Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Dec 19, 2020)
Date Submitted: Aug 29, 2020
Open Peer Review Period: Aug 29, 2020 - Sep 6, 2020
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Real-time autOmatically updated data warehOuse in healThcare (ROOT: An Innovative and Automated Data Collection System)
ABSTRACT
Background:
The American Society of Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often not recorded in an organized way, and converting them into a structured format can be time-consuming. The Clinical Data Warehouse is a database that consolidates data from various clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated in clinical practice, so applying them to clinical research is difficult because they are not structured and formatted to find key-point contents.
Objective:
To determine how best to organize a huge amount of clinical data to evaluate the clinical features and outcomes of upper aerodigestive tract cancers, including cancers of the head and neck, esophagus, lung, and thymus and mesothelioma.The Real-time autOmatically updated data warehOuse in healThcare uses six main areas to describe the journey of cancer patients.
Methods:
In this study, we developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. We used data from patients diagnosed at Samsung Medical Center from 2008 to 2020.
Results:
We collected comprehensive clinical data for 67,617 patients across 6 tumor types: 28,954 with non-small-cell lung cancer; 2,540 with small-cell lung cancer; 30,035 with head and neck cancer; 4,950 with esophageal cancer; 966 with thymic cancer; and 172 with mesothelioma. The results of a longitudinal molecular study, such as EGFR mutations, ALK tests, and NGS, were also included. Scattered information was integrated and automatically built up to match the cohort, allowing users to instantly capture most updated test results and treatment outcomes.
Conclusions:
This is a landmark study documenting the successful construction of a real-time updating system for big medical data based on the CDW program.
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Copyright
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