A "Patch" to the NYU Emergency Department Visit Algorithm.
Ontology highlight
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.
SUBMITTER: Johnston KJ
PROVIDER: S-EPMC5517669 | biostudies-literature | 2017 Aug
REPOSITORIES: biostudies-literature
Publications
A "Patch" to the NYU Emergency Department Visit Algorithm.
Johnston Kenton J KJ Allen Lindsay L Melanson Taylor A TA Pitts Stephen R SR
Health services research 20170801 4
<h4>Objective</h4>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.<h4>Data sources</h4>The Nationwide Emergency Department Sample.<h4>Study design</h4>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.<h4 ...[more]
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