Journal:Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research
| Full article title | Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research |
|---|---|
| Journal | Journal of Pathology Informatics |
| Author(s) | Durant, Thomas J.S.; Gong, Guannan; Price, Nathan; Schilz, Wade L. |
| Author affiliation(s) | Yale New Haven Hospital, Yale New Haven Health |
| Primary contact | Email: Log in required |
| Year published | 2020 |
| Volume and issue | 11 |
| Article # | 14 |
| DOI | 10.4103/jpi.jpi_15_20 |
| ISSN | 2153-3539 |
| Distribution license | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License |
| Website | http://www.jpathinformatics.org/article.asp |
| Download | http://www.jpathinformatics.org/temp/JPatholInform11114-7412119_203521.pdf |
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Abstract
Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research.
Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting.
Results: Between June 2018 and December 2018, we identified 2,992 unique specimens belonging to 2,815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure e-mail notifications were sent to investigators to automatically notify them of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2,000 results per second.
Conclusion: This work demonstrates that real-world clinical data can be analyzed in real-time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification.
Keywords: biobanking, biomedical research, biospecimen science, clinical specimens, real-time identification, translational research
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This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added.
