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J Digit Imaging. 2019 Dec;32(6):1103-1111. doi: 10.1007/s10278-019-00241-z.

Automated Billing Code Retrieval from MRI Scanner Log Data.

Author information

1
Institute of Medical Engineering, Technical University of Applied Sciences Amberg-Weiden, Weiden, Germany. jonas.denck@siemens-healthineers.com.
2
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany. jonas.denck@siemens-healthineers.com.
3
Siemens Healthineers, Erlangen, Germany. jonas.denck@siemens-healthineers.com.
4
Siemens Healthineers, Erlangen, Germany.
5
Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland.
6
Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
7
Institute of Medical Engineering, Technical University of Applied Sciences Amberg-Weiden, Weiden, Germany.

Abstract

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.

KEYWORDS:

Machine learning; Magnetic resonance imaging; Medical coding; Reimbursement; Workflow enhancement

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