Department of Radiology, Veterans Affairs Maryland Healthcare System, 10 North Greene Street, Baltimore, MD, 21201, USA.
J Digit Imaging. 2017 Dec;30(6):657-660. doi: 10.1007/s10278-017-0005-3.
In conventional radiology peer review practice, a small number of exams (routinely 5% of the total volume) is randomly selected, which may significantly underestimate the true error rate within a given radiology practice. An alternative and preferable approach would be to create a data-driven model which mathematically quantifies a peer review risk score for each individual exam and uses this data to identify high risk exams and readers, and selectively target these exams for peer review. An analogous model can also be created to assist in the assignment of these peer review cases in keeping with specific priorities of the service provider. An additional option to enhance the peer review process would be to assign the peer review cases in a truly blinded fashion. In addition to eliminating traditional peer review bias, this approach has the potential to better define exam-specific standard of care, particularly when multiple readers participate in the peer review process.
在传统放射学的同行评议实践中，随机选择一小部分检查（通常为总工作量的 5%），这可能会大大低估给定放射科实践中的真实错误率。另一种替代且更可取的方法是创建一个数据驱动的模型，该模型可以为每个单独的检查数学量化同行评议风险评分，并使用该数据识别高风险的检查和读者，并选择性地针对这些检查进行同行评议。还可以创建类似的模型来协助根据服务提供商的特定优先级分配这些同行评议案例。增强同行评议过程的另一个选择是真正盲法分配同行评议案例。除了消除传统的同行评议偏见外，这种方法还有可能更好地定义特定于检查的护理标准，特别是当多个读者参与同行评议过程时。
