Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.

中文翻译：

---

从MRI扫描仪日志数据中自动检索计费代码。

尽管放射学中的数字化和自动化水平稳步提高，但放射科中磁共振成像（MRI）考试的计费编码仍基于技术人员的手动输入。考试完成后，技术人员会在放射学信息系统中输入与计费代码关联的相应考试代码。此外，根据执行的过程，添加或删除其他计费代码。此工作流程很耗时，我们证明技术人员报告的帐单代码中包含错误。编码工作流程可以受益于自动化系统，因此在这项工作中开发了基于MRI日志数据自动分配MRI检查计费代码的预测模型。据我们所知，这是首次尝试根据模式日志数据预测计费代码。MRI日志数据可提供各种信息，包括已执行的MR序列集，MR扫描仪台移动以及给定的造影剂。MR序列名称使用启发式方法进行了标准化，并纳入了预测功能。该预测模型在9754例MRI考试中进行了训练，并在1个月的日志数据（423例MRI考试）中进行了测试，这些日志数据来自瑞士医疗关税系统Tarmed的放射学站点的两个MRI扫描仪。所开发的模型是一个以多层感知器为基础分类器的分类器链的集成，它预测的MRI检查医疗账单代码的平均F1分数为97.8％（召回率为98.1％，精度为97.5％）。手动编码达到98.1％的微平均F1得分（召回率97.4％，精度98）。8％）。因此，自动编码的性能接近于人类的性能。这项工作集成到临床环境中，有可能使技术人员从无附加值的管理任务中解放出来，从而增强MRI工作流程并防止编码错误。