Journal:Development of an informatics system for accelerating biomedical research

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Full article title Development of an informatics system for accelerating biomedical research (Version 2)
Journal F1000Research
Author(s) Navale, Vivek; Ji, Micehle; Vovk, Olga; Misquitta, Leonie; Gebremichael, Tsega; Garcia, Alison; Fann, Yang; McAuliffe, Matthew
Author affiliation(s) National Institutes of Health; General Dynamics Information Technology, Inc.; Sapient Government Services
Primary contact Email: Vivek dot Navale at nih dot gov
Year published 2020
Volume and issue 8
Article # 1430
DOI 10.12688/f1000research.19161.2
ISSN 2046-1402
Distribution license Creative Commons Attribution 4.0 International
Website https://f1000research.com/articles/8-1430/v2
Download https://f1000research.com/articles/8-1430/v2/pdf (PDF)
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

The Biomedical Research Informatics Computing System (BRICS) was developed to support multiple disease-focused research programs. Seven service modules are integrated together to provide a collaborative and extensible web-based environment. The modules—Data Dictionary, Account Management, Query Tool, Protocol and Form Research Management System, Meta Study, Data Repository, and Globally Unique Identifier—facilitate the management of research protocols, including the submission, processing, curation, access, and storage of clinical, imaging, and derived genomics data within the associated data repositories. Multiple instances of BRICS are deployed to support various biomedical research communities focused on accelerating discoveries for rare diseases, traumatic brain injuries, Parkinson’s disease, inherited eye diseases, and symptom science research. No personally identifiable information is stored within the data repositories. Digital object identifiers are associated with the research studies. Reusability of biomedical data is enhanced by common data elements (CDEs), which enable systematic collection, analysis, and sharing of data. The use of CDEs with a service-oriented informatics architecture enabled the development of disease-specific repositories that support hypothesis-based biomedical research.

Keywords: informatics system, biomedical repository, translational research, FAIR

Introduction

Biomedical informatics systems can be used for the management of heterogeneous data, testing of data analysis methods, dissemination of translational research, and the generation of high-throughput hypotheses.[1][2] In the past, many disease-focused research programs have collected data in dissimilar ways, which has resulted in difficulties for data aggregation and comparative analyses. For example, non-standard methods of data collection in traumatic brain injury (TBI) research have led to many different types of injuries to be classified within the same class of injury. To overcome this problem, in October 2007, the National Institute of Neurological Disorders and Stroke (NINDS), National Institute on Disability and Rehabilitation Research (NIDRR), the Defense and Veterans Brain Injury Center, and the Brain Injury Association of America sponsored a workshop to examine barriers to TBI clinical trial effectiveness. The workshop recommendation of improving data discoverability and integration in TBI research resulted in the development and implementation of common data elements (CDEs) and the Federal Interagency Traumatic Brain Injury Research (FITBIR) informatics system.[3]

A CDE is defined as a fixed representation of a variable collected within a specified clinical domain, interpretable unambiguously in human and machine-computable terms.[4] It consists of a precisely defined question with a set of permissible values as responses. Typically, CDE development for biomedical disease programs involves multiple steps: identifying a need for a CDE or group of CDEs, bringing together stakeholders and expert groups for selection, implementing various iterations and updates to initial CDE development based on ongoing input from the broader community, and finally endorsing of the CDEs for widespread usage and adoption by the stakeholder community.[5] Use of CDEs enhances data quality and consistency, which facilitates data reuse for clinical and translational research.

CDEs are used in various programs of clinical research, including in neuroscience[6], rare diseases research[7], and management of chronic conditions.[8] For clinical data lifecycle management, the use of CDEs provides a structured data collection process, which enhances the likelihood for data to be pooled and combined for meta-analyses, modelling, and post-hoc construction of synthetic cohorts for exploratory analyses.[9] Investigators working to develop protocols for data collection can also consult the NIH Common Data Element Resource Portal for using established CDEs for disease programs.[10]


References

  1. ↑ Sarkar, I.N. (2010). "Biomedical informatics and translational medicine". Journal of Translational Medicine 8: 22. doi:10.1186/1479-5876-8-22. PMC PMC2837642. PMID 20187952. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837642. 
  2. ↑ Payne, P.R.O. (2012). "Chapter 1: Biomedical knowledge integration". PLoS Computational Biology 8 (12): e1002826. doi:10.1371/journal.pcbi.1002826. PMC PMC3531314. PMID 23300416. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531314. 
  3. ↑ Thompson, H.J.; Vavilala, M.S.; Rivara, F.P. (2015). "Chapter 1: Common Data Elements and Federal Interagency Traumatic Brain Injury Research Informatics System for TBI Research". Annual Review of Nursing Research 33 (1): 1–11. doi:10.1891/0739-6686.33.1. PMC PMC4704986. PMID 25946381. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704986. 
  4. ↑ Silva, J.; Wittes, R. (1999). "Role of clinical trials informatics in the NCI's cancer informatics infrastructure". Proceedings AMIA Symposium: 950–4. PMC PMC2232686. PMID 10566501. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232686. 
  5. ↑ Zentzis, B. (15 May 2017). "Common Data Element (CDE)". Clinfowiki. https://clinfowiki.org/wiki/index.php/Common_Data_Element_(CDE). Retrieved 03 April 2018. 
  6. ↑ National Institutes of Health. "NINDS Commond Data Elements". National Institutes of Health. https://www.commondataelements.ninds.nih.gov/. Retrieved 03 April 2018. 
  7. ↑ Rubinstein, Y.R.; McInnes, P. (2015). "NIH/NCATS/GRDR Common Data Elements: A leading force for standardized data collection". Contemporary Clinical Trials 42: 78–80. doi:10.1016/j.cct.2015.03.003. PMC PMC4450118. PMID 25797358. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450118. 
  8. ↑ Moore, S.M.; Schiffman, R.; Waldrop-Valverde, D. et al. (2016). "Recommendations of Common Data Elements to Advance the Science of Self-Management of Chronic Conditions". Journal of Nursing Scholarship 48 (5): 437–47. doi:10.1111/jnu.12233. PMC PMC5490657. PMID 27486851. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490657. 
  9. ↑ Sheehan, J.; Hirschfeld, S.; Foster, E. et al. (2016). "Improving the value of clinical research through the use of Common Data Elements". Clinical Trials 13 (6): 671–76. doi:10.1177/1740774516653238. PMC PMC5133155. PMID 27311638. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133155. 
  10. ↑ "Common Data Element (CDE) Resource Portal". National Library of Medicine. National Institutes of Health. 3 January 2013. https://www.nlm.nih.gov/cde/glossary.html. Retrieved 03 April 2018. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.

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