Detecting Unanticipated Increases in Emergency Department Chief Complaint Keywords | Zendy

Ramona Lall | Zendy; Alison Levin-Rector | Zendy; Robert Mathes | Zendy; Don Weiss | Zendy

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Detecting Unanticipated Increases in Emergency Department Chief Complaint Keywords

Author(s) -

Ramona Lall,

Alison Levin-Rector,

Robert Mathes,

Don Weiss

Publication year - 2014

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v6i1.5069

Subject(s) - complaint , emergency department , word lists by frequency , situation awareness , word (group theory) , set (abstract data type) , computer science , a priori and a posteriori , field (mathematics) , medicine , medical emergency , information retrieval , engineering , mathematics , nursing , political science , philosophy , geometry , epistemology , pure mathematics , law , programming language , aerospace engineering

The chief complaint (CC) text field is a rich source of information, but its current use for syndromic surveillance is limited to a fixed set of syndromes defined a priori using keywords. To identify unanticipated sudden increases in word frequency, we developed a simple method that compares the frequency of every word in the CC text field on a given day against the average frequency of the same word during a baseline period. This could prove useful for situational awareness during routine surveillance and emergencies.

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