Identifying patients presenting in pain to the adult emergency department: A binary classification task and description of prevalence

Abstract

AbstractBackgroundAccurate, reliable and efficient measures of pain-related presentations are essential to evaluate and improve pain care in the ED. Estimates of pain prevalence on arrival to the emergency department (ED) vary depending on the methods used. Artificial intelligence (AI) approaches are likely to be the future for identifying patients in pain from electronic health records (EHR). However, we need a robust method to identify these patients before this can occur. This study aims to identify patients presenting in pain to the ED using binary classification and to describe the population, treatment and outcomes.MethodsThis study employs a cross-sectional design using retrospective data routinely collected in the EHR at a single ED. A random sample of 10 000 patients was selected for inclusion over three years. Triage nursing assessment underwent binary classification by three expert clinicians. The prevalence of pain on arrival is the primary outcome. Patients with pain were compared to those without pain on arrival regarding demographics, treatment and outcomes.ResultsThe prevalence of pain on arrival was 55.2% (95%CI 54.2% - 56.2%). Patients who presented in pain differed from those without pain in terms of age, country of birth, socioeconomic status, mode of arrival, urgency and discharge destination. The median time to first analgesic medication was 65min (IQR 38 – 114 min), and 45.6% (95% CI 44.3% - 46.9%) of patients arriving in pain received analgesic medication.ConclusionsThe prevalence of pain on arrival compares well with previously reported figures using similar methods. Differences in the cohort presenting in pain compared to the population may represent differences in the prevalence or be an extension of previous bias seen in the documentation of pain. This work has set a rigorous methodology for identifying patients presenting with pain from the EHR. It will form the basis for future applications of AI to identify patients presenting in pain to the ED.