A “Patch” to the NYU Emergency Department Visit Algorithm

Kenton J. Johnston*, Lindsay Allen, Taylor A. Melanson, Stephen R. Pitts

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Objective: To document erosion in the New York University Emergency Department (ED) visit algorithm's capability to classify ED visits and to provide a “patch” to the algorithm. Data Sources: The Nationwide Emergency Department Sample. Study Design: We used bivariate models to assess whether the percentage of visits unclassifiable by the algorithm increased due to annual changes to ICD-9 diagnosis codes. We updated the algorithm with ICD-9 and ICD-10 codes added since 2001. Principal Findings: The percentage of unclassifiable visits increased from 11.2 percent in 2006 to 15.5 percent in 2012 (p <.01), because of new diagnosis codes. Our update improves the classification rate by 43 percent in 2012 (p <.01). Conclusions: Our patch significantly improves the precision and usefulness of the most commonly used ED visit classification system in health services research.

Original languageEnglish (US)
Pages (from-to)1264-1276
Number of pages13
JournalHealth Services Research
Volume52
Issue number4
DOIs
StatePublished - Aug 2017
Externally publishedYes

Keywords

  • Emergency department visit algorithm
  • emergency department use
  • health services research

ASJC Scopus subject areas

  • Health Policy

Fingerprint

Dive into the research topics of 'A “Patch” to the NYU Emergency Department Visit Algorithm'. Together they form a unique fingerprint.

Cite this