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Oncology Information System Data - Implications for 'Big Data'.

Alexis Andrew Miller1

  • 1Department of Radiation Oncology, Illawarra Cancer Care Centre, Illawarra Shoalhaven Local Health District, Wollongong NSW Australia.

Studies in Health Technology and Informatics
|July 4, 2018
PubMed
Summary
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Oncology Information Systems data completion rates are low, hindering Big Data initiatives. Improving data quality is crucial for accurate cancer research and analysis.

Area of Science:

  • Medical Informatics
  • Oncology Data Management

Background:

  • Oncology Information Systems (OIS) are crucial for managing cancer patient data.
  • A minimal medical dataset was analyzed over a 10-year period up to 2016.

Purpose of the Study:

  • To assess the completeness and quality of medical data within an OIS.
  • To evaluate the suitability of this data for Big Data analytics.

Main Methods:

  • Analysis of 12,906 diagnoses (ICD10: C00-C80) registered between 2006 and 2016.
  • Evaluation of data completion rates across different cancer sites and quality-assured sites.

Main Results:

  • Overall data completion rate was only 18.84%, with significant site-specific variations.
  • Quality-assured Head & Neck and Rectum sites showed high completion rates (97.4% and 88.74%).
Keywords:
Big DataIncomplete dataMOSAIQOncology information systemQuality Assurance

Related Experiment Videos

  • Sites lacking clinician attention, such as Lung and Brain, exhibited poor to largely incomplete data (23.23% and 2.01% completion).

    • Conclusions:

    • The low and variable data completion rates compromise the reliability of oncological data for Big Data efforts.
    • Current data quality is insufficient for advanced analytics like data extrapolation or mining without domain expert input.
    • The fundamental requirements for Big Data are not met by the analyzed oncological dataset.