Use of Natural Language Processing to Identify Significant Abnormalities for Follow-up in a Large Accumulation of Non-delivered Radiology Reports

Objective: A radiology information system failure affected too many radiology reports (13,601) for manual review and detection of findings requiring clinical action, and required a semi-automated screening system to find such patients in a timely manner. Materials and methods: A novel SNOMED CT based healthcare platform was used to automatically find reports with actionable findings requiring clinical intervention. Record triage and abstraction was accomplished through a process which included data ingestion, user configuration, filter construction, and radiologist team review workflow. A lead radiologist optimised filters for American College of Radiology Category 3 actionable findings and against various exclusion criteria through a visual query construction interface and observed cohort results through a variety of graphical display renderings. A random sample of excluded reports was checked in order to confirm a statistically significant confidence level. Results: The computer filtered subset of 2878 reports was then reviewed by a team of radiologists through a computer assisted chart abstraction process leading to 12 records for follow-up, and a single patient requiring semiurgent imaging. Discussion: This project used standard software that was interactively configured by the investigating radiologist to interrogate big data, rather than requiring specialised query design by nonclinical experts. Conclusion: This project illustrates the practical application of a generic ontology based big-data healthcare analytics system to address a specific clinical challenge. Benefits included rapid processing, reduced human workload, and improved workflow.