Cooperative management method and system for clinical test items

Abstract

The embodiment of the invention relates to the technical field of data management, in particular to a collaborative management method and system for clinical test items, and the method comprises the steps: carrying out the live collection of each unit region of a test site, and obtaining the live information of the test site; the project progress of each clinical test project performed in the test site is monitored in real time to generate the test demand information of the subsequent time, the test capability of the test site in the subsequent time is analyzed based on the live information to obtain an unoccupied scheduling list, and the test demand information is subjected to implementation simulation in combination with the unoccupied scheduling list and the live information. And generating a project scheduling plan, sending a scheduling instruction to an executor of each clinical test project according to the project scheduling plan, and continuously collecting response information of the executor to the scheduling instruction so as to correct the project scheduling plan.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the technical field of data management, and more specifically, the embodiments of this disclosure relate to a collaborative management method and system for clinical trial projects. Background Technology

[0002] With the continuous deepening of medical research and the increasing demand for health, the number of clinical trial projects has increased dramatically. At the same time, the research fields are constantly expanding, and the types of diseases, treatment methods and technical means involved are becoming more and more complex. The trial process is becoming more cumbersome. The traditional clinical trial management model often relies on manual communication and paper document records. The problems of untimely and inaccurate information transmission are quite prominent. There are information barriers between different departments and personnel, which leads to problems such as unreasonable resource allocation and conflicting task assignments. Summary of the Invention

[0003] In view of this, this disclosure provides a collaborative management method and system for clinical trial projects to ensure the rational allocation of clinical trial resources.

[0004] According to a first aspect of this disclosure, a collaborative management method for clinical trial programs is provided, comprising: Real-time data was collected from each unit area of ​​the test site to obtain real-time information about the test site. Real-time monitoring of the progress of various clinical trial projects conducted within the testing site to generate experimental requirements information for subsequent periods; Based on the real-time information, the testing capacity of the test site in the subsequent time is analyzed to obtain a list of available scheduling slots. By combining the available scheduling list with the real-time information, the experimental requirements are simulated to generate a project scheduling plan. According to the project scheduling plan, scheduling instructions are sent to the implementers of each clinical trial project, and the response information of the implementers to the scheduling instructions is continuously collected in order to revise the project scheduling plan.

[0005] According to a second aspect of this disclosure, a collaborative management system for clinical trial projects is provided, for implementing the collaborative management method for clinical trial projects as described in any one of the first aspects, comprising: The real-time data acquisition module is used to acquire real-time data from each unit area of ​​the test site to obtain real-time information about the test site. The progress monitoring module is used to monitor the progress of various clinical trial projects conducted within the trial site in real time, so as to generate experimental requirements information for subsequent times. The capacity analysis module is used to analyze the test site's test capacity in subsequent time based on the real-time information and obtain a list of available scheduling slots. The simulation scheduling module is used to simulate the implementation of the experimental requirements by combining the available scheduling list with the real-time information, and generate a project scheduling plan. The scheduling optimization module is used to send scheduling instructions to the executors of each clinical trial project according to the project scheduling plan, and continuously collect the executors' response information to the scheduling instructions in order to modify the project scheduling plan.

[0006] The technical solution disclosed herein has the following beneficial effects: This disclosure comprehensively collects real-time information, accurately grasps the status of the test site, provides a foundation for subsequent management, monitors project progress in real time and generates demand information, allows for advance resource planning to avoid resource idleness or shortage, analyzes test capacity to generate a list of available resources, achieves reasonable resource allocation, simulates implementation to generate contingency plans, improves the scientific nature of scheduling, and modifies contingency plans based on the executor's response to ensure that the plans are in line with reality, improves project execution efficiency and success rate, and ensures the smooth conduct of clinical trials. Attached Figure Description

[0007] Figure 1 This illustration shows a step diagram of a collaborative management method for a clinical trial project according to this exemplary embodiment; Figure 2 This diagram illustrates the structure of a collaborative management system for a clinical trial project according to this exemplary embodiment. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this disclosure clearer, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. Unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0009] The term "comprising" and any variations thereof in the specification and claims of this disclosure are intended to cover non-exclusive protection. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0010] In this disclosure, there are one or more embodiments; "multiple" refers to two or more. "And / or" describes the relationship between the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following associated objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0011] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order, sequence, size, or priority. For example, the terms "first dialogue information" and "second dialogue information" in the embodiments of this disclosure are merely used to distinguish different dialogue information. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0012] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, which are schematic illustrations of this disclosure and are not necessarily drawn to scale. Some block diagrams shown in the drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in hardware modules or integrated circuits, or in networks, processors, or microcontrollers. Implementations can be carried out in various forms and should not be construed as limited to the examples set forth herein. The features, structures, or characteristics described in this disclosure can be combined in any suitable manner in one or more implementations. Numerous specific details are provided in the following description to give a thorough description of the embodiments of this disclosure. However, those skilled in the art will recognize that one or more specific details may be omitted when implementing the technical solutions of this disclosure, or other methods, components, apparatuses, steps, etc., may be used to replace one or more specific details. It should be noted that in the embodiments of this disclosure, the dissemination and use of data comply with relevant national laws and regulations.

[0013] Please see Figure 1 As shown in the embodiments of this disclosure, a collaborative management method for clinical trial projects is provided, including: S1: Collect real-time data from each unit area of ​​the test site to obtain real-time information about the test site; S2: Real-time monitoring of the progress of various clinical trial projects conducted within the trial site to generate experimental requirements information for subsequent periods; S3: Based on the real-time information, analyze the testing capacity of the test site in the subsequent time to obtain a list of available scheduling slots; S4: Combine the available scheduling list with the real-time information to simulate the implementation of the experimental requirements and generate a project scheduling plan; S5: Send scheduling instructions to the executors of each clinical trial project according to the project scheduling plan, and continuously collect the executors' response information to the scheduling instructions in order to revise the project scheduling plan.

[0014] In step S1 of the embodiments provided in this disclosure, various types of sensors, such as temperature sensors, humidity sensors, pressure sensors, and cameras, are pre-deployed at the test site to form a sensor cluster. These sensors perform all-round, multi-angle real-time monitoring of the test site, collecting various physical parameters and image information within the test site. This monitoring data is then imported into a pre-constructed digital map, which is a virtual model of the test site, intuitively displaying its layout and structure. The sensor cluster can comprehensively and in real-time acquire various information from the test site, providing a rich data source for subsequent analysis. The digital map provides a visual platform for data integration and display, facilitating an intuitive understanding and analysis of the overall situation of the test site.

[0015] By utilizing monitoring data integrated into the digital map, each unit area of ​​the test site is analyzed individually. The environmental characteristics analyzed include, but are not limited to, temperature, humidity, light intensity, and air quality. By analyzing these environmental characteristics, the specific environmental conditions of each unit area are determined. Different clinical trial projects have different environmental requirements. Understanding the environmental characteristics of each unit area helps to rationally plan the trial projects and ensure the accuracy and reliability of the test results. For example, some trials require specific temperature and humidity conditions; if the environmental conditions do not meet the requirements, it will affect the progress and results of the trial.

[0016] By interacting with the control system of the testing equipment, the current tasks being performed by each piece of equipment can be obtained. Simultaneously, the historical work records of each piece of equipment can be reviewed to analyze its work efficiency and task completion patterns. This allows for an assessment of the current task completion progress. Integrating the current tasks and progress helps to define the current task characteristics of the testing equipment. Understanding the current tasks and progress of the testing equipment helps in the rational scheduling of new testing projects. If a piece of equipment is nearing completion of its current task, its subsequent use can be planned in advance. If a piece of equipment is currently heavily loaded with tasks, new testing projects should be scheduled for other idle equipment, thereby improving equipment utilization and testing efficiency.

[0017] By integrating the regional environmental characteristics of each unit area obtained in the previous steps with the current task characteristics of each test device, a comprehensive information set is formed, namely the actual situation information of the test site. The actual situation of the test site is determined by both environmental characteristics and equipment task status. Combining the two can yield a complete and accurate actual situation information of the test site, providing a comprehensive basis for subsequent test capability analysis, project scheduling, etc.

[0018] In step S2 of the embodiments provided in this disclosure, a cloud management platform is used. This platform stores detailed information on all clinical trial projects within the trial site. The trial task chain for each clinical trial project is extracted from the platform. The trial task chain is an ordered sequence of tasks that clearly defines the specific tasks to be completed from the beginning to the end of each project and their order. The trial task chain is the overall planning blueprint of the project, which clearly shows the various stages and processes of the project. Obtaining the trial task chain is the basis for subsequent analysis of project progress and determination of experimental requirements. Only by clarifying the task chain can we accurately determine the current stage of the project and what tasks still need to be completed.

[0019] The test task execution records of each test device are collected from the control system or related recording equipment of the test equipment. These records contain information such as the time and completion status of the equipment's tasks. By comparing these execution records with the test task chain, the completed and incomplete tasks of each clinical trial project can be analyzed, thereby determining the actual progress of the project. The execution records of the test equipment are an objective reflection of the actual progress of the project. By analyzing the execution records, the actual completion status of the project can be accurately understood, avoiding deviations caused by judging progress solely based on the planned time. This helps to promptly identify problems in the project execution process, such as task delays and equipment failures, and take corresponding measures to make adjustments.

[0020] Based on the project progress analyzed above, the completed and incomplete tasks are identified in the experimental task chain. The incomplete tasks are then extracted to form the subsequent task chain for each clinical trial project. The subsequent task chain clarifies the specific tasks that still need to be completed after the current stage of the project. Focusing on the subsequent task chain allows us to concentrate on the incomplete parts of the project and avoid wasting energy on the completed tasks. It provides a direct basis for determining the subsequent experimental needs, because each task in the subsequent task chain corresponds to specific resource requirements such as experimental equipment, reagents, and personnel.

[0021] A detailed analysis of each task in each subsequent task chain is conducted to determine the experimental equipment required to complete each task. These equipment requirements are then organized according to the chronological order of the tasks to form an equipment requirement chain. Combining the equipment requirement chains of all clinical trial projects yields the experimental requirements information for the subsequent period. This experimental requirement information is crucial for the rational allocation of experimental resources. By converting subsequent task chains into equipment requirement chains, the specific requirements of each project for experimental equipment in the subsequent period can be clearly understood, including the type, quantity, and usage time of the equipment. This helps to make advance arrangements and preparations for equipment, avoiding project delays due to insufficient equipment or usage conflicts.

[0022] In step S3 of the embodiments provided in this disclosure, based on the previously collected current task characteristics of the test equipment, including the current work task and completion progress, and combined with the equipment's operating rules and historical data, the time required for each test equipment to complete the current task is estimated, that is, the remaining time before each test equipment reaches its idle state is obtained. Understanding the remaining idle time of each test equipment is the basis for analyzing the subsequent testing capabilities of the test site. Only by clarifying when the equipment can be idle can the subsequent test project arrangements be further planned to avoid scheduling new tasks when the equipment is busy, thus avoiding resource conflicts.

[0023] By continuously collecting test task execution records from various testing devices over a long period, such as task completion time, task type, and execution efficiency, and placing these data onto a pre-constructed time axis, the ability of each testing device to perform different types of test projects at different time periods can be evaluated. This includes aspects such as the device's accuracy, speed, and stability. This provides information on the testing capabilities of each testing device. Different test projects have different performance requirements for the equipment, and the execution capabilities of the equipment may also vary at different time periods. By analyzing the test task execution records on the time axis, we can comprehensively and accurately understand the testing capability characteristics of each device, providing a basis for the subsequent rational allocation of test projects and ensuring that projects can be completed efficiently on appropriate equipment.

[0024] Based on the remaining time before each experimental device reaches its idle state, mark the time point from the current moment until each device completes its current task and becomes idle on the time axis, as well as the time interval within a subsequent period. This interval is the idle time interval for each experimental device to execute new clinical trial projects in the subsequent time. Clearly defining the idle time interval of each device can intuitively show the availability of the device in the subsequent time. This helps managers quickly find suitable devices and time points when scheduling new experimental projects, improve resource utilization efficiency, and avoid equipment idleness or overuse.

[0025] By combining the testing capability information of each testing device, the previously generated spare time intervals are further subdivided. For example, based on the device's ability to execute different types of test items in different time periods, the spare time intervals are divided into sub-intervals suitable for different test items. Each sub-interval corresponds to a specific type of test item that the device can execute efficiently. Different test items have different performance requirements for the device. By refining the spare time intervals into test item sub-intervals, test items and equipment resources can be matched more accurately. This can improve the execution quality and efficiency of test items and avoid test errors or delays caused by equipment mismatch.

[0026] The available time intervals for each experimental device and their corresponding experimental project sub-intervals are organized and arranged to form a complete list, namely the available scheduling list. This list clearly shows the availability of each device in the experimental site in subsequent time and the types of experimental projects suitable for execution. The available scheduling list is a comprehensive reflection of the experimental site's subsequent experimental capacity. It provides a comprehensive and intuitive reference for project scheduling. Based on this list and the needs of each clinical trial project, managers can quickly and reasonably arrange experimental equipment and time to achieve optimal allocation of experimental resources.

[0027] In step S4 of the embodiments provided in this disclosure, priority data for each clinical trial project is obtained from the project management system or related documents. This data can be determined based on factors such as the importance and urgency of the project. The equipment demand chains in the experimental demand information are matched with the available time intervals of each experimental device in the available scheduling list. Based on the priority data, projects of different priorities are reasonably arranged in terms of equipment available time, generating multiple different scheduling possibilities. That is, several candidate scheduling plans for each clinical trial project relative to each experimental device are obtained. Different clinical trial projects have differences in importance, time requirements, etc. By clarifying the priority, it can be ensured that resources are allocated to more important and urgent projects first, improving the overall project efficiency. Mapping processing can find all possible time matching schemes between equipment and projects, providing more choices for subsequent selection of the optimal scheduling plan.

[0028] Based on the regional environmental characteristics (such as temperature, humidity, cleanliness, etc.) of each unit area in the real-time information, the adaptability of each test device to different clinical trial projects is evaluated, it is determined whether the environment where the equipment is located meets the project requirements, and the project queuing space around each test device is analyzed, i.e. whether there is enough space to store the materials required for the trial, arrange personnel for operation, etc. Based on the analysis results of project adaptability and project queuing space, the value of each candidate scheduling plan is evaluated, taking into account factors such as the possibility of project completion, efficiency, cost, etc. Finally, the plan with the highest value is selected from the candidate scheduling plans as the final project scheduling plan.

[0029] The experimental project has specific environmental requirements. If the environment where the equipment is located is unsuitable for the project, it may affect the accuracy and reliability of the test results. Therefore, analyzing the project's adaptability ensures the quality of the test. The project queuing space affects the convenience and safety of the test operation. Sufficient space can avoid problems such as material chaos and inconvenience for personnel operation, thereby improving test efficiency. Evaluating the value of each candidate scheduling plan and selecting the best plan can ensure that the final project scheduling plan achieves optimal resource utilization and project execution, maximizing overall benefits.

[0030] In step S5 of the embodiments provided in this disclosure, the executors of each clinical trial project are determined according to the generated project scheduling plan, and detailed scheduling instructions are sent to their pre-bound information terminals (such as mobile phones, tablets, dedicated work terminals, etc.) through the information system. The scheduling instructions include key information such as project task content, execution time, equipment used, and operation requirements. Timely and accurate transmission of scheduling instructions to the executors is the foundation for the smooth execution of the project. The information terminal is the direct channel for the executors to obtain work arrangements. In this way, it can be ensured that the executors understand the task details as soon as possible, make corresponding preparations, and ensure that the project starts as planned.

[0031] Upon receiving a dispatch instruction, the executor will send interactive instructions through the information terminal, such as providing feedback on the feasibility of the task, proposing resource requests, and reporting unexpected problems. Simultaneously, the information terminal's positioning function will be used to obtain the executor's location information in real time. The interactive instructions and location data will be integrated as the executor's response information to the dispatch instruction. The interactive instructions can reflect the executor's understanding of the dispatch instruction, the actual problems faced, and feedback on the task, which helps to promptly identify unreasonable aspects in the dispatch plan. The location data can help understand whether the executor arrives at the work position on time and whether there are any deviations from the plan during task execution, providing an important basis for judging the actual progress of task execution.

[0032] The collected response information is systematically analyzed. For each scheduling action in the project scheduling plan, its executability parameters are evaluated, such as whether the task can start on time, whether there are sufficient resources to support it, and whether there are any technical difficulties. Then, the interrelationships between the executability parameters of each scheduling action are analyzed. For example, whether the delay of one scheduling action will affect the execution of other related scheduling actions. This provides information on the overall execution status of the project scheduling plan. By analyzing the response information, the feasibility and potential risks of each scheduling action in the scheduling plan can be quantitatively evaluated. Considering the correlation between each scheduling action, a comprehensive understanding of the overall situation of the project scheduling plan in actual execution can be obtained, avoiding chain reactions in the entire project due to local problems and ensuring the overall progress of the project.

[0033] The overall execution status information of the project scheduling plan obtained from the previous analysis is input into a pre-trained management optimization intelligent model. Based on a large amount of historical data and optimization algorithms, this model can readjust and optimize the order of scheduling behaviors in the project scheduling plan according to the current execution status information, and generate a new scheduling scheme. The management optimization intelligent model has powerful data analysis and optimization capabilities, and can comprehensively consider various factors and complex relationships to find a better order of scheduling behaviors. By dynamically optimizing the scheduling plan, the efficiency of project execution can be improved, resource waste can be reduced, and various changes that occur during actual execution can be better responded to, ensuring that the project can proceed along the optimal path.

[0034] In one possible implementation, the step of collecting real-time data from each unit area of ​​the test site to obtain real-time information about the test site includes: S11: The test site is monitored from multiple angles by a sensor cluster pre-set at the test site, and the monitoring data is fed into a pre-built digital map. S12: Based on the digital map, analyze the environmental characteristics of each unit area of ​​the test site to obtain the regional environmental characteristics of each unit area; S13: Obtain the current working task of each test device in each unit area of ​​the test site, evaluate the completion progress of each test device for the current working task based on the historical working record of each test device, and generate the current task feature of the test device by combining the current working task and the completion progress. S14: Combine the regional environmental characteristics of each unit area with the current task characteristics to obtain the real-time information of the test site.

[0035] Various types of sensors, such as temperature sensors, humidity sensors, light sensors, pressure sensors, and cameras, are pre-installed at key locations within the test site to form a sensor cluster. These sensors can collect physical data and image information from different aspects within the test site in real time. The sensor cluster works continuously, collecting various types of data from the test site and transmitting this monitoring data to the data processing center via wired or wireless communication. The collected monitoring data is then input into a pre-constructed digital map of the test site according to certain rules and formats. The digital map is a virtual model of the test site that can intuitively display the layout and structure of the site. After the data is input, the various elements on the map can correspond to the actual physical state.

[0036] The multi-type sensors in the sensor cluster can monitor the test site from different angles, ensuring that comprehensive and accurate site information is obtained, providing a rich data foundation for subsequent analysis and decision-making. The digital map provides a visual display platform for the monitoring data. By inputting the data into the map, the real-time status of each area of ​​the test site can be seen intuitively, making it easier for managers to grasp the overall situation and quickly locate problems.

[0037] Based on the functional layout and actual needs of the test site, the test site in the digital map is divided into several unit areas. Each unit area has relatively independent functions and characteristics. Using the monitoring data stored in the digital map, the environmental characteristics of each unit area are extracted and analyzed. For example, parameters such as temperature, humidity, light intensity, and air quality of the area are extracted, and statistical calculations are performed to obtain the average environmental parameters and fluctuation range of each unit area. The environmental characteristics of each unit area are summarized and recorded to form an environmental characteristic report for each unit area, clarifying the environmental characteristics and applicable scope of the area.

[0038] Different clinical trial projects have different requirements for environmental conditions. By analyzing the environmental characteristics of each unit area, the project can be matched with the most suitable area, improving the accuracy and reliability of the trial. Understanding the environmental characteristics of each unit area helps to assess the environmental risks that may be faced during the trial, and take corresponding measures in advance for prevention and control, ensuring the smooth progress of the trial.

[0039] By interacting with the control system or management software of the testing equipment, detailed information on the tasks currently being performed by each piece of testing equipment is obtained, including task name, task objective, and task start time. Historical work records of each piece of testing equipment are reviewed to analyze its work efficiency, completion time, and other data in similar tasks. Based on this historical data and the actual situation of the current task, the progress of each piece of testing equipment in completing the current task is evaluated. The information on the current task and the completion progress are integrated to generate the current task characteristics of each piece of testing equipment. The current task characteristics may include information such as the remaining time, remaining workload, and estimated completion time of the task.

[0040] Understanding the current task characteristics of each testing device allows for the rational arrangement of new testing projects, avoiding overuse or idleness of equipment, improving equipment utilization and testing efficiency. By evaluating the progress of equipment completion, the progress of each testing project can be monitored in real time, potential problems can be identified in a timely manner, and corresponding measures can be taken to adjust the situation, ensuring that the project is completed on time.

[0041] The regional environmental characteristics of each unit area obtained in the previous steps and the current task characteristics of each test device are integrated to form a comprehensive information set. The regional environmental characteristics are associated with the current task characteristics of the test devices in that area to establish a correspondence between the regional environment and the equipment tasks. The integrated and associated data is sorted and analyzed to generate real-time information of the test site. The real-time information can be presented in the form of reports, charts or visualization interfaces to intuitively show the overall status of the test site.

[0042] The actual conditions of the trial site are determined by both environmental characteristics and equipment tasks. Combining these two factors to obtain real-time information can provide a comprehensive and accurate basis for decision-making in the collaborative management of clinical trial projects. By updating and analyzing real-time information in real time, changes and problems within the trial site can be identified in a timely manner, enabling dynamic management and optimization of the trial project and improving the overall execution effect of the project.

[0043] In one possible implementation, the step of monitoring the progress of various clinical trial projects conducted at the trial site in real time to generate experimental demand information for subsequent times includes: S21: Obtain the trial task chain for each clinical trial project conducted within the trial site through a cloud management platform; S22: Collect the test task execution records of each test device in the test site, and analyze the project progress of the test task chain based on the test task execution records to obtain the project progress of each clinical trial project; S23: Based on the project progress, extract the unexecuted portion of the trial task chain for each clinical trial project to obtain the subsequent task chain for each clinical trial project; S24: Perform test equipment requirement analysis on each of the subsequent task chains, and convert each of the subsequent task chains into equipment requirement chains based on the analysis results. Combine the equipment requirement chains to obtain test requirement information.

[0044] Through a specific network connection method, a stable and secure communication connection is established between the system and the cloud management platform. Login is performed using pre-assigned account and password information. After the platform's identity verification and authorization process, access to data related to clinical trial projects is obtained. In the cloud management platform's database, according to specific query rules and conditions, the trial task chain data of each clinical trial project within the trial site is extracted. The trial task chain is usually a list containing multiple ordered trial tasks, which details the steps that need to be completed from the start to the end of the project.

[0045] As a centralized data storage and management center, the cloud management platform ensures that the task chain information of all clinical trial projects is managed in a unified and standardized manner, facilitating data sharing and collaboration among different departments and personnel. By acquiring data through the cloud platform, it is guaranteed that the obtained trial task chain information is the latest and most accurate, avoiding information errors caused by data dispersion or untimely updates.

[0046] Each test device is equipped with a corresponding data acquisition interface. These interfaces can collect test task execution records in real time through sensors, log recording systems, etc., including information such as task start time, end time, execution status, and completion status. The execution records of each test device are transmitted to the data processing center through wired or wireless communication, and the data is integrated and cleaned to remove invalid or erroneous data.

[0047] The integrated trial task execution records are compared and matched with the trial task chain. Based on the completion status and time nodes of the tasks, the actual progress of each clinical trial project is calculated. For example, the project progress can be measured by indicators such as the number of completed tasks and the proportion of time consumed to the total planned time.

[0048] The execution records of the testing equipment are an objective reflection of the actual progress of the project. By analyzing these records, we can accurately understand the completion status of the project at each stage, promptly identify potential problems and delays in the project execution process, and provide an important basis for project management decisions. Managers can adjust resource allocation and optimize task arrangements in a timely manner based on the actual progress to ensure that the project can proceed smoothly according to plan.

[0049] Based on the project progress obtained from the previous analysis, the dividing point between completed and incomplete tasks is determined in the trial task chain. The incomplete tasks are extracted from the trial task chain to form the subsequent task chain for each clinical trial project. The subsequent task chain clarifies the specific tasks and sequence that need to be completed after the current stage of the project.

[0050] Focusing on the subsequent task chain allows management to concentrate on the unfinished parts of the project, avoiding wasting too much energy and resources on completed tasks. The subsequent task chain provides a direct basis for subsequent resource planning and scheduling. Only by clarifying the subsequent tasks can the required equipment, personnel, materials and other resources be accurately determined.

[0051] A detailed analysis of each task in each subsequent task chain is conducted to determine the required types, quantities, specifications, and usage time of experimental equipment needed to complete each task. The equipment requirements for each task are then organized and summarized according to the order of the tasks to form an equipment requirement chain. The equipment requirement chain clarifies the specific needs for different experimental equipment at different time periods during the subsequent project execution. The equipment requirement chains of various clinical trial projects are combined, and duplicate and conflicting requirements are removed to obtain the experimental requirements information for the entire experimental site in the subsequent time.

[0052] By converting subsequent task chains into equipment demand chains, we can clearly understand the specific equipment resource requirements of subsequent experiments, thereby achieving precise allocation of equipment resources and avoiding equipment idleness or shortage. Experiment demand information provides an important reference for project coordination and optimization. Managers can reasonably arrange the execution sequence and time of projects based on equipment demand, thereby improving equipment utilization and overall project efficiency.

[0053] In one possible implementation, the step of analyzing the test site's testing capacity in subsequent times based on the real-time information to obtain the available scheduling list includes: S31: Based on the current task characteristics in the real-time information, analyze the remaining time of the idle state of each test device in the test site to obtain the remaining time of each test device until the device is in an idle state. S32: Substitute the continuously collected test task execution records of each test device into the pre-constructed time coordinate axis to analyze the test project execution capability of each test device and obtain the test capability information of each test device; S33: Mark the time coordinate axis with time intervals based on the remaining time before each test device is in a free state, so as to generate the free time interval for each test device to execute new clinical trial projects in the subsequent time. S34: Based on the test capability information of each test device, the idle time intervals are refined to divide the idle time intervals into several sub-intervals for each test item. S35: Combine the available time intervals that are divided into sub-intervals of each test project to obtain an available scheduling list.

[0054] Extract the current task characteristics of each experimental device from the real-time information, including the currently executing task content, task start time, estimated completion time, and progress. Based on the current task's progress and estimated completion time, calculate the time required for each experimental device to complete the current task, i.e., the remaining time before the device becomes idle. For example, if a task is estimated to last 10 hours, has already been executed for 3 hours, and is 30% complete, it is estimated that approximately 7 hours are needed to complete the remaining 70% of the task. Understanding the remaining time before each experimental device becomes idle is the foundation for subsequent experimental capacity analysis and scheduling. Only by clearly identifying when the equipment will become idle can new clinical trial projects be rationally arranged, avoiding overuse or idleness of the equipment and improving equipment utilization.

[0055] Continuously collect test task execution records from each testing device, including task type, task completion time, task execution quality (such as accuracy, error rate, etc.), and equipment operating parameters. Organize and classify these records, and pre-construct a time axis with time as the horizontal axis. Mark the test task execution records of each testing device on the axis in chronological order. By analyzing the execution records on the axis, evaluate the execution capabilities of each testing device for different types of test projects in different time periods. For example, analyze the efficiency, quality stability, and adaptability of the device in completing tasks in different time periods, thereby obtaining the test capability information of each testing device.

[0056] Different testing equipment may have varying performance capabilities at different times and may also have different adaptability to different types of testing projects. By analyzing the execution records on the time axis, we can gain a comprehensive and accurate understanding of the testing capabilities of each piece of equipment, providing a basis for the rational allocation of testing projects in the future.

[0057] Based on the remaining time before each experimental device reaches its idle state, determine the time point from the current moment to the start of idleness after completing the current task on the time axis, and mark the time interval for the subsequent period. This interval is the idle time interval for the device to execute new clinical trial projects in the subsequent time. Clearly mark the idle time interval of each experimental device on the time axis and record relevant information, such as the start time and end time of the interval.

[0058] Clearly defining the available time intervals for each experimental device allows for a clear view of the device's availability in subsequent times. This helps in quickly identifying suitable devices and time points when scheduling new clinical trial projects, thereby improving the efficiency and accuracy of resource allocation.

[0059] By combining the testing capability information of each testing device, the device's ability to perform different types of testing projects in different time periods is analyzed. For example, a certain device may be more suitable for performing high-precision testing projects in certain time periods, while it may be more efficient at performing ordinary-precision testing projects in other time periods. Based on the results of the capability matching analysis, the spare time interval of each testing device is further refined into several sub-intervals for testing projects. Each sub-interval corresponds to a specific type of testing project that the device is suitable to perform in that time period. For example, an 8-hour spare time interval is divided into two 4-hour sub-intervals, one sub-interval is suitable for performing type A testing projects, and the other sub-interval is suitable for performing type B testing projects.

[0060] Different clinical trial projects have different equipment performance requirements. By refining the spare time interval into sub-intervals for each trial project, we can more accurately match trial projects with equipment resources. This can improve the execution quality and efficiency of the trial projects and avoid trial errors or delays caused by equipment incompatibility.

[0061] The available time intervals for each testing device and their corresponding sub-intervals for testing projects are collected and organized. This information is then arranged side-by-side according to device number or other rules to form a complete list, namely the available scheduling list. The list should include detailed information such as device name, available time interval, and sub-interval for testing projects. The available scheduling list comprehensively reflects the subsequent testing capacity of the testing site, providing a comprehensive and intuitive reference for project scheduling. Based on this list and the needs of each clinical trial project, managers can quickly and rationally allocate testing equipment and time, achieving optimal allocation of testing resources.

[0062] In one possible implementation, the step of combining the available scheduling list with the real-time information to simulate the implementation of the experimental requirements and generate a project scheduling plan includes: S41: Obtain priority data for each clinical trial project, and map each equipment demand chain of the experimental demand information relative to the available time interval of each experimental equipment in the available scheduling list according to the priority data, so as to obtain several alternative scheduling plans for each clinical trial project relative to each experimental equipment. S42: Based on the regional environmental characteristics of each unit area in the real-time information, analyze the project adaptability and project queuing space of each test equipment, and evaluate the value of each candidate scheduling plan based on the analysis results, and select the best candidate scheduling plan as the project scheduling plan.

[0063] Priority data for each clinical trial project is obtained from the project management system, relevant documents, or decision-making level. Priority is set based on factors such as the importance and urgency of the project, funding, and impact on the company's strategy. For example, some clinical trial projects related to the treatment of major diseases have higher priority.

[0064] For each equipment requirement chain in the experimental requirement information, according to the priority data, it is matched with the available time interval of each test equipment in the available scheduling list in descending order. For each equipment requirement, it is checked whether there is a suitable test equipment that can meet the requirement within its available time interval. If so, the matching relationship is recorded to form a possible scheduling scheme.

[0065] Repeat the above process to find all possible combinations of equipment and time for each clinical trial project, thereby obtaining several alternative scheduling plans for each clinical trial project relative to each trial equipment. For example, if project A requires equipment X to work for 3 hours, and equipment X is available in the available scheduling list from 9:00 to 12:00 and from 14:00 to 17:00, then two possible scheduling plans will be formed: project A can use equipment X from 9:00 to 12:00 or from 14:00 to 17:00.

[0066] Different clinical trial projects have varying degrees of importance and urgency. By using priority data to guide the mapping between equipment demand chains and available time intervals, we can ensure that high-priority projects receive appropriate equipment resources first, preventing resources from being occupied by low-priority projects. This improves the rationality and efficiency of resource allocation. Generating several alternative scheduling plans can provide more possibilities for subsequent decision-making, making it easier to comprehensively consider various factors and select the optimal scheduling scheme.

[0067] Based on the regional environmental characteristics (such as temperature, humidity, cleanliness, and light) of each unit area in the real-time information, assess the adaptability of each test device to different clinical trial projects. Different test projects may have specific environmental requirements. For example, some biological experiments need to be carried out under specific temperature and humidity conditions. Check whether the environment of the unit area where the device is located can meet the requirements of the project. If it does not meet the requirements, the device is poorly adapted to the project. If it does meet the requirements, further assess the degree of adaptability, such as the degree of matching between environmental conditions and project requirements.

[0068] Analyze the project queuing space around each test equipment, i.e. whether there is enough space to store the materials required for the test and arrange personnel for operation. For example, some large test equipment requires a large operating space and material storage space. Assess whether the queuing space will affect the project's execution efficiency and safety. If the space is insufficient, it will lead to problems such as inconvenience in operation and disorder of materials, thereby reducing the quality of project execution.

[0069] Based on the analysis results of project adaptability and project queuing space, a value assessment is conducted on each candidate scheduling plan. Some assessment indicators can be set, such as the probability of project completion, efficiency, cost, and environmental impact. Each indicator is assigned a corresponding weight, and then the score of each candidate scheduling plan on each indicator is calculated. The total value score of each plan is obtained by combining the results.

[0070] By comparing the total value scores of each candidate scheduling plan, the candidate scheduling plan with the highest score is selected as the project scheduling plan. This plan is considered to be the best solution to meet the project requirements and achieve optimal resource allocation, taking into account factors such as project adaptability and project queuing space.

[0071] Project adaptability analysis ensures that experimental projects are conducted under suitable environmental and equipment conditions, thereby improving the accuracy and reliability of experimental results and ensuring project quality. Project queuing space analysis helps avoid operational inconvenience and inefficiency caused by insufficient space, improving project execution efficiency. By evaluating and selecting the value of each candidate scheduling plan, the optimal project scheduling plan can be selected from multiple possible solutions, achieving optimized resource allocation and efficient project execution.

[0072] In one possible implementation, the steps of sending scheduling instructions to the implementers of each clinical trial project according to the project scheduling plan, and continuously collecting the implementers' response information to the scheduling instructions in order to revise the project scheduling plan include: S51: Send scheduling instructions to the information terminals of the executors pre-assigned to each clinical trial project according to the project scheduling plan; S52: Obtain the interactive instructions sent by the executor through the information terminal, and monitor the location data of the information terminal in real time, so as to serve as the executor's response information to the scheduling instructions; S53: Perform an overall analysis of each response information to determine the executability parameters of each scheduling behavior in the project scheduling plan, and perform an overall correlation analysis of the executability parameters of each scheduling behavior to obtain the overall execution status information of the project scheduling plan. S54: Substitute the overall execution status information into the pre-trained management optimization intelligent model to optimize the order of scheduling actions in the project scheduling plan.

[0073] Key information relevant to the implementers of each clinical trial project, such as task content, execution time, equipment used, and operational requirements, is extracted from the project scheduling plan and compiled into clear and concise scheduling instruction texts. Information terminals pre-assigned to the implementers of each clinical trial project are determined. These terminals can be mobile devices such as smartphones, tablets, and smartwatches, or fixed work terminals. The scheduling instructions are matched with the corresponding implementer's information terminal and sent to the implementer's information terminal via appropriate communication methods (such as SMS, instant messaging software, or a dedicated work management app). During transmission, the instructions are ensured to be accurately conveyed, and the sending time and reception status are recorded. Timely and accurate communication of the task arrangements in the project scheduling plan to the implementers, enabling them to clearly understand their work content and requirements, is the foundation for the smooth execution of the project. Ensuring that all implementers receive the same scheduling instructions at the same time helps coordinate the actions of all parties and avoids work chaos caused by untimely or inaccurate information transmission.

[0074] Establish corresponding feedback channels on the executors' information terminals to receive interactive instructions sent by them through the terminals. Interactive instructions can include confirmation of dispatch instructions, questions, feedback on difficulties, resource requests, etc. Utilize the positioning function of the information terminals (such as GPS positioning) to obtain the executors' location information in real time, set an appropriate monitoring frequency, and ensure that the executors' movement trajectory and current location can be grasped in a timely manner. Integrate interactive instructions and positioning data to form the executors' response information to dispatch instructions. Classify and organize this information for subsequent analysis.

[0075] Interactive instructions reflect the executor's understanding of the scheduling instructions, the actual problems they face, and the progress of their work; location data visually shows whether the executor arrives at the work location on time and whether they deviate from the plan during task execution. By combining these two aspects of information, a comprehensive understanding of the executor's status can be obtained. Based on the executor's response information, project managers can promptly identify potential inconsistencies in the scheduling plan or unexpected situations that arise during execution, thereby making corresponding adjustment decisions.

[0076] A thorough analysis of the response information is conducted, and for each scheduling action in the project scheduling plan, its executability parameters are determined. The executability parameters may include whether the task can start on time, whether there are sufficient resources to support it, whether there are technical difficulties, and whether the executor's capabilities are suitable. For example, if the executor reports a lack of necessary test reagents, then the corresponding scheduling action has low executability in terms of resources.

[0077] Analyzing the overall correlation between the executability parameters of each scheduling action reveals that the executability of one scheduling action is affected by other scheduling actions. For example, the usage time of a certain device will affect the execution of subsequent related tasks. By analyzing this correlation, we can understand the mutual constraints and influences between various links in the project scheduling plan. By combining the analysis results of executability parameters and correlations, we can evaluate the overall execution status of the project scheduling plan. We can use quantitative indicators or qualitative descriptions to represent the overall execution status, such as good execution status, certain risks, or facing significant challenges.

[0078] By analyzing the response information as a whole, the feasibility and rationality of the project scheduling plan can be evaluated from multiple perspectives. This avoids focusing only on local issues while ignoring the overall execution. Analyzing the correlation between the executability parameters of each scheduling behavior helps to identify potential problems and chain reactions that may occur during project execution, and allows for proactive measures to prevent and resolve them.

[0079] The overall execution status information of the project scheduling plan is input into a pre-trained intelligent management optimization model. This model, trained based on a large amount of historical data and optimization algorithms, is capable of performing complex data analysis and decision-making based on the input information. Using built-in algorithms and rules, the intelligent management optimization model recalculates and optimizes the order of scheduling actions in the project scheduling plan based on the overall execution status information. The model considers various factors, such as task priority, resource availability, and the correlation between various scheduling actions, to find the optimal scheduling order. The model outputs an optimized project scheduling plan, in which the order of scheduling actions has been adjusted and optimized. The new scheduling plan is compared with the original plan to analyze the optimization effect and changes.

[0080] The intelligent management optimization model has powerful data analysis and optimization capabilities. It can comprehensively consider various complex factors and find a better sequence of scheduling behaviors. By optimizing the scheduling sequence, waiting time and resource conflicts between tasks can be reduced, and the project execution efficiency can be improved. Various changes and uncertainties may occur during project execution. By incorporating the overall execution status information into the model for optimization, the scheduling plan can be better adapted to the actual situation, thereby improving the project success rate.

[0081] Please see Figure 2 As shown, this disclosure provides a collaborative management system for clinical trial projects, used to implement the collaborative management method for clinical trial projects as described in any one of the first aspects, including: The real-time data acquisition module is used to acquire real-time data from each unit area of ​​the test site to obtain real-time information about the test site. The progress monitoring module is used to monitor the progress of various clinical trial projects conducted within the trial site in real time, so as to generate experimental requirements information for subsequent times. The capacity analysis module is used to analyze the test site's test capacity in subsequent time based on the real-time information and obtain a list of available scheduling slots. The simulation scheduling module is used to simulate the implementation of the experimental requirements by combining the available scheduling list with the real-time information, and generate a project scheduling plan. The scheduling optimization module is used to send scheduling instructions to the executors of each clinical trial project according to the project scheduling plan, and continuously collect the executors' response information to the scheduling instructions in order to modify the project scheduling plan.

[0082] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0083] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be referred to as "circuit," "module," or "system," respectively.

[0084] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A collaborative management method for clinical trial projects, characterized in that, include: Real-time data was collected from each unit area of ​​the test site to obtain real-time information about the test site. Real-time monitoring of the progress of various clinical trial projects conducted within the testing site to generate experimental requirements information for subsequent periods; Based on the real-time information, the testing capacity of the test site in the subsequent time is analyzed to obtain a list of available scheduling slots. By combining the available scheduling list with the real-time information, the experimental requirements are simulated to generate a project scheduling plan. According to the project scheduling plan, scheduling instructions are sent to the implementers of each clinical trial project, and the response information of the implementers to the scheduling instructions is continuously collected in order to revise the project scheduling plan.

2. The collaborative management method for clinical trial projects as described in claim 1, characterized in that, The steps for collecting real-time data from each unit area of ​​the test site to obtain the real-time information of the test site include: The test site is monitored from multiple angles by a sensor cluster pre-set at the test site, and the monitoring data is fed into a pre-constructed digital map. Based on the digital map, the environmental characteristics of each unit area of ​​the test site are analyzed to obtain the regional environmental characteristics of each unit area. The current working task of each test device set in each unit area of ​​the test site is obtained, and the completion progress of each test device for the current working task is evaluated based on the record of the time required for each test device to perform various tasks in the past. The current task and completion progress are combined to generate the current task characteristics of the test device. By combining the regional environmental characteristics of each unit area with the current task characteristics, the real-time information of the test site is obtained.

3. The collaborative management method for clinical trial projects as described in claim 1, characterized in that, The steps for real-time monitoring of the progress of various clinical trial projects conducted at the trial site to generate experimental requirements information for subsequent times include: The trial task chain for each clinical trial project conducted within the trial site is obtained through a cloud management platform; Collect test task execution records of each test device in the test site, and analyze the project progress of the test task chain based on the test task execution records to obtain the project progress of each clinical trial project; Based on the project progress, the pending tasks of each clinical trial project are extracted from the trial task chain to obtain the subsequent task chain of each clinical trial project; The requirements for experimental equipment are analyzed for each subsequent task chain. Based on the analysis results, each subsequent task chain is converted into an equipment requirement chain. The equipment requirement chains are then combined to obtain experimental requirement information.

4. The collaborative management method for clinical trial projects as described in claim 3, characterized in that, The steps for analyzing the testing capacity of the test site in subsequent times based on the real-time information and obtaining the available scheduling list include: Based on the current task characteristics within the real-time information, the remaining time of the idle state of each test device in the test site is analyzed to obtain the remaining time of each test device until the device reaches the idle state. The test task execution records of each test device that are continuously collected are substituted into the pre-constructed time coordinate axis to analyze the test project execution capabilities of each test device and obtain the test capability information of each test device. The time axis is marked with time intervals based on the remaining time before each test device reaches its idle state, so as to generate the idle time interval for each test device to execute new clinical trial projects in the subsequent time. Based on the testing capability information of each testing device, the idle time intervals are further refined to divide the idle time intervals into several sub-intervals for each testing item. The spare time intervals, which are divided into sub-intervals of each test project, are combined in parallel to obtain a spare scheduling list.

5. The collaborative management method for clinical trial projects as described in claim 4, characterized in that, The steps for simulating the implementation of the experimental requirements by combining the available scheduling list with the real-time information to generate a project scheduling plan include: Priority data for each clinical trial project is obtained, and based on the priority data, each equipment demand chain of the experimental demand information is mapped to the available time interval of each experimental equipment in the available scheduling list to obtain several alternative scheduling plans for each clinical trial project relative to each experimental equipment. Based on the regional environmental characteristics of each unit area in the real-time information, the project adaptability and project queuing space of each test equipment are analyzed. Based on the analysis results, the value of each candidate scheduling plan is evaluated, and the best candidate scheduling plan is selected as the project scheduling plan.

6. The collaborative management method for clinical trial projects as described in claim 1, characterized in that, The steps of sending scheduling instructions to the implementers of each clinical trial project according to the project scheduling plan, and continuously collecting the implementers' response information to the scheduling instructions in order to revise the project scheduling plan include: According to the project scheduling plan, scheduling instructions are sent to the information terminals of the executors pre-assigned to each clinical trial project; The system acquires interactive instructions sent by the executor through the information terminal and monitors the location data of the information terminal in real time, so as to serve as the executor's response information to the scheduling instructions. The response information is analyzed as a whole to determine the executability parameters of each scheduling action in the project scheduling plan, and the overall correlation of the executability parameters of each scheduling action is analyzed to obtain the overall execution status information of the project scheduling plan. The overall execution status information is fed into a pre-trained intelligent management optimization model to optimize the order of scheduling actions in the project scheduling plan.

7. A collaborative management system for clinical trial projects, characterized in that, A collaborative management method for a clinical trial project according to any one of claims 1-6 includes: The real-time data acquisition module is used to acquire real-time data from each unit area of ​​the test site to obtain real-time information about the test site. The progress monitoring module is used to monitor the progress of various clinical trial projects conducted within the trial site in real time, so as to generate experimental requirements information for subsequent times. The capacity analysis module is used to analyze the test site's test capacity in subsequent time based on the real-time information and obtain a list of available scheduling slots. The simulation scheduling module is used to simulate the implementation of the experimental requirements by combining the available scheduling list with the real-time information, and generate a project scheduling plan. The scheduling optimization module is used to send scheduling instructions to the executors of each clinical trial project according to the project scheduling plan, and continuously collect the executors' response information to the scheduling instructions in order to modify the project scheduling plan.

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