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Abstract #2635

Automated Billing Code Prediction from MRI Log Data

Jonas Denck1, Wilfried Landsch├╝tz2, Knud Nairz3, Johannes T. Heverhagen3, Andreas Maier1, and Eva Rothgang4

1University of Erlangen-Nuremberg, Erlangen, Germany, 2Siemens Healthineers, Erlangen, Germany, 3Inselspital, University of Bern, Bern, Switzerland, 4Technical University of Applied Sciences Amberg-Weiden, Amberg, Germany

We developed an algorithm that is capable of retrieving MRI billing codes from MRI log data. This proof-of-concept work is applied to Tarmed, the Swiss fee-for-service tariff system for outpatient services, and is tested on two MRI scanners, a MAGNETOM Aera and a MAGNETOM Skyra (Siemens Healthcare, Erlangen, Germany), of a single radiology site. A machine learning approach for automated MRI billing code retrieval from MRI log data is implemented. The proposed algorithm reliably predicts medical billing codes for MRI exams (F1-score: 97.1%). Integrated in the clinical environment, this work has the potential to reduce the workload for technologists, prevent coding errors and enable scanner-specific expense and turnover analysis.

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