Surveillance of COVID-19 using a keyword search for symptoms in reports from emergency medical communication centers in Gironde, France: a 15 year retrospective cross-sectional study

Abstract

During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic. To determine the performance of keyword-search algorithm in call reports to emergency medical communication centers (EMCC) to describe trends in symptoms during the COVID-19 crisis. We retrospectively retrieved all free text call reports from the EMCC of the Gironde department (SAMU 33), France, between 2005 and 2020 and classified them with a simple keyword-based algorithm to identify symptoms relevant to COVID-19. A validation was performed using a sample of manually coded call reports. The six selected symptoms were fever, cough, muscle soreness, dyspnea, ageusia and anosmia. We retrieved 38,08,243 call reports from January 2005 to October 2020. A total of 8539 reports were manually coded for validation and Cohen’s kappa statistics ranged from 75 (keyword anosmia) to 59% (keyword dyspnea). There was an unprecedented peak in the number of daily calls mentioning fever, cough, muscle soreness, anosmia, ageusia, and dyspnea during the COVID-19 epidemic, compared to the past 15 years. Calls mentioning cough, fever and muscle soreness began to increase from February 21, 2020. The number of daily calls reporting cough reached 208 on March 3, 2020, a level higher than any in the previous 15 years, and peaked on March 15, 2020, 2 days before lockdown. Calls referring to dyspnea, anosmia and ageusia peaked 12 days later and were concomitant with the daily number of emergency room admissions. Trends in symptoms cited in calls to EMCC during the COVID-19 crisis provide insights into the natural history of COVID-19. The content of calls to EMCC is an efficient epidemiological surveillance data source and should be integrated into the national surveillance system.

Public Health surveillance from emergency call center data: visualization dashboard and NLP of call reports

“…Misspelling and syntax variations were taken into account using the same method. The procedure applied to symptoms was previously developed and validated for a list of common symptoms on the 2005-2020 database (Gil-Jardiné et al 2021a).…”

“…Keyword search applied to symptoms achieved a Cohen's kappa ranging from 59 to 75 for the following symptoms: agueusia, anosmia, cough, fever, muscle soreness, dyspnea. Text extraction results are detailed in a previous work (Gil-Jardiné et al 2021a).…”

“…With ∼300 thousand calls per year, the SAMU of the Gironde department (1.6 million inhabitants) is the second largest EMCC of France (Gil-Jardiné et al 2021a). Its information system allows exhaustive archiving of all the calls and their treatment since 2005.…”

Qualité en régulation médicale : critères et évaluation

“…Cependant, l'évaluation de sa performance via un essai randomisé n'a pas démontré une amélioration sur le temps d'identification de l'arrêt ou sur le délai d'envoi des secours (30). Cet axe est en cours de développement, pour d'autres motifs d'appels, par différentes équipes de recherches françaises (31,32).…”

Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges

“…These nonexhaustive procedures are time and resource consuming and are mostly based on voluntary work. Automatic signal extraction from EHR would allow real-time monitoring and ensure the responsiveness sought in any surveillance system [ 98 , 99 ]. The use of new state-of-the-art NLP models such as Transformers would bypass the difficulties in extracting fine-grained and standardized data from the most frequently used entries (free text) in ED and EMD.…”