scholarly journals Clinical research data management in the United States: Where we've been and where we're going

2021 ◽  
Author(s):  
Mary Banach ◽  
Kaye H Fendt ◽  
Johann Proeve ◽  
Dale Plummer ◽  
Samina Qureshi ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Clinical Data ◽  
Management Practices ◽  
The United States ◽  
Research Data ◽  
Fda Guidance ◽  
Research Data Management ◽  
Challenges And Opportunities ◽  
Clinical Research Data

With the focus of the COVID-19 pandemic, we wanted to reach all stakeholders representing communities concerned with good clinical data management practices. We wanted to represent not only data managers but bio-statisticians, clinical monitors, data scientists, informaticians, and all those who collect, organize, analyze, and report on clinical research data. In our paper we will discuss the history of clinical data management in the US and its evolution from the early days of FDA guidance. We will explore the role of biomedical research focusing on the similarities and differences in industry and academia clinical research data management and what we can learn from each other. We will talk about our goals for recruitment and training for the CDM community and what we propose for increasing the knowledge and understanding of good clinical data practice to all – particularly our front-line data collectors i.e., nurses, medical assistants, patients, other data collectors. Finally, we will explore the challenges and opportunities to see CDM as the hub for good clinical data research practices in all of our communities.We will also discuss our survey on how the COVID-19 pandemic has affected the work of CDM in clinical research.

scholarly journals VARIABILITY IN CLINICAL RESEARCH DATA MANAGEMENT PRACTICES: LESSONS FROM THE MALARIA COMMUNITY

2017 ◽  
Vol 2 (Suppl 2) ◽  
pp. A19.1-A19
Author(s):  
Amélie Julé ◽  
Hazel Ashurst ◽  
Laura Merson ◽  
Piero Olliaro ◽  
Vicki Marsh ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

Biomedical and Clinical Research Data Management

2021 ◽  
pp. 532-543
Author(s):  
Matthias Ganzinger ◽  
Enrico Glaab ◽  
Jules Kerssemakers ◽  
Sven Nahnsen ◽  
Ulrich Sax ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

scholarly journals Health Sciences Libraries Advancing Collaborative Clinical Research Data Management in Universities

2018 ◽  
Vol 7 (2) ◽  
pp. e1130
Author(s):  
Tania Bardyn ◽  
◽  
Emily Patridge ◽  
Michael Moore ◽  
Jane Koh ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Health Sciences ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

scholarly journals Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective

2019 ◽  
Vol 28 (01) ◽  
pp. 140-151 ◽  
Author(s):  
Jonathan P. Bona ◽  
Fred W. Prior ◽  
Meredith N. Zozus ◽  
Mathias Brochhausen
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Clinical Research ◽  
Data Management ◽  
Clinical Data ◽  
Research Data ◽  
Biomedical Ontologies ◽  
One To One ◽  
Data Elements ◽  
Clinical Research Data

Objectives: There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms “semantic integration" and “knowledge representation“. This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). Methods: We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. Results: We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. Conclusions: We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.

scholarly journals Analysis of professional competencies for the clinical research data management profession: implications for training and professional certification

2017 ◽  
Vol 24 (4) ◽  
pp. 737-745 ◽  
Author(s):  
Meredith N Zozus ◽  
Angel Lazarov ◽  
Leigh R Smith ◽  
Tim E Breen ◽  
Susan L Krikorian ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Scientific Discipline ◽  
Research Data ◽  
Professional Competencies ◽  
Degree Programs ◽  
Professional Certification ◽  
Research Data Management ◽  
Clinical Research Informatics ◽  
Clinical Research Data

Abstract Objective: To assess and refine competencies for the clinical research data management profession. Materials and Methods: Based on prior work developing and maintaining a practice standard and professional certification exam, a survey was administered to a captive group of clinical research data managers to assess professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. Results: Respondents confirmed a set of 91 professional competencies. As expected, differences were seen in job tasks between early- to mid-career and mid- to late-career practitioners. Respondents indicated growing variability in types of studies for which they managed data and types of data managed. Discussion: Respondents adapted favorably to the separate articulation of professional competencies vs foundational knowledge. The increases in the types of data managed and variety of research settings in which data are managed indicate a need for formal education in principles and methods that can be applied to different research contexts (ie, formal degree programs supporting the profession), and stronger links with the informatics scientific discipline, clinical research informatics in particular. Conclusion: The results document the scope of the profession and will serve as a foundation for the next revision of the Certified Clinical Data ManagerTM exam. A clear articulation of professional competencies and necessary foundational knowledge could inform the content of graduate degree programs or tracks in areas such as clinical research informatics that will develop the current and future clinical research data management workforce.

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