Review of Clinical Research Informatics

Abstract

Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

Health Equity in Clinical Research Informatics

“…Clinical research informatics (CRI) is a sub-discipline within biomedical and health informatics that focuses on the analysis, interpretation, and presentation of clinical knowledge generated through informatics [1]. This definition by Embi and Payne [1], dates back over a decade ago and has previously been mentioned in this journal by Solomonides [2]. Acknowledging and including topics that have flourished since Embi and Payne's definition [2], the most notable addition is Artificial Intelligence (AI), referring in a broad sense to the ability of technology to resemble functions and processes of human beings.…”

“…This definition by Embi and Payne [1], dates back over a decade ago and has previously been mentioned in this journal by Solomonides [2]. Acknowledging and including topics that have flourished since Embi and Payne's definition [2], the most notable addition is Artificial Intelligence (AI), referring in a broad sense to the ability of technology to resemble functions and processes of human beings. Machine Learning (ML) [3], and Natural Language Processing (NLP) [4,5], are notable subgroups of AI technologies using relevant clinical data sets to advance the representation and understanding of a problem.…”