COMMENTARY
New Tools for Estimating the EMS Transport Interval: Implications for Policy and Patient Care
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s far back as 1973, the Emergency Medical Services Systems Act (EMSSA) defined ”access to care” as one of the 15 core areas of emergency medical services (EMS) systems, recognizing the important role of EMS, both ground and air, in providing transport to definitive care.1 Rapid, evidence-based emergency intervention has since been shown to reduce morbidity and mortality in time-sensitive conditions, such as acute myocardial infarction, stroke, and trauma.2 EMS providers are critically important for quickly recognizing these conditions, administering initial treatment, and rapidly transporting patients to the most appropriate receiving centers.3,4 A 2006 Institute of Medicine report on the future of emergency care noted widespread fragmentation and limited regional coordination of patients transported to the optimal, ready facility; it recommended more integrated, coordinated, and regionalized prehospital care.5 To meet these objectives, EMS resources must be allocated and available to serve patients in all geographic areas and be held accountable for performance. Accurately estimating and monitoring EMS time intervals is critical for regional planning to ensure timely access to health care services for all citizens. Calculating EMS intervals can highlight areas of unmet need and allow local, regional, and national officials to assess the value of changing prehospital and hospital supply and distribution to match population needs. In 1993, Spaite and colleagues6 proposed a timeinterval model of the EMS response that has become a standard in studying what actually happens in the field. In the terminology of this model, a ”time” is the discrete moment when an event takes place; an ”interval” is the temporal distance between two times. The ”transport interval” was defined as the temporal distance between when the ambulance leaves the scene and when it arrives at the hospital. A number of studies have used this temporal distance from the hospital to define a population’s access to time-sensitive clini-
The authors have no relevant financial information or potential conflicts of interest to disclose. A related article appears on page 9
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ISSN 1069-6563 PII ISSN 1069-6563583
cal services, such as the percentage of the population in a metropolitan area who can reach a trauma center in 60 minutes or a stroke center in 30 minutes.7,8 Similar studies examining the response interval (the temporal distance between the time an EMS unit is notified of the call and the arrival of that unit at the scene) have been used to examine other features of EMS systems, such as optimal placement of helipads, to improve access to air medical services in a geographic region.9 In this issue of Academic Emergency Medicine, Wallace and colleagues10 examine three different methods of estimating the prehospital transport interval. They report ”moderate” accuracy, with estimates within 5 minutes of the actual transport interval in about four of five cases for each method, although the road network methods were a bit more accurate. Likely the most striking finding of the study was the differences in 20-minute driving distance catchment areas determined by the three methods, as defined by both area (198, 310, and 352 square miles) and population (698,049, 883,615, and 960,844 persons). Because prehospital and hospital resource planning may be based on linear arc areas, refinements in population coverage estimates using the road network methods may lead to changes in health care resource needs and allocation. For example, a 38% increase in the hospital 20-minute catchment area (as was seen here when Pittsburgh 20-minute drive times were estimated using ArcGIS compared with linear arc) may require more inpatient hospital beds. Changes in transport interval estimates may also influence regionalization planning and individual patient-preferred receiving centers. Further, ground transport may actually be feasible in locations previously thought to require air evacuation. However, studies of this type necessarily rely on potentially inaccurate time data for the criterion standard (observed transport interval) against which the accuracy of estimated transport interval is compared. Most notable in this study is that the ”leave scene” and ”arrive hospital” times were either manually entered into mobile data terminals by the crewmember driving the ambulance (in one of the two systems studied) or
© 2013 by the Society for Academic Emergency Medicine doi: 10.1111/acem.12278
ACADEMIC EMERGENCY MEDICINE • January 2014, Vol. 21, No. 1 • www.aemj.org
called in by radio to the dispatcher (in the other system). The accuracy of these times might be improved with the use of global positioning systems (GPS) technology that is becoming more commonplace in ambulances and can automatically detect location. The authors’ recommendation to validate predicted access with empiric data seems quite reasonable, and the widespread availability of automated data such as that used in this study should make this quite feasible. Further, empiric transport interval data might be incorporated into future models. ”Big data” is a popular buzzword and in health care refers to the massive amount of electronic data being collected and analyzed to improve health care costs, efficiency, and quality, including delivering more personalized health care.11 The EMS system is currently contributing to big data with electronic dispatch data, electronic patient care reports (ePCR), and physiologic monitoring data. A community’s electronic dispatch or ePCR prehospital transport intervals could be retrospectively analyzed to predict future response and transport intervals alone or in combination with the other estimation methods described. Using continuously updated data, the EMS system would continuously learn from experience to generate the most accurate interval estimates.12 The finding that transport interval estimates become less accurate as distances increase implies these estimates should be used with caution for populations with greater than 20-minute drive times and may have limited value for rural health care systems planning. These populations may particularly benefit from validation with more empiric data or using the big data approach. It is also notable that the King County EMS database, the larger of the two databases used in this study, excludes cardiac arrest and trauma patient transports. This is an important limitation, since these two categories of patients are at the forefront of discussions on access to and regionalization of care. The addition of the southwestern Pennsylvania Resuscitation Outcomes Consortium Epistry-Cardiac Arrest study data provided approximately 1,000 cardiac arrest patients; however, no trauma patients were included in either cohort. To increase generalizability, future studies should include all EMS call types (possibly using EMS dispatch data sources), as well as more geographical diversity, including areas with large rural populations. A recent study by Fleischman and colleagues13 examined the transports of nearly 50,000 patients in the Portland, Oregon, area to investigate whether a road network system (ArGIS 9.1 Network Analyst) could accurately predict the transport interval. Comparing the network-predicted intervals to the actual intervals, as calculated from the depart scene and arrive hospital times logged by the EMS crews via automated data terminal, the study found that the street network predicted arrival within 5 minutes of actual arrival time only 15% of the time. However, a linear regression model that incorporated daylight versus not, rush hour versus not, and lights and sirens versus not, improved this accuracy to 72.8%, similar to the accuracy found in the study by Wallace et al.10 However, it should be noted that Wal-
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lace et al.10 did not include use of lights and sirens in their model; the data from Fleishman et al. suggest that lights and sirens may be a more important parameter than previously thought,13 and perhaps its inclusion could render these estimates even more accurate. The authors also acknowledge that more than half of the transports in the two databases were excluded from the study due to missing or nongeocodable exact starting (scene) locations. This has obvious limitations for any system of predicting transport intervals in real time; however, ambulances equipped with GPS can allow for accurate geocoding of the starting location and the transport interval calculation when the vehicle departs the scene. Estimates of the transport interval have other uses beyond examining population-based access to care. The most obvious lies in allowing the receiving emergency department (ED) to prepare for inbound ambulance traffic. An accurate estimated time of arrival can allow, for example, the trauma team to assemble in the ED prior to patient arrival, but not so far in advance as to prematurely draw those personnel away from other tasks elsewhere in the hospital. It has been demonstrated that such estimates made by EMS personnel are inaccurate,14,15 often resulting in either too much or too little preparedness at the ED and perhaps in some cases altering how a direct medical oversight physician might manage a patient.15 Fleischman and colleagues developed a prototype web-based application that allows hospitals to estimate ambulance’s arrival time using Google Maps.13 Advanced 9-1-1 dispatch centers may already be using GPS to track and display real-time ambulance location on large, wall-mounted displays; this functionality could be extended to the hospital setting, allowing hospital-based providers to monitor the ambulance’s location and predicted arrival time in real time. Prehospital providers could also use real-time estimated transport intervals to help select between two or more otherwise equivalent receiving facilities. These applications are immediately plausible; other industries, including taxis and trucking companies, are already leveraging these technologies to track and improve service delivery. Road network estimation techniques with or without regression are important advances over current linear arc interval estimates. However, there is opportunity to improve the accuracy, particularly in rural areas with longer transport intervals. Continued mapping algorithm advancements, empiric big data transport intervals, and GPS-based locations are promising ways to further refine transport interval estimates. EMS road network transport interval estimation techniques can be immediately applied to emergency care and regionalization planning, and new electronic tools can use these estimates to improve operations and efficiency in dispatch centers, ambulances, and receiving EDs. David C. Cone, MD (
[email protected]) Editor-in-Chief, Academic Emergency Medicine Yale University School of Medicine New Haven, CT
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Cone and Landman • NEW TOOLS FOR TRANSPORT INTERVALS FOR EMS
Adam B. Landman, MD Brigham and Women’s Hospital Boston, MA
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Supervising Editor: Gary Gaddis, MD.
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References
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1. United States Congress. Emergency Medical Services Systems Act of 1973, In: 93rd Congress (ed). Washington, DC: US Government Printing Office, 1973. 2. Cairns CB, Glickman SW. Time makes a difference to everyone, everywhere: the need for effective regionalization of emergency and critical care. Ann Emerg Med. 2012; 60:638–40. 3. Cone DC, Lerner EB, Band RA, et al. Prehospital care and new models of regionalization. Acad Emerg Med. 2010; 17:1337–45. 4. Landman AB, Spatz ES, Cherlin EJ, Krumholz HM, Bradley EH, Curry LA. Hospital collaboration with emergency medical services in the care of patients with acute myocardial infarction: perspectives from key hospital staff. Ann Emerg Med. 2013; 61: 185–95. 5. Institutes of Medicine. Emergency Medical Services: At the Crossroads. Washington, DC: The National Academies Press, 2007. 6. Spaite DW, Valenzuela TD, Meislin HW, Criss EA, Hinsberg P. Prospective validation of a new model for evaluating emergency medical services systems by in-field observation of specific time intervals in
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