Department of Radiology, Veterans Affairs Maryland Healthcare System, 10 North Greene Street, Baltimore, MD, 21201, USA.
J Digit Imaging. 2017 Oct;30(5):530-533. doi: 10.1007/s10278-017-0004-4.
Conventional peer review practice is compromised by a number of well-documented biases, which in turn limit standard of care analysis, which is fundamental to determination of medical malpractice. In addition to these intrinsic biases, other existing deficiencies exist in current peer review including the lack of standardization, objectivity, retrospective practice, and automation. An alternative model to address these deficiencies would be one which is completely blinded to the peer reviewer, requires independent reporting from both parties, utilizes automated data mining techniques for neutral and objective report analysis, and provides data reconciliation for resolution of finding-specific report differences. If properly implemented, this peer review model could result in creation of a standardized referenceable peer review database which could further assist in customizable education, technology refinement, and implementation of real-time context and user-specific decision support.
传统的同行评审实践受到多种有据可查的偏见的影响，这些偏见反过来又限制了标准护理分析，而标准护理分析是确定医疗事故的基础。除了这些内在的偏见之外，当前的同行评审还存在其他缺陷，包括缺乏标准化、客观性、回顾性实践和自动化。解决这些缺陷的一种替代模式是完全对同行评审员视而不见，要求双方独立报告，利用自动化数据挖掘技术进行中立和客观的报告分析，并提供数据协调以解决特定发现的报告差异。如果正确实施，这种同行评审模式可以创建一个标准化的可参考的同行评审数据库，从而进一步有助于定制化教育、技术改进以及实时上下文和用户特定决策支持的实施。
