Publication date: Jul 11, 2024
To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm. A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic. 55. 16% (95%CI 54. 95%-55. 36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment. Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED. Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights.
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| Concepts | Keywords |
|---|---|
| Algorithm | Artificial intelligence |
| Australian | COVID-19 |
| Nursing | Deep learning models |
| Pandemic | Electronic health records |
| Pain | |
| Prevalence | |
| Symptoms |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | emergency |
| disease | IDO | algorithm |
| disease | VO | time |
| disease | VO | population |
| disease | MESH | Covid-19 pandemic |
| disease | IDO | intervention |
| disease | IDO | process |
| drug | DRUGBANK | Coenzyme M |
| disease | IDO | disposition |
| drug | DRUGBANK | Etoperidone |
| drug | DRUGBANK | Ilex paraguariensis leaf |
| drug | DRUGBANK | Gold |
| disease | MESH | clinical significance |
| drug | DRUGBANK | Pentaerythritol tetranitrate |
| disease | VO | age |
| disease | IDO | country |
| drug | DRUGBANK | Amlodipine |
| disease | MESH | Death |
| disease | IDO | facility |
| disease | IDO | symptom |
| disease | VO | volume |
| disease | MESH | metastases |