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Analyzing pain patterns in the emergency department: Leveraging clinical text deep learning models for real-world insights.
Hughes, James A; Wu, Yutong; Jones, Lee; Douglas, Clint; Brown, Nathan; Hazelwood, Sarah; Lyrstedt, Anna-Lisa; Jarugula, Rajeev; Chu, Kevin; Nguyen, Anthony.
Afiliación
  • Hughes JA; School of Nursing, Queensland University of Technology, Brisbane, Australia; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia. Electronic address: J1.hughes@qut.edu.au.
  • Wu Y; Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
  • Jones L; QIMR-Berghoffer Research Institute, Brisbane, Australia.
  • Douglas C; School of Nursing, Queensland University of Technology, Brisbane, Australia; Metro North Health, Queensland, Australia.
  • Brown N; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia; Faculty of Medicine, University of Queensland, Brisbane, Australia.
  • Hazelwood S; Emergency Department, The Prince Charles Hospital, Queensland, Australia.
  • Lyrstedt AL; School of Nursing, Queensland University of Technology, Brisbane, Australia; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia.
  • Jarugula R; Emergency Department, The Prince Charles Hospital, Queensland, Australia.
  • Chu K; Emergency and Trauma Centre, Royal Brisbane and Women's Hospital, Brisbane, Australia; Faculty of Medicine, University of Queensland, Brisbane, Australia.
  • Nguyen A; Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
Int J Med Inform ; 190: 105544, 2024 Oct.
Article en En | MEDLINE | ID: mdl-39003790
ABSTRACT

OBJECTIVE:

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. MATERIALS AND

METHODS:

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.

RESULTS:

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.

DISCUSSION:

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.

CONCLUSION:

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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor / Servicio de Urgencia en Hospital / Aprendizaje Profundo / COVID-19 Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Oceania Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dolor / Servicio de Urgencia en Hospital / Aprendizaje Profundo / COVID-19 Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Oceania Idioma: En Revista: Int J Med Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Irlanda