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A "Patch" to the NYU Emergency Department Visit Algorithm
Kenton J Johnston1, Lindsay Allen2, Taylor A Melanson2
1Department of Health Management & Policy and Center for Outcomes Research, Saint Louis University, St. Louis, MO.
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.
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