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Automated Billing Code Retrieval from MRI Scanner Log Data.

Jonas Denck1,2,3, Wilfried Landschütz4, Knud Nairz5

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Summary
This summary is machine-generated.

Automating billing code assignment for magnetic resonance imaging (MRI) exams using AI significantly reduces errors and saves time. This AI model closely matches human accuracy, improving radiology workflow efficiency.

Keywords:
Machine learningMagnetic resonance imagingMedical codingReimbursementWorkflow enhancement

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Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Manual billing code assignment for magnetic resonance imaging (MRI) exams is time-consuming and prone to errors.
  • Current digitalization in radiology has not fully automated the billing coding process for MRI procedures.
  • Technologists manually input billing codes post-exam, leading to potential inaccuracies and workflow inefficiencies.

Purpose of the Study:

  • To develop and evaluate a prediction model for automated assignment of billing codes for MRI exams.
  • To leverage MRI log data for accurate billing code prediction, reducing manual input.
  • To assess the performance of an automated system compared to manual coding in radiology.

Main Methods:

  • Developed a prediction model using MRI log data, including MR sequences and contrast medium information.
  • Standardized MR sequence names and incorporated them as features for the prediction model.
  • Trained the model on 9754 MRI exams and tested it on 423 exams using an ensemble of classifier chains with a multilayer perceptron base classifier.

Main Results:

  • The automated system achieved a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%) for predicting medical billing codes.
  • Manual coding achieved a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%).
  • The automated coding performance is comparable to human performance, indicating high accuracy.

Conclusions:

  • Automated billing code assignment for MRI exams is feasible and highly accurate, closely matching human performance.
  • This AI-driven approach can free technologists from administrative tasks, enhancing MRI workflow efficiency.
  • The developed model has the potential to significantly reduce coding errors in radiology departments.