PDF(Pubmed)
Abstract：
Current Procedural Terminology Codes is a numerical coding system used to bill for medical procedures and services and crucially, represents a major reimbursement pathway. Given that pathology services represent a consequential source of hospital revenue, understanding instances where codes may have been misassigned or underbilled is critical. Several algorithms have been proposed that can identify improperly billed CPT codes in existing datasets of pathology reports. Estimation of the fiscal impacts of these reports requires a coder (i.e., billing staff) to review the original reports and manually code them again. As the re-assignment of codes using machine learning algorithms can be done quickly, the bottleneck in validating these reassignments is in this manual re-coding process, which can prove cumbersome. This work documents the development of a rapidly deployable dashboard for examination of reports that the original coder may have misbilled. Our dashboard features the following main components: (1) a bar plot to show the predicted probabilities for each CPT code, (2) an interpretation plot showing how each word in the report combines to form the overall prediction, and (3) a place for the user to input the CPT code they have chosen to assign. This dashboard utilizes the algorithms developed to accurately identify CPT codes to highlight the codes missed by the original coders. In order to demonstrate the function of this web application, we recruited pathologists to utilize it to highlight reports that had codes incorrectly assigned. We expect this application to accelerate the validation of re-assigned codes through facilitating rapid review of false-positive pathology reports. In the future, we will use this technology to review thousands of past cases in order to estimate the impact of underbilling has on departmental revenue.
摘要：
当前的程序术语代码是一种数字编码系统，用于为医疗程序和服务开具账单，代表了主要的报销途径。鉴于病理服务是医院收入的相应来源，了解代码可能被错误分配或欠费的情况是至关重要的。已经提出了几种算法，可以在现有的病理报告数据集中识别不正确的计费CPT代码。估计这些报告的财政影响需要一个编码器(即，计费人员)来查看原始报告并再次手动编码。由于使用机器学习算法可以快速完成代码的重新分配，验证这些重新分配的瓶颈是在手动重新编码过程中，这可以证明是麻烦的。这项工作记录了可快速部署的仪表板的开发，用于检查原始编码器可能有错误计费的报告。我们的仪表板具有以下主要组件：（1）条形图显示每个CPT代码的预测概率，（2）解释图，显示报告中的每个单词如何组合以形成整体预测，和(3)用户输入他们选择分配的CPT代码的地方。该仪表板利用开发的算法来准确地识别CPT代码以突出显示原始编码器错过的代码。为了演示此Web应用程序的功能，我们招募了病理学家，利用它来突出显示错误分配代码的报告。我们希望此应用程序通过促进快速审查假阳性病理报告来加速重新分配代码的验证。在未来,我们将使用这项技术来审查过去的数千个案例，以估计账单不足对部门收入的影响。
