COMMENTARY

New Tools for Estimating the EMS TransportInterval: Implications for Policy and PatientCare

As far back as 1973, the Emergency Medical Ser-vices Systems Act (EMSSA) defined ”access tocare” as one of the 15 core areas of emergency

medical services (EMS) systems, recognizing the impor-tant role of EMS, both ground and air, in providingtransport to definitive care.1 Rapid, evidence-basedemergency intervention has since been shown to reducemorbidity and mortality in time-sensitive conditions,such as acute myocardial infarction, stroke, andtrauma.2 EMS providers are critically important forquickly recognizing these conditions, administering ini-tial treatment, and rapidly transporting patients to themost appropriate receiving centers.3,4

A 2006 Institute of Medicine report on the future ofemergency care noted widespread fragmentation andlimited regional coordination of patients transported tothe optimal, ready facility; it recommended more inte-grated, coordinated, and regionalized prehospital care.5

To meet these objectives, EMS resources must be allo-cated and available to serve patients in all geographicareas and be held accountable for performance. Accu-rately estimating and monitoring EMS time intervals iscritical for regional planning to ensure timely access tohealth care services for all citizens. Calculating EMSintervals can highlight areas of unmet need and allowlocal, regional, and national officials to assess the valueof changing prehospital and hospital supply and distri-bution to match population needs.

In 1993, Spaite and colleagues6 proposed a time-interval model of the EMS response that has becomea standard in studying what actually happens in thefield. In the terminology of this model, a ”time” is thediscrete moment when an event takes place; an ”inter-val” is the temporal distance between two times. The”transport interval” was defined as the temporal dis-tance between when the ambulance leaves the sceneand when it arrives at the hospital. A number of stud-ies have used this temporal distance from the hospitalto define a population’s access to time-sensitive clini-

cal services, such as the percentage of the populationin a metropolitan area who can reach a trauma centerin 60 minutes or a stroke center in 30 minutes.7,8 Sim-ilar studies examining the response interval (the tem-poral distance between the time an EMS unit isnotified of the call and the arrival of that unit at thescene) have been used to examine other features ofEMS systems, such as optimal placement of helipads,to improve access to air medical services in a geo-graphic region.9

In this issue of Academic Emergency Medicine, Wal-lace and colleagues10 examine three different methodsof estimating the prehospital transport interval. Theyreport ”moderate” accuracy, with estimates within5 minutes of the actual transport interval in about fourof five cases for each method, although the road net-work methods were a bit more accurate.

Likely the most striking finding of the study was thedifferences in 20-minute driving distance catchmentareas determined by the three methods, as defined byboth area (198, 310, and 352 square miles) and popula-tion (698,049, 883,615, and 960,844 persons). Becauseprehospital and hospital resource planning may bebased on linear arc areas, refinements in populationcoverage estimates using the road network methodsmay lead to changes in health care resource needsand allocation. For example, a 38% increase in thehospital 20-minute catchment area (as was seen herewhen Pittsburgh 20-minute drive times were estimatedusing ArcGIS compared with linear arc) may requiremore inpatient hospital beds. Changes in transportinterval estimates may also influence regionalizationplanning and individual patient-preferred receivingcenters. Further, ground transport may actually be fea-sible in locations previously thought to require airevacuation.

However, studies of this type necessarily rely onpotentially inaccurate time data for the criterion stan-dard (observed transport interval) against which theaccuracy of estimated transport interval is compared.Most notable in this study is that the ”leave scene” and”arrive hospital” times were either manually enteredinto mobile data terminals by the crewmember drivingthe ambulance (in one of the two systems studied) or

The authors have no relevant financial information or potentialconflicts of interest to disclose.A related article appears on page 9

76 PII ISSN 1069-6563583 doi: 10.1111/acem.12278ISSN 1069-6563 © 2013 by the Society for Academic Emergency Medicine

called in by radio to the dispatcher (in the othersystem). The accuracy of these times might be improvedwith the use of global positioning systems (GPS) tech-nology that is becoming more commonplace in ambu-lances and can automatically detect location.

The authors’ recommendation to validate predictedaccess with empiric data seems quite reasonable, andthe widespread availability of automated data such asthat used in this study should make this quite feasible.Further, empiric transport interval data might be incor-porated into future models. ”Big data” is a popularbuzzword and in health care refers to the massiveamount of electronic data being collected and analyzedto improve health care costs, efficiency, and quality,including delivering more personalized health care.11

The EMS system is currently contributing to big datawith electronic dispatch data, electronic patient carereports (ePCR), and physiologic monitoring data. Acommunity’s electronic dispatch or ePCR prehospitaltransport intervals could be retrospectively analyzed topredict future response and transport intervals alone orin combination with the other estimation methodsdescribed. Using continuously updated data, the EMSsystem would continuously learn from experience togenerate the most accurate interval estimates.12

The finding that transport interval estimates becomeless accurate as distances increase implies these esti-mates should be used with caution for populations withgreater than 20-minute drive times and may havelimited value for rural health care systems planning.These populations may particularly benefit from valida-tion with more empiric data or using the big dataapproach. It is also notable that the King County EMSdatabase, the larger of the two databases used in thisstudy, excludes cardiac arrest and trauma patienttransports. This is an important limitation, since thesetwo categories of patients are at the forefront of dis-cussions on access to and regionalization of care. Theaddition of the southwestern Pennsylvania Resuscita-tion Outcomes Consortium Epistry-Cardiac Arreststudy data provided approximately 1,000 cardiac arrestpatients; however, no trauma patients were included ineither cohort. To increase generalizability, future stud-ies should include all EMS call types (possibly usingEMS dispatch data sources), as well as more geo-graphical diversity, including areas with large ruralpopulations.

A recent study by Fleischman and colleagues13 exam-ined the transports of nearly 50,000 patients in the Port-land, Oregon, area to investigate whether a roadnetwork system (ArGIS 9.1 Network Analyst) couldaccurately predict the transport interval. Comparing thenetwork-predicted intervals to the actual intervals, ascalculated from the depart scene and arrive hospitaltimes logged by the EMS crews via automated data ter-minal, the study found that the street network predictedarrival within 5 minutes of actual arrival time only 15%of the time. However, a linear regression model thatincorporated daylight versus not, rush hour versus not,and lights and sirens versus not, improved this accuracyto 72.8%, similar to the accuracy found in the study byWallace et al.10 However, it should be noted that Wal-

lace et al.10 did not include use of lights and sirens intheir model; the data from Fleishman et al. suggest thatlights and sirens may be a more important parameterthan previously thought,13 and perhaps its inclusioncould render these estimates even more accurate. Theauthors also acknowledge that more than half of thetransports in the two databases were excluded from thestudy due to missing or nongeocodable exact starting(scene) locations. This has obvious limitations for anysystem of predicting transport intervals in real time;however, ambulances equipped with GPS can allow foraccurate geocoding of the starting location and thetransport interval calculation when the vehicle departsthe scene.

Estimates of the transport interval have other usesbeyond examining population-based access to care. Themost obvious lies in allowing the receiving emergencydepartment (ED) to prepare for inbound ambulancetraffic. An accurate estimated time of arrival can allow,for example, the trauma team to assemble in the EDprior to patient arrival, but not so far in advance as toprematurely draw those personnel away from othertasks elsewhere in the hospital. It has been demon-strated that such estimates made by EMS personnel areinaccurate,14,15 often resulting in either too much or toolittle preparedness at the ED and perhaps in some casesaltering how a direct medical oversight physician mightmanage a patient.15 Fleischman and colleagues devel-oped a prototype web-based application that allowshospitals to estimate ambulance’s arrival time usingGoogle Maps.13 Advanced 9-1-1 dispatch centers mayalready be using GPS to track and display real-timeambulance location on large, wall-mounted displays;this functionality could be extended to the hospital set-ting, allowing hospital-based providers to monitor theambulance’s location and predicted arrival time in realtime. Prehospital providers could also use real-time esti-mated transport intervals to help select between two ormore otherwise equivalent receiving facilities. Theseapplications are immediately plausible; other industries,including taxis and trucking companies, are alreadyleveraging these technologies to track and improve ser-vice delivery.

Road network estimation techniques with or withoutregression are important advances over current lineararc interval estimates. However, there is opportunity toimprove the accuracy, particularly in rural areas withlonger transport intervals. Continued mapping algo-rithm advancements, empiric big data transport inter-vals, and GPS-based locations are promising ways tofurther refine transport interval estimates. EMS roadnetwork transport interval estimation techniques can beimmediately applied to emergency care and regionaliza-tion planning, and new electronic tools can use theseestimates to improve operations and efficiency in dis-patch centers, ambulances, and receiving EDs.

David C. Cone, MD(david.cone@yale.edu)Editor-in-Chief, Academic Emergency MedicineYale University School of MedicineNew Haven, CT

ACADEMIC EMERGENCY MEDICINE • January 2014, Vol. 21, No. 1 • www.aemj.org 77

Adam B. Landman, MDBrigham and Women’s HospitalBoston, MA

Supervising Editor: Gary Gaddis, MD.

References

1. United States Congress. Emergency Medical Ser-vices Systems Act of 1973, In: 93rd Congress (ed).Washington, DC: US Government Printing Office,1973.

2. Cairns CB, Glickman SW. Time makes a differenceto everyone, everywhere: the need for effectiveregionalization of emergency and critical care. AnnEmerg Med. 2012; 60:638–40.

3. Cone DC, Lerner EB, Band RA, et al. Prehospitalcare and new models of regionalization. AcadEmerg Med. 2010; 17:1337–45.

4. Landman AB, Spatz ES, Cherlin EJ, Krumholz HM,Bradley EH, Curry LA. Hospital collaboration withemergency medical services in the care of patientswith acute myocardial infarction: perspectivesfrom key hospital staff. Ann Emerg Med. 2013; 61:185–95.

5. Institutes of Medicine. Emergency Medical Services:At the Crossroads. Washington, DC: The NationalAcademies Press, 2007.

6. Spaite DW, Valenzuela TD, Meislin HW, Criss EA,Hinsberg P. Prospective validation of a new modelfor evaluating emergency medical services systemsby in-field observation of specific time intervals in

prehospital care. Ann Emerg Med. 1993; 22:638–45.

7. Branas CC, MacKenzie EJ, Williams JC, et al. Accessto trauma centers in the United States. JAMA. 2005;293:2626–33.

8. Carr BG, Branas CC, Metlay JP, Sullivan AF,Camargo CA Jr. Access to emergency care in theUnited States. Ann Emerg Med. 2009; 54:261–9.

9. Foo CP, Ahghari M, MacDonald RD. Use ofgeographic information systems to determine newhelipad locations and improve timely response whilemitigating risk of helicopter emergency medicalservices operations. Prehosp Emerg Care. 2010; 14:461–8.

10. Wallace DJ, Kahn JM, Angus DC, et al. Accuracy ofprehospital transport time estimation. Acad EmergMed. 2014; 21:9–16.

11. Adler-Milstein J, Jha AK. Healthcare’s ”big data”challenge. Am J Manag Care. 2013; 19:537–8.

12. Smith M, Halvorson G, Kaplan G. What’s needed isa health care system that learns: recommendationsfrom an IOM report. JAMA. 2012; 308:1637–8.

13. Fleischman RJ, Lundquist M, Jui J, Newgard CD,Warden C. Predicting ambulance time of arrival tothe emergency department using global positioningsystem and Google Maps. Prehosp Emerg Care.2013; 17:458–65.

14. Jurkovich GJ, Campbell D, Padrta J, Luterman A.Paramedic perception of elapsed field time. JTrauma. 1987; 27:892–7.

15. Propp DA, Rosenberg CA. A comparison of prehos-pital estimated time of arrival and actual time ofarrival to an emergency department. Am J EmergMed. 1991; 9:301–3.

78 Cone and Landman • NEW TOOLS FOR TRANSPORT INTERVALS FOR EMS