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Comparing methods for identifying pancreatic cancer patients using electronic data sources.

Jeff Friedlin1, Marc Overhage, Mohammed A Al-Haddad

  • 1Regenstrief Institute, Inc.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary

Natural language processing (NLP) technology offers a more accurate method for identifying pancreatic cancer in patients with pancreatic cysts compared to International Classification of Diseases, 9th Edition (ICD-9) codes, improving diagnostic specificity.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Health Services Research

Background:

  • Accurate identification of pancreatic cancer is crucial for patient management.
  • Electronic health record data, including International Classification of Diseases, 9th Edition (ICD-9) codes, are often used for cohort identification.
  • Limitations in coding accuracy can impact research and clinical care.

Purpose of the Study:

  • To evaluate the accuracy of two electronic methods for identifying pancreatic cancer in patients with pancreatic cysts.
  • To compare the performance of International Classification of Diseases, 9th Edition (ICD-9) codes versus natural language processing (NLP) technology.
  • To investigate factors contributing to identification failures.

Main Methods:

  • A cohort of pancreatic cyst patients was analyzed.

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  • Pancreatic cancer identification was performed using ICD-9 codes and NLP technology.
  • Both methods were validated against a human-reviewed surgical database (gold standard).

    • Main Results:

    • Both ICD-9 codes and NLP demonstrated high sensitivity for pancreatic cancer detection.
    • The NLP method significantly outperformed ICD-9 codes in specificity and positive predictive value (PPV).
    • NLP required only a marginal increase in time and effort compared to ICD-9 coding.

    Conclusions:

    • Natural language processing (NLP) technology provides a more accurate electronic method for identifying pancreatic cancer in this cohort.
    • ICD-9 coding accuracy is influenced by factors such as the identification algorithm, cancer type, and co-existing precancerous or similar conditions.
    • NLP is a valuable tool for improving the precision of cancer cohort identification in electronic health records.