Rare diseases are characterized by very low incidence and prevalence rates,complex genetic mechanisms,and diverse clinical phenotypes,posing significant diagnostic and therapeutic challenges in clinical research.In principle,the design of clinical research protocols for rare diseases does not differ significantly from general clinical research.However,the difficulties mainly stem from the unique characteristics of rare diseases,which amplify the challenges and limitations inherent in general clinical research.These challenges typically involve five aspects:data collection,data management,technical methods,ethical regulations,and patient engagement.However,with the rapid development of digital technologies such as information technology,artificial intelligence(AI),and blockchain,particularly in the innovative applications of data collection,storage,analysis,sharing,and management,new opportunities have emerged for the implementation and optimization of rare disease clinical research.Strategies for conducting rare disease clinical research using digital technologies are often applied to rare disease clinical research and patient management based on digitalized registration platforms,the development of AI-driven diagnostic aids to improve the accuracy of rare disease diagnosis,the use of digital technologies for decentralized rare disease clinical research,and the promotion of data fusion from multiple sources and modalities.However,during the application process,new challenges have gradually been identified.Despite of many challenges that still exist in terms of data privacy,algorithmic fairness,and ethical norms,with the continuous maturation of technology and the improvement of ethical frameworks,digitally-intelligent-driven clinical research on rare diseases remains promising.

Objective:To construct an evaluation index system for the operational management capability in tertiary public hospitals and validate its feasibility,providing a reference for accurately assessing the operational management capability of public hospitals.Methods:A literature review and Delphi method were employed to construct the index system.The Analytic Hierarchy Process was used to calculate weights,and a weighted comprehensive index method was applied to evaluate the operational management capability of sample hospitals.Results:A hierarchical indicator system was established,comprising 6 first-level indicators,16 second-level indicators,and 40 third-level indicators,covering the dimensions of resource allocation,service efficiency,economic operation,risk prevention and control,development capacity,and social benefits.Empirical results indicated that the composite score of the sample hospital decreased from 71.5 in 2019 to 53.8 in 2020 and rebounded to 57.9 by 2023.Conclusions:The empirical outcomes align well with the operational capacity as understood by the operational management personnel of the sample hospital,demonstrating that the indicator system is scientifically sound and rational,possessing a high evaluative capability.

Objective:To construct an evaluation index system for the operational management capability in tertiary public hospitals and validate its feasibility,providing a reference for accurately assessing the operational management capability of public hospitals.Methods:A literature review and Delphi method were employed to construct the index system.The Analytic Hierarchy Process was used to calculate weights,and a weighted comprehensive index method was applied to evaluate the operational management capability of sample hospitals.Results:A hierarchical indicator system was established,comprising 6 first-level indicators,16 second-level indicators,and 40 third-level indicators,covering the dimensions of resource allocation,service efficiency,economic operation,risk prevention and control,development capacity,and social benefits.Empirical results indicated that the composite score of the sample hospital decreased from 71.5 in 2019 to 53.8 in 2020 and rebounded to 57.9 by 2023.Conclusions:The empirical outcomes align well with the operational capacity as understood by the operational management personnel of the sample hospital,demonstrating that the indicator system is scientifically sound and rational,possessing a high evaluative capability.

Rare diseases are characterized by very low incidence and prevalence rates,complex genetic mechanisms,and diverse clinical phenotypes,posing significant diagnostic and therapeutic challenges in clinical research.In principle,the design of clinical research protocols for rare diseases does not differ significantly from general clinical research.However,the difficulties mainly stem from the unique characteristics of rare diseases,which amplify the challenges and limitations inherent in general clinical research.These challenges typically involve five aspects:data collection,data management,technical methods,ethical regulations,and patient engagement.However,with the rapid development of digital technologies such as information technology,artificial intelligence(AI),and blockchain,particularly in the innovative applications of data collection,storage,analysis,sharing,and management,new opportunities have emerged for the implementation and optimization of rare disease clinical research.Strategies for conducting rare disease clinical research using digital technologies are often applied to rare disease clinical research and patient management based on digitalized registration platforms,the development of AI-driven diagnostic aids to improve the accuracy of rare disease diagnosis,the use of digital technologies for decentralized rare disease clinical research,and the promotion of data fusion from multiple sources and modalities.However,during the application process,new challenges have gradually been identified.Despite of many challenges that still exist in terms of data privacy,algorithmic fairness,and ethical norms,with the continuous maturation of technology and the improvement of ethical frameworks,digitally-intelligent-driven clinical research on rare diseases remains promising.