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

Emergency medicine and its services have reached a breaking point during the COVID-19 pandemic. This pandemic has highlighted the failures of a system that needs to be reconsidered, and novel approaches need to be considered. Artificial intelligence (AI) has matured to the point where it is poised to fundamentally transform health care, and applications within the emergency field are particularly promising. In this viewpoint, we first attempt to depict the landscape of AI-based applications currently in use in the daily emergency field. We review the existing AI systems; their algorithms; and their derivation, validation, and impact studies. We also propose future directions and perspectives. Second, we examine the ethics and risk specificities of the use of AI in the emergency field.

Mental and physical health among the French population before and during the first and second COVID-19 lockdowns: Latent class trajectory analyses using longitudinal data.

BACKGROUND: The French government issued national COVID-19-related confinement and stay-at-home orders depending on different epidemic levels in a bid to stem the coronavirus pandemic and its resurgence. The long-term impact of lockdown measures on the general population may vary. We aimed to identify and characterize self-reported mental and physical health trajectories in the French population from pre-lockdown to the first and second COVID-19 lockdowns and to identify factors associated with health status variation patterns. METHODS: We did a secondary analysis of the MAVIE cohort in France. Volunteers of this national cohort were recruited between November 2014 and December 2019, and information was collected at recruitment (pre-lockdown), April-May 2020 (the first lockdown), and October-December 2020 (the second lockdown). Latent class mixed models were built to identify distinct anxiety (as measured by GAD-7) and depressive (as measured by PHQ-9) symptoms, and self-perceived mental and physical health trajectories. Factors associated with status variation were identified by logistic or multinomial regression. RESULTS: A total of 613 participants with data in all three data collection waves were included. Respondents spent almost half as much time on traditional media, websites and social media during the second lockdown as during the first. Mean anxiety scores were 1.96, 2.37 and 2.82 at pre-lockdown, and the first and second lockdowns, respectively. Mean depressive scores were 3.12, 3.36 and 3.95, respectively. Latent class mixed models fitted two and three distinct trajectory classes respectively for anxiety symptoms ('no pre-pandemic anxiety, slightly increase', 58.9%; 'consistently fair', 41.1%) and depressive symptoms ('consistently very low', 34.6%; 'consistently low', 56.1%; 'increasing and clinically significant at the second lockdown', 9.3%), and four classes for self-perceived mental and physical health. Females were more likely to belong to trajectories of the most vulnerable one as regard to the symptoms of anxiety and depression, and self-perceived mental and physical health. The younger participants were also more vulnerable to anxiety symptoms and those with a clinical diagnosis or a positive COVID-19 test for the participant or relatives were more likely to belong to vulnerable trajectories for depressive symptoms and self-perceived mental health. CONCLUSION: A continuing increase in the mean scores of anxiety and depression symptoms was observed throughout the two lockdown periods in France. Further analyses revealed distinct patterns with a small fraction of volunteers experiencing worsening mental and physical health symptoms. This vulnerable small part of the population requires targeted support.

Cohort profile: MAVIE a web-based prospective cohort study of home, leisure, and sports injuries in France.

MAVIE is a web-based prospective cohort study of Home, Leisure, and Sports Injuries with a longitudinal follow-up of French general population volunteers. MAVIE participants are voluntary members of French households, including overseas territories. Participation in the cohort involves answering individual and household questionnaires and relevant exposures and prospectively reporting injury events during the follow-up. Recruitment and data collection have been in progress since 2014. The number of participants as of the end of the year 2019 was 12,419 from 9,483 households. A total of 8,640 participants provided data during follow-up. Respondents to follow-up were composed of 763 children aged 0-14, 655 teenagers and young adults aged 15-29, 6,845 adults, and 377 people aged 75 or more. At the end of the year 2019, 1,698 participants had reported 2,483 injury events. Children, people aged 50 and more, people with poor self-perceived physical and mental health, people who engage in sports activities, and people with a history of injury during the year before recruitment were more likely to report new injuries. An interactive mobile/web application (MAVIE-Lab) was developed to help volunteers decide on personalized measures to prevent their risks of HLIs. The available data provides an opportunity to analyse multiple exposures at both the individual and household levels that may be associated with an increased risk of trauma. The ongoing analysis includes HLI incidence estimates, the determination of health-related risk factors, a specific study on the risk of home injury, another on sports injuries, and an analysis of the role of cognitive skills and mind wandering. Volunteers form a community that constitutes a population laboratory for preventative initiatives.

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.

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.

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.

OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. METHODS: We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports. RESULTS: The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems. DISCUSSION: The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising. CONCLUSION: The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.

A longitudinal study of mental health before and during COVID-19 lockdown in the French population.

BACKGROUND: The impact of general population lockdown implemented in the face of the COVID-19 epidemic needs to be evaluated. We describe here a longitudinal study on the mental health of adults in France. METHODS: We did a secondary analysis of a web-based cohort, initially set up to study home and leisure injuries, in order to measure the consequences of the national lockdown implemented in France from 17 March 2020 to 11 May 2020, and to assess potential vulnerability and resilience factors. Eligible participants were invited to answer an online questionnaire designed to assess their living conditions and health during lockdown. Comparisons were done with answers provided 4.8 years earlier on average. RESULTS: On 15th April 2020, we sent email invitations to 9598 participants recruited between November 2014 and December 2019 and 1237 volunteers took part in the study by completing the online questionnaire. The proportion of those with anxiety symptoms markedly increased from 17.3 to 20.1%. The average self-rated level of mental health decreased from 7.77 to 7.58. Women, the elderly and the youngest appeared to be more vulnerable. A small living space (less than 30 m2) was associated with an increase in depression symptoms (PHQ-9 score), and poorer self-rated physical health at recruitment was associated with an increase in anxiety symptoms (GAD-7 score). On the contrary, the average self-rated level of physical health markedly increased from 7.44 to 7.94 between recruitment and lockdown, and the proportion of those who reported a level of 9 or 10 jumped from 25.7% at recruitment to 43.1% during lockdown. CONCLUSIONS: Mental health deteriorated during lockdown in France during the 2020 COVID-19 crisis. Overall, self-rated physical health improved but those who experienced a worse physical health were more likely to report anxiety symptoms.