Evaluation of the NCPDP Structured and Codified Sig Format for e-prescriptions

J Am Med Inform Assoc. Sep-Oct 2011;18(5):645-51. doi: 10.1136/amiajnl-2010-000034. Epub 2011 May 25.

Abstract

Objective: To evaluate the ability of the structure and code sets specified in the National Council for Prescription Drug Programs Structured and Codified Sig Format to represent ambulatory electronic prescriptions.

Design: We parsed the Sig strings from a sample of 20,161 de-identified ambulatory e-prescriptions into variables representing the fields of the Structured and Codified Sig Format. A stratified random sample of these representations was then reviewed by a group of experts. For codified Sig fields, we attempted to map the actual words used by prescribers to the equivalent terms in the designated terminology.

Measurements: Proportion of prescriptions that the Format could fully represent; proportion of terms used that could be mapped to the designated terminology.

Results: The fields defined in the Format could fully represent 95% of Sigs (95% CI 93% to 97%), but ambiguities were identified, particularly in representing multiple-step instructions. The terms used by prescribers could be codified for only 60% of dose delivery methods, 84% of dose forms, 82% of vehicles, 95% of routes, 70% of sites, 33% of administration timings, and 93% of indications.

Limitations: The findings are based on a retrospective sample of ambulatory prescriptions derived mostly from primary care physicians.

Conclusion: The fields defined in the Format could represent most of the patient instructions in a large prescription sample, but prior to its mandatory adoption, further work is needed to ensure that potential ambiguities are addressed and that a complete set of terms is available for the codified fields.

Publication types

  • Evaluation Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Ambulatory Care
  • Drug Labeling*
  • Electronic Prescribing / standards*
  • Forms and Records Control / standards*
  • Humans
  • Medication Errors / prevention & control*
  • Natural Language Processing*
  • Systematized Nomenclature of Medicine
  • United States