Checking radiology reports and reviewing patient records: an IT solution for preventing missed limb fractures.

In order to prevent missed limb fractures the efficacy of an IT solution will be evaluated.

Grant ID: EMPJ-11-158-CHU-RADIOLOGY

Lay Summary

Patients experiencing pain and swelling in their limbs following an accident will often have X-Rays in the Emergency Department. The doctor will look at these X-rays for signs of a fracture and then treat the patient accordingly. The X-Ray specialist elsewhere in the hospital will also look at these X-Rays and write a report. However, this report may not be available until after the patient and doctor have both gone home. If the X-Ray specialist’s report identifies a fracture, other staff working in the Emergency Department will need to go back and double-check the patient’s records to make sure the fracture was picked up by the treating doctor and that the patient was appropriately treated.
The procedure for checking X-Ray reports and checking that the patient was appropriately treated is laborious and time consuming. Moreover, due to resourcing problems, it is often done days after the patient’s initial presentation to the Emergency Department. A more timely and efficient process is required.


Outcomes

The results of this study will ensure that patients have a safe, timely and efficient process for checking that their fractures are not missed.


Dissemination

Peer reviewed publications
Wagholikar AS, Lawley MJ, Hansen DP, Chu K. Identifying symptom groups from Emergency Department presenting complaint free text using SNOMED CT. AMIA Annu Symp Proc. 2011;2011:1446-53. Epub 2011 Oct 22. PubMed PMID: 22195208; PubMed Central PMCID: PMC3243271.

K. Chu, J. Greenslade, S. Martin, J. O'Dwyer, A. Wagholikar, J. Crilly, G. Keijzers, N. Philips, "Developing Computer Software to Read Limb X Rays Reports" Emerg Med Austral 2013;S1, 3

Zuccon G, Wagholikar AS, Nguyen AN, Butt L, Chu K, Martin S, Greenslade J. Automatic Classification of Free-Text Radiology Reports to Identify Limb Fractures using Machine Learning and the SNOMED CT Ontology. AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:300-4. eCollection 2013. PubMed PMID: 24303284; PubMed Central PMCID: PMC3845773.

Wagholikar A, Zuccon G, Nguyen A, Chu K, Martin S, Lai K, Greenslade J. Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. Australas Med J. 2013 May 30;6(5):301-7. doi: 10.4066/AMJ.2013.1651. Print 2013. PubMed PMID: 23745152; PubMed Central PMCID: PMC3674422.

Wagholikar A, Zuccon G, Nguyen A, Chu K, Martin S, Lai K, Greenslade J. Automated classification of limb fractures from free-text radiology reports using a clinician-informed gazetteer methodology. Australas Med J. 2013 May 30;6(5):301-7. doi: 10.4066/AMJ.2013.1651. Print 2013. PubMed PMID: 23745152; PubMed Central PMCID: PMC3674422.

Koopman, Bevan; Zuccon, Guido; Wagholikar, Amol; Chu, Kevin; O'Dwyer, John; Nguyen, Anthony. Automated Reconciliation of Radiology Reports and Discharge Summaries. In: AMIA; 14-18 November 2015; San Francisco. AMIA; 2015. 775-784.

Conference Proceedings/Abstracts
Chu K. “Automated reconciliation of radiology reports & discharge summaries”, 2015 Australian e-Health Research Colloquium, 31 March 2015, Brisbane, Queensland, Australia


SHARE

Amount Awarded
$139,001


Program


Grant Scheme


Status
Complete


Principal Investigator:
A/Prof Kevin Chu


Co Investigators:
Dr Jamie Lind
Dr Amol Walholikar
A/Prof Julia Crilly
Mr John O’Dwyer
Dr Natalie Philips
Dr Jaimi Greenslade
Dr Gerben Keijzers


Associate Investigators:
Dr Anthony Nguyen
Dr Michael Lawley


Institution


Collaborating Institutions


CONTACT US +61 7 3720 5700 info@emfoundation.org.au 2/15 Lang Parade Milton Qld 4064