Abstract

ObjectiveThis study aims to determine whether redeploying junior doctors to assist at triage represents good value for money and a good use of finite staffing resources.MethodsWe undertook a cost-minimisation analysis to produce new evidence, from an economic perspective, about the costs associated with reallocating junior doctors in the emergency department. We built a decision-analytic model, using a mix of prospectively collected data, routinely collected administrative databases and hospital costings to furnish the model. To measure the impact of uncertainty on the model’s inputs and outputs, probabilistic sensitivity analysis was undertaken, using Monte Carlo simulation.ResultsThe mean costs for usual care were $27,035 (95% CI $27,016 to $27,054), while the mean costs for the new model of care were $25,474, (95% CI $25,453 to $25,494). As a result, the mean difference was -$1,561 (95% CI -$1,533 to -$1,588), with the new model of care being a less costly approach to managing staffing allocations, in comparison to the usual approach.ConclusionOur study shows that redeploying a junior doctor from the fast-track area of the department to assist at triage provides a modest reduction in cost. Our findings give decision-makers who seek to maximise benefit from their finite budget, support to reallocate personnel within the ED.

Highlights

  • Our study shows that redeploying a junior doctor from the fast-track area of the department to assist at triage provides a modest reduction in cost

  • Overcrowding is associated with patients waiting longer to be seen and treated, and this can lead to poorer health outcomes [4, 5]

  • Rostering consultant emergency physicians to work at Emergency Departments (EDs) triage has been shown to reduce waiting times, decrease total patient length of stay, decrease the number of patients leaving without complete assessment and decrease door-to-physician time in other jurisdictions [6]

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Summary

Methods

To measure the impact of uncertainty on the model’s inputs and outputs, probabilistic sensitivity analysis was undertaken, using Monte Carlo simulation

Results
Conclusion
Study design
Discussion
Limitations
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