.valueOf(sn1)*2-1} D1;

[0083] Cycle ${Integer.valueOf(sn1)*2-1} D15;

[0084] Cycle ${Integer.valueOf(sn1)*2} D1;

[0085] Cycle ${Integer.valueOf(sn1)*2} D15;

[0086] Sn1 is uniformly set to 3;

[0087] Cycle 1 D1;

[0088] Cycle 1 D15;

[0089] Cycle 2 D1;

[0090] Cycle 2 D15;

[0091] Cycle 3 D1;

[0092] Cycle 3 D15;

[0093] Cycle 4 D1;

[0094] Cycle 4 D15;

[0095] Cycle 5 D1;

[0096] Cycle 5 D15;

[0097] Cycle 6 D1;

[0098] Cycle 6 D15.

[0099] S1323. Calculate the planned follow-up date.

[0100] In this embodiment, the planned follow-up date refers to the planned time of the follow-up.

[0101] Specifically, follow-up calculations before and after enrollment are performed according to the clinical trial protocol to obtain planned follow-up dates. Based on the clinical trial protocol, the planned follow-up dates for each subject are calculated. These dates will be used for subsequent follow-up data collection and management.

[0102] In one embodiment, see Figure 5 , the above-mentioned step S1323 may include steps S13231~S13233.

[0103] S13231. Exclude groups without inclusion labels and hidden follow-up items.

[0104] In this example, before calculating the follow-up date, we first need to ensure that all data are related to enrolled subjects. This step involves filtering the dataset to remove groups without an enrollment label (i.e., subjects who did not formally enter the trial).

[0105] Identify and remove these invalid groupings through database queries or data screening tools to ensure that subsequent calculations are based only on valid subject data.

[0106] It is also necessary to exclude follow-up items that are set as "hidden" in the trial. These items are usually required by the trial design and may not be followed up in all participants.

[0107] Make sure that the follow-up table contains only the required, visible follow-up items to simplify subsequent calculations.

[0108] S13232. The number of dynamic follow-up visits was carried out and combined with the static follow-up visits.

[0109] In this embodiment, the number of follow-up visits is determined for each subject based on their dynamic follow-up settings. For example, if a subject is scheduled for monthly follow-up and requires three follow-up visits, three follow-up records will be generated. This process may require traversing the subject's follow-up plan to determine the specific time nodes for each follow-up visit.

[0110] Merge the time points generated by dynamic follow-up with the static follow-up time points. Static follow-up refers to follow-up at predetermined time nodes, usually fixed in time. The merging process needs to ensure the rationality of time, avoid duplication or conflict. For example, if the dynamic follow-up is scheduled before the static follow-up, the order needs to be reasonably arranged to ensure the accuracy of the records.

[0111] S13233, calculate the planned follow-up date according to the date formula type.

[0112] Within each group, the system determines whether the follow-up can calculate the planned date according to the date formula type:

[0113] Preset symbol (PPD, PAD): Determine whether the planned date of the last follow-up in the group can be obtained.

[0114] Yes: trial is successful;

[0115] No: trial failed;

[0116] Preset symbol (FPD, FAD): Determine whether the planned date of the first follow-up in the group can be obtained.

[0117] Yes: trial is successful;

[0118] No: trial failed;

[0119] Preset symbol (SCR): directly determine as trial failed.

[0120] Preset symbol (ENR): directly determine as trial successful.

[0121] Fixed formula: in the form of $(Visit Scheme ID).actualDate(+|-)*.days.

[0122] Substitute the corresponding static or dynamic follow-up item to determine:

[0123] Static follow-up: use the trial result of the corresponding follow-up item.

[0124] Dynamic follow-up: use the trial result of the last follow-up item.

[0125] Note: Visit Scheme ID must be the same group follow-up item, otherwise it is determined as trial failed.

[0126] Directly determine as trial failed: all cases not belonging to the first five categories are classified as "category 5".

[0127] Through the above process, the system can efficiently determine the trial result of each follow-up item to ensure the accuracy of the follow-up plan.

[0128] Specifically, a pre-defined date formula is used to calculate the specific date of each follow-up visit. The formula usually includes the baseline date plus the corresponding time interval (such as day, week, month).

[0129] For example, if the baseline date is January 1, 2024, and the first follow-up visit is set one month after the baseline, the calculation formula is: January 1, 2024 + 1 month = February 1, 2024.

[0130] According to the formula type, the specific dates for each dynamic and static follow-up time point are calculated one by one, and the rationality and feasibility of these dates are ensured.

[0131] In this process, programming tools or database functions can be used to automate the calculation process to improve efficiency and accuracy.

[0132] Through these detailed steps, the planned follow-up date for each subject can be accurately calculated, laying the foundation for subsequent follow-up work. These dates will help researchers systematically track the subject's health status and research progress, ensuring the smooth progress of the clinical trial.

[0133] S1324. Generate a subject follow-up table based on the baseline, follow-up label, follow-up item name, and planned follow-up date.

[0134] In this embodiment, all of the above information is summarized to form a complete subject follow-up table. This table will include basic information of each subject, the specific date of each follow-up, the follow-up content and evaluation results, etc., which will facilitate the research team to conduct subsequent data collection and analysis.

[0135] Through this series of steps, a systematic follow-up table for subjects can be generated, providing solid data support for clinical trials. This not only improves the standardization of research, but also provides important guarantees for the safety of subjects and the evaluation of treatment effects.

[0136] This embodiment uses an engine to implement the above-mentioned respondent follow-up table. Specifically, an in-depth observation of more than 4,000 clinical trial projects is conducted, and advanced filtering algorithms, such as discrete Fourier transform, are used to identify and extract stable periodic patterns in the data. Clinical trials can generally be divided into fixed follow-up and periodic follow-up, and each follow-up has a sequence. Therefore, if the first follow-up is used as the baseline, subsequent follow-ups are carried out on this basis. Therefore, the following formula is summarized.

[0137] The formula is as follows:

[0138] $(n).plannedDate: the planned follow-up date for the specified item in the follow-up plan;

[0139] $(n).actualDate: specifies the actual visit date of the follow-up record;

[0140] PPD: planned visit date of the last visit;

[0141] PAD: actual visit date of the last visit;

[0142] FPD: planned visit date of the first visit of the subject;

[0143] FAD: actual visit date of the first visit of the subject;

[0144] SCR: screening date of the subject;

[0145] ENR: enrollment date of the subject;

[0146] Actual formula examples:

[0147] $(128).actualDate+7.days;

[0148] $(256).plannedDate+10.days;

[0149] PAD+21.days;

[0150] FAD+1.week;

[0151] These rules are converted into formulas and embedded in the "configure trial schema", while also reserving the setting of the window period.

[0152] At the same time, this example supports the anchoring of follow-up and subject milestones (such as anchoring a follow-up to enrollment, that is, once the subject participates in the follow-up, it becomes an enrolled state, and the system automatically changes the subject's status to enrolled), making the setting of landmark milestones such as screening, enrollment, and completion of research automated.

[0153] By recognizing periodic patterns, the system can better understand the trends of trial data and help researchers make more accurate decisions. According to the extracted rules, the trial schema can be configured more scientifically, improving the success rate of the trial.

[0154] The system can automatically draw a detailed follow-up schedule for each project, based on the information of the subjects responsible for the clinical coordinator (directly obtained from the subject database), to generate personalized follow-up tables.

[0155] Each coordinator can generate a unique follow-up plan based on the subjects they are responsible for, ensuring the relevance and practicality of the information. Automated generation of schedules reduces the time for manual arrangement and improves work efficiency.

[0156] S130, sending and updating the subject follow-up table according to the clinical trial protocol.

[0157] In this embodiment, a schedule reminder is automatically sent to the terminal device (including mobile devices and computer terminals) of the clinical coordinator on the scheduled date. The coordinator can check the system schedule to understand the subject follow-up arrangement and specific detection items that need to be performed daily.

[0158] The coordinator can obtain the latest follow-up arrangement at any time and any place, which includes follow-up date, department, detection item, sampling standard and quantity, and provides the corresponding department and recommended value for each detection item (the system will automatically update these information based on the overall project data by identifying the frequency of repetition and hospital characteristics), improving the flexibility, timeliness and accuracy of work. The automatic reminder function reduces the omission caused by human error, ensuring that each subject can complete the follow-up on time and according to the requirements of the protocol.

[0159] After completing the follow-up, the coordinator only needs to update and enter the follow-up date and detection results and other key data on the system terminal. The system will automatically identify the label of each follow-up event, accurately update the status of the subject, and synchronize the information to the entire system.

[0160] Automatic label recognition and status update reduce human input errors and ensure data reliability. Instant information synchronization enables each business module to obtain the latest data, improving the collaboration efficiency between teams.

[0161] The system greatly improves the efficiency of clinical trials and the accuracy of data management through advanced algorithms and automated management. Its personalized follow-up arrangement, timely reminder mechanism and real-time data update provide strong support for clinical coordinators, ensuring that each trial link can be accurately followed up and monitored, thereby promoting the success and innovation of clinical research.

[0162] S140, detecting and warning the updated subject follow-up table.

[0163] In an embodiment, referring to Figure 6 The above step S140 can include steps S141-S145.

[0164] S141, obtaining all follow-up item names in the updated subject follow-up table.

[0165] In this embodiment, all Page Names are extracted from the subject follow-up protocol and de-duplicated to ensure that each Page Name is only processed once in the subsequent steps.

[0166] Extracting the latest follow-up item names ensures that clinical coordinators are aware of the examination items that currently require attention and avoid omissions. Updates to the follow-up table reflect changes in patient status, allowing subsequent treatment to be based on the latest information.

[0167] S142: traverse each of the follow-up item names and filter detection items of available status from the data dictionary.

[0168] In this embodiment, a loop is performed on each acquired Page Name to analyze and match related detection items one by one. Detection items with a status of "available" are filtered out from the data dictionary to ensure that subsequent matching items are valid.

[0169] By automatically filtering the available status of the test items, the time and effort of manual query is reduced. Selecting only the available test items can avoid the error records caused by inappropriate status.

[0170] S143. Match detection items in the data dictionary for each follow-up item name, and take the first item when multiple items are matched.

[0171] In this embodiment, an exhaustive search is performed for each Page Name to find the corresponding abbreviation from the filtered available detection items.

[0172] If a Page Name matches multiple detection items, the first matching item will be selected as the corresponding detection item.

[0173] Automatic matching reduces the decision-making burden on clinical coordinators and improves work efficiency. By setting the selection to only the first matching item, data entry consistency is ensured, reducing data inconsistencies caused by different coordinators selecting different items.

[0174] S144. Generate a test record and assign a value, wherein the test record includes the test name, standard name, standard department and prompt content.

[0175] In this embodiment, the test name is: the Page Name in the subject follow-up plan is assigned as the name of the test record.

[0176] Standard Name: The name given to the matched detection item.

[0177] Standard Department: Add the standard department information of the matched test items to the record.

[0178] Prompt content: Add prompt content for detection items to facilitate understanding and use.

[0179] If no detection item is matched in S144, the system will still generate a detection record, but will only assign a detection name to maintain the integrity of the record.

[0180] In addition, the clinical coordinator adds the subject number in the subject pool of the relevant center according to the actual follow-up, which ensures the data de-identification and privacy protection.

[0181] Through the automated matching of detection items and the generation of records, the time and workload of manual operations are greatly reduced. De-duplication processing and available state screening ensure data consistency and reduce the risk of false matching. Clear steps and standardized record generation processes help improve the management level of clinical trials, ensuring that each link has a basis. The generated detection records include detailed information, facilitating subsequent tracking and auditing, and improving data transparency. De-identification is used when adding subject numbers to ensure the privacy and safety of subjects, in line with ethical requirements.

[0182] In summary, the design of this process not only improves work efficiency, but also enhances data standardization and security, providing a solid guarantee for the smooth progress of clinical research.

[0183] Integrating various relevant information into the detection records facilitates subsequent viewing and management. Standardized record formats help reduce human errors during data entry and improve information accuracy.

[0184] S145, warning according to the detection record.

[0185] By setting key parameters such as target values, contract values, etc., combined with the generated follow-up table and data entered by clinical coordinators, precise early warning of missing follow-ups can be achieved from multiple dimensions such as projects, research centers, and clinical coordinators. This function significantly improves the monitoring and management efficiency of project progress, ensuring that each node and follow-up is completed on time, thereby improving the execution efficiency and quality of the entire project.

[0186] The warning mechanism can timely notify clinical coordinators to pay attention to upcoming follow-ups, ensuring that there is no omission. Through real-time warning, it ensures that patients can perform necessary detection on time, improving the quality of clinical services.

[0187] The above-mentioned intelligent follow-up method for clinical trial subjects generates and sends subject follow-up tables according to clinical trial schemes, and performs corresponding updates. Subsequently, the updated follow-up tables are detected and warned to ensure data accuracy and timeliness, optimize research processes, reduce operating costs, and improve clinical research quality, enhancing internal communication efficiency, real-time tracking of trial progress, and rapid identification of problems, thereby improving overall efficiency and quality.

[0188] Figure 7 is a schematic block diagram of a clinical trial subject intelligent follow-up device 300 provided by an embodiment of the present application. As shown in Figure 7Corresponding to the intelligent follow-up method of the clinical trial subject as shown above, the present application also provides an intelligent follow-up device 300 of the clinical trial subject. The intelligent follow-up device 300 of the clinical trial subject includes units for executing the intelligent follow-up method of the clinical trial subject as described above, and the device can be configured in a server. Specifically, please refer to Figure 7 The intelligent follow-up device 300 of the clinical trial subject includes a scheme acquisition unit 301, a follow-up table generation unit 302, a sending and updating unit 303, and a detection and early warning unit 304.

[0189] The scheme acquisition unit 301 is configured to acquire a clinical trial scheme; the follow-up table generation unit 302 is configured to generate a subject follow-up table according to the clinical trial scheme; the sending and updating unit 303 is configured to send and update the subject follow-up table according to the clinical trial scheme; and the detection and early warning unit 304 is configured to detect and early warn the updated subject follow-up table.

[0190] In an embodiment, the follow-up table generation unit 302 includes:

[0191] A setting subunit is configured to set down the clinical trial scheme and set the follow-up type;

[0192] A generation subunit is configured to anchor the baseline of the set clinical trial scheme and generate the subject follow-up table.

[0193] In an embodiment, the generation subunit includes:

[0194] A baseline label setting module is configured to set the baseline and the corresponding follow-up label of the set clinical trial scheme;

[0195] A name determination module is configured to replace the number with a specified format and perform calculation according to a preset rule to obtain the follow-up item name;

[0196] A date determination module is configured to calculate the planned follow-up date;

[0197] A follow-up table determination module is configured to generate the subject follow-up table according to the baseline, the follow-up label, the follow-up item name, and the planned follow-up date.

[0198] In an embodiment, the date determination module is configured to calculate the follow-up before and after enrollment according to the clinical trial scheme to obtain the planned follow-up date.

[0199] In an embodiment, the date determination module further includes:

[0200] An exclusion sub-module is configured to exclude the grouping without the enrollment label and the hidden follow-up item;

[0201] The merging submodule is used to expand the number of dynamic follow-ups and merge them with the static follow-ups;

[0202] The date calculation submodule is used to calculate the planned follow-up date according to the date formula type.

[0203] In one embodiment, the detection and early warning unit 304 includes:

[0204] The name acquisition subunit is used to obtain the names of all follow-up items in the updated subject follow-up table;

[0205] A screening subunit, configured to traverse each of the follow-up item names and screen the detection items in the available state from the data dictionary;

[0206] The matching subunit is used to match the detection item in the data dictionary for each follow-up item name, and when multiple items are matched, the first item is taken;

[0207] A generation subunit is used to generate and assign values ​​to a test record, wherein the test record includes the test name, standard name, standard department, and prompt content;

[0208] The early warning subunit is used to issue early warnings based on detection records.

[0209] It should be noted that those skilled in the art can clearly understand that the above-mentioned sub-units are used for the specific implementation process of the device and each unit, and reference can be made to the corresponding description in the aforementioned method embodiment. For the convenience and brevity of the description, they will not be repeated here.

[0210] The above subunits can be implemented as a computer program. Figure 8 Runs on the computer device shown.

[0211] See also Figure 8 , Figure 8 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0212] See Figure 8 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0213] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, can enable the processor 502 to execute a method for intelligent follow-up of clinical trial subjects.

[0214] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0215] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an intelligent follow-up method for clinical trial subjects.

[0216] The network interface 505 is used to communicate with other devices through the network. Figure 8 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0217] The processor 502 is configured to execute a computer program 5032 stored in the memory to implement the following steps:

[0218] Obtain a clinical trial protocol; generate a subject follow-up form according to the clinical trial protocol; send and update the subject follow-up form according to the clinical trial protocol; and perform detection and early warning on the updated subject follow-up form.

[0219] In one embodiment, when the processor 502 implements the step of generating a subject follow-up table according to the clinical trial protocol, the processor 502 specifically implements the following steps:

[0220] The clinical trial protocol is decentralized and the follow-up type is set; a baseline is anchored for the set clinical trial protocol, and a subject follow-up table is generated.

[0221] The follow-up type includes dynamic follow-up and static follow-up. When the set follow-up type is dynamic follow-up, the cycle type, number of times, dependencies between items and super-window range are specified.

[0222] In one embodiment, when implementing the steps of anchoring the baseline for the set clinical trial protocol and generating a subject follow-up table, the processor 502 specifically implements the following steps:

[0223] A baseline and a corresponding follow-up label are set for the clinical trial protocol after setting up; numbers are replaced using a specified format and calculated according to preset rules to obtain the name of the follow-up item; the planned follow-up date is calculated; and a subject follow-up table is generated based on the baseline, follow-up label, follow-up item name, and planned follow-up date.

[0224] In an embodiment, the processor 502 implements the following steps when implementing the step of calculating the planned follow-up date according to the clinical trial plan:

[0225] The follow-up before and after enrollment is calculated according to the clinical trial plan to obtain the planned follow-up date.

[0226] In an embodiment, the processor 502 implements the following steps when implementing the step of calculating the planned follow-up date according to the clinical trial plan:

[0227] Excluding groups without enrollment labels and hidden follow-up items; expanding the number of dynamic follow-ups and merging with static follow-ups; calculating the planned follow-up date according to the date formula type.

[0228] In an embodiment, the processor 502 implements the following steps when implementing the step of expanding the number of dynamic follow-ups:

[0229] Obtaining all follow-up item names in the updated subject follow-up table; traversing each of the follow-up item names to filter the detection items with available states from the data dictionary; matching the detection items in the data dictionary for each follow-up item name, and taking the first item when multiple items are matched; generating a detection record and assigning values, wherein the detection record includes the detection name, the standard name, the standard department, and the prompt content; and performing early warning according to the detection record.

[0230] It should be understood that, in the embodiments of the present application, the processor 502 can be a central processing unit (CPU), and the processor 502 can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0231] It can be understood by those skilled in the art that all or part of the processes in the method of the above embodiment can be completed by a computer program instructing related hardware. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the above-mentioned embodiment of the method.

[0232] Therefore, the application further provides a storage medium. The storage medium can be a computer readable storage medium. The storage medium stores a computer program, wherein the computer program is executed by a processor to enable the processor to perform the following steps:

[0233] obtaining a clinical trial scheme; generating a subject follow-up table according to the clinical trial scheme; sending and updating the subject follow-up table according to the clinical trial scheme; detecting and warning the updated subject follow-up table.

[0234] In an embodiment, when the processor executes the computer program to implement the step of generating the subject follow-up table according to the clinical trial scheme, the processor specifically implements the following steps:

[0235] anchoring a baseline to the set clinical trial scheme and generating the subject follow-up table.

[0236] The follow-up types include dynamic follow-up and static follow-up, and when the set follow-up type is dynamic follow-up, a cycle type, a number of times, a dependency relationship between items, and a super window range are specified.

[0237] In an embodiment, when the processor executes the computer program to implement the step of anchoring the baseline to the set clinical trial scheme and generating the subject follow-up table, the processor specifically implements the following steps:

[0238] setting a baseline and a corresponding follow-up label to the set clinical trial scheme; replacing numbers and performing calculation according to a preset rule using a specified format to obtain a follow-up item name; calculating a planned follow-up date; and generating the subject follow-up table according to the baseline, the follow-up label, the follow-up item name, and the planned follow-up date.

[0239] In an embodiment, when the processor executes the computer program to implement the step of calculating the planned follow-up date, the processor specifically implements the following steps:

[0240] calculating the planned follow-up date according to the clinical trial scheme before and after enrollment.

[0241] In an embodiment, when the processor executes the computer program to implement the step of calculating the planned follow-up date according to the clinical trial scheme before and after enrollment, the processor specifically implements the following steps:

[0242] excluding a group without an enrollment label and a hidden follow-up item; expanding a number of times of dynamic follow-up and merging the dynamic follow-up with static follow-up; and calculating the planned follow-up date according to a date formula type.

[0243] In an embodiment, the processor, when executing the computer program to implement the detecting and early warning step of the updated subject follow-up table, specifically implements the following steps:

[0244] All follow-up item names in the updated subject follow-up table are acquired; each follow-up item name is traversed, and a detection item of an available state is screened from a data dictionary; for each follow-up item name, a detection item is matched in the data dictionary, and when multiple items are matched, the first item is taken; a detection record is generated and is assigned a value, wherein the detection record includes a detection name, a standard name, a standard department, and prompt content; and early warning is performed according to the detection record.

[0245] The storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, and various computer readable storage media that can store program codes.

[0246] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software, or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0247] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed.

[0248] The steps in the method embodiments of the present application can be adjusted, combined, and reduced in sequence according to actual needs. The units in the device embodiments of the present application can be combined, divided, and reduced according to actual needs. In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0249] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a storage medium. Based on such an understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application.

[0250] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent follow-up method for clinical trial subjects, characterized in that: include: Obtain clinical trial protocols; Generate a subject follow-up form according to the clinical trial protocol; Send and update the subject follow-up form according to the clinical trial protocol; Conduct testing and early warning on the updated subject follow-up form; Generating a subject follow-up table according to the clinical trial protocol includes: Decentralize the clinical trial protocol and set the follow-up type; Anchor the baseline for the clinical trial protocol after setting up and generate the subject follow-up form; The follow-up type includes dynamic follow-up and static follow-up. When the set follow-up type is dynamic follow-up, the cycle type, number of times, dependencies between items and super-window range are specified; The clinical trial protocol is anchored to the baseline and a subject follow-up table is generated, including: Set the baseline and corresponding follow-up labels for the clinical trial protocol after setting up; According to the preset rules, the numbers are replaced with the specified format and calculated to obtain the name of the follow-up item; Calculate planned follow-up date; Generate a subject follow-up table based on the baseline, follow-up label, follow-up item name, and planned follow-up date; The calculation of the planned follow-up date includes: Calculate the follow-up before and after enrollment according to the clinical trial protocol to obtain the planned follow-up date; The follow-up calculation before and after enrollment according to the clinical trial protocol to obtain the planned follow-up date includes: Groups without inclusion labels and hidden follow-up items were excluded; The number of dynamic follow-up visits was expanded and combined with the static follow-up visits; Calculates the planned follow-up date by date formula type.

2. The intelligent follow-up method for clinical trial subjects according to claim 1, characterized in that: The testing and early warning of the updated subject follow-up table includes: Get the names of all follow-up items in the updated subject follow-up table; Traversing each of the follow-up item names, and filtering detection items of available status from the data dictionary; For each follow-up item name, match the test item in the data dictionary. When multiple items are matched, take the first one. Generate test records and assign values, where the test records include the test name, standard name, standard department, and prompt content; Issue early warning based on test records.

3. An intelligent follow-up device for clinical trial subjects, the device using the intelligent follow-up method for clinical trial subjects according to any one of claims 1 to 2, characterized in that: include: Protocol acquisition unit, used to obtain clinical trial protocols; A follow-up table generating unit, configured to generate a subject follow-up table according to the clinical trial protocol; a sending and updating unit, configured to send and update the subject follow-up form according to the clinical trial protocol; The detection and early warning unit is used to detect and issue early warnings on the updated subject follow-up form.

4. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 2 when executing the computer program.

5. A storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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