Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center

Overview

Journal JCO Clin Cancer Inform

Specialty Medical Informatics

Date 2023 Mar 31

PMID 37001040

Authors

Jeremy Petch

Joel Kempainnen

Christopher Pettengell

Steven Aviv

Bill Butler

Greg Pond

Ashirbani Saha

Jessica Bogach

Alexandria Allard-Coutu

Peter Sztur

Jonathan Ranisau

Mark Levine

Affiliations

Soon will be listed here.

Abstract

Purpose: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system.

Methods: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine.

Results: The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95.

Conclusion: This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

Citing Articles

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Moulson R, Law J, Sacher A, Liu G, Shepherd F, Bradbury P Curr Oncol. 2024; 31(4):1947-1960.

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