ORCID Profile
0000-0001-7582-9145
Current Organisation
Alfred Health
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 04-2023
Publisher: Wiley
Date: 05-2022
DOI: 10.1111/ANS.17611
Publisher: Wiley
Date: 06-10-2021
DOI: 10.1111/ANS.17259
Publisher: CSIRO Publishing
Date: 27-03-2023
DOI: 10.1071/AH23030
Abstract: Optimising junior doctor rosters is a common subject of debate both in Australia and overseas. While total work hours are recognised to increase the risk of fatigue-related complications for both junior doctors and their patients, patterns of work are less commonly described. Multiple low quality evidence recommendations exist to guide rostering practices to reduce predominantly the risk of fatigue-associated error and burnout, but also to avoid disruptions to continuity of care and provide adequate training opportunities. Given available evidence is poor, further centre and specialty-specific studies are required to delineate optimal rostering patterns for Australian junior doctors.
Publisher: Springer Science and Business Media LLC
Date: 18-11-2022
DOI: 10.1038/S41598-022-24504-Y
Abstract: Rapid detection of intracranial haemorrhage (ICH) is crucial for assessing patients with neurological symptoms. Prioritising these urgent scans for reporting presents a challenge for radiologists. Artificial intelligence (AI) offers a solution to enable radiologists to triage urgent scans and reduce reporting errors. This study aims to evaluate the accuracy of an ICH-detection AI software and whether it benefits a high-volume trauma centre in terms of triage and reducing diagnostic errors. A peer review of head CT scans performed prior to the implementation of the AI was conducted to identify the department’s current miss-rate. Once implemented, the AI software was validated using CT scans performed over one month, and was reviewed by a neuroradiologist. The turn-around-time was calculated as the time taken from scan completion to report finalisation. 2916 head CT scans and reports were reviewed as part of the audit. The AI software flagged 20 cases that were negative-by-report. Two of these were true-misses that had no follow-up imaging. Both patients were followed up and exhibited no long-term neurological sequelae. For ICH-positive scans, there was an increase in TAT in the total s le (35.6%), and a statistically insignificant decrease in TAT in the emergency (− 5.1%) and outpatient (− 14.2%) cohorts. The AI software was tested on a s le of real-world data from a high-volume Australian centre. The diagnostic accuracy was comparable to that reported in literature. The study demonstrated the institution’s low miss-rate and short reporting time, therefore any improvements from the use of AI would be marginal and challenging to measure.
No related grants have been discovered for Calvin Fletcher.