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Abstract：
Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient\'s tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.
摘要：
数字数据处理彻底改变了医疗文档，并实现了跨医院的患者数据汇总。诸如AO基金会关于骨折治疗的倡议(AOSammelstudie，1986),关于生存的主要创伤结局研究(MTOS)，创伤审计和研究网络(TARN)开创了多医院数据收集的先河。大型创伤登记处，像德国创伤登记处(TR-DGU)有助于提高证据水平,但仍然受到预定义的数据集和有限的生理参数的限制.对病理生理反应的理解的提高证实了有关骨折护理的决策导致了患者量身定制的动态方法的发展，例如安全最终手术算法。在未来,人工智能(AI)可以通过潜在地改变裂缝识别和/或结果预测来提供进一步的步骤。向灵活决策和人工智能驱动创新的演变可能会有进一步的帮助。当前的手稿总结了从本地数据库和随后的创伤注册到基于AI的算法的大数据的发展，例如Parkland创伤死亡率指数和IBMWatsonPathwayExplorer。
