Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions.

Data-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical. Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner.

Emergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field.

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition.