Use of an Electronic Health Record to Optimize Site Performance in Randomized Clinical Trials

2014 ◽  
Vol 05 (01) ◽  
Author(s):  
Yvette Henry Valerie Harkins
2020 ◽  
Vol 17 (4) ◽  
pp. 402-404
Author(s):  
Jill Schnall ◽  
LingJiao Zhang ◽  
Jinbo Chen

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.


2019 ◽  
Vol 16 (3) ◽  
pp. 306-315 ◽  
Author(s):  
Vanita R Aroda ◽  
Patricia R Sheehan ◽  
Ellen M Vickery ◽  
Myrlene A Staten ◽  
Erin S LeBlanc ◽  
...  

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.


2020 ◽  
Vol 15 (1) ◽  
pp. 5-21
Author(s):  
Konstantinos Vezertzis ◽  
George I. Lambrou ◽  
Dimitrios Koutsouris

Background: According to European legislation, a clinical trial is a research involving patients, which also includes a research end-product. The main objective of the clinical trial is to prove that the research product, i.e. a proposed medication or treatment, is effective and safe for patients. The implementation, development, and operation of a patient database, which will function as a matrix of samples with the appropriate parameterization, may provide appropriate tools to generate samples for clinical trials. Aim: The aim of the present work is to review the literature with respect to the up-to-date progress on the development of databases for clinical trials and patient recruitment using free and open-source software in the field of endocrinology. Methods: An electronic literature search was conducted by the authors from 1984 to June 2019. Original articles and systematic reviews selected, and the titles and abstracts of papers screened to determine whether they met the eligibility criteria, and full texts of the selected articles were retrieved. Results: The present review has indicated that the electronic health records are related with both the patient recruitment and the decision support systems in the domain of endocrinology. The free and open-source software provides integrated solutions concerning electronic health records, patient recruitment, and the decision support systems. Conclusions: The patient recruitment relates closely to the electronic health record. There is maturity at the academic and research level, which may lead to good practices for the deployment of the electronic health record in selecting the right patients for clinical trials.


2020 ◽  
Vol 17 (3) ◽  
pp. 237-242 ◽  
Author(s):  
Monica M Bertagnolli ◽  
Brian Anderson ◽  
Andre Quina ◽  
Steven Piantadosi

Clinical trials provide evidence essential for progress in health care, and as the complexity of medical care has increased, the demand for such data has dramatically expanded. Conducting clinical trials has also become more complicated, evolving to meet increasing challenges in delivering clinical care and meeting regulatory requirements. Despite this, the general approach to data collection remains the same, requiring that researchers submit clinical data in response to study treatment protocols, using precisely defined data structures made available in study-specific case report forms. Currently, research data management is not integrated within the patient’s clinical care record, creating added burden for clinical staff and opportunities for error. During the past decade, the electronic health record has become standard across the US healthcare system and is increasingly used to collect and analyze data reporting quality metrics for clinical care delivery. Recently, electronic health record data have also been used to address clinical research questions; however, this approach has significant drawbacks due to the unstructured and incomplete nature of current electronic health record data. This report describes steps necessary to use the electronic health record as a tool for conducting high-quality clinical research.


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