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Clinical Research Informatics
Clinical Research Informatics (CRI) is a multidisciplinary application of Health Information Technology. CRI involves storing and mining data from a clinical data warehouse for research and development. It helps clinicians manage new information that is discovered during research. Clinical Research Informatics is a form of informatics that utilizes new knowledge pertaining to health. It is a way for clinicians to manage new information that research creates. Clinical research informatics emerged from Biomedical informatics and it has become a very vital aspect of health informatics. As defined by the United States National Institutes of Health (NIH), “Clinical research falls into three categories, Patient-oriented, epidemiological and behavioral, and outcome and health services research.” The need for research has remained constant, while many other aspects of health services have began to speed up. Clinical Research Informatics is needed to speed up clinical research and make is more easily accessible. Due to evolution in the medical industry, new tools were needed that could be applied to clinical Research Informatics. Columbia University defines the major goals of Clinical Research Informatics as Sharing big health data, learning health system, improve workflow, and support interdisciplinary team science. It attempts to create efficiency within the process of “research, clinical trials, medical centers, and community practice.” Clinical research Informatics is also defined as combining humans and information technology to the technology can help the human better perform, not to replace but to aid in human research (Richesson & Andrews)
CRI uses a systematic approach of analyzing data and managing knowledge base of health information to provide more efficient and better health care, with support of clinical research, and assisting & providing clinical tools to healthcare researchers (Embi et all). CRI incorporates the most sophisticated and advanced technologies to develop, collect, discover, process and analyze substantial patient data to further improve and strengthen clinical research capabilities and help researchers meet objectives of new diagnostic tests, new treatments and much more. (Kahn, Weng).
With the increased use of EHR and the vast amount of stored medical data, there is a growing need and opportunity for clinical data mining for analytic purposes. (Simpao et al 2014). CRI itself is a fairly new field of medical or bioinformatics that presently has many challenges such as under resourced, trust in sharing of information, informed consent, and recruiting patients to participant. Nonetheless, there is also a future chance of advancement and success of CRI with the growing areas of specialized clinical data sets available.
One area in particular that CRI is involved with is the data-sharing infrastructure in cancer research - CaBIG, (cancer Biomedical Informatics Grid). The caBIG is an open source, open access network for sharing and storing clinical data to assist cancer patients worldwide. It is a way to share data through multiple hospitals, clinics, labs and more so as not to have to repeat test, procedures or therapies. Based on the needs assessment, CRI tools like CaBIG and others provide a way to collaborate, and share analysis, results and other clinical information to enhance cancer diagnosis, prevention and possible treatment. Clinical Research Informatics can be used to convert new knowledge and scientifically findings into applicable procedures or therapies. It applies to health informatics because it helps combine the work of a researcher, clinician, and patient through the use of technology. CRI can help research and clinical trials become actual patented technology, such as mobile health applications or even something as large as a Clinical Data Warehouse. According to the CRITC, Clinical Research Informatics has been utilized by creating registries for new diseases and distributing that information as well as patient education kiosk in clinic waiting rooms.
Another CRI application of major importance is clinical trials involving cardiovascular, diabetes, rare diseases, and other health related issues. Many CRI tools to statistically analyze, explore, and share are necessary in order to manage the vast amount of clinical data and images generated from all new clinical trials. Multiple new interfaces are being developed to aid humans in the industry of clinical data.
For example, a program called OpenClinica was designed to organize and manage clinical data trials. It helps design individual studies, extract, monitor and submit data.
American Medical Informatics Association
US National Library of Medicine National Institutes of Health
FSU Library- ProQuest, SAGE
Clinical Research Informatics
Translational Bioinformatics is the use of computers, software, and other technology along with information and biological sciences to analyze data before and after clinical trials to transform into useable comparable information.
A Clinical Data Warehouse is necessary to store clinical data from multiple sources, able to provide statistical and other health analysis across groups.
EHR- Electronic Health Records are important in the exchange of information for the purpose of clinical research.
AMIA - American Medical Informatics Association is the professional organized group of health care professionals in the field of information technology research, bioinformatics, and health informatics. This association realizes the importance of CRI and provides opportunity for members to get actively involved. AMIA currently is promoting CRI and is offering an upcoming summit for
professionals in this rapidly growing area to network, interact and share designs and information with peers.
CRITC-The Clinical Research Informatics and Technology Consultation is a consulting group that aids with data management and informatics as well as research.
NIH: National Institute of Health supports the nations scientific studies and health discoveries.
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Embi, P.J., Payne, P.R.O. (2009). Clinical research information: challenges, opportunities, and definition for an emerging domain. Journal of American Medical Informatics Association.16(3).316-327 Retrieved from doi:10.1197/jamia.3005
Kahn, M.G., Weng, C. (2012). Clinical research informatics: a conceptual perspective. Journal of American Medical Information Association. 19(1) 36-42 Retrieved from doi:10.1136/amiajnl-2012-000968
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Clinical Data Capture
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Richesson, Rachel L., and James E. Andrews.
Clinical Research Informatics
. London: Springer, 2012. Print.
Simpao, A.F., Ahumada, L. M., Calvez, J.A., Rehman, M.A., (2014). A review of Analytics and clinical informatics in health care. Journal of Medical Systems. 38(45) 1-7. Retrieved from doi:10.1007/sl0916-014-0045x
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