Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system

Our plain-English summary of Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system, by H.A. Jung, O. Jeong, D.K. Chang, et al (Translational Lung Cancer Research, 2021).

The study

• Authors: H.A. Jung, O. Jeong, D.K. Chang, S. Park, J.M. Sun, S.H. Lee, J.S. Ahn, M.J. Ahn, K. Park
• Journal: Translational Lung Cancer Research
• Published: 2021;10(10):3865-3874
• Cited by: 13 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Background: The American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers' clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.

Methods: The Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008-2020 were used.

Results: Comprehensive clinical data for 67,617 patients across six 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 were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.

Conclusions: This landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.

2021 Translational Lung Cancer Research. All rights reserved.

In plain English

The authors described ROOT, a computer system they built at the Samsung Medical Center in Seoul, Republic of Korea. It automatically pulled data from hospital electronic records and organized them for cancer research. It covered 67,617 patients diagnosed from January 2008 to January 2020: 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 system gathered more than 700 clinical data points, processed free text and updated every 24 hours. In a check, 200 randomly selected patients per cohort were compared against medical records. Clinical stage was confirmed in 95.6% of non-small cell lung cancer patients, and survival data were more than 95% accurate. Recurrence data for stage I to III non-small cell lung cancer patients after curative-intent surgery were 95% accurate. The article did not study asbestos and gave no mesothelioma results beyond that cohort count. The authors concluded the system was successfully developed but still at an early stage.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Description of the development and validation of an automated hospital data collection system (not an epidemiological or clinical outcome study)
• Who was studied: 67,617 patients diagnosed at Samsung Medical Center, Seoul, from January 2008 to January 2020: 28,954 with non-small cell lung cancer, 2,540 with small cell lung cancer, 30,035 with head and neck cancer (23,578 of them thyroid cancer), 4,950 with esophageal cancer, 966 with thymic cancer and 172 with mesothelioma (site of mesothelioma not specified)
• Exposure: No asbestos, fiber or occupational exposure was studied
• Main results:
  • Across the six main areas of each patient's cancer journey, the system collected more than 70 essential elements and 700 clinical data points
  • Extracted data were automatically uploaded every 24 hours
  • For validation, 200 patients were randomly selected from each cohort and compared with the actual medical records
  • Clinical stage was confirmed in 95.6% of non-small cell lung cancer patients
  • Survival data were more than 95% accurate and had no missing values
  • Recurrence data, checked in stage I to III non-small cell lung cancer patients after curative-intent surgery, were 95% accurate compared with chart review by a medical oncologist
• The authors' conclusion: The authors concluded that they had successfully developed an algorithm to retrieve, analyze and automatically update clinical data warehouse data on a cancer patient cohort in real time
• Limitations the authors state:
  • The system was at an early stage and had constraints in sensitivity and data specificity; algorithm development was ongoing
  • Applying the algorithm to other organs, cancers or diseases would need to be checked for problems

Topics

Medical terms in this study, explained in our Lexicon: Mesothelioma, Disease Level, Lung Cancer, Esophageal Cancer, EGFR, Next-Generation Sequencing (NGS).

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