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Testing Three Problem List Terminologies in a simulated data entry environment.

Kin Wah Fung1, Junchuan Xu, S Trent Rosenbloom

  • 1National Library of Medicine, Bethesda, MD, USA. kwfung@nlm.nih.gov

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
PubMed
Summary
This summary is machine-generated.

The CORE Problem List Subset (PLT) demonstrated comparable coverage to larger terminologies like SNOMED CT, while requiring less time for concept retrieval. This suggests smaller, focused problem list subsets can enhance clinical data entry efficiency.

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Area of Science:

  • Medical Informatics
  • Clinical Terminology
  • Health Data Standards

Background:

  • Problem List Terminologies (PLT) are crucial for organizing patient health information in electronic medical records (EMR).
  • Evaluating the efficiency and coverage of different PLTs is essential for optimizing clinical data entry and retrieval.
  • Existing PLTs vary in size and structure, potentially impacting their usability and effectiveness.

Purpose of the Study:

  • To compare the coverage and coding efficiency of three distinct Problem List Terminologies (PLTs).
  • To assess the performance of the CORE Problem List Subset of SNOMED CT against a larger clinical subset and a current clinical practice PLT.

Main Methods:

  • A web-based application simulating clinical data entry was utilized.
  • Physician reviewers searched for concepts within three PLTs (CORE Subset, Clinical SNOMED, Mayo Clinic PLT) matching 450 free-text problem statements (15 per reviewer).
  • Coverage (exact/partial matches) and time to find a concept were measured.

Main Results:

  • The CORE Problem List Subset showed coverage comparable to the Clinical SNOMED subset for exact or partial matches.
  • The CORE Subset required significantly less time for physicians to locate a concept.
  • The smaller size of the CORE Subset's pick lists may contribute to faster concept retrieval.

Conclusions:

  • The CORE Problem List Subset offers a viable alternative to larger terminologies, balancing comprehensive coverage with improved efficiency.
  • Smaller, curated problem list subsets can enhance the speed and potentially the accuracy of clinical data entry.
  • Further research into the impact of PLT size on coding efficiency in real-world clinical settings is warranted.