Redefining the Practice of Peer Review Through Intelligent Automation Part 2: Data-Driven Peer Review Selection and Assignment.

Bruce I Reiner

Publication/Presentation Date

12-1-2017

Abstract

In conventional radiology peer review practice, a small number of exams (routinely 5% of the total volume) is randomly selected, which may significantly underestimate the true error rate within a given radiology practice. An alternative and preferable approach would be to create a data-driven model which mathematically quantifies a peer review risk score for each individual exam and uses this data to identify high risk exams and readers, and selectively target these exams for peer review. An analogous model can also be created to assist in the assignment of these peer review cases in keeping with specific priorities of the service provider. An additional option to enhance the peer review process would be to assign the peer review cases in a truly blinded fashion. In addition to eliminating traditional peer review bias, this approach has the potential to better define exam-specific standard of care, particularly when multiple readers participate in the peer review process.

Volume

30

Issue

6

First Page

657

Last Page

660

ISSN

1618-727X

Disciplines

Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology

PubMedID

28752322

Department(s)

Department of Radiology and Diagnostic Medical Imaging

Document Type

Article

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