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Informatics

Correlate: A PACS- and EHR-integrated Tool Leveraging Natural Language Processing to Provide Automated Clinical Follow-up

Published Online:Sep 12 2017https://doi.org/10.1148/rg.2017160195

Abstract

This article describes Correlate, a Web-based software program that uses natural language processing to allow the user to efficiently search an electronic health record prospectively from a picture archiving and communication system client for clinically relevant follow-up information regarding a radiologic study.

A major challenge for radiologists is obtaining meaningful clinical follow-up information for even a small percentage of cases encountered and dictated. Traditional methods, such as keeping medical record number follow-up lists, discussing cases with rounding clinical teams, and discussing cases at tumor boards, are effective at keeping radiologists informed of clinical outcomes but are time intensive and provide follow-up for a small subset of cases. To this end, the authors developed a picture archiving and communication system–accessible electronic health record (EHR)-integrated program called Correlate, which allows the user to easily enter free-text search queries regarding desired clinical follow-up information, with minimal interruption to the workflow. The program uses natural language processing (NLP) to process the query and parse relevant future clinical data from the EHR. Results are ordered in terms of clinical relevance, and the user is e-mailed a link to results when these are available for viewing. A customizable personal database of queries and results is also maintained for convenient future access. Correlate aids radiologists in efficiently obtaining useful clinical follow-up information that can improve patient care, help keep radiologists integrated with other specialties and referring physicians, and provide valuable experiential learning. The authors briefly review the history of automated clinical follow-up tools and discuss the design and function of the Correlate program, which uses NLP to perform intelligent prospective searches of the EHR.

© RSNA, 2017

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Article History

Received: Oct 10 2016
Revision requested: Feb 24 2017
Revision received: Apr 21 2017
Accepted: May 23 2017
Published online: Sept 12 2017
Published in print: Sept 2017