Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS

• Published in: Journal of biomedical semantics Vol. 10; no. S1; pp. 21 - 9
• Main Authors: Piotrkowicz, Alicja, Johnson, Owen, Hall, Geoff
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.11.2019; BioMed Central; BMC
• Subjects: Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Brain research; Cancer metastasis; Computer industry; Data Mining; Encyclopedias and dictionaries; Feasibility Studies; Feasibility study; Humans; Hydronephrosis; Hydronephrosis - diagnostic imaging; Information retrieval; Information storage and retrieval; National Health Programs; Natural Language Processing; Patient care; Radiology; Research Report; Rule-based system; Socialized medicine; Software; Technology; Time; United Kingdom; United Kingdom; Rule-based system; Radiology; Information retrieval; Natural language processing; Feasibility study
• DOI: 10.1186/s13326-019-0213-5
• ISSN: 2041-1480

Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinicians have limited time to read such large amounts of free-text and are at risk of information overload and consequently missing information vital to patient care. Automatically identifying relevant information at the point of care has the potential to reduce these risks but represents a considerable research challenge. One software solution that has been proposed in industry is the IBM Watson analytics suite which includes rule-based analytics capable of processing large document collections at scale. In this paper we present an overview of IBM Watson Content Analytics and a feasibility study using Content Analytics with a large-scale corpus of clinical free-text reports within a UK National Health Service (NHS) context. We created dictionaries and rules for identifying positive incidence of hydronephrosis and brain metastasis from 5.6 m radiology reports and were able to achieve 94% precision, 95% recall and 89% precision, 94% recall respectively on a sample of manually annotated reports. With minor changes for US English we applied the same rule set to an open access corpus of 0.5 m radiology reports from a US hospital and achieved 93% precision, 94% recall and 84% precision, 88% recall respectively. We were able to implement IBM Watson within a UK NHS context and demonstrate effective results that could provide clinicians with an automatic safety net which highlights clinically important information within free-text documents. Our results suggest that currently available technologies such as IBM Watson Content Analytics already have the potential to address information overload and improve clinical safety and that solutions developed in one hospital and country may be transportable to different hospitals and countries. Our study was limited to exploring technical aspects of the feasibility of one industry solution and we recognise that healthcare text analytics research is a fast-moving field. That said, we believe our study suggests that text analytics is sufficiently advanced to be implemented within industry solutions that can improve clinical safety.