A Multi-Faceted Strategy for Evidence Translation Reduces Healthcare Waiting Time: A Mixed Methods Study Using the RE-AIM Framework.
Katherine E Harding, Annie K Lewis, David A Snowdon and 2 others
PMID 36188815WHAT IT FOUND
About half of surveyed services adopted the STAT waiting time reduction model.
Managers and staff support were key facilitators. Large existing waitlists and resource shortages were main barriers. Early adopters reported reduced waiting times, but data was anecdotal.
Key findings
01Forty respondents (56%) reported implementing or being in the process of implementing the STAT model at their service.
02The most common challenges were large existing waiting lists, lack of resources, and perceived supply/demand imbalance, each reported by about a third of respondents.
03Support from management was the most commonly reported factor facilitating implementation, followed by staff support and organizational culture.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTsWhat it means for SLPs
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What it does not show
The survey response rate was low (24% of individuals), though higher at the organizational level (54%). Effectiveness data was anecdotal and uncontrolled, relying on self-reported observations rather than measured outcomes. The timeframe between training and survey was short, so maintenance of changes was not fully assessed. Respondents were more likely to be senior clinicians and those who joined the community of practice, suggesting potential selection bias. The study did not measure actual waiting time changes objectively in the adopting services.
Declared interests
Funding was provided by the National Health and Medical Research Council, Australia, and the Victorian Department of Health and Human Services. The authors declared no commercial or financial conflicts of interest.
The easy way to misread this
Do not interpret the reported reductions in waiting time as proven efficacy. These were anecdotal, self-reported observations from a small subset of adopters, not objective outcome measures. The study confirms the model's adoption and perceived benefits, not its statistical effectiveness in real-world settings.