Mr. Jean-Daniel Fardel Profile

Jean-Daniel Fardel

at 3R Swiss Imaging Network SA

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Publications (1)

Proceedings Article | 29 March 2024 Poster + Paper

Structured radiology report text analysis using natural language processing for automatic billing

Nicolas Devanthery, Natalie Heracleous, Benoit Dufour, Jean-Daniel Fardel, Benoit Rizk, Hugues Brat, Cyril Thouly, Henning Müller, Federica Zanca, Lluis Borràs Ferrís

Proceedings Volume 12929, 129290Y (2024) https://doi.org/10.1117/12.3005518

KEYWORDS: Ultrasonography, Abdomen, Algorithm development, Radiology, Doppler effect, Quality control, Medical imaging, Data modeling, Data mining, Tissues

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Purpose: The aim of this study was to develop an algorithm for automated quality control of structured radiology reports and to automatically obtain the correct invoicing codes for the performed exam. Ultrasound (US) exams of the abdomen were selected as use case, including Doppler exams. Method: To build a correct algorithm for automatic billing, the billing tree for the Ultrasound exams was studied. In Switzerland, TARMED, is the tariff structure used for billing outpatient medical services. The 4600 services listed in TARMED are divided into chapters that group together all services with a well-defined common characteristic. For example, Chapter 39 covers all medical imaging services. These chapters are further subdivided into subchapters for greater precision. Using this information a modular Natural Language Processing algorithm based on the Natural Language Toolkit (NLTK) library was developed. A second NLP algorithm based on SPACY was also developed, with the objective of a double validation of the first developed NLP algorithm. To train and test the algorithm a dataset of 170 exams corresponding to US abdominal examinations along with their radiology report were extracted from our RIS. The results of the algorithm were validated by an experienced technologists which identified possible discrepancy between the algorithm results and the correct billing. This check was carried out on a batch of data containing 95 samples. A confusion matrix was used to analyze the results. Results: In all 95 data samples, the NLTK algorithm was able to detect the billing codes correctly 100% of the time. In all our 95 data samples, the Spacy algorithm was able to detect the billing codes correctly in 86.3% of cases. This algorithm tends to overestimate the type of abdominal examination present in the report. Indeed, the 13 cases in which the algorithm made an error were cases where it detected a full abdominal ultrasound when the examination was a simple lower or upper abdomen. Conclusion: The NLTK model provides reliable and efficient estimation of billing codes for abdominal ultrasound, facilitating the task of the technologies who saves time and avoids possible human errors.

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