Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

Etran Bouchouar,Brendan Hanley,Benjamin M Hetman

doi:10.1186/s12889-021-11132-w

Abstract

BackgroundAutomated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada.MethodsSyndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms.ResultsA daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset.ConclusionsThe development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

Highlights

Emergency department-based syndromic surveillance systems (ED-Syndromic surveillance system (SyS)) are effective tools that complement laboratory-based surveillance methods for the detection of infectious diseases and other public health aberrations, and enable effective control measures in a timely response [1,2,3,4]
By measuring the contribution different sources of free-text (e.g., clinical triage notes (CN), and discharge diagnosis (DD) fields) with standardized codes (e.g., Canadian Emergency Department Information System (CEDIS)), we aim to identify an optimal set of natural language processing algorithms to be used as part of a local Emergency Department syndromic surveillance systems (ED-SyS) to be implemented for surge support as well as lasting capacity in a resourceconstrained setting where staff and technological infrastructure are limited
Data source The Territorial Epidemiologist collaborated with health information analysts at Yukon’s Whitehorse General Hospital (WGH), the only hospital in Whitehorse and Yukon’s primary hospital to access records from the newly implemented real-time emergency department electronic records collection tool: Meditech ED-Tracker system. This system comprises of an electronic medical records database that captures the following information at the time of patient assessment (1) demographic characteristics and date of visit (2) clinical notes (CN) (e.g., free-text describing a brief history of the stated complaint, the recorded temperature at triage) (3) the chief complaint (CC) containing the Canadian Emergency Department Information System (CEDIS) code [40, 41] and (4) the physician discharge diagnosis (DD) which is a free-text field providing the diagnosis at patient discharge from the ED

Summary

Introduction

Emergency department-based syndromic surveillance systems (ED-SyS) are effective tools that complement laboratory-based surveillance methods for the detection of infectious diseases and other public health aberrations, and enable effective control measures in a timely response [1,2,3,4]. ED-SyS are routinely used in urban locations to monitor diseases of public health importance and have been leveraged globally during mass gathering events [5,6,7,8] Many of these systems depend on (1) dedicated technical human resources (e.g., epidemiologists and data scientists), (2) sophisticated technological IT platforms (e.g., ESSENCE, BioSense, and NC-Detect in the U.S.; PHIDO and ACES in Canada) [9,10,11,12], and the use of (3) resource intensive syndrome case validation methods [1, 13]. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada

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