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. 2024 Apr 19;31(5):1126-1134.
doi: 10.1093/jamia/ocae042.

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

Jacqueline Honerlaw  1   2 , Yuk-Lam Ho  1   2 , Francesca Fontin  1   2 , Michael Murray  1   2 , Ashley Galloway  1   2 , David Heise  3 , Keith Connatser  3 , Laura Davies  3 , Jeffrey Gosian  1   2 , Monika Maripuri  1   2 , John Russo  1   2   4 , Rahul Sangar  1   2 , Vidisha Tanukonda  1   5 , Edward Zielinski  1   2 , Maureen Dubreuil  2   6 , Andrew J Zimolzak  7   8 , Vidul A Panickan  2   9 , Su-Chun Cheng  2   9 , Stacey B Whitbourne  2   10   11   12 , David R Gagnon  2   13 , Tianxi Cai  2   9   14 , Katherine P Liao  2   12   15   9 , Rachel B Ramoni  16 , J Michael Gaziano  2   10   11   12 , Sumitra Muralidhar  16 , Kelly Cho  1   2   10   11   12
Affiliations

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

Jacqueline Honerlaw et al. J Am Med Inform Assoc. .

Abstract

Objective: Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research.

Materials and methods: The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site.

Results: The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development.

Discussion: The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping.

Conclusion: CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

Keywords: algorithms; electronic health records; library; phenomics.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Figure 1.
Figure 1.
CIPHER library user workflow. Researchers can contribute phenotype algorithms using our webform, browse definitions in the CIPHER phenotype knowledgebase, and visualize data in connected tools which link back to the knowledgebase.
Figure 2.
Figure 2.
Phenotype components in the knowledgebase. ICD, International Classification of Diseases; LOINC, Logical Observation Identifiers Names and Codes; MeSH, Medical Subject Headings. The knowledgebase collects and stores phenotype metadata elements based on the CIPHER standard.
Figure 3.
Figure 3.
CIPHER phenotype knowledgebase search interface. Researchers can use text search (A) and filters (B) to browse phenotypes.
Figure 4.
Figure 4.
Phenotype entry user interface. The phenotype webform allows validation of standard vocabularies (A), collection of phenotype metadata using the CIPHER standard (B), and viewing of phenotype submission status in the user’s personal phenotype dashboard (C).
Figure 5.
Figure 5.
Data visualization tools in CIPHER. The Phecode to ICD Map facilitates the understanding of ICD-9 and -10 codes to phecode mappings (A). The KESER network visualizes the network structure of EHR codes, learned using co-occurrence summary level data from VA and Mass General Brigham (B). Users can navigate from a phenotype present in the KESER network to the phenotype article describing the algorithm in the knowledgebase (C).
See this image and copyright information in PMC

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