Content and structure of clinical problem lists: a corpus analysis. Academic Article uri icon

Overview

MeSH

  • Algorithms
  • Artificial Intelligence
  • Clinical Protocols
  • New York
  • Subject Headings

MeSH Major

  • Information Storage and Retrieval
  • Medical History Taking
  • Medical Records Systems, Computerized
  • Medical Records, Problem-Oriented
  • Natural Language Processing
  • Pattern Recognition, Automated

abstract

  • In the interest of designing an automated high-level, longitudinal clinical summary of a patient record, we analyze traditional ways in which medical problems pertaining to the patient are summarized in the electronic health record. The patient problem list has become a commonly used proxy for a summary of patient history and automated methods have been proposed to generate it. However, little research has been conducted on how to structure the problem list in a manner most effective for supporting clinical care. This study analyzes the structure and content of the Past Medical History (PMH) sections of a large corpus of clinical notes, as a proxy for problem lists. Findings show that when listing patients history, physicians convey several semantic types of information, not only problems. Furthermore, they often group related concepts in a single line of the PMH. In contrast, traditional problem lists allow only a simple enumeration of coded terms. Content analysis goes on to reiterate the value of more complex representations as well as provide valuable data and guidelines for automated generation of a clinical summary.

publication date

  • 2008

has subject area

  • Algorithms
  • Artificial Intelligence
  • Clinical Protocols
  • Information Storage and Retrieval
  • Medical History Taking
  • Medical Records Systems, Computerized
  • Medical Records, Problem-Oriented
  • Natural Language Processing
  • New York
  • Pattern Recognition, Automated
  • Subject Headings

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2655994

PubMed ID

  • 18999284

Additional Document Info

start page

  • 753

end page

  • 757