School of Science, Mathematical Sciences, RMIT University, Australia; Rozetta Institute (formerly Capital Markets Cooperative Research Centre, CMCRC), Australia.
School of Science, Mathematical Sciences, RMIT University, Australia.
Int J Med Inform. 2020 Apr;136:104086. doi: 10.1016/j.ijmedinf.2020.104086. Epub 2020 Feb 5.
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.
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.
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.
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.
在基于活动的资金系统中，住院病例诊断相关分组（DRG）的错误分类会对医疗服务提供者的收入产生重大影响。弱信息贝叶斯模型可用于估计病例 DRG 分类错误的概率。
本研究提出了一种新的混合先验方法，该方法利用临床编码审核员的猜测，并在信息不足时切换到非信息先验。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。
基于重复的 5 折交叉验证，混合先验模型的分类性能最佳，在 20 次试验中的 14 次中实现了最佳分类准确性，显著优于基准模型。
通过混合先验纳入启发式专家猜测，大大提高了 DRG 错误检测的准确性；因此，在医疗服务提供者实施时，它有能力提高临床编码审核的效率。
