Research Ideas and Outcomes :
Conference Abstract
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Corresponding author: Marisa L Conte (meese@umich.edu)
Received: 30 Sep 2022 | Published: 12 Oct 2022
© 2022 Marisa Conte, Allen Flynn, Peter Boisvert, Zach Landis-Lewis, Rachel Richesson, Charles Friedman
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Conte ML, Flynn AJ, Boisvert P, Landis-Lewis Z, Richesson RL, Friedman CP (2022) Computable phenotypes for cohort identification: core content for a new class of FAIR Digital Objects. Research Ideas and Outcomes 8: e95856. https://doi.org/10.3897/rio.8.e95856
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Introduction
We present current work to develop and define a class of digital objects that facilitates patient cohort identification for clinical studies, such that these objects are Findable, Accessible, Interoperable, and Reusable (FAIR) (
Defining computable phenotypes
In biomedical informatics, 'phenotyping' describes a data-driven approach to identifying a group of individuals sharing observable characteristics of interest, generally related to a disease or condition, and a 'computable phenotype' (CP) is a machine-processable expression of a phenotypic pattern of these characteristics (
For the purposes of this work, we are interested in CPs derived from data contained in electronic health record (EHR) systems. This includes both structured data, e.g. codes for diseases, diagnoses, procedures, or laboratory tests, and unstructured data, e.g. free text including patient histories, clinical observations, discharge summaries, and reports. Thus, we define computable phenotype FDOs (CP-FDOs) as a class of FDO that packages an executable EHR-derived CP together with documentation needed to implement and use it effectively for creating cohorts of individuals with similar observable characteristics from EHR data sets.
Importance of portable and FAIR CPs
There is tremendous excitement for using real-world EHR data to discover important findings about human health and well-being. However, for discovery to happen, researchers need mechanisms like CPs to identify study cohorts for analysis. Beginning in the early 2010s, a growing literature explores various methods for the secondary use of EHR data for patient phenotyping to arrive at consistent study cohorts (
Our current focus is on packaging CPs inside FDOs for classifying patients as having or not having a phenotype of interest. This can be done within an individual health system, or at scale across a clinical data research network. Using CPs for cohort identification can reduce the time and expense of traditional data set building and clincal trial recruitment, and expand the potential scope of a study population(
Creating and validating CPs requires time, resources, and both clinical and technical expertise. One estimate is that it can take 6-10 months to develop and validate a CP (
There may also be significant advantages to making CPs FAIR. These include transparency in cohort selection, and better generalizability of results. FAIR CPs may also increase the potential for robust comparisons of data from related studies, leading to better evidence synthesis to improve delivery of care and ultimately human health.
Defining a new class of FDOs to hold and convey CPs
We believe that packaging validated CPs inside digital objects may alleviate many of the pressures mentioned above, and contributes to making both the processes and products of clinical research more FAIR. To this end, our current work focuses on packaging a validated CP inside a machine-processable FDO. The phenotype of interest identifies pediatric and adult patients with a rare disease (
The goals of this work are:
Conclusion
Computable phenotypes, packaged as FDOs, may increase the potential both for the portability of a phenotype and the reusability of data resulting from its implementation. Providing CPs as executable FDOs may also reduce barriers to portability and local implementation. In this presentation, we describe our work to develop a FDO computable phenotype from an existing validated phenotype. Lessons learned from this process will increase our understanding of both the technical requirements, and how to address necessary components of abstraction, binding, and encapsulation so that these can function as FAIR Digital Objects.
computable biomedical knowledge, portability, reuse
Marisa L Conte
First International Conference on FAIR Digital Objects, presentation