Routine testing for SARS-CoV-2 in healthcare workers (HCWs) has been suggested to prevent healthcare facilities becoming persistent reservoirs of infectivity . Group-testing strategies have been proposed to increase capacity, but these have been designed to facilitate mass population testing and do not prioritize turnaround time, an important consideration for HCW screening . We propose a non-adaptive combinatorial (NAC) group-testing strategy to increase throughput whilst facilitating rapid turnaround . NAC matrices were constructed for sample sizes of 700 , 350 and 250 . Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1-10% . NAC matrices were compared to Dorfman Sequential (DS) group-testing approaches . NAC matrices performed well at low prevalence levels with an average of 97% of samples resolved after a single round of testing via the n=700 matrix at a prevalence of 1% . In simulations of low to medium (0.1% -3 %) prevalence all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required . At very high prevalence levels (10 %) the DS matrix was marginally superior, however both group-testing approaches performed poorly at high prevalence levels . This strategy maximises the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs . Using this methodology, laboratories can adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.