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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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CUSTOM-SEQ: a prototype for oncology rapid learning in a comprehensive EHR environment.

Jeremy L Warner1, Lucy Wang2, William Pao3

  • 1Department of Medicine, Division of Hematology/Oncology, Vanderbilt University, Nashville, TN, USA. Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. jeremy.warner@vanderbilt.edu.

Journal of the American Medical Informatics Association : JAMIA
|March 25, 2016
PubMed
Summary

CUSTOM-SEQ automatically tracks cancer mutation outcomes using electronic health records. This system provides real-time survival statistics to advance precision oncology and evidence-based treatment decisions.

Keywords:
electronic health recordsgenomicshealth information managementinformation scienceneoplasmsprecision medicine

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Precision oncology relies on understanding complex cancer genomes.
  • Tracking mutation-specific outcomes is challenging with current methods.
  • A need exists for automated systems to analyze real-world data.

Purpose of the Study:

  • To develop CUSTOM-SEQ, a system for automatically calculating and displaying mutation-specific survival statistics.
  • To support evidence-based querying in precision oncology.
  • To create a proof-of-principle for a continuously updating outcomes system.

Main Methods:

  • Included patients with cancer genotyping and extracted clinical data.
  • Utilized algorithms to process electronic health record data.
  • Regularly refreshed results and highlighted significant findings.

Main Results:

  • Analyzed 4310 patients with a median follow-up of 17 months.
  • Confirmed EGFR mutations in lung cancer correlate with superior survival (HR=0.53, P<.001).
  • Identified GNAQ mutations in melanoma associated with inferior survival (HR=3.42, P<.001).
  • Found smoking status not prognostic for EGFR-mutated lung cancer.

Conclusions:

  • CUSTOM-SEQ is a novel rapid learning system for precision oncology.
  • Continuous data updates enhance the evidence base beyond retrospective studies.
  • Future work will enable interactive population exploration for deeper insights.