Intelligent drug management method and system in drug clinical test process

Abstract

The invention relates to an intelligent drug management method and system in a drug clinical test process, and the method comprises the steps: obtaining a clinical test scheme, determining a drug clinical test standard according to the clinical test scheme, searching potential subjects, and transmitting a recommendation list to the potential subjects; in the clinical test process, acquiring multi-dimensional clinical test information of a subject corresponding to the target drug, extracting target data associated with the safety of the subject corresponding to the clinical test scheme in the multi-dimensional clinical test information, acquiring risk index data through the target data, and performing risk assessment; after a clinical test is finished, visit information is configured according to a clinical test scheme, the visit information comprises a visit base line, a visit window, a visit period and visit examination items, a visit schedule table is generated based on the visit information, remote monitoring is further carried out under the condition of being not affected by area and time factors, and the visit schedule table is displayed on the basis of all doctor-seeing data of a subject. A subject panoramic data view is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of information-based storage and use management of drugs for clinical trial patients, and particularly to an intelligent drug management method and system during the process of drug clinical trials. Background Art

[0002] During the process of drug clinical trials, traditional management methods have many limitations. For example, the recruitment efficiency of subjects is low, relying on manual screening and matching, which is prone to errors and time-consuming. In terms of the collection and analysis of clinical trial data, traditional methods usually only focus on single or a few indicators, lacking comprehensive multi-dimensional dataset construction and analysis methods. In addition, the risk assessment and management of clinical trials are also relatively complex, and it is difficult for traditional means to monitor the safety and adverse events of subjects in real time and accurately.

[0003] With the development of artificial intelligence and big data technologies, the digital and intelligent transformation of clinical trials has become possible. For example, through AI technology, precise recruitment of subjects can be achieved, and the efficiency of data collection and analysis can be improved. At the same time, the application of remote monitoring technology has also provided new solutions for the management of clinical trials and data quality. However, the current intelligent management methods still have deficiencies, especially in multi-dimensional data integration, the accuracy of risk assessment, and the intelligent management of the entire process of clinical trials. Summary of the Invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide an intelligent drug management method during the process of drug clinical trials, which can effectively solve many problems in traditional clinical trial management and promote the development of drug clinical trials towards the direction of intelligence, high efficiency, and precision.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] An intelligent drug management method during the process of drug clinical trials, comprising:

[0007] Obtain a clinical trial protocol, determine drug clinical trial criteria according to the clinical trial protocol, find potential subjects based on the drug clinical trial criteria, and send a recommendation form to the potential subjects;

[0008] During the clinical trial process, obtain multi-dimensional clinical trial information of subjects corresponding to the target drug, extract target data associated with the safety of subjects corresponding to the clinical trial protocol from the multi-dimensional clinical trial information, obtain risk index data through the target data, and conduct a risk assessment;

[0009] After the clinical trial ends, according to the clinical trial protocol, visit information is configured. The visit information includes: visit baseline, visit window, visit cycle, visit inspection items. Based on the visit information, a visit schedule is generated. Further, remote monitoring is carried out without being affected by regional and time factors. Based on all the medical data of the subjects, a panoramic data view of the subjects is formed.

[0010] Optionally, the target data includes:

[0011] Data related to adverse events and serious adverse events includes: the number of occurrences of adverse events, the incidence rate of adverse events, the number of subjects with the most occurrences of a certain adverse event, the number of subjects with adverse events that have not been resolved, the number of occurrences of particularly concerned adverse events, the timeliness of reporting of adverse events, the type analysis of adverse events, the number of adverse events occurring between two visits, the number of occurrences of serious adverse events, the incidence rate of serious adverse events, the number of subjects with the most occurrences of a certain serious adverse event, the number of subjects with serious adverse events that have not been resolved, the number of occurrences of particularly concerned serious adverse events, the timeliness of reporting of serious adverse events, the type analysis of serious adverse events, and the timely reporting rate of serious adverse events;

[0012] Data related to discontinuation events includes: discontinuation rate, the number of subjects with temporary discontinuation, the type analysis of discontinuation events, and the discontinuation rate caused by serious adverse events.

[0013] Optionally, obtaining the risk index data includes:

[0014]

[0015] Wherein, R ij is the risk index data, x ij is the mean or median of the jth target data of the ith hospital, u j is the mean or median of the jth target data of all hospitals, σ j is the standard deviation of the jth target data of all hospitals.

[0016] Optionally, risk assessment of the risk index data includes:

[0017]

[0018] Wherein, M i is the risk assessment result of the jth target data, m is the total number of target data, w j is the weight of the jth target data, and the weight can be adjusted according to the importance and relevance of the target data, R ij is the risk score of the jth target data of the ith hospital.

[0019] Optionally, generating the visit schedule includes:

[0020] Based on the visit information, by adding the visit baseline and the visit cycle, obtain the next visit time, and add the required visit inspection items to the next visit time to generate the visit schedule. At the same time, determine whether the subject has completed the visit within the visit window. If the subject fails to complete the visit within the visit window, it is necessary to set the number of days of visit extension and adjust the subsequent visit plan.

[0021] Optionally, the all medical treatment data includes: medical treatment records, examinations, tests, medical records, and doctor's orders.

[0022] To achieve the above object, the present invention provides a drug intelligent management system in the process of drug clinical trials, including:

[0023] A clinical trial recommendation module, configured to obtain a clinical trial protocol, determine drug clinical trial criteria according to the clinical trial protocol, find potential subjects based on the drug clinical trial criteria, and send a recommendation form to the potential subjects;

[0024] A clinical trial evaluation module, configured to obtain multi-dimensional clinical trial information of a subject corresponding to a target drug during the clinical trial, extract target data associated with the safety of the subject corresponding to the clinical trial protocol from the multi-dimensional clinical trial information, obtain risk index data through the target data, and conduct a risk assessment;

[0025] A visit and remote monitoring module, configured to, after the clinical trial ends, configure visit information according to the clinical trial protocol, where the visit information includes: visit baseline, visit window, visit cycle, visit inspection items, generate a visit schedule based on the visit information, and further conduct remote monitoring without being affected by regional and time factors, and form a panoramic data view of the subject based on all the medical treatment data of the subject.

[0026] Optionally, the clinical trial evaluation module includes:

[0027] A target data extraction unit, configured to obtain multi-dimensional clinical trial information of a subject corresponding to a target drug during the clinical trial, and extract target data associated with the safety of the subject corresponding to the clinical trial protocol from the multi-dimensional clinical trial information;

[0028] A clinical trial evaluation unit, configured to obtain risk index data through the target data:

[0029]

[0030] Wherein, R ijis risk index data, x ij is the mean or median of the j-th target data of the i-th hospital, u j is the mean or median of the j-th target data of all hospitals, σ j is the standard deviation of the j-th target data of all hospitals;

[0031] Performing risk assessment on the risk index data includes:

[0032]

[0033] where M i is the risk assessment result of the j-th target data, m is the total number of target data, w j is the weight of the j-th target data, and the weight is adjusted according to the importance and relevance of the target data, R ij is the risk score of the j-th target data of the i-th hospital.

[0034] Optionally, the visit and remote monitoring module includes:

[0035] A visit plan generation unit, which is used to configure visit information according to the clinical trial protocol after the end of the clinical trial. The visit information includes: visit baseline, visit window, visit cycle, visit inspection items. Based on the visit information, by adding the visit baseline and the visit cycle, the next visit time is obtained, and the required visit inspection items are added to the next visit time to generate the visit schedule. At the same time, it is judged whether the subject has completed the visit within the visit window. If the subject fails to complete the visit within the visit window, the visit extension days need to be set and the subsequent visit plan is adjusted;

[0036] A panoramic data view generation unit, which is used to perform remote monitoring without being affected by regional and time factors, and form a panoramic data view of the subject based on all the medical treatment data of the subject.

[0037] The beneficial effects of the present invention are:

[0038] Improve the efficiency of subject recruitment: The present invention can quickly screen out eligible subjects by intelligently matching the clinical trial protocol with potential subjects, and send recommendation forms in a timely manner, significantly improving the recruitment efficiency.

[0039] Comprehensive multi-dimensional data analysis: The present invention can collect and analyze multi-dimensional clinical trial information of subjects in real time, including key data such as adverse events and drug withdrawal events, providing more comprehensive support for the evaluation of the effectiveness and safety of clinical trials.

[0040] Precise risk assessment: The risk index calculation and assessment method of the present invention based on multi-dimensional data can monitor the safety and adverse events of subjects in real time, discover potential risks in a timely manner and take measures.

[0041] Optimized visit plan and remote monitoring: The present invention generates a visit schedule and remote monitoring function through intelligent means, which is not restricted by region and time, improving the management efficiency and data quality of clinical trials.

[0042] Enhanced data integration and management capabilities: The present invention integrates all the medical treatment data of subjects into a panoramic data view, facilitating researchers to comprehensively understand the situation of subjects and improving the utilization value of data.

[0043] Accelerating the clinical trial process: The present invention optimizes all aspects of clinical trials through intelligent means, reduces manual intervention, and lowers the error rate, thereby shortening the clinical trial cycle and accelerating the listing of new drugs.

[0044] In summary, the present invention can effectively solve many problems in traditional clinical trial management, and promote the development of drug clinical trials towards the direction of intelligence, high efficiency and precision. Brief Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 It is a flowchart of a drug intelligent management method in the process of a drug clinical trial according to an embodiment of the present invention;

[0047] Figure 2 It is a flowchart of finding potential subjects according to an embodiment of the present invention. Detailed Description of the Embodiments

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0049] To make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0050] Such as Figure 1As shown in the figure, this embodiment discloses a drug intelligent management method during the drug clinical trial process, including: obtaining the clinical trial protocol, determining the drug clinical trial standards according to the clinical trial protocol, searching for potential subjects based on the drug clinical trial standards, and sending a recommendation form to the potential subjects; during the clinical trial process, obtaining multi-dimensional clinical trial information of the subjects corresponding to the target drug, extracting the target data associated with the safety of the subjects corresponding to the clinical trial protocol from the multi-dimensional clinical trial information, obtaining risk index data through the target data, and conducting risk assessment; after the clinical trial ends, configuring the visit information according to the clinical trial protocol, where the visit information includes: visit baseline, visit window, visit cycle, visit inspection items, generating a visit schedule based on the visit information, and further conducting remote monitoring without being affected by regional and time factors, and forming a panoramic data view of the subjects based on all the medical treatment data of the subjects.

[0051] This embodiment discloses a drug intelligent management method during the drug clinical trial process, including:

[0052] Obtaining the clinical trial protocol and screening potential subjects: Obtain the clinical trial protocol, and parse the key information in the protocol through natural language processing technology, such as the trial purpose, drug characteristics, subject inclusion and exclusion criteria, etc.

[0053] According to the parsed clinical trial protocol, use machine learning algorithms to construct a potential subject screening model. This model combines multi-source data such as electronic health record (EHR) data, medical imaging data, and genetic testing data to accurately search for potential subjects.

[0054] Generate a personalized recommendation form for the screened potential subjects. The content of the recommendation form includes an introduction to the trial drug, potential benefits and risks, participation process, etc., and push it to the potential subjects through methods such as text messages, emails, or mobile medical applications.

[0055] Real-time risk monitoring and assessment during the clinical trial process:

[0056] During the clinical trial process, real-time collect multi-dimensional physiological data (such as heart rate, blood pressure, blood sugar, etc.) and clinical trial information (such as drug dosage, administration time, etc.) of the subjects through Internet of Things devices (such as wearable devices, intelligent medical devices).

[0057] Use deep learning algorithms to extract features from the collected multi-dimensional data, and screen out the key target data associated with the safety of the subjects, such as the occurrence frequency of adverse events, abnormal drug metabolism indicators, etc.

[0058] Build a dynamic risk assessment model based on the target data. This model compares real-time data with historical data, combines preset risk thresholds, and evaluates the safety risks of subjects during the clinical trial in real time. Once the risk exceeds the threshold, an alarm is immediately triggered, and the researcher is notified to take corresponding measures.

[0059] Intelligent visits and remote monitoring after the clinical trial:

[0060] According to the clinical trial protocol, automatically generate a visit information template covering key elements such as visit baseline, visit window, visit cycle, and visit inspection items. Researchers can adjust and optimize the template according to the actual situation.

[0061] Combined with the geographical location information and traffic conditions of the subjects, use intelligent algorithms to optimize the visit schedule, reasonably arrange the visit time and route, and improve the visit efficiency.

[0062] In terms of remote monitoring, use blockchain technology to ensure the security and integrity of all medical treatment data of the subjects. Through data mining and visualization technologies, generate a panoramic data view of the subjects, comprehensively display information such as the health status and drug reactions of the subjects during and after the clinical trial, and provide decision-making support for researchers.

[0063] Data-driven optimization and feedback of clinical trials:

[0064] During the clinical trial, regularly collect and analyze the feedback information of the subjects, such as the feeling of drug efficacy and the experience of adverse reactions. Through text mining technology, conduct sentiment analysis and topic extraction on the feedback information to discover potential problems and improvement directions.

[0065] Combined with clinical trial data and subject feedback information, use reinforcement learning algorithms to dynamically adjust the clinical trial protocol, such as optimizing drug doses and adjusting dosing frequencies, to improve the trial effect and subject compliance.

[0066] After the clinical trial, conduct a comprehensive analysis and summary of the data during the entire trial process to generate a detailed trial report. The report content includes trial results, risk assessment results, panoramic data views of the subjects, etc., and is intuitively displayed through visualization technologies. At the same time, feedback the trial data and experience to the drug R & D system to provide reference for subsequent drug R & D.

[0067] Furthermore, finding potential subjects and sending recommendation sheets to potential subjects include: intelligent screening. Lymphoma has a variety of disease types and a long median survival time for patients. The traditional method of screening subjects is for researchers to look through medical records to find eligible potential subjects. This method requires a lot of human resources and is slow, resulting in a long enrollment cycle. Intelligent screening assists research teams and researchers to quickly locate potential subjects suspected of meeting the conditions and push them to the research team for follow-up by deeply mining existing and incremental medical records based on big data algorithms combined with intelligent entry and exclusion. The core of intelligent screening is to exclude patients based on data managed by the clinical research data center, and to retrieve eligible subjects according to the clinical trial recruitment conditions. The system provides researchers with easy-to-use graphical inclusion and exclusion tools for rapid screening. For example, the recruitment conditions include treatment plans, efficacy, treatment time intervals, genes, etc. These data need to be refined according to the specific clinical trials. Data processing and production, and inclusion and exclusion strategies should be formulated according to actual conditions to improve the recall rate and accuracy of recruitment. For clinical trials with complex recruitment conditions, we first perform medical decomposition on the recruitment conditions and break them down into fields that can be expressed by computers. For example, treatment plans need to be logically judged based on prescriptions. Then, we use big data technology to formulate search statements for retrieval, and return eligible subjects in seconds. Based on the returned results, we iteratively optimize the production of inclusion fields and big data retrieval. The intelligent screening system is connected with the doctor's workstation (HIS) and the hospital's Internet hospital platform to reach eligible subjects, fully ensuring that subjects in need can find suitable clinical trials and be enrolled based on the best benefit-risk profile. The doctor's workstation reaches the reminder process, such as Figure 2 .

[0068] Furthermore, the target data include: data related to adverse events and serious adverse events include: the number of adverse events, the incidence of adverse events, the number of subjects with the most adverse events of a certain type, the number of subjects with adverse events but not resolved, the number of adverse events of special concern, the timeliness of reporting of adverse events, the type analysis of adverse events, the number of adverse events occurring between two visits, the number of serious adverse events, the incidence of serious adverse events, the number of subjects with the most serious adverse events of a certain type, the number of subjects with serious adverse events but not resolved, the number of serious adverse events of special concern, the timeliness of reporting of serious adverse events, the type analysis of serious adverse events and the timeliness reporting rate of serious adverse events; data related to discontinuation events include: the discontinuation rate, the number of subjects who temporarily discontinued medication, the type analysis of discontinuation events and the discontinuation rate caused by serious adverse events.

[0069] Furthermore, obtaining risk indicator data includes:

[0070]

[0071] Among them, R ij is the risk index data, x ij is the mean or median of the j-th target data of the i-th hospital, u j is the mean or median of the j-th target data of all hospitals, σ j is the standard deviation of the j-th target data of all hospitals.

[0072] Furthermore, the risk assessment of the risk index data includes:

[0073]

[0074] Among them, M i is the risk assessment result of the j-th target data, m is the total number of target data, w j is the weight of the j-th target data, and the weight can be adjusted according to the importance and relevance of the target data, R ij is the risk score of the j-th target data of the i-th hospital.

[0075] Furthermore, generating a visit schedule includes: based on the visit information, adding the visit baseline and the visit cycle to obtain the next visit time, and adding the required visit examination items to the next visit time to generate a visit schedule. At the same time, it is judged whether the subject completes the visit within the visit window. If the subject fails to complete the visit within the visit window, the visit extension days need to be set and the subsequent visit plan needs to be adjusted.

[0076] Specifically: The intelligent visit for lymphoma clinical research has a high patient follow-up frequency. According to the clinical trial protocol, the visit baseline, visit window, visit cycle, and visit examination items are configured, and the corresponding visit version is released. The system automatically calculates the next visit time, conducts automatic planning and calendar display, forms a visit timeline, reminds the subject and the researcher of the visit time and visit items of each subject, and automatically captures various medical treatment and examination items in the medical big data for visit tracking. Its advantages are: ① The intelligent subject visit system makes the visit plan of the researcher / Clinical Research Coordinator (CRC) more organized. ② The intelligent subject visit system brings convenience to the patients. ③ The intelligent subject visit system improves the institution's control over the visit and helps the institution better conduct statistical analysis.

[0077] Furthermore, forming a panoramic data view of the subjects includes: Our hospital took the lead in launching a remote monitoring system on February 10, 2020 for the use of the sponsor and the contract research organization, enabling the monitoring work to be unaffected by factors such as the epidemic, region, and time. The follow-up of lymphoma clinical research patients is long. The remote monitoring system integrates all the medical data of the subjects in the hospital according to the permissions and organizes the panoramic data view of the subjects in dimensions such as medical visits, examinations, tests, medical records, and doctor's orders. According to the requirements of relevant personal privacy protection laws and GCP regulations, the source data of the electronic health records of the subjects during the clinical trial is intercepted, and sensitive information such as the names, ID numbers, phone numbers, and addresses of the subjects is desensitized. An account watermark is added to the panoramic page of the subjects to achieve the isolation and security protection of clinical trial data and ensure the data security of the subjects. In the clinical trial, the non-electronic data of the subjects and the researcher's folder are uploaded to the remote monitoring system through manual desensitization, so as to achieve the remote monitoring of all the materials in the clinical trial. The new monitoring mode of remote monitoring and on-site monitoring can not only avoid crowd gathering, maximize the health and safety of all participants in the clinical trial, but also reduce the clinical cost, improve the monitoring efficiency, make the monitoring a continuous behavior, continuously monitor the trial, timely discover monitoring problems, take intervention measures, improve the trial quality, and enhance the data compliance.

[0078] Furthermore, all the medical data includes: medical records, examinations, tests, medical records, and doctor's orders.

[0079] This embodiment also provides a drug intelligent management system during the drug clinical trial process, including: a clinical trial recommendation module, configured to obtain a clinical trial protocol, determine drug clinical trial standards according to the clinical trial protocol, search for potential subjects based on the drug clinical trial standards, and send a recommendation form to the potential subjects; a clinical trial evaluation module, configured to obtain multi-dimensional clinical trial information of the subjects corresponding to the target drug during the clinical trial process, extract the target data associated with the safety of the subjects corresponding to the clinical trial protocol from the multi-dimensional clinical trial information, obtain risk index data through the target data, and conduct a risk assessment; a visit and remote monitoring module, configured to, after the clinical trial ends, configure visit information according to the clinical trial protocol, where the visit information includes: visit baseline, visit window, visit cycle, visit inspection items, generate a visit schedule based on the visit information, and further conduct remote monitoring without being affected by factors such as region and time, and form a panoramic data view of the subjects based on all the medical data of the subjects.

[0080] Specifically:

[0081] This embodiment also provides a drug intelligent management system during the drug clinical trial process, including:

[0082] A clinical trial recommendation module:

[0083] Protocol Analysis Unit: Using natural language processing technology, analyze the clinical trial protocol, extract key information, and provide data support for subsequent processes.

[0084] Subject Screening Unit: Based on machine learning algorithms, build a potential subject screening model by integrating multi-source data, accurately find potential subjects, and generate personalized recommendation forms.

[0085] Recommendation Form Push Unit: Push the recommendation form to potential subjects through various channels to improve the willingness of subjects to participate and the efficiency of trial recruitment.

[0086] Clinical Trial Evaluation Module:

[0087] Data Collection Unit: Use Internet of Things devices to collect multi-dimensional data of subjects in real time to ensure the timeliness and accuracy of data.

[0088] Feature Extraction Unit: Use deep learning algorithms to extract features from the collected data, and screen out key target data related to the safety of subjects.

[0089] Risk Assessment Unit: Build a dynamic risk assessment model to evaluate the safety risks of subjects in real time. Once the risk exceeds the threshold, immediately trigger an alarm and notify the researchers.

[0090] On-site and Remote Monitoring Module:

[0091] On-site Visit Plan Generation Unit: Automatically generate an on-site visit information template according to the clinical trial protocol, and optimize the visit schedule by combining the geographical location information and traffic conditions of the subjects.

[0092] Remote Monitoring Unit: Use blockchain technology to ensure the security of subject data, generate a panoramic data view of subjects through data mining and visualization technologies, and achieve remote monitoring.

[0093] Feedback and Optimization Unit: Collect and analyze subject feedback information, combine it with clinical trial data, use reinforcement learning algorithms to dynamically adjust the clinical trial protocol, and generate a detailed report after the trial and feedback it to the drug R & D system.

[0094] Multi-source Data Fusion and Precise Screening: Combine multi-source data such as electronic health records, medical images, and genetic testing, and use machine learning algorithms to accurately find potential subjects, improving the accuracy and efficiency of subject screening. Real-time Risk Monitoring and Dynamic Assessment: Collect data in real-time through Internet of Things devices, use deep learning algorithms to extract key features, build a dynamic risk assessment model, and achieve real-time monitoring and dynamic assessment of the safety risks of subjects, ensuring the safety of clinical trials. Intelligent Visits and Remote Monitoring: Automatically generate visit information templates and optimize the visit schedule, use blockchain technology to ensure data security, generate a panoramic data view of subjects, and achieve remote monitoring without regional and time restrictions, improving the efficiency and quality of monitoring. Data-driven Trial Optimization and Feedback: Combine subject feedback information and clinical trial data, use reinforcement learning algorithms to dynamically adjust the trial plan, improve the trial effect and subject compliance, and feedback the trial data and experience to the drug R & D system to provide reference for subsequent R & D, forming a closed-loop optimization of drug R & D.

[0095] Further, the clinical trial evaluation module includes: a target data extraction unit, which is used to obtain multi-dimensional clinical trial information of subjects corresponding to the target drug during the clinical trial process, and extract target data related to the safety of subjects corresponding to the clinical trial protocol from the multi-dimensional clinical trial information; a clinical trial evaluation unit, which is used to obtain risk index data through the target data:

[0096]

[0097] Wherein, R ij is the risk index data, x ij is the mean or median of the j-th target data of the i-th hospital, u j is the mean or median of the j-th target data of all hospitals, σ j is the standard deviation of the j-th target data of all hospitals;

[0098] Performing risk assessment on the risk index data includes:

[0099]

[0100] Wherein, M i is the risk assessment result of the j-th target data, m is the total number of target data, w j is the weight of the j-th target data, and the weight can be adjusted according to the importance and relevance of the target data, R ij is the risk score of the j-th target data of the i-th hospital.

[0101] Furthermore, the on-site visit and remote monitoring module includes: a visit plan generation unit, which is used to configure visit information according to the clinical trial protocol after the clinical trial ends. The visit information includes: visit baseline, visit window, visit cycle, and visit inspection items. Based on the visit information, by adding the visit baseline and the visit cycle, the next visit time is obtained, and the required visit inspection items are added to the next visit time to generate a visit schedule. At the same time, it is judged whether the subject has completed the visit within the visit window. If the subject fails to complete the visit within the visit window, the visit extension days need to be set and the subsequent visit plan is adjusted; a panoramic data view generation unit, which is used to conduct remote monitoring without being affected by regional and time factors, and form a panoramic data view of the subject based on all the medical treatment data of the subject.

[0102] The above embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A drug intelligent management method in the process of drug clinical trials, characterized in that: include: Obtaining a clinical trial protocol, determining drug clinical trial standards based on the clinical trial protocol, searching for potential subjects based on the drug clinical trial standards, and sending a recommendation form to the potential subjects; In the process of conducting clinical trials, multi-dimensional clinical trial information of subjects corresponding to the target drug is obtained, target data associated with the safety of the subjects corresponding to the clinical trial protocol is extracted from the multi-dimensional clinical trial information, risk indicator data is obtained through the target data, and risk assessment is performed; After the clinical trial is completed, visit information is configured according to the clinical trial plan. The visit information includes: visit baseline, visit window, visit cycle, and visit examination items. A visit schedule is generated based on the visit information. Remote monitoring is further performed without being affected by regional and time factors. A panoramic data view of the subject is formed based on all the medical data of the subject.

2. The intelligent drug management method in the process of drug clinical trials according to claim 1, characterized in that: The target data includes: Data related to adverse events and serious adverse events include: the number of adverse events, the incidence of adverse events, the number of subjects with the most adverse events of a certain type, the number of subjects with adverse events that were not resolved, the number of adverse events of special concern, the timeliness of reporting of adverse events, analysis of types of adverse events, the number of adverse events occurring between two visits, the number of serious adverse events, the incidence of serious adverse events, the number of subjects with the most serious adverse events of a certain type, the number of subjects with serious adverse events that were not resolved, the number of serious adverse events of special concern, the timeliness of reporting of serious adverse events, analysis of types of serious adverse events and the timeliness rate of reporting of serious adverse events; Data related to discontinuation events included: discontinuation rate, number of subjects who temporarily discontinued the drug, analysis of the types of discontinuation events, and discontinuation rate due to serious adverse events.

3. The intelligent drug management method in the process of drug clinical trials according to claim 1, characterized in that: Acquiring the risk indicator data includes: Among them, R ij is the risk indicator data, x ij is the mean or median of the jth target data of the i-th hospital, u j is the mean or median of the jth target data of all hospitals, σ j is the standard deviation of the j-th target data of all hospitals.

4. The intelligent drug management method in the process of drug clinical trials according to claim 3, characterized in that: The risk assessment of the risk indicator data includes: Among them, M i is the risk assessment result of the jth target data, m is the total number of target data, w j is the weight of the jth target data. The weight can be adjusted according to the importance and relevance of the target data. ij Calculate the risk score of the jth target data of the i-th hospital.

5. The intelligent drug management method in the process of drug clinical trials according to claim 1, characterized in that: Generating the visit schedule includes: Based on the visit information, the next visit time is obtained by adding the visit baseline to the visit cycle, and the required visit examination items are added to the next visit time to generate the visit schedule. At the same time, it is determined whether the subject completes the visit within the visit window. If the subject fails to complete the visit within the visit window, it is necessary to set the visit extension days and adjust the subsequent visit plan.

6. The intelligent drug management method in the process of drug clinical trials according to claim 1, characterized in that: The complete medical data includes: medical records, examinations, tests, medical records and doctor's orders.

7. A drug intelligent management system in a drug clinical trial process, which uses the drug intelligent management method in a drug clinical trial process according to any one of claims 1 to 6, comprising: A clinical trial recommendation module is used to obtain a clinical trial protocol, determine drug clinical trial standards based on the clinical trial protocol, search for potential subjects based on the drug clinical trial standards, and send a recommendation form to the potential subjects; A clinical trial evaluation module is used to obtain multi-dimensional clinical trial information of subjects corresponding to the target drug during the clinical trial, extract target data associated with the safety of the subjects corresponding to the clinical trial scheme from the multi-dimensional clinical trial information, obtain risk indicator data through the target data, and perform risk evaluation; The visit and remote monitoring module is used to configure the visit information according to the clinical trial plan after the clinical trial is completed. The visit information includes: visit baseline, visit window, visit cycle, visit examination items, and generate a visit schedule based on the visit information. It also conducts remote monitoring without being affected by regional and time factors, and forms a panoramic data view of the subject based on all the medical data of the subject.

8. The intelligent drug management system in the process of drug clinical trials according to claim 7, characterized in that: The clinical trial evaluation module includes: A target data extraction unit is used to obtain multi-dimensional clinical trial information of subjects corresponding to the target drug during the clinical trial, and extract target data associated with the safety of the subjects corresponding to the clinical trial scheme from the multi-dimensional clinical trial information; The clinical trial evaluation unit is used to obtain risk indicator data through the target data: Among them, R ij is the risk indicator data, x ij is the mean or median of the jth target data of the i-th hospital, u j is the mean or median of the jth target data of all hospitals, σ j is the standard deviation of the j-th target data of all hospitals; The risk assessment of the risk indicator data includes: Among them, M i is the risk assessment result of the jth target data, m is the total number of target data, w j is the weight of the jth target data, and the weight is adjusted according to the importance and relevance of the target data. ij Calculate the risk score of the jth target data of the i-th hospital.

9. The intelligent drug management system in the process of drug clinical trials according to claim 7, characterized in that: The visit and remote monitoring module includes: A visit plan generating unit is used to configure visit information according to the clinical trial protocol after the clinical trial is completed, wherein the visit information includes: visit baseline, visit window, visit cycle, and visit examination items. Based on the visit information, the next visit time is obtained by adding the visit baseline to the visit cycle, and the required visit examination items are added to the next visit time to generate the visit schedule, and at the same time, it is determined whether the subject completes the visit within the visit window. If the subject fails to complete the visit within the visit window, it is necessary to set the visit extension days and adjust the subsequent visit plan; The panoramic data view generation unit is used to perform remote monitoring without being affected by regional and time factors, and to form a panoramic data view of the subject based on all the medical data of the subject.

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