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Refining unspecific clinical codes in electronic health records using clinical text improves data specificity. This study demonstrates the feasibility of enhancing observational research and patient care through better data detail.

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Area of Science:

  • Health Informatics
  • Medical Data Analysis
  • Natural Language Processing in Healthcare

Background:

  • Electronic health record (EHR) databases are crucial for observational studies.
  • Unspecific clinical codes limit the precision of medical information in EHRs.
  • Refining these codes is essential for accurate data analysis and research.

Purpose of the Study:

  • To assess the feasibility of enhancing unspecific condition codes in a Dutch general practitioner (GP) EHR database.
  • To leverage clinical free text for improving code specificity.
  • To evaluate the impact of code refinement on data analysis.

Main Methods:

  • Employed text classification techniques: search queries, semi-supervised learning, and supervised learning.
  • Improved specificity for ten unspecific International Classification of Primary Care (ICPC-1) codes.
  • Evaluated two text representations and three machine learning algorithms for (semi-)supervised models.

Main Results:

  • Classification models generally performed well across most codes.
  • No single approach consistently outperformed others; performance varied by code and method.
  • Class imbalance and limited data impacted (semi-)supervised models, but search queries were effective.
  • Over half of unspecific code occurrences showed improved specificity.

Conclusions:

  • Utilizing clinical text information is feasible for enhancing unspecific codes in EHR databases.
  • Even limited machine learning techniques and modest training data yield improvements.
  • Future research should explore transfer learning, structured data integration, and cross-setting validation.
  • Enhanced data detail benefits observational research and patient care.