Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials

For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.

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Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence

BMC Medical Informatics and Decision Making ◽

10.1186/1472-6947-13-37 ◽

2013 ◽

Vol 13 (1) ◽

Cited By ~ 50

Author(s):

Felix Köpcke ◽

Benjamin Trinczek ◽

Raphael W Majeed ◽

Björn Schreiweis ◽

Joachim Wenk ◽

...

Keyword(s):

Clinical Trials ◽

Electronic Health Record ◽

Retrospective Analysis ◽

Patient Recruitment ◽

Health Record ◽

Data Completeness ◽

Electronic Health ◽

Evaluation Of Data

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Establishing an electronic health record–supported approach for outreach to and recruitment of persons at high risk of type 2 diabetes in clinical trials: The vitamin D and type 2 diabetes (D2d) study experience

Clinical Trials ◽

10.1177/1740774519839062 ◽

2019 ◽

Vol 16 (3) ◽

pp. 306-315 ◽

Cited By ~ 4

Author(s):

Vanita R Aroda ◽

Patricia R Sheehan ◽

Ellen M Vickery ◽

Myrlene A Staten ◽

Erin S LeBlanc ◽

...

Keyword(s):

Type 2 Diabetes ◽

Vitamin D ◽

Clinical Trials ◽

Electronic Health Record ◽

System Level ◽

Health Record ◽

Recruitment Methods ◽

Clinical Environments ◽

Electronic Health

Aims To establish recruitment approaches that leverage electronic health records in multicenter prediabetes/diabetes clinical trials and compare recruitment outcomes between electronic health record–supported and conventional recruitment methods. Methods Observational analysis of recruitment approaches in the vitamin D and type 2 diabetes (D2d) study, a multicenter trial in participants with prediabetes. Outcomes were adoption of electronic health record–supported recruitment approaches by sites, number of participants screened, recruitment performance (proportion screened who were randomized), and characteristics of participants from electronic health record–supported versus non–electronic health record methods. Results In total, 2423 participants were randomized: 1920 from electronic health record (mean age of 60 years, 41% women, 68% White) and 503 from non–electronic health record sources (mean age of 56.9 years, 58% women, 61% White). Electronic health record–supported recruitment was adopted by 21 of 22 sites. Electronic health record–supported recruitment was associated with more participants screened versus non–electronic health record methods (4969 vs 2166 participants screened), higher performance (38.6% vs 22.7%), and more randomizations (1918 vs 505). Participants recruited via electronic health record were older, included fewer women and minorities, and reported higher use of dietary supplements. Electronic health record–supported recruitment was incorporated in diverse clinical environments, engaging clinicians either at the individual or the healthcare system level. Conclusion Establishing electronic health record–supported recruitment approaches across a multicenter prediabetes/diabetes trial is feasible and can be adopted by diverse clinical environments.

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Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

Yearbook of Medical Informatics ◽

10.1055/s-0038-1638592 ◽

2008 ◽

Vol 17 (01) ◽

pp. 128-144 ◽

Cited By ~ 98

Author(s):

G. K. Savova ◽

K. C. Kipper-Schuler ◽

J. F. Hurdle ◽

S. M. Meystre

Keyword(s):

Systematic Review ◽

Data Mining ◽

Electronic Health Record ◽

Information Extraction ◽

System Performance ◽

Health Record ◽

Clinical Text ◽

Text Corpora ◽

Electronic Health ◽

Published Research

Summary Objectives We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.

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