From Big Data to the Bedside: answering big questions in emergency department pain care using artificial intelligence and patient-reported outcomes

Grant ID: EMLE-166R34-2020-HUGHES

Lay Summary

One of the main reasons that acute pain is not well treated in the emergency department (ED) setting is that pain is difficult to measure. While patient-reported outcome measures (PROMS) are commonly used to help guide treatment of pain in settings such as chronic pain care, cancer care and migraine care, there are no similar tools available for patients with acute pain in the ED. Further hampering efforts to provide better ED pain care is poor overall understanding of the numbers and types of patients that experience pain.

Since it is a symptom rather than a diagnosis, information about pain is not systematically collected and is often obscured within free-text clinical notes. The lack of readily-available data makes it difficult to determine who exactly has experienced pain, and to design research studies to evaluate new and existing treatments.

Researchers aim to validate a PROM for pain care in the ED by administering to 400 patients who present with pain to one of two large hospital EDs. The aim is to find out the incidence and characteristics of patients who present with pain to the Royal Brisbane and Women’s Hospital ED, by using novel machine- and deep-learning techniques to process free-text information from clinical notes. This study will provide new knowledge and techniques that are essential for clinician-researchers to design and conduct studies that will ultimately improve pain care in the ED.


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Amount Awarded
$97,326


Program


Grant Scheme


Status
Active


Principal Investigator:
Dr James Hughes


Co Investigators:
A/Prof Kevin Chu
Dr Anthony Nguyen
Prof. Clint Douglas
Dr Nathan Brown
Ms Lee Jones
Dr Thanh Vu
Dr Rajeev Jarugula


Institution


Collaborating Institutions


CONTACT US +61 7 3720 5700 info@emfoundation.org.au Suite 1B, Terraces, 19 Lang Parade, Milton Qld 4064