For several decades, hospital emergency departments have been experiencing congestion, sometimes reaching saturation point, where they are no longer able to fulfil their primary mission: to prioritise patients requiring immediate care due to a clinical situation that could be life-threatening or functionally debilitating. The main reason for this situation is a structural mismatch between medical needs, which have increased due to population ageing, and outpatient care supply, which has remained relatively stable in order to contain healthcare expenditure. As a result, a large proportion of people visiting hospital emergency departments are individuals who have been unable to find a solution to their medical needs in the community and have turned to the hospital as a last resort. These are patients seeking urgent, unscheduled care who have been unable to obtain an appointment with their general practitioner or another primary care professional. In times of extreme pressure, as sometimes happens in France during the summer, access to hospital emergency departments is limited to patients who have received prior authorisation to attend. Similarly, new ways of managing these requests for urgent or unscheduled care are being sought in the field of medical regulation. Triage of patients by telephone appears to be an essential step in medical regulation prior to access to hospital emergency departments. Indeed, if solutions are available in the city for patients who do not need to go to the emergency department, this triage will optimise the resources of the healthcare system. However, quickly assessing patients without visual contact (who may be in a state of emotional distress or face a language barrier) is a particularly delicate task. Several triage algorithms are available to assist telephone operators. However, these require structured clinical information that is not easily and quickly accessible during calls. For several years now, artificial intelligence (AI) has emerged as a promising alternative for assisting operators, as it enables the management of large amounts of unstructured data, particularly audio exchanges. AI-based classification models using audio data have shown that they could be useful in medical regulation, particularly in cases of cardiac arrest, stroke or myocardial infarction. However, to our knowledge, previous studies have focused on specific disorders, and their models are not capable of handling the vast range of cases inherent in the classification of general front-line emergency calls. In this context, researchers have developed an AI-based model to identify patients requiring referral to hospital emergency departments among outpatients seeking emergency or unscheduled care through medical call centres. To do so they used telephone calls and medical records from SOS Médecins Grand Paris, a group of approximately 150 general practitioners and emergency doctors who mainly offer same-day home visits in Paris and its neighbouring departments (more than 6.5 million inhabitants). The objective of this study is to evaluate the model's ability to identify patients requiring hospitalization based on (1) new data from SOS Médecins Grand Paris, but also (2) data from Corsica, (3) to compare the model's predictions with those of a physician, and (4) to determine the general conditions for using the predictions in current practice.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
The area under the ROC (receiver operating characteristic) curve (AROC)
Timeframe: From the telephone call to the end of the home visit (maximum 24 hours)
Laurent RIGAL, Dr