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Objective It aims to construct an evaluation index system for the development level of intelligent health insurance,which can serve as a reference for health insurance management departments in assessing the develop-ment level of intelligent health insurance and the implementation of health insurance informatization.Methods Key events in intelligent health insurance were identified based on event system theory and text analysis.The evaluation index system was determined through a combination of expert interviews and Delphi expert consultations.The entro-py method was used to calculate the weights of each index,followed by the assessment of the current and ideal de-velopment levels.Results A total of 16 experts were consulted.After two rounds of Delphi expert consultation,two first-level indicators and 18 second-level indicators were finally included in the system.The current development level of intelligent health insurance in China is at the intelligent development stage(2.524 points),while the ideal de-velopment level is at the intelligent improvement stage(4.073 points).The positivity coefficient of both rounds of Del-phi expert consultation was 100%,with an authority coefficient of 0.842,and the degree of expert coordination im-proved with each round.Conclusion The constructed evaluation index system exhibits high scientificity,stability,and generalizability.It can provide an effective evaluation tool for the development of intelligent health insurance in various pooled areas.
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Taking the reform of DRG payment methods as the background,it discusses how the medical in-surance department uses information technology to achieve refined monitoring and management of medical institu-tions,so as to improve the quality and efficiency of medical services and control the unreasonable growth of medical expenses.The three stages of"precision monitoring-refined supervision-precision governance"of medical insurance DRG based on"refined theory"are proposed;taking Nanjing's"medical insurance high-speed railway"as an example,a DRG refined supervision and governance model framework is constructed,and its analysis is carried out monitoring elements and governance elements,and finally put forward implementation suggestions,including hori-zontal collaboration led by medical insurance,establishing a service and cost evaluation mechanism that combines in-ternal and external services.
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It dified framework of health system resilience analysis.The research integrates practical elements from the case of the online pandemic material procurement and allocation hall in Nanjing,categorizing the resilience-building of local health systems via informatization into two distinct dimensions:static foundation and dynamic endowment.It conducts an in-depth examination of the logical pathways that leverage informatization to bolster resilience,and further investigates the inherent advantages and potential areas for optimization within informatization.The findings suggest that the synergistic empowerment of both static foundation and dynamic endowment effectively amplifies the risk defense capability and resilience of local health systems.
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Information collaboration is an important realisation path to deepen the reform of the medical and health system and to promote the collaborative development and governance of the"Three Medicine"during the"14th Five-Year Plan"period.It employs the SFIC model and makes appropriate modifications to it.The analytical framework comprises six elements:"initial conditions-external environment-catalytic leadership-institutional de-sign-collaborative process-results feedback".This framework is used to dissect the current collaborative dilemma in the"Three Medicine"information collaboration and governance.Based on this analysis,an optimized path for infor-mation collaborative governance is proposed:consolidating the foundation of"Three Medicine"information collabora-tion,enhancing the catalytic leadership capability of meta-governance,optimizing the institutional design of informa-tion life-cycle governance,reshaping the information collaboration process,and focusing on the evaluation feedback mechanism.
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Digital transformation is driving the repositioning of government work and the reshaping of public ser-vice models.It uses TOE theory combined with a technology analysis framework as a theoretical perspective and a single-case study approach to explore the operational mechanism and optimization path of health insurance gover-nance modernization.The findings show that the digital transformation of health insurance is in line with the three-stage path of"structuring the enabling mechanism-forming digital service capacity-enabling value creation".The next stage is to promote the implementation of digital coding standards,accelerate the application of technology integration,respond to the needs of the insured,improve the supporting measures for the linkage of the three health care systems,and bring into play the effectiveness of modern governance of health care.It expands the scope of government governance modernisation research and has both theoretical and practical value.
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SARS-CoV-2 is the pathogen responsible for the COVID-19 pandemic. The SARS-CoV-2 papain-like cysteine protease has been implicated in virus maturation, dysregulation of host inflammation and antiviral immune responses. We showed that PLpro preferably cleaves the K48-ubiquitin linkage while also being capable of cleaving ISG15 modification. The multiple functions of PLpro render it a promising drug target. Therefore, we screened an FDA-approved drug library and also examined available inhibitors against PLpro. Inhibitor GRL0617 showed a promising IC50 of 2.1 M. The co-crystal structure of SARS-CoV-2 PLpro-C111S in complex with GRL0617 suggests that GRL0617 is a non-covalent inhibitor. NMR data indicate that GRL0617 blocks the binding of ISG15 to PLpro. The antiviral activity of GRL0617 reveal that PLpro is a promising drug target for therapeutically treating COVID-19. One Sentence SummaryCo-crystal structure of PLpro in complex with GRL0617 reveals the druggability of PLpro for SARS-CoV-2 treatment.