Objective:This study aims to construct a quality evaluation index system for specialized disease databases. Through systematic assessment and optimization, it seeks to comprehensively enhance the quality and standardization of specialized disease cohort data. This initiative will provide more precise and reliable data support for disease research, the development of innovative drugs and medical devices, as well as policy formulation.Methods:By conducting a thorough analysis of domestic and international literature and policies related to clinical research data quality evaluation systems, preliminary quality evaluation indicators for specialized disease databases were established. Utilizing the Delphi method in two rounds, a quality evaluation system for specialized disease databases was constructed. The Analytic Hierarchy Process (AHP) and YAAHP 7.5 software were then employed to calculate the relative weights of indicators at various levels and their composite weights.Results:The two rounds of expert consultation achieved a 100.00% valid response rate, with an expert authority coefficient of 0.81 in both rounds. In the second round, the Kendall′s coordination coefficients for the first-level and second-level indicators reached 0.311 and 0.218, respectively ( P<0.05), indicating a good level of consensus among experts. The final specialized disease database quality evaluation system consists of 3 first-level indicators, 10 second-level indicators, and 32 third-level indicators. The first-level indicators include database construction, data quality, and cohort development, with weight coefficients of 31.82%, 41.49%, and 26.69%, respectively. The scientific validity of the indicator system was confirmed through reliability and validity analyses. When applied to assessing 58 specialized disease database projects from 36 medical institutions in a certain city, the results showed significant improvements in scores for database construction, data quality, and cohort development, with the most notable improvement observed in database construction. Conclusions:This study successfully developed a scientific, practical, and rationally weighted quality evaluation system for specialized disease databases, demonstrating high expert consensus and broad applicability.Validation studies have shown that this system effectively enhances the standardization and data quality of databases, providing robust technical support and assurance for specialized disease research and data resource sharing.

Objective:This study aims to construct a quality evaluation index system for specialized disease databases. Through systematic assessment and optimization, it seeks to comprehensively enhance the quality and standardization of specialized disease cohort data. This initiative will provide more precise and reliable data support for disease research, the development of innovative drugs and medical devices, as well as policy formulation.Methods:By conducting a thorough analysis of domestic and international literature and policies related to clinical research data quality evaluation systems, preliminary quality evaluation indicators for specialized disease databases were established. Utilizing the Delphi method in two rounds, a quality evaluation system for specialized disease databases was constructed. The Analytic Hierarchy Process (AHP) and YAAHP 7.5 software were then employed to calculate the relative weights of indicators at various levels and their composite weights.Results:The two rounds of expert consultation achieved a 100.00% valid response rate, with an expert authority coefficient of 0.81 in both rounds. In the second round, the Kendall′s coordination coefficients for the first-level and second-level indicators reached 0.311 and 0.218, respectively ( P<0.05), indicating a good level of consensus among experts. The final specialized disease database quality evaluation system consists of 3 first-level indicators, 10 second-level indicators, and 32 third-level indicators. The first-level indicators include database construction, data quality, and cohort development, with weight coefficients of 31.82%, 41.49%, and 26.69%, respectively. The scientific validity of the indicator system was confirmed through reliability and validity analyses. When applied to assessing 58 specialized disease database projects from 36 medical institutions in a certain city, the results showed significant improvements in scores for database construction, data quality, and cohort development, with the most notable improvement observed in database construction. Conclusions:This study successfully developed a scientific, practical, and rationally weighted quality evaluation system for specialized disease databases, demonstrating high expert consensus and broad applicability.Validation studies have shown that this system effectively enhances the standardization and data quality of databases, providing robust technical support and assurance for specialized disease research and data resource sharing.

Purpose/Significance To solve the problem that regional clinical research data are difficult to integrate efficiently,and to promote"Chinese evidence"and"Chinese protocol"in the global clinical research community.Method/Process Based on the standard-ization theory,the data standardization system is proposed,and the construction and application methods of the regional clinical research data platform are explored with the integration of multi-center clinical research data as the starting point.Result/Conclusion The theo-retical framework of the regional clinical research data platform has been preliminarily established,and the clinical research capabilities of tertiary hospitals in Shanghai have been significantly improved.

Centralized monitoring is a risk-based remote monitoring mode that can effectively improve monitoring efficiency and quality. From July to September 2023, this study developed a centralized monitoring scheme for investigator initiated trials(IIT). This scheme utilized electronic data collection system and clinical research document management system, using programming techniques to compare the consistency of key project processes and data, monitor data filling, distribution trends, logical relationships, as well as documents such as informed consent forms, protocol violation records, and adverse event reports. It could timely identify problems in clinical trials and develop targeted response measures. From October to December 2023, 6 experts conducted centralized monitoring for 153 IIT projects using this scheme and found common issues in program execution(86 projects), ethical approvals(68 projects), and informed consent forms(67 projects), and so on. At the scome time, corresponding measures were developed. The process took a total of 20 days, with an average time of 6.27 hours per project. Compared with traditional on-site monitoring, the centralized monitoring scheme developed in this study had shown certain advantages in terms of timeliness, which could help guide the efficient implementation of on-site monitoring work and provide references for tertiary public hospitals to improve the quality of clinical trials.

Centralized monitoring is a risk-based remote monitoring mode that can effectively improve monitoring efficiency and quality. From July to September 2023, this study developed a centralized monitoring scheme for investigator initiated trials(IIT). This scheme utilized electronic data collection system and clinical research document management system, using programming techniques to compare the consistency of key project processes and data, monitor data filling, distribution trends, logical relationships, as well as documents such as informed consent forms, protocol violation records, and adverse event reports. It could timely identify problems in clinical trials and develop targeted response measures. From October to December 2023, 6 experts conducted centralized monitoring for 153 IIT projects using this scheme and found common issues in program execution(86 projects), ethical approvals(68 projects), and informed consent forms(67 projects), and so on. At the scome time, corresponding measures were developed. The process took a total of 20 days, with an average time of 6.27 hours per project. Compared with traditional on-site monitoring, the centralized monitoring scheme developed in this study had shown certain advantages in terms of timeliness, which could help guide the efficient implementation of on-site monitoring work and provide references for tertiary public hospitals to improve the quality of clinical trials.

Objective:To constructe an evaluation index system for clinical research innovation in medical institutions, for references for enhancing the research and innovation capabilities of medical institutions and formulating policies related to clinical research innovation.Methods:From March 2022 to May 2023, relevant literature and policies on the evaluation system of scientific and technological innovation at home and abroad were analyzed to establish the preliminary screening clinical research innovation indicators. Two rounds of Delphi method were used to construct a clinical research innovation index evaluation system, analytic hierarchy process was used to calculate the weights of each indicator.Results:The effective response rates of the two rounds of consultation questionnaires were both 100.00%, with expert authority coefficients of 0.95 and Kendall coordination coefficients of 0.85 and 0.87, respectively. The clinical research innovation index evaluation system ultimately established 4 primary indicators, 13 secondary indicators, and 42 tertiary indicators. The first level indicators included infrastructure construction, innovation support environment, clinical research activity, and innovation effectiveness, with weight coefficients of 18.00%, 21.00%, 30.00%, and 31.00%, respectively.Conclusions:The clinical research innovation evaluation index system constructed in this study covered the investment, environment, and output aspects of research innovation, and could comprehensively and objectively reflect the clinical research innovation ability of medical institutions.

By comparing the training status of clinical research methods between United States and China, this article introduces comprehensive training system in the United States in and after the university, and discusses the problems in clinical research methodology training in China. These issues include that when medical students at school do not receive the training of professional clinical research courses, and after they go to the work, they also lack professional and accurate clinical research methodology training, which make it difficult for doctors to independently conduct clinical research. Therefore, it is recommended that Chinese medical schools should systematically establish clinical research methodology courses for undergraduates and graduate students to cultivate the clinical research capabilities of medical students. Secondly, according to the clinical research levels of doctors, different training models are proposed in the study. The existing social resources should be scientifically guided. We hope our work would provide some references for the improvement of clinical research methodology in China, to promote clinicians to be independently responsible for conducting clinical research, and improve the national medical level.

Effective supervision of the clinical research management department can guarantee and improve the quality of the investigator initiated trials(IIT). The authors analyzed relevant clinical research regulations and literature and summarized the current situation of risk-based IIT project process quality management. On such basis, they determined the risk-based IIT project process quality management method in combination with the previous research of the research group.From 2021 to 2022, this method was used to implement process quality management for 353 IIT projects in Shanghai′s tertiary hospitals. More than 3 000 risk points were identified through centralized supervision, and then on-site supervision was carried out to correct the problems found. As proven by the results, the method could find existing problems in time and define the risk level of the project, and also formulate an individualized risk supervision plan accordingly, so as to effectively ensure the data reliability and scientific results. It is suggested that the clinical research management department implement risk based management for the whole process of IIT projects, increase funding and staffing, and implement hierarchical management for the projects by research types, so as to promote the sustainable development of IITs.

Objective:Investigator Initiated Trials (IIT) play a key role in promoting comprehensively the development and homogeneity of clinical diagnosis and treatment, thus, this article aims to explore a set of recommendations for the construction and management of clinical research institutes that support IIT.Methods:Through the combination of literature review and institutional construction practice cases, based on the experience of domestic and foreign universities and well-functioning medical institutions in building clinical research centers, as well as summarizing the construction cases of the clinical research institute of Shanghai Jiao Tong University School of Medicine, to discuss the construction and management plan of such centers.Results:Propose recommendations for the construction and management standards of clinical research centers that support IIT, covering the principles of center construction, basic settings, organizational structure, functional departments, basic platforms, staffing, document management and institutional evaluation.Conclusions:We hope this study can provide reference to universities and medical institutions for the construction of the clinical research institute.

Objective:To assess possible risk factors and their respective levels in the whole process of investigator initiated trial(IIT)projects proposed in the proposal stage, for reference in formulation of risk management plans.Methods:Through literature analysis and research group discussions, the risk factors of IIT projects and risk level assessment criteria were preliminarily identified, and a consultation questionnaire was developed as a result. Delphi method was used to further optimize the risk factors and determine their risk levels. Data obtained from the consulfation were analysied by descriptive.Results:The recovery rates of two rounds of expert consultation were both 100%, and the degree of expert authority was 0.942. The survey finalized 38 risk factors, including extremely high risk, high risk, medium risk, low risk and very low risk factors of 17(44.7%), 15(39.5%), 3(7.9%), 2(5.3%) and 1(2.6%) respectively.Conclusions:This study determined a risk evaluation system of IIT projects in the proposal stage. This system can identify risks of IIT projects at an early stage, facilitating early intervention of problems existing in such projects, and minimize risks to the rights and safety of patients.