CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data

Hazlehurst, Brian; Kurtz, Stephen E.; Masica, Andrew L.; Stevens, Victor J.; McBurnie, Mary Ann; Puro, Jon; Vijayadeva, Vinutha; Au, David H.; Brannon, Elissa D.; Sittig, Dean F.

doi:10.1016/j.ijmedinf.2015.06.002

Cited by 17 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the findings are not necessarily generalizable to commercial EHRs and non-VA institutions. However, our previous research suggests trigger portability given use of standardized codes (eg, those from Current Procedural Terminology and International Classification of Diseases), which could be particularly effective in the large data repositories of other health systems, both within and outside the United States, 25,44,45 or with health information exchanges 46 that are becoming more common. Furthermore, if American College of Radiology recommendations for standardizing lung imaging reports are adopted, triggers could become more widely used.…”

Section: Discussionmentioning

confidence: 99%

Computerized Triggers of Big Data to Detect Delays in Follow-up of Chest Imaging Results

Murphy

Meyer

Bhise

et al. 2016

Chest

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Computerized Triggers of Big Data to Detect Delays in Follow-up of Chest Imaging Results

Murphy

Meyer

Bhise

et al. 2016

Chest

Self Cite

View full text Add to dashboard Cite

show abstract

“…48,49 In the context of many healthcare systems, these initiatives have found ways to securely perform the following tasks: 1) distribute queries from authorized researchers through network software; 2) execute the queries against the local data; and 3) return aggregated results to the researcher. 47,50 Concurrent with interoperability efforts in technology and clinical data standards, 51 these distributed research networks use common data models to harmonize codified elements to be included in datasets. In this way, these networks are able to link disparate data to increase the scale of each research study.…”

Section: Sharing Datamentioning

confidence: 99%

Leveraging electronic health records for clinical research

Raman

Curtis

Andersson

et al. 2018

American Heart Journal

View full text Add to dashboard Cite

Electronic health records (EHRs) can be a major tool in the quest to decrease costs and timelines of clinical trial research, generate better evidence for clinical decision making, and advance health care. Over the past decade, EHRs have increasingly offered opportunities to speed up, streamline, and enhance clinical research. EHRs offer a wide range of possible uses in clinical trials, including assisting with prestudy feasibility assessment, patient recruitment, and data capture in care delivery. To fully appreciate these opportunities, health care stakeholders must come together to face critical challenges in leveraging EHR data, including data quality/completeness, information security, stakeholder engagement, and increasing the scale of research infrastructure and related governance. Leaders from academia, government, industry, and professional societies representing patient, provider, researcher, industry, and regulator perspectives convened the Leveraging EHR for Clinical Research Now! Think Tank in Washington, DC (February 18-19, 2016), to identify barriers to using EHRs in clinical research and to generate potential solutions. Think tank members identified a broad range of issues surrounding the use of EHRs in research and proposed a variety of solutions. Recognizing the challenges, the participants identified the urgent need to look more deeply at previous efforts to use these data, share lessons learned, and develop a multidisciplinary agenda for best practices for using EHRs in clinical research. We report the proceedings from this think tank meeting in the following paper.

show abstract

“…Several of the publications described large-scale efforts to improve the state of CRI through regional, national, or international consortia or funding models. Infrastructure initiatives included interoperable electronic health records, cloud computing, management of big data sources (such as genomics and imaging), collection of patient-reported outcomes, and multi-institution integration for comparative effectiveness research [9][10][11][12][13]. One crucial aspect of systems architectures for CRI is the ability to protect confidentiality of participants; articles in this group covered methods for securely sharing data across sites, detecting protected health information and pseudonymization [14][15][16].…”

Section: Architectures and Standardsmentioning

confidence: 99%

Clinical Research Informatics: Supporting the Research Study Lifecycle

Johnson¹

2017

Yearb Med Inform

View full text Add to dashboard Cite

SummaryObjectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years [2015][2016]. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

show abstract

CER Hub: An informatics platform for conducting comparative effectiveness research using multi-institutional, heterogeneous, electronic clinical data

Cited by 17 publications

References 30 publications

Computerized Triggers of Big Data to Detect Delays in Follow-up of Chest Imaging Results

Computerized Triggers of Big Data to Detect Delays in Follow-up of Chest Imaging Results

Leveraging electronic health records for clinical research

Clinical Research Informatics: Supporting the Research Study Lifecycle

Contact Info

Product

Resources

About