Journal Article

Text-mining in electronic healthcare records can be used as efficient tool for screening and data collection in cardiovascular trials: a multicenter validation study

Fiolet, Aernoud T.L., Alings, Marco, Schuit, Ewoud, de Vries, Martine C., Groenwold, Rolf H.H., van Dijk, Wouter B., Asselbergs, Folkert W., Mosterd, Arend, Sammani, Arjan, Grobbee, Diederick E., Schaap, Jeroen, van der Graaf, Rieke, Groenhof, T. Katrien J.

2021

Overview

This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial. In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel. Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4–8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7–92.8%). Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Accuracy / Automation / Cardiology / Cardiovascular / Cardiovascular disease / Clinical trials / Costs / Data collection / Data mining / Data points / Data-collections / Drug use / Electronic health records / Electronic healthcare records (EHRs) / Electronic medical records (EMRs) / Enrollments / Epidemiology / Health care / Health care facilities / Hypertension / Identification / Internal Medicine / LoDoCo2 / Multicenter / Participation / Patients / Personnel / Recruitment / Screening / Text-mining / Trials / Validation studies