Common data elements for substance use disorders in electronic health records: the NIDA Clinical Trials Network experience

Addiction ◽  
2012 ◽  
Vol 108 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Udi E. Ghitza ◽  
Robert E. Gore-Langton ◽  
Robert Lindblad ◽  
David Shide ◽  
Geetha Subramaniam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Substance Use ◽  
Electronic Health Records ◽  
Substance Use Disorders ◽  
Common Data Elements ◽  
Health Records ◽  
Electronic Health ◽  
Data Elements

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

scholarly journals Phenotyping issues for exploring electronic health records to design clinical trials

2020 ◽  
Vol 17 (4) ◽  
pp. 402-404
Author(s):  
Jill Schnall ◽  
LingJiao Zhang ◽  
Jinbo Chen
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Electronic Health Record ◽  
Statistical Methods ◽  
Gold Standard ◽  
The Other ◽  
Health Record ◽  
Health Records ◽  
Electronic Health ◽  
Downstream Analysis

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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