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Comparison-Bot: an Automated Preliminary-Final Report Comparison System.

Amit D Kalaria¹, Ross W Filice²

¹MedStar Georgetown University Hospital, 3800 Reservoir Rd NW, Washington, DC, 20008, USA. akalaria@gmail.com.

Journal of Digital Imaging

|November 6, 2015

Summary

This summary is machine-generated.

A new system automatically summarizes radiology report differences for trainees, improving education by highlighting changes and providing easy access to images. This tool enhances learning and offers valuable departmental insights.

Keywords:

Automated feedback Health Level 7 (HL7)Internship and residency Interpretation errors Medical education Medical informatics applications Performance measurement Quality assurance Radiology information systems Radiology reporting Radiology workflow Resident-attending discrepancy

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Area of Science:

Radiology Education
Medical Informatics
Health Systems Science

Background:

Substantial discrepancies exist between preliminary and final radiology reports, hindering resident and fellow education.
Barriers to learning from these discrepancies include high study volume, remote finalization, and difficulty accessing prior reports.

Purpose of the Study:

To develop and evaluate a system for automatically summarizing and presenting differences between preliminary and final radiology reports.
To enhance the educational experience of radiology trainees by facilitating efficient review of report changes.

Main Methods:

Developed an automated system to compile and email weekly summaries of report differences.
Implemented a trainee-accessible dashboard with custom reporting and direct links to studies in Picture Archiving and Communication Systems (PACS).
Highlighted reports with significant changes, particularly in the impression, for focused review.

Main Results:

Departmental surveys indicated the system is easy to understand and improves the educational experience.
The system provides descriptive statistics on report changes by trainee level, attending, and exam type.
The system is designed for easy portability to other departments with Health Level 7 (HL7) data access.

Conclusions:

The automated report difference summary system effectively addresses barriers to learning from preliminary-to-final report discrepancies.
This tool enhances radiology trainee education by providing accessible, highlighted feedback and direct image access.
The system offers valuable data for departmental quality improvement and can be broadly implemented.