BACKGROUND In activity based funding systems, the misclassification of inpatient episode Diagnostic Related Groups (DRGs) can have significant impacts on the revenue of health care providers. Weakly informative Bayesian models can be used to estimate an episode's probability of DRG misclassification. METHODS This study proposes a new, Hybrid prior approach which utilises guesses that are elicited from a clinical coding auditor, switching to non-informative priors where this information is inadequate. This model's ability to detect DRG revision is compared to benchmark weakly informative Bayesian models and maximum likelihood estimates. RESULTS Based on repeated 5-fold cross-validation, classification performance was greatest for the Hybrid prior model, which achieved best classification accuracy in 14 out of 20 trials, significantly outperforming benchmark models. CONCLUSIONS The incorporation of elicited expert guesses via a Hybrid prior produced a significant improvement in DRG error detection; hence, it has the ability to enhance the efficiency of clinical coding audits when put into practice at a health care provider.

中文翻译：

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将专家知识纳入诊断相关组分类错误的统计检测中。

背景技术在基于活动的筹资系统中，住院发作的诊断相关组（DRG）的错误分类可能对卫生保健提供者的收入产生重大影响。信息量弱的贝叶斯模型可用于估计剧集DRG分类错误的可能性。方法本研究提出了一种新的混合先验方法，该方法利用了从临床编码审核员那里得出的猜测，并在信息不足的情况下切换到非信息先验。将该模型检测DRG修订的能力与基准弱信息贝叶斯模型和最大似然估计进行了比较。结果基于重复的5倍交叉验证，Hybrid先前模型的分类性能最高，在20个试验中有14个获得了最佳分类精度，明显优于基准模型。结论通过Hybrid先验技术引入专家的猜测可以显着改善DRG错误检测。因此，当在医疗保健提供者中投入实践时，它具有提高临床编码审核效率的能力。