医疗大数据背景下临床诊断软件应用模式与管理机制

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摘要

伴随着医疗大数据技术的蓬勃发展，临床诊断软件用于疾病筛查、影像识别和临床决策支持等方面变得日益广泛。然而，在实际应用的过程当中，仍然存在着数据整合方面的困难、系统兼容性表现不足、医护人员在使用上的黏性不算高以及风险责任界定比较模糊之类的问题。基于医疗大数据环境呈现出来的特征，本文从管理视角出发，系统分析了临床诊断软件的应用现状与主要问题，进而构建出一种“数据整合—医机协同—流程嵌入”的三维应用模式框架，与此同时提出了一套包含数据治理、运行管理、风险控制与组织保障机制在内的“四位一体”管理体系。本文研究认为，完善制度设计以及优化管理机制，是推动临床诊断软件实现高质量应用的关键路径。这为医疗机构数字化转型与智慧医院建设方面提供了理论参考与实践建议。

文献引用

• [1]丁帅,郝聪颖,王浩,等.我国数字医疗发展现状、挑战与对策研究[J].中国工程科学,2025,27(06):1-8. [2]杨金铭,王纳,胡业勋,等.人工智能医疗中的法律风险防范[J].四川大学学报(医学版),2025,56(01):143-148. [3]张胜发,马玉环,张敬晨,等.基于数据安全的健康医疗科学数据分级指南研究[J].医学信息学杂志,2023,44(08):19-24. [4]阎小妍,董冲亚,姚晨.大数据时代的循证医学研究[J].中国循证医学杂志,2017,17(03):249-254. [5]Abraham R, Schneider J, vom Brocke J.Data governance: A conceptual framework, structured review, and research agenda[J].Data,2018,1(4):43. [6]Alotaibi A, Federico F.Interoperability of heterogeneous health information systems: a systematic literature review[J].International Journal of Medical Informatics,2022,162:104587. [7]Amann J, Blass A, Vayena E, et al.Explainability for artificial intelligence in healthcare: a multidisciplinary perspective[J].BMC Medical Informatics and Decision Making,2020,20(1):310. [8]Dash S, Shakyawar S K, Sharma M, & Kaushik S. (2019).Big data in healthcare: management, analysis and future prospects.Journal of Big Data,6(1),1-25. [9]Donnelly, K., et al. SNOMED CT: The universal health language[J].Studies in Health Technology and Informatics,2006,121:279-291. [10]Ghassemi M, et al.A review of challenges and opportunities in machine learning for health[J].AMIA Joint Summits on Translational Science Proceedings,2020:191-200. [11]Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019).Key challenges for delivering clinical impact with artificial intelligence.BMC Medicine,17(1),1-9. [12]Liu X, Faes L, Kale A U, et al.A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis[J].The Lancet Digital Health,2019,1(6):e271-e297. [13]Longoni C, et al. Resistance to Medical Artificial Intelligence[J].Journal of Consumer Research,2019,46(4):629-650. [14]Panesar M, Shaw T, McGregor D, et al.Evolutionary role of physician leaders in healthcare informatics and digital health transformation[J].Health Informatics Journal,2024,30(3). [15]Reddy, Sandeep, et al."A governance model for the application of AI in health care[J]."Journal of the American medical informatics association.27.3(2020):491-497. [16]Rony, Moustaq Karim Khan, et al."‘I wonder if my years of training and expertise will be devalued by machines’: concerns about the replacement of medical professionals by artificial intelligence."SAGE open nursing 10(2024):23779608241245220. [17]Shortliffe E H, Sepúlveda M J.Clinical Decision Support in the Era of Artificial Intelligence[J].JAMA,2018,320(21):2199-2200. [18]Sutton R T, Pincock D, Baumgart D C, et al.An overview of clinical decision support systems: benefits, risks, and strategies for success[J].npj Digital Medicine,2020,3(1):17. [19]Thorsen-Meyer H C, Nielsen A B, Nielsen A P, et al.Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit[J].npj Digital Medicine,2020,3(1). [20]Topol, E. J. (2019).High-performance medicine: the convergence of human and artificial intelligence.Nature Medicine,25(1),44-56. Trist E.The evolution of socio-technical systems[J].Issues in the Quality of Working Life,1981,2:1-67.