%A Cole,Justin %A Beare,Richard %A Phan,Thanh G. %A Srikanth,Velandai %A MacIsaac,Andrew %A Tan,Christianne %A Tong,David %A Yee,Susan %A Ho,Jesslyn %A Layland,Jamie %D 2018 %J Frontiers in Cardiovascular Medicine %C %F %G English %K • ST elevation myocardial infarction,Time to PCI,STEMI protocol,Staff travel time,catheterization laboratory activation %Q %R 10.3389/fcvm.2017.00089 %W %L %M %P %7 %8 2018-January-08 %9 Original Research %+ Jamie Layland,Peninsula Health Heart Service,Australia,jlayland@phcn.vic.gov.au %+ Jamie Layland,Peninsula Clinical School, Monash University,Australia,jlayland@phcn.vic.gov.au %+ Jamie Layland,Department of Cardiology, St Vincent’s Hospital,Australia,jlayland@phcn.vic.gov.au %# %! Time to reperfusion could be affected by traffic %* %< %T Staff Recall Travel Time for ST Elevation Myocardial Infarction Impacted by Traffic Congestion and Distance: A Digitally Integrated Map Software Study %U https://www.frontiersin.org/articles/10.3389/fcvm.2017.00089 %V 4 %0 JOURNAL ARTICLE %@ 2297-055X %X BackgroundRecent evidence suggests hospitals fail to meet guideline specified time to percutaneous coronary intervention (PCI) for a proportion of ST elevation myocardial infarction (STEMI) presentations. Implicit in achieving this time is the rapid assembly of crucial catheter laboratory staff. As a proof-of-concept, we set out to create regional maps that graphically show the impact of traffic congestion and distance to destination on staff recall travel times for STEMI, thereby producing a resource that could be used by staff to improve reperfusion time for STEMI.MethodsTravel times for staff recalled to one inner and one outer metropolitan hospital at midnight, 6 p.m., and 7 a.m. were estimated using Google Maps Application Programming Interface. Computer modeling predictions were overlaid on metropolitan maps showing color coded staff recall travel times for STEMI, occurring within non-peak and peak hour traffic congestion times.ResultsInner metropolitan hospital staff recall travel times were more affected by traffic congestion compared with outer metropolitan times, and the latter was more affected by distance. The estimated mean travel times to hospital during peak hour were greater than midnight travel times by 13.4 min to the inner and 6.0 min to the outer metropolitan hospital at 6 p.m. (p < 0.001). At 7 a.m., the mean difference was 9.5 min to the inner and 3.6 min to the outer metropolitan hospital (p < 0.001). Only 45% of inner metropolitan staff were predicted to arrive within 30 min at 6 p.m. compared with 100% at midnight (p < 0.001), and 56% of outer metropolitan staff at 6 p.m. (p = 0.021).ConclusionOur results show that integration of map software with traffic congestion data, distance to destination and travel time can predict optimal residence of staff when on-call for PCI.