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

Abstract

BackgroundAccurate, 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.

Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights

“…The outcome of this deployment is that the prevalence of pain on arrival was identified as 55.16% of all presentations to the ED, with a significant difference between pre-and post-pandemic prevalence. Pain remains the most common symptom on presentation to the ED, with over 55% of all patients experiencing pain on arrival in this department (4). Fundamentally the patient arriving in pain to the ED is different in terms of age, sex, socioeconomic status, ethnicity, residence, and urgency to those not in pain.…”

“…Inadequate documentation about pain and pain intensity can have immediate consequences for the quality of clinical care, and ongoing consequences for research and quality improvement because it makes it difficult to identify patients who have presented in pain. Instead, researchers typically rely on indirect measures, such as diagnosis or the administration of analgesic medication, to identify their target cohort when auditing medical records (4). However, such indirect measures will likely introduce bias into any resulting samples.…”

“…Patients arriving to the ED with pain can be identified in audits of medical records by skilled clinicians reviewing information in the initial clinical assessment, even when pain severity is not documented (4,6). However, the labor-intensive manual abstraction that is required limits the amount of data that can be reviewed.…”

The use and outcomes of non-pharmacological analgesia in the adult emergency department

“…Evaluating the effectiveness of pain management in the ED has been difficult due to a lack of appropriate tools. The recent adaptation (8) and validation (9) of the Revised American Pain Society -Patient Outcome Questionnaire in the ED setting (APS-POQ-RED) provides the means to assess the usage and effectiveness of pain relief in the ED including non-pharmacological analgesia.…”

Enhancing pain care with the American Pain Society Patient Outcome Questionnaire for use in the Emergency Department (APS-POQ-RED): validating a patient-reported outcome measure

“…Pain is the most common symptom in people who present to emergency departments (ED) (1, 2). However, due to a lack of well-validated tools to measure patient-reported outcomes (PROs) of pain care in the ED setting, outcomes of pain care from the perspective of the patient are not well understood (3).…”