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Determining Follow-Up Imaging Study Using Radiology Reports.

Sandeep Dalal1, Vadiraj Hombal2, Wei-Hung Weng3

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Summary
This summary is machine-generated.

This study developed an automated method to track follow-up imaging compliance. The algorithm accurately identifies completed radiology recommendations, improving patient care and reducing administrative burden.

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Area of Science:

  • Radiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Radiology reports frequently include follow-up imaging recommendations.
  • Non-adherence to these recommendations can result in delayed treatment, adverse patient outcomes, and increased healthcare costs.

Purpose of the Study:

  • To develop a scalable, automated approach for identifying the completion of follow-up imaging studies recommended in prior radiology reports.
  • To improve adherence to crucial follow-up imaging recommendations.

Main Methods:

  • Utilized a dataset of 559 follow-up imaging recommendations and subsequent reports from an academic practice.
  • Developed a ground-truth dataset by having three radiologists identify appropriate follow-up examinations.
  • Trained an Extremely Randomized Trees classifier using recommendation attributes, study metadata, and text similarity.

Main Results:

  • The classifier achieved an F-score of 0.807 in identifying follow-up examinations.
  • Inter-annotator agreement among radiologists ranged from 0.853 to 0.868.
  • The methodology demonstrated a high degree of accuracy in linking recommendations to completed studies.

Conclusions:

  • Automated methods can effectively identify follow-up imaging completions, supporting integration into management applications.
  • This technology can enhance adherence to follow-up imaging recommendations, leading to better patient outcomes.
  • Radiology administrators can leverage this system to monitor compliance and implement proactive reminder strategies.