Quantifying clinical narrative redundancy in an electronic health record. Academic Article uri icon

Overview

MeSH

  • Algorithms
  • Humans
  • New York
  • Retrospective Studies

MeSH Major

  • Electronic Health Records
  • Forms and Records Control
  • Hospital Information Systems
  • Information Storage and Retrieval
  • Software Design

abstract

  • Although electronic notes have advantages compared to handwritten notes, they take longer to write and promote information redundancy in electronic health records (EHRs). We sought to quantify redundancy in clinical documentation by studying collections of physician notes in an EHR. We implemented a retrospective design to gather all electronic admission, progress, resident signout and discharge summary notes written during 100 randomly selected patient admissions within a 6 month period. We modified and applied a Levenshtein edit-distance algorithm to align and compare the documents written for each of the 100 admissions. We then identified and measured the amount of text duplicated from previous notes. Finally, we manually reviewed the content that was conserved between note types in a subsample of notes. We measured the amount of new information in a document, which was calculated as the number of words that did not match with previous documents divided by the length, in words, of the document. Results are reported as the percentage of information in a document that had been duplicated from previously written documents. Signout and progress notes proved to be particularly redundant, with an average of 78% and 54% information duplicated from previous documents respectively. There was also significant information duplication between document types (eg, from an admission note to a progress note). The study established the feasibility of exploring redundancy in the narrative record with a known sequence alignment algorithm used frequently in the field of bioinformatics. The findings provide a foundation for studying the usefulness and risks of redundancy in the EHR.

publication date

  • February 2010
  • January 2010

has subject area

  • Algorithms
  • Electronic Health Records
  • Forms and Records Control
  • Hospital Information Systems
  • Humans
  • Information Storage and Retrieval
  • New York
  • Retrospective Studies
  • Software Design

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2995640

Digital Object Identifier (DOI)

  • 10.1197/jamia.M3390

PubMed ID

  • 20064801

Additional Document Info

start page

  • 49

end page

  • 53

volume

  • 17

number

  • 1