A "Patch" to the NYU Emergency Department Visit Algorithm

Kenton J Johnston; Lindsay Allen; Taylor A Melanson; Stephen R Pitts

doi:10.1111/1475-6773.12638

A "Patch" to the NYU Emergency Department Visit Algorithm

Health Serv Res. 2017 Aug;52(4):1264-1276. doi: 10.1111/1475-6773.12638.

Authors

Kenton J Johnston¹, Lindsay Allen², Taylor A Melanson², Stephen R Pitts^{3

4}

Affiliations

¹ Department of Health Management & Policy and Center for Outcomes Research, Saint Louis University, St. Louis, MO.
² Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GA.
³ Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA.
⁴ Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA.

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.

Keywords: Emergency department visit algorithm; emergency department use; health services research.

MeSH terms

Algorithms*
Emergency Service, Hospital / statistics & numerical data*
Health Care Surveys
Health Services Research
Humans
New York