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.

COVID-19 and healthcare professionals: The psychological impact of the pandemic on healthcare professionals and the use of EMDR early interventions

Recent research has provided new information on the impact of COVID-19 and previous pandemics on the mental health of healthcare professionals (HCP). Several studies have found that HCP are greatly affected by pandemics and may develop anxiety disorders, mood disorders, and posttraumatic stress disorder. The stress caused by the intense working conditions and the fear of contracting and transmitting the virus are major vulnerability factors for these workers, increasing their risk of developing a mental health condition. It is therefore essential to provide appropriate support to this population in order to reduce and avoid the psychological burden of the current pandemic on their mental health. Considering the data previously published on the COVID-19 pandemic and past epidemics, the present article aims to provide an epidemiological review of the psychological impact of a pandemic on healthcare professionals. Furthermore, it examines, from a theoretical perspective, whether EMDR early interventions (EEI) may constitute an effective solution in order to provide psychological support to HCP in hospitals. Lastly, the article will identify various protocols for EEI, which, it argues, should be the approaches of choice for providing early support following a potentially traumatic event. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

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.

Assessment of the SpO2/FiO2 ratio as a tool for hypoxemia screening in the emergency department.

OBJECTIVE: We assessed the performance of the ratio of peripheral arterial oxygen saturation to the inspired fraction of oxygen (SpO2/FiO2) to predict the ratio of partial pressure arterial oxygen to the fraction of inspired oxygen (PaO2/FiO2) among patients admitted to our emergency department (ED) during the SARS-CoV-2 outbreak. METHODS: We retrospectively studied patients admitted to an academic-level ED in France who were undergoing a joint measurement of SpO2 and arterial blood gas. We compared SpO2 with SaO2 and evaluated performance of the SpO2/FiO2 ratio for the prediction of 300 and 400 mmHg PaO2/FiO2 cut-off values in COVID-19 positive and negative subgroups using receiver-operating characteristic (ROC) curves. RESULTS: During the study period from February to April 2020, a total of 430 arterial samples were analyzed and collected from 395 patients. The area under the ROC curves of the SpO2/FiO2 ratio was 0.918 (CI 95% 0.885-0.950) and 0.901 (CI 95% 0.872-0.930) for PaO2/FiO2 thresholds of 300 and 400 mmHg, respectively. The positive predictive value (PPV) of an SpO2/FiO2 threshold of 350 for PaO2/FiO2 inferior to 300 mmHg was 0.88 (CI95% 0.84-0.91), whereas the negative predictive value (NPV) of the SpO2/FiO2 threshold of 470 for PaO2/FiO2 inferior to 400 mmHg was 0.89 (CI95% 0.75-0.96). No significant differences were found between the subgroups. CONCLUSIONS: The SpO2/FiO2 ratio may be a reliable tool for hypoxemia screening among patients admitted to the ED, particularly during the SARS-CoV-2 outbreak.

Authors' response: Novel criteria for dyspnea patients

Dear Editor, We would like to thank the authors of the recent letter “Novel Criteria for Dyspnea Patients” for sharing their concerns after reading our manuscript. In this study, we discussed the possibility of improving early patient assessment and identifying the presence of significant hypoxemia by using the SpO2/FiO2 ratio for all patients admitted for respiratory symptoms. Patients characteristics N = 395 Age, median (IQR) 60 (44–78) Gender, male, N (%) 193 (48.9%) Comorbidities Cardiac, N (%) 91 (23.0%) Heart failure, N (%) 71 (18.0%) Atrial fibrillation, N (%) 40 (10.1%) Coronary artery disease, N (%) 30 (7.6%) Other cardiac disease, N (%) 33 (8.4%) Pulmonary, N (%) 118 (29.9%) Asthma, N (%) 48 (12.2%) Chronic obstructive pulmonary disease(COPD), N (%) 51 (12.9%) Lung carcinosis, N (%) 10 (2.53%) Restrictive lung disease, N (%) 16 (4.05%) Other, N (%) 10 (2.53%) Acute kidney failure, N (%) 33 (8.4%) Chronic kidney disease, N (%) 29 (7.34%) COVID-19 status Positive, N (%) 90 (22.8%) Negative, N (%) 305 (77.2%) Diagnosis at discharge Pulmonary disease, N (%) 291 (73.7%) SARS-CoV-2, N (%) 90 (22.8%) COPD exacerbation, N (%) 21 (5.32%) Acute asthma, N (%) 18 (4.56%) Pneumonia, N (%) 49 (12.4%) Cardiogenic pulmonary oedema, N (%) 21 (5.32%) Respiratory acute viral syndrome (undetermined), N (%) 72 (18.3%) Others, N (%) 20 (5.06%) Extra-respiratory disease, N (%) 104 (26.3%) Vital signs Heart rate, median (IQR) 88 (77–101) Systolic blood pressure, median (IQR) 129 (115–144) Diastolic blood pressure, median (IQR) 77 (68–87) SpO2, median (IQR) 97 (95–99) Ventilatory rate, median (IQR) 22 (18–28) Temperature (°C), median (IQR) 37 (36.6–37.5) Blood gas analysis N = 430 Sample from COVID-19 patient, N (%) 94 (21.9%) FiO2 (%), median (IQR) 21 (21–29) PaO2 (mm Hg), median (IQR) 83.3 (71.3–97.5) SaO2 (%), median (IQR) 97.9 (96.4–98.9) PaCO2 (mm Hg), median (IQR) 36.0 (32.3–40.5) pH, median (IQR) 7.44 (7.41–7.47) PaO2/FiO2 (mm Hg), median (IQR) 364.3 (269.5–450) PaO2/FiO2 > 400, N (%) 164 (38.1%) PaO2/FiO2 300–400, N (%) 132 (30.7%) PaO2/FiO2 < 300, N (%) 134 (31.2%) SaO2/FiO2, median (IQR) 461.2 (339.0–470.5) SpO2/FiO2, median (IQR) 452.4 (337.9–466.7) Table 1 Characteristics of study subjects and samples.

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.