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A “Patch” to the NYU Emergency Department Visit Algorithm
Author(s) -
Johnston Kenton J.,
Allen Lindsay,
Melanson Taylor A.,
Pitts Stephen R.
Publication year - 2017
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12638
Subject(s) - emergency department , algorithm , diagnosis code , medicine , machine learning , emergency medicine , computer science , population , environmental health , psychiatry
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.