Clinical test object intelligent follow-up visit method and device, computer equipment and storage medium

Abstract

The invention discloses an intelligent follow-up visit method and device for clinical test subjects, 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 a subject follow-up table according to the clinical test scheme; and carrying out detection and early warning on the updated subject follow-up table. By implementing the method, the research process can be optimized, the operation cost can be reduced, the clinical research quality can be improved, the internal communication efficiency can be enhanced, the test progress can be tracked in real time, and the problem can be quickly identified, so that the overall efficiency and quality can be improved.

Description

Technical Field

[0001] The present invention relates to artificial intelligence, and more specifically to an intelligent follow-up method, device, computer equipment and storage medium for clinical trial subjects. Background Art

[0002] As clinical research moves towards informatization, the traditional follow-up method is mainly a separate follow-up method. Insufficient information sharing between central institutions has led to patient data being scattered in different systems, making integration and analysis difficult. This data island phenomenon limits the effectiveness and comprehensiveness of clinical research. Traditional manual records and telephone follow-up methods are not only time-consuming and error-prone, but also difficult to support large-scale follow-up needs. This inefficient follow-up method affects the timeliness and accuracy of the research. Due to the single existing follow-up method, lack of personalization and interactivity, patient participation and satisfaction are generally low. This not only affects the quality of patient feedback, but also reduces the reliability of research results. With the increase in the amount of follow-up data, it becomes 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] This shows that traditional follow-up methods can no longer meet the growing demand and patients' expectations 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 trial progress in real time, and quickly identify problems, thereby improving overall efficiency and quality. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide a method, device, computer equipment and storage medium for intelligent follow-up of clinical trial subjects.

[0006] To achieve the above object, the present invention adopts the following technical solution: a method for intelligent follow-up of clinical trial subjects, comprising: Obtain clinical trial protocols; Generate a subject follow-up table according to the clinical trial protocol; Send and update the subject follow-up form according to the clinical trial protocol; Conduct monitoring and early warning on the updated follow-up form of the subjects.

[0007] A further technical solution is: generating a subject follow-up table according to the clinical trial protocol includes: Decentralize the clinical trial protocol and set up the follow-up type; Anchor the baseline for the clinical trial protocol after setting up and generate a subject follow-up form.

[0008] A further technical solution is as follows: 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.

[0009] A further technical solution is: anchoring the baseline of the clinical trial program after setting and generating a subject follow-up table, 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 names of the follow-up items; Calculate the planned follow-up date; A subject follow-up table is generated based on the baseline, follow-up label, follow-up item name, and planned follow-up date.

[0010] A further technical solution is: the calculation of the planned follow-up date includes: Follow-up calculations before and after enrollment were performed according to the clinical trial protocol to obtain the planned follow-up date.

[0011] A further technical solution is: the follow-up calculation before and after enrollment is performed according to the clinical trial protocol to obtain the planned follow-up date, including: 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.

[0012] A further technical solution is: the detection 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 detection 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 test name, standard name, standard department and prompt content; Issue early warning based on test records.

[0013] The present invention also provides an intelligent follow-up device for clinical trial subjects, comprising: Protocol acquisition unit, used to obtain clinical trial protocols; A follow-up table generating unit, used for generating a subject follow-up table according to the clinical trial protocol; A sending and updating unit, used for sending and updating the subject follow-up form according to the clinical trial protocol; The detection and early warning unit is used to detect and warn the updated subject follow-up table.

[0014] The present invention further provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0015] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the present invention generates and sends a subject follow-up form through a clinical trial protocol and updates it accordingly. Subsequently, the updated follow-up form is tested and warned to ensure the accuracy and timeliness of the data, 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, and quickly identify problems, thereby improving overall efficiency and quality.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of an application scenario of the intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process flow of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 3 A schematic diagram of a sub-process of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 4 A schematic diagram of a sub-process of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 5 A schematic diagram of a sub-process of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 6 A schematic diagram of a sub-process of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention; Figure 7 A schematic block diagram of an intelligent follow-up device for clinical trial subjects provided by an embodiment of the present invention; Figure 8 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0022] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0023] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0024] See also Figure 1 and Figure 2 , Figure 1 A schematic diagram of an application scenario of the intelligent follow-up method for clinical trial subjects provided in an embodiment of the present invention. Figure 2A schematic flow chart of an intelligent follow-up method for clinical trial subjects provided in an embodiment of the present invention. The intelligent follow-up method for clinical trial subjects is applied to a server. The server interacts with the terminal for data, and improves the overall efficiency and quality of the research through systematic process optimization. First, a follow-up table for subjects is generated based on the clinical trial protocol, which realizes standardized management and reduces the error rate of manual operation. Secondly, the setting of dynamic and static follow-up types makes the follow-up process more flexible, can adapt to different research needs, and further saves operating costs. Furthermore, the timeliness of follow-up is ensured and the reliability of data is improved through the accurate calculation of baseline anchoring and planned follow-up dates. The updated follow-up table is fully tested and warned, and potential problems can be identified in real time to ensure the smooth progress of the trial. This method also promotes internal communication, and team members can quickly obtain key information and improve collaboration efficiency. In short, intelligent follow-up management significantly optimizes the research process and improves the overall quality of clinical research.

[0025] Figure 2 FIG. 1 is a flow chart of an intelligent follow-up method for clinical trial subjects provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S140.

[0026] S110. Obtain clinical trial plan.

[0027] In this embodiment, obtaining the clinical trial protocol is the first crucial step in clinical research, which involves collecting and organizing specific information such as trial design, objectives, methods, subject standards, data collection process and expected results from relevant literature, institutions or databases to form a clinical trial protocol.

[0028] S120. Generate a subject follow-up table according to the clinical trial protocol.

[0029] In this embodiment, the subject follow-up form is a document used in clinical trials to record and track the information and progress of subjects during the research process. This document not only helps researchers collect data systematically, but also ensures the scientificity and effectiveness of the research.

[0030] Specifically, the subject follow-up form 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. In one embodiment, see Figure 3 , the above-mentioned step S120 may include steps S131~S132.

[0031] S131. Decentralize the clinical trial plan and set up follow-up types.

[0032] In this 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, dependencies between items and super-window range are specified.

[0033] Specifically, the follow-up type settings: Dynamic follow-up: specifies follow-up during the study based on the subject's status or research progress, and flexibly adjusts the follow-up time point.

[0034] It is necessary to set the cycle type (for example, weekly, monthly), number of times (for example, a total of 5 follow-up visits are required), and the dependencies between the items (for example, the results of a certain follow-up visit affect the schedule of subsequent follow-ups).

[0035] The super-window range (e.g., the allowed deviation in follow-up time) also needs to be determined.

[0036] Static follow-up: Regular follow-up according to a predetermined schedule, usually with assessments at fixed time points (e.g., each follow-up is one month after treatment).

[0037] S132. Anchor the baseline for the clinical trial protocol and generate a subject follow-up table.

[0038] In one embodiment, see Figure 5 The above-mentioned step S132 may include steps S1321~S1324.

[0039] S1321. Set a baseline and corresponding follow-up labels for the clinical trial protocol.

[0040] In this embodiment, the baseline refers to the subject's status before the intervention begins, usually including initial health status, laboratory test results, etc. This data is used for comparison of subsequent follow-up results.

[0041] Determine the label for each follow-up time point (e.g., Visit 1, Visit 2) for easy identification and recording.

[0042] Ensure that baseline data and follow-up labels are accurate and clear to facilitate subsequent data collection and analysis.

[0043] S1322. According to preset rules, numbers are replaced using a specified format and calculations are performed to obtain a follow-up item name.

[0044] 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.

[0045] Specifically, the rules for generating the names of dynamic follow-up items and dynamically generating names in a loop are as follows: {} contains only sn: single-point loop, sn dynamically replaces the self-incrementing number; {} contains Integer.valueOf(sn): a single-point loop, sn dynamically replaces the self-incrementing number and participates in the calculation; {} contains sn1, sn2, sn3...: group loop, sn dynamically replaces the self-incrementing number; It should be noted that in the same scheme, the follow-ups containing sn1 are merged into one group, the follow-ups containing sn2 are merged into one group, and so on.

[0046] For example: Cycle ${Integer.valueOf(sn1)*2-1} D1; Cycle ${Integer.valueOf(sn1)*2-1} D15; Cycle ${Integer.valueOf(sn1)*2} D1; Cycle ${Integer.valueOf(sn1)*2} D15; Sn1 is uniformly set to 3; Cycle 1 D1; Cycle 1 D15; Cycle 2 D1; Cycle 2 D15; Cycle 3 D1; Cycle 3 D15; Cycle 4 D1; Cycle 4 D15; Cycle 5 D1; Cycle 5 D15; Cycle 6 D1; Cycle 6 D15.

[0047] S1323. Calculate the planned follow-up date.

[0048] In this embodiment, the planned follow-up date refers to the planned time of the follow-up.

[0049] Specifically, the follow-up calculations before and after enrollment are performed according to the clinical trial protocol to obtain the planned follow-up dates. According to the settings of 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.

[0050] In one embodiment, see Figure 5 , the above-mentioned step S1323 may include steps S13231~S13233.

[0051] S13231. Exclude groups without inclusion labels and hidden follow-up items.

[0052] In this example, before calculating the follow-up date, it is first necessary to ensure that all data are related to the enrolled subjects. This step involves screening the data set and eliminating groups without enrollment labels (i.e., subjects who have not officially entered the trial).

[0053] 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.

[0054] It is also necessary to exclude those 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 subjects.

[0055] Make sure that the follow-up table contains only the required, visible follow-up items to simplify subsequent calculations.

[0056] S13232. The number of dynamic follow-ups is carried out and combined with the static follow-ups.

[0057] In this embodiment, for each subject, the number of follow-up visits is expanded according to the dynamic follow-up settings. For example, if a subject is set to be followed up once a month and needs to be followed up three times, three follow-up records need to be generated. This process may require traversing the follow-up plan of the subject to determine the specific time node of each follow-up visit.

[0058] Merge the time points generated by the above dynamic follow-up with the static follow-up time points. Static follow-up refers to follow-up conducted at a predetermined time node, usually at a fixed time. The merging process needs to ensure the rationality of the time to avoid duplication or conflict. For example, if the dynamic follow-up is scheduled before the static follow-up, the order needs to be arranged reasonably to ensure the accuracy of the record.

[0059] S13233. Calculate the planned follow-up date by date formula type.

[0060] In each group, the system determines whether the follow-up can calculate the planned date according to the date formula type: Preset symbols (PPD, PAD): Determine whether the planned date of the last follow-up visit of the group to which it belongs can be obtained.

[0061] Yes: trial success; No: Trial calculation failed; Preset symbols (FPD, FAD): Determine whether the planned date of the first follow-up of the group to which it belongs can be obtained.

[0062] Yes: trial success; No: Trial calculation failed; Preset symbol (SCR): Directly judged as trial calculation failure.

[0063] Preset symbol (ENR): Directly judged as trial calculation success.

[0064] Fixed formula: The format is $(Visit Scheme ID).actualDate(+|-)*.days.

[0065] Substitute the corresponding static or dynamic follow-up items for judgment: Static follow-up: the calculation results of the corresponding follow-up items shall prevail.

[0066] Dynamic follow-up: The calculation result of the last follow-up item shall prevail.

[0067] Note: The Visit Scheme ID must be a follow-up item in the same group, otherwise it will be considered a trial calculation failure.

[0068] Directly judged as trial calculation failure: All situations that do not belong to the first five categories are classified as "Category 5".

[0069] Through the above process, the system can efficiently determine the trial calculation results of each follow-up item to ensure the accuracy of the follow-up plan.

[0070] Specifically, a preset 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).

[0071] For example, if the baseline date is January 1, 2024, and the first follow-up is set one month after the baseline, the calculation formula is: January 1, 2024 + 1 month = February 1, 2024.

[0072] According to the formula type, the specific dates of each dynamic and static follow-up time point are calculated one by one, and the rationality and feasibility of these dates are ensured.

[0073] In this process, programming tools or database functions can be used to automate the calculation process to improve efficiency and accuracy.

[0074] 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 health status and research progress of the subjects and ensure the smooth progress of the clinical trial.

[0075] S1324. Generate a subject follow-up table based on the baseline, follow-up label, follow-up item name, and planned follow-up date.

[0076] In this embodiment, all the above information is summarized to form a complete subject follow-up table. The table will include basic information of each subject, specific date of each follow-up, follow-up content and evaluation results, etc., to facilitate the research team to carry out subsequent data collection and analysis.

[0077] Through this series of steps, a systematic follow-up table for subjects can be generated to provide solid data support for clinical trials. This not only improves the standardization of the research, but also provides important guarantees for the safety of subjects and the evaluation of treatment effects.

[0078] For the above-mentioned respondent follow-up table, this embodiment uses an engine to implement it. Specifically, more than 4,000 clinical trial projects are deeply observed, 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-ups and periodic follow-ups, and each follow-up has a sequence. Therefore, if the first follow-up is taken as the baseline, subsequent follow-ups are carried out on this basis, so the following formula is summarized.

[0079] The formula is as follows: $(n).plannedDate: the planned follow-up date for the specified item in the follow-up plan; $(n).actualDate: specifies the actual follow-up date of the follow-up record; PPD: planned follow-up date of the last follow-up visit; PAD: actual follow-up date of last follow-up; FPD: planned follow-up date of the subject’s first follow-up visit; FAD: actual follow-up date of the subject’s first follow-up visit; SCR: subject screening date; ENR: subject enrollment date; Example of actual formula: $(128).actualDate+7.days; $(256).plannedDate+10.days; PAD+21.days; FAD+1.week; These rules are converted into formulas and embedded in the "Configuration Test Plan", and window period settings are also reserved.

[0080] At the same time, this example supports the anchoring of follow-up visits and subject milestones (for example, anchoring a certain follow-up visit with enrollment, that is, once a subject participates in the follow-up visit, he or she becomes enrolled, and the system automatically changes the subject's status to enrolled), automating the setting of landmark milestones such as screening, enrollment, and completion of the study.

[0081] By identifying periodic patterns, the system can better understand the trends of experimental data and help researchers make more accurate decisions. Based on the extracted patterns, the experimental plan can be configured more scientifically to improve the success rate of the experiment.

[0082] The system can automatically draw up a detailed follow-up schedule for each project and generate a personalized follow-up form based on the subject information (obtained directly from the subject database) for which the clinical coordinator is responsible.

[0083] Each coordinator can generate a unique follow-up plan based on the subjects they are responsible for, ensuring that the information is targeted and practical. Automatically generating a schedule reduces the time for manual scheduling and improves work efficiency.

[0084] S130. Send and update the subject follow-up form according to the clinical trial protocol.

[0085] In this embodiment, a schedule reminder is automatically sent to the clinical coordinator's terminal device (including mobile devices and computers) based on the set planned date. The coordinator can check the system schedule to understand the subject follow-up arrangements and specific test items that need to be carried out every day.

[0086] Coordinators can obtain the latest follow-up arrangements anytime and anywhere, which include: follow-up date, department, test items, sampling standards and quantity, etc., and provide the corresponding department and recommended values ​​for each test (the system will automatically update this information based on the overall project data by identifying the repetition frequency and hospital characteristics), improving the flexibility, timeliness and accuracy of work. The automatic reminder function reduces omissions caused by manual omissions, ensuring that each subject can complete the follow-up on time and according to the protocol requirements.

[0087] After completing the follow-up, the coordinator only needs to update and enter key data such as the follow-up date and test results on the system terminal. The system will automatically identify the label of each follow-up event, accurately update the subject's status, and synchronize the information to the entire system.

[0088] Automatic tag recognition and status updates reduce manual input errors and ensure data reliability. Instant synchronization of information enables each business module to obtain the latest data and improves collaboration efficiency among teams.

[0089] This system greatly improves the efficiency of clinical trials and the accuracy of data management through advanced algorithms and automated management. Its personalized follow-up arrangements, timely reminder mechanisms, and real-time data updates 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.

[0090] S140. Conduct testing and early warning on the updated follow-up list of the subjects.

[0091] In one embodiment, see Figure 6 The above-mentioned step S140 may include steps S141~S145.

[0092] S141. Obtain the names of all follow-up items in the updated subject follow-up table.

[0093] In this embodiment, all Page Names are extracted from the follow-up plan of the subject and deduplication is performed to ensure that each Page Name is processed only once in subsequent steps.

[0094] Extracting the latest follow-up item names ensures that clinical coordinators are aware of the examination contents that need attention at the moment to avoid omissions. The update of the follow-up table reflects the changes in the patient's status, so that subsequent processing can be carried out based on the latest information.

[0095] S142, traverse each of the follow-up item names, and filter detection items of available status from the data dictionary.

[0096] In this embodiment, a loop process is performed for each acquired Page Name so as to analyze and match related detection items one by one. Detection items with a status of "available" are screened out in the data dictionary to ensure that subsequent matching items are valid.

[0097] 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 erroneous records caused by inappropriate status.

[0098] S143. Match the detection items in the data dictionary for each follow-up item name, and take the first item when multiple items are matched.

[0099] In this embodiment, an exhaustive search is performed for each Page Name to find the corresponding abbreviation from the filtered available detection items.

[0100] If a Page Name matches multiple detection items, the first hit item will be selected as the corresponding detection item.

[0101] Automatic matching reduces the decision-making burden of clinical coordinators and improves work efficiency. By setting only the first matching item to be selected, the consistency of data entry is ensured, reducing the problem of inconsistent data caused by different coordinators selecting different items.

[0102] S144. Generate a test record and assign a value, wherein the test record includes the test name, standard name, standard department and prompt content.

[0103] In this embodiment, the test name is: the Page Name in the subject follow-up plan is assigned to the name of the test record.

[0104] Standard Name: The name given to the matched detection.

[0105] Standard Department: Add the standard department information of the matched test items to the record.

[0106] Prompt content: Add prompt content for detection items to facilitate understanding and use.

[0107] 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.

[0108] In addition, the clinical coordinator added the subject number to the subject database of the relevant center based on the actual follow-up situation. This process ensured the desensitization and privacy protection of the data.

[0109] By automating the process of matching test items and generating records, the time and workload of manual operations are greatly reduced. Deduplication and available status screening ensure data consistency and reduce the risk of mismatching. Clear steps and standardized record generation processes help improve the management level of clinical trials and ensure that each link has a basis. The generated test records include detailed information, which is convenient for subsequent tracking and auditing, and improves the transparency of the data. Desensitization is used when adding subject numbers to ensure the privacy and security of the subjects and meet ethical requirements.

[0110] In summary, the design of this process can not only improve work efficiency, but also enhance the standardization and security of data, providing a solid guarantee for the smooth progress of clinical research.

[0111] Integrate all kinds of relevant information into the test record for easy subsequent review and management. Standardized record formats help reduce human errors during data entry and improve the accuracy of information.

[0112] S145. Issue an early warning based on the detection records.

[0113] By setting key parameters (such as target values, contract values, etc.), combined with the generated follow-up table and the data entered by the clinical coordinator, accurate warning of follow-up absences can be provided from multiple dimensions such as projects, research centers, and clinical coordinators. This function significantly improves the efficiency of monitoring and management 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.

[0114] The early warning mechanism can promptly notify clinical coordinators to pay attention to the follow-up items that are about to expire to ensure that nothing is missed. Through real-time early warning, patients can be guaranteed to undergo necessary tests on time, thus improving the quality of clinical services.

[0115] The above-mentioned intelligent follow-up method for clinical trial subjects generates and sends the subject follow-up form through the clinical trial plan and updates it accordingly. Subsequently, the updated follow-up form is tested and warned to ensure the accuracy and timeliness of the data, 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, and quickly identify problems, thereby improving overall efficiency and quality.

[0116] Figure 7 is a schematic block diagram of an intelligent follow-up device 300 for clinical trial subjects provided by an embodiment of the present invention. Figure 7 As shown, corresponding to the above clinical trial subject intelligent follow-up method, the present invention also provides a clinical trial subject intelligent follow-up device 300. The clinical trial subject intelligent follow-up device 300 includes a unit for executing the above clinical trial subject intelligent follow-up method, and the device can be configured in a server. Figure 7 The clinical trial subject intelligent follow-up device 300 includes a plan acquisition unit 301, a follow-up table generation unit 302, a sending and updating unit 303 and a detection and early warning unit 304.

[0117] The protocol acquisition unit 301 is used to acquire the clinical trial protocol; the follow-up table generation unit 302 is used to generate a subject follow-up table according to the clinical trial protocol; the sending and updating unit 303 is used to send and update the subject follow-up table according to the clinical trial protocol; the detection and early warning unit 304 is used to detect and warn the updated subject follow-up table.

[0118] In one embodiment, the follow-up table generating unit 302 includes: Setting up subunits for delegating the clinical trial protocol and setting up follow-up types; The generation subunit is used to anchor the baseline for the clinical trial protocol after setting up and generate the subject follow-up table.

[0119] In one embodiment, the generating subunit comprises: A baseline label setting module is used to set a baseline and corresponding follow-up label for the clinical trial program after setting; A name determination module, used to replace numbers in a specified format and perform calculations according to preset rules to obtain a name for a follow-up item; a date determination module, used to calculate the planned follow-up date; A follow-up table determination module is used to generate a subject follow-up table based on the baseline, follow-up label, follow-up item name and planned follow-up date.

[0120] In one embodiment, the date determination module is used to perform follow-up calculations before and after enrollment according to the clinical trial protocol to obtain a planned follow-up date.

[0121] In one embodiment, the date determination module further includes: The exclusion submodule is used to exclude groups without entry labels and hidden follow-up items; The merging submodule is used to expand the number of dynamic follow-ups and merge them with the static follow-ups; The date calculation submodule is used to calculate the planned follow-up date according to the date formula type.

[0122] In one embodiment, the detection and early warning unit 304 includes: A name acquisition subunit is used to acquire the names of all follow-up items in the updated subject follow-up table; A screening subunit, used for traversing each of the follow-up item names and screening the detection items of available status from the data dictionary; 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; A generation subunit is used to generate and assign values ​​to a test record, wherein the test record includes a test name, a standard name, a standard department, and prompt content; The early warning subunit is used to issue early warnings based on the detection records.

[0123] 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 can refer to the corresponding description in the aforementioned method embodiment. For the convenience and conciseness of the description, it will not be repeated here.

[0124] The above subunits can be implemented as a computer program for the device. The computer program can be implemented as a computer program for the device. Figure 8 Runs on the computer device shown.

[0125] See also Figure 8 , Figure 85 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.

[0126] See also 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 .

[0127] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a method for intelligent follow-up of clinical trial subjects.

[0128] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0129] 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.

[0130] The network interface 505 is used to communicate with other devices over 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0131] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps: Obtain the clinical trial plan; generate a subject follow-up table according to the clinical trial plan; send and update the subject follow-up table according to the clinical trial plan; and detect and warn the updated subject follow-up table.

[0132] 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: 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.

[0133] The follow-up types include 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.

[0134] In one embodiment, when the processor 502 implements the step of anchoring the baseline of the clinical trial scheme after setting and generating the subject follow-up table, the processor 502 specifically implements the following steps: A baseline and a corresponding follow-up label are set for the clinical trial program after setting; numbers are replaced using a specified format and calculated according to preset rules to obtain a follow-up item name; a planned follow-up date is calculated; and a subject follow-up table is generated according to the baseline, follow-up label, follow-up item name and planned follow-up date.

[0135] In one embodiment, when the processor 502 implements the step of calculating the planned follow-up date, the processor 502 specifically implements the following steps: Follow-up calculations before and after enrollment were performed according to the clinical trial protocol to obtain the planned follow-up date.

[0136] In one embodiment, when the processor 502 implements the step of performing follow-up calculations before and after enrollment according to the clinical trial protocol to obtain a planned follow-up date, the processor 502 specifically implements the following steps: Exclude groups without inclusion labels and hidden follow-up items; expand the number of dynamic follow-ups and merge them with static follow-ups; calculate the planned follow-up date by date formula type.

[0137] In one embodiment, when implementing the step of expanding the number of dynamic follow-ups, the processor 502 specifically implements the following steps: Obtain all follow-up item names in the updated subject follow-up table; traverse each of the follow-up item names and filter the test items in the available state from the data dictionary; match the test items in the data dictionary for each follow-up item name, and take the first item when multiple items are matched; generate a test record and assign a value, wherein the test record includes the test name, standard name, standard department and prompt content; and issue an early warning based on the test record.

[0138] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0139] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. 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 embodiment of the above method.

[0140] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps: Obtain the clinical trial plan; generate a subject follow-up table according to the clinical trial plan; send and update the subject follow-up table according to the clinical trial plan; and detect and warn the updated subject follow-up table.

[0141] In one embodiment, when the processor executes the computer program to implement the step of generating a subject follow-up table according to the clinical trial protocol, the processor specifically implements the following steps: 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.

[0142] The follow-up types include 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.

[0143] In one embodiment, when the processor executes the computer program to implement the step of anchoring the baseline for the set clinical trial scheme and generating a subject follow-up table, the processor specifically implements the following steps: A baseline and a corresponding follow-up label are set for the clinical trial program after setting; numbers are replaced using a specified format and calculated according to preset rules to obtain a follow-up item name; a planned follow-up date is calculated; and a subject follow-up table is generated according to the baseline, follow-up label, follow-up item name and planned follow-up date.

[0144] In one 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: Follow-up calculations before and after enrollment were performed according to the clinical trial protocol to obtain the planned follow-up date.

[0145] In one embodiment, when the processor executes the computer program to implement the step of performing follow-up calculations before and after enrollment according to the clinical trial protocol to obtain a planned follow-up date, the processor specifically implements the following steps: Exclude groups without inclusion labels and hidden follow-up items; expand the number of dynamic follow-ups and merge them with static follow-ups; calculate the planned follow-up date by date formula type.

[0146] In one embodiment, when the processor executes the computer program to implement the step of detecting and warning the updated subject follow-up table, the processor specifically implements the following steps: Obtain all follow-up item names in the updated subject follow-up table; traverse each of the follow-up item names and filter the test items in the available state from the data dictionary; match the test items in the data dictionary for each follow-up item name, and take the first item when multiple items are matched; generate a test record and assign a value, wherein the test record includes the test name, standard name, standard department and prompt content; and issue an early warning based on the test record.

[0147] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0148] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0150] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0152] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on 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 table 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 follow-up form of the subjects; Wherein, 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 names of the follow-up items; Calculate the planned follow-up date; Generate a subject follow-up table according to the baseline, follow-up label, follow-up item name, and planned follow-up date; 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 detection 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 test name, standard name, standard department and prompt content; Issue early warning based on test records.

2. The intelligent follow-up method for clinical trial subjects according to claim 1, characterized in that: Generating a subject follow-up table according to the clinical trial protocol includes: Decentralize the clinical trial protocol and set up the follow-up type; Anchor the baseline for the clinical trial protocol after setting up and generate a subject follow-up form.

3. The intelligent follow-up method for clinical trial subjects according to claim 2, characterized in that: The calculation of the planned follow-up date includes: Follow-up calculations before and after enrollment were performed according to the clinical trial protocol to obtain the planned follow-up date.

4. The intelligent follow-up method for clinical trial subjects according to claim 3, characterized in that: 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.

5. An intelligent follow-up device for clinical trial subjects, characterized in that: include: Protocol acquisition unit, used to obtain clinical trial protocols; A follow-up table generating unit, used for generating a subject follow-up table according to the clinical trial protocol; A sending and updating unit, used for sending and updating the subject follow-up form according to the clinical trial protocol; A detection and early warning unit, used to detect and warn the updated subject follow-up form; Wherein, 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 names of the follow-up items; Calculate the planned follow-up date; Generate a subject follow-up table according to the baseline, follow-up label, follow-up item name, and planned follow-up date; 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 detection 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 test name, standard name, standard department and prompt content; Issue early warning based on test records.

6. A computer device, characterized in that: The computer device comprises 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 4 when executing the computer program.

7. 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 4 is implemented.

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