Redefining the Practice of Peer Review Through Intelligent Automation-Part 3: Automated Report Analysis and Data Reconciliation.
Publication/Presentation Date
2-1-2018
Abstract
One method for addressing existing peer review limitations is the assignment of peer review cases on a completely blinded basis, in which the peer reviewer would create an independent report which can then be cross-referenced with the primary reader report of record. By leveraging existing computerized data mining techniques, one could in theory automate and objectify the process of report data extraction, classification, and analysis, while reducing time and resource requirements intrinsic to manual peer review report analysis. Once inter-report analysis has been performed, resulting inter-report discrepancies can be presented to the radiologist of record for review, along with the option to directly communicate with the peer reviewer through an electronic data reconciliation tool aimed at collaboratively resolving inter-report discrepancies and improving report accuracy. All associated report and reconciled data could in turn be recorded in a referenceable peer review database, which provides opportunity for context and user-specific education and decision support.
Volume
31
Issue
1
First Page
1
Last Page
4
ISSN
1618-727X
Published In/Presented At
Reiner B. I. (2018). Redefining the Practice of Peer Review Through Intelligent Automation-Part 3: Automated Report Analysis and Data Reconciliation. Journal of digital imaging, 31(1), 1–4. https://doi.org/10.1007/s10278-017-0006-2
Disciplines
Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology
PubMedID
28744581
Department(s)
Department of Radiology and Diagnostic Medical Imaging
Document Type
Article