Development & Deployment of a Real-time Healthcare Predictive Analytics Platform

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR)...

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Detalles Bibliográficos
Autores principales: Boussina, Aaron, Shashikumar, Supreeth, Amrollahi, Fatemeh, Pour, Hayden, Hogarth, Michael, Nemati, Shamim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10120788/
https://www.ncbi.nlm.nih.gov/pubmed/37090509
http://dx.doi.org/10.1101/2023.04.10.23288373
Descripción
Sumario:The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.

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