Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2020  |  Volume : 11  |  Issue : 1  |  Page : 14

Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research

1 Department of Laboratory Medicine, Yale University School of Medicine; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
2 Center for Outcomes Research and Evaluation, Yale New Haven Hospital; Interdepartmental Program in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA
3 Department of Information Technology, Yale New Haven Health, New Haven, CT, USA

Correspondence Address:
Dr. Wade L Schulz
Department of Laboratory Medicine, 55 Park Street PS502A, New Haven, CT
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_15_20

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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 2992 unique specimens belonging to 2815 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 of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 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.

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