Login / Signup

Evaluating the effectiveness of intensive versus non-intensive image interpretation education for radiographers: a randomised control trial study protocol.

Michael J NeepTom SteffensPatrick EastgateSteven M McPhail
Published in: Journal of medical radiation sciences (2018)
Radiographer commenting systems have not been successfully implemented in many Australian hospitals, despite evidence of their benefit and adoption elsewhere, such as the United Kingdom. An important contributor to the lack of widespread adoption of radiographer commenting in Australia (and likely elsewhere) is the limited availability of accessible education options for radiographers. The purpose of this randomised controlled trial is to compare the effectiveness of the same image interpretation education program delivered over an intensive 2-day period (intensive format) versus a series of shorter regular workshops (non-intensive format). The study design is a multicentre, stratified (by years of experience) two group parallel-arm single-blind (assessor blinded) randomised controlled trial. Participants will be allocated to one of the two groups: (1) intensive format of education or (2) non-intensive format of education in a 1:1 ratio. Participants will undergo assessments before education, at 1 week post-intervention completion and at 12 weeks post-intervention completion. Findings from this trial will be of relevance to radiographers seeking image interpretation training as well as organisations providing image interpretation education to prepare clinical staff for participation in a radiographer commenting system. A limitation of the trial is that the sample will be inclusive of radiographers, and findings may not be able to be directly extrapolated to other clinical disciplines (e.g. junior doctors, physiotherapists or nurse practitioners).
Keyphrases
  • study protocol
  • randomized controlled trial
  • healthcare
  • quality improvement
  • clinical trial
  • deep learning
  • primary care
  • open label
  • systematic review
  • machine learning
  • cross sectional
  • electronic health record