Clinical research informatics

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Clinical research informatics (CRI) is a subdomain of biomedical and health informatics that focuses on the application of informatics to the discovery and management of new knowledge relating to health and disease. It includes management of information related to clinical trials, and also involves informatics related to secondary research use of clinical data. Clinical research informatics and Translational Bioinformatics are the primary domains related to informatics activities that support translational research[1].


The definition of CRI is in flux as it emerges as a subdiscipline. A 2009 definition focused CRI specifically on the domain of clinical research (human clinical trials and studies) but acknowledged that CRI also touches on the domain of translational research[2] (in medicine, translational research activities are those which precede and follow human clinical research activities; sometimes referred to as "bench to bedside" and and "bedside to community," respectively]).

A 2012 definition, however, took a wider view, suggesting that CRI "...focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials, clinical trials to academic health center practice, diffusion and implementation to community practice, and 'real world' outcomes"[3]. If this broader definition becomes widely adopted, CRI could merge with another emerging informatics subdomain, translational research informatics (TRI).

CRI related standards

CDISC develops several standards. Some of them are adopted by FDA for regulatory submissions. Operational Data Mode (ODM) is the most generic CRI CDISC standard.

CRI historical development

CRI is rapidly evolving and growing, in part due to increasingly complex clinical research workflow and information management challenges[2]. Underlying reasons for this evolution and growth include:

  • The rapid pace of biomedical science and the need for advances in medicine, which create pressure for clinical research to be conducted in a timely and efficient manner and also produce high-quality results[2]
  • The associated need to make clinical care data available for secondary use in support of clinical research[2]
  • The use of sophisticated biomedical research techniques that generate massive and ever-growing data sets (aka Big Data)[4]
  • The need for computer programs and other tools that can evaluate, combine, and visualize these large quantities of data not only on supercomputers, but also on PCs and workstations[4]
  • Challenges presented by the regulatory requirements associated with conducting clinical studies, including a trend toward conducting clinical trials in community practice settings instead of large academic health centers (AHCs)[2]

In addition to these factors, CRI development has been accelerated by an increase in the scope and pace of clinical and translational science advancements funded by programs such as the National Institutes of Health's (NIH) Roadmap for Medical Research initiative[2]. Roadmap programs related to CRI include:

Applications of CRI

Interventional Research

Traditionally, one main area of focus in CRI is supporting clinical trials that aim to evaluate the intervention or treatment by randomized controlled trials (RCTs)[6]. With the recent widespread adoption of EHR systems, CRI may be able to better support other approaches to interventional research such pragmatic clinical trials (PCTs) [6]. In contrast to RCTs, pragmatic clinical trials aim to evaluate the effectiveness of new treatments and interventions in real-world conditions [7]. Research cohorts within PCTs are determined using patient features and/or clinical features identified through the EHR which may offer a more accurate representation of the true patient population [7]. Despite the need to track the efficacy of new treatments after adoption into practice, there are still many challenges with conducting PCTs. Some informatics hurdles include challenges with data integration across multiple databases, identification of appropriate population cohorts and standardization of disease severity and progression [6].

Observational Research

In addition to interventional research, clinical research informatics can also provide the necessary infrastructure to support for observational research efforts. Observational research objectives center around evaluating treatment and/or patient outcomes that occur as a result of routine healthcare delivery. [6]. This non-experimental approach to research can be designed to evaluate based on cohort or cross-sectional grouping and can evaluate outcomes prospectively and retrospectively [6]. Essential research activities such as cohort identification, quality measures and treatment outcomes, all rely on querying and extraction of data from the EHR. The uptick in adoption of Common Data Models, such as i2b2 and OMOP, have allowed for better data standardization across institutions and has resulted in more opportunities for research based on “real world data” (RWD) [8]. Real world data often refers to data derived from real-world setting such as during healthcare delivery or health-related applications on mobile devices [8]. In addition, the use of large research networks such as the National Patient-Centered Clinical Research Network (PCORnet) have allowed for more large-scale observational studies to be conducted across different research institutions. One notable research initiative extracted data from the PCORnet network to understand the relationship between antibiotics administration and growth patterns in children [9]

Cohort Discovery

Clinical trials routinely struggle to meet their patient recruitment targets, which can result in early trial termination [10]. Many clinical research informatics efforts have centered round improving patient identification to improve overall clinical trial participant recruitment. Improvements in phenotyping EHR data could be a promising approach to improving participant recruitment in clinical trials.[8] One recommendation published in 2018 from the Clinical Trials Transformation Institutive called for use of “electronic health record queries, ICD-9 and ICD-10 de-identified records and geo-targeting disease data” as an avenue to improve cohort discovery and ultimately increase participant recruitment [10].

Clinical Data Mining

In recent years, Machine Learning has become a prominent approach to mining clinical data for research purposes. In a one notable paper by Rajkomar et al, the authors implemented deep learning techniques to mine EHR data to search for basic research questions such as clinical outcomes, risk of death, quality of care and risk of readmission [11]. Despite these authors illustrating significant progress in applying Machine Learning to retrospective research, there are still significant challenges associated with using this type of AI approach to build predicative models for prospective research studies [8].

Additional efforts

Other major initiatives, programs, and activities related to CRI include:

Related Articles

Related concepts


  1. American Medical Informatics Association (AMIA). Informatics areas: clinical research informatics [Online]. 2012 [cited 2012 Nov 25]; Available from: URL:
  2. Embi PJ, Payne PR. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc 2009;16:323, 325.
  3. Kahn MG, Weng C. Clinical research informatics: a conceptual perspective. J Am Med Inform Assoc 2012 Apr [cited 2012 Nov 25]; 19(e1):[e36-42]. Available from: URL:
  4. National Institutes of Health (NIH). Common fund makes new FY2010 wwards for National Centers for Biomedical Computing [Online]. [cited 2012 Nov 25]; Available from: URL:
  5. NIH National Center for Advancing Translational Sciences (NCATS). Clinical and Translational Science Awards [Online]. [cited 2012 Nov 25]; Available from: URL:
  6. Richesson RL, Horvath MM, Rusincovitch SA. Clinical research informatics and electronic health record data. Yearb Med Inform. 2014;9(1):215-23.
  7. Kalkman S, van Thiel G, Grobbee DE, van Delden JJM. Pragmatic clinical trials: ethical imperatives and opportunities. Drug Discov Today. 2018;23(12):1919-21.
  8. Solomonides A. Review of Clinical Research Informatics. Yearb Med Inform. 2020;29(1):193-202.
  9. Huang GD, Bull J, Johnston McKee K, Mahon E, Harper B, Roberts JN. Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative. Contemp Clin Trials. 2018;66:74-9.
  10. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18
  11. Block JP, Bailey LC, Gillman MW, Lunsford D, Boone-Heinonen J, Cleveland LP, et al. PCORnet Antibiotics and Childhood Growth Study: Process for Cohort Creation and Cohort Description. Acad Pediatr. 2018;18(5):569-76.

External resources

Submitted by Deb Woodcock

Applications of CRI submitted by Marisa Capachietti Lopez