26-Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime Effect.

MILITARY MEDICINE, 00, 0/0: 1 , 2023Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime EffectLt Col Andrew Hall, MD<a href="https://orcid.org/0000-0002-6621-1084"><img src="/media/202408//1724838597.404032.png" /><img src="/media/202408//1724838597.409586.png" />*;</a> Cara Olsen, DrPH<a href="https://orcid.org/0000-0002-2734-4511"><img src="/media/202408//1724838597.4142962.png" /></a><a href="#bookmark1">†;</a> William Dribben, MD<a href="#bookmark2">‡;</a> Jacob Glaser, MD<a href="#bookmark3">§,</a><a href="#bookmark4">||;</a> Matthew Hanson, MD<a href="#bookmark5">¶</a>Downloaded from <a href="https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336bygueston30November2023">https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336 by guest on 30 November 2023</a>ABSTRACT Introduction:Organizational profciency increases with experience, which is known as a learning curve. A theoretical peacetime effect occurs when knowledge and skills degrade during peacetime. In this study, the intertheater evacuation system was examined for evidence of a military learning curve and peacetime effect.Materials and Methods:Data on medical evacuations from U.S. Central Command occurring between January 1, 2003,and December 31, 2022, were acquired from the TRANSCOM Regulating and Command &amp; Control Evacuation System. Priority mission evac- uation time corresponding to peak periods of activity in Iraq and Afghanistan and minimal activity in Afghanistan was analyzed. Any reduction or increase in the delivery time of casualties would be considered a change in profciency.Results:There was a marginal monthly decline of 0.019 days (27.4 min) to perform a priority evacuation from Iraq (95% con- fdence interval [CI], 0.009 to 0.028 days, P &lt; .001) and a decline of 0.010 days (14.4 min) from Afghanistan (95% CI, 0.003 to 0.016 days, P = .004) over 40 months from peak monthly average times. There was a monthly marginal increase in priority evacuation average time from Afghanistan of 0.008 days (11.5 min) (95% CI, 0.005 to 0.011, P &lt; .001) between January 2013 and December 2020. The number of monthly evacuations estimated to maintain or improve monthly average evacuation time is approximately 50.Conclusions:An intertheater aeromedical evacuation system increased in profciency during periods of confict and declined during relative peacetime. There is evidence of a peacetime effect on intertheater aeromedical evacuation.INTRODUCTIONThe delivery of military medicine steadily improved because of practitioners’ collective experiences during the height of the Iraq and Afghanistan conficts. The gain of organizational profciency as a function of experience is known as a learn- <a id="bookmark6"></a>ing curve<a href="#bookmark7">.1</a> Learning curve principles have been evaluated in <a id="bookmark8"></a>diverse felds such as economics and psychology.<a href="#bookmark9">2</a><a href="#bookmark10">,3</a> If a gain in experience results in increased profciency, intuitively, a loss of experience will result in a loss of profciency. Within military medicine, the concept that casualty care outcomes improve with confict and then decline with peace is known <a id="bookmark11"></a>as the “peacetime effect<a href="#bookmark12">.”4,5</a><a id="bookmark13"></a>*USCENTCOM Offce of the Command Surgeon, MacDill AFB, FL 33621, USA<a id="bookmark1"></a>† Department of Preventive Medicine &amp; Biostatistics, Uniformed ServicesUniversity of the Health Sciences, Bethesda, MD 20814, USA<a id="bookmark2"></a>‡ USTRANSCOM, TPMRC-A, Scott AFB, IL 62225, USA<a id="bookmark3"></a>§ Department of Surgery, Uniformed Services University of the HealthProvidence Regional Medical Center, Everett, WA 98201, USA<a id="bookmark4"></a>Scie||nces, Bethesda, MD 20814, USA<a id="bookmark5"></a>¶ AFSOC Offce of the Command Surgeon, Hurlburt Field, FL 32544, USAdoi:https://doi. org/10 . 1093/milmed/usad353Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.A recent battlefeld advancement is the rapid aeromedi- cal evacuation of wound, ill, and injured personnel to large medical facilities capable of defnitive care (Role 4 Military Treatment Facility). This transfer from one theater of mili- tary operations to another is intertheater patient movement. Over the course of recent conficts, data were systemati- cally collected on each patient’s movement. For intertheater aeromedical evacuation, profciency can be quantifed by the time it takes to deliver a casualty to a role 4 facility.Numerous variables go into moving casualties from dis- tributed points of injury to established facilities providing defnitive care. Individual actions by people and organizations collectively contribute to profciency gains. As an example, in 2007, one scheduled cargo aircraft mission and three contin- gency cargo aircraft missions per week were made available for the evacuation of patients from the feld hospital in Balad, Iraq. The result of this action was likely to reduce evacua- tion times. The provided example likely occurred amid many other changes though, and the effect cannot be independently analyzed in isolation. Ultimately, with a strategic-level under- standing of the rate of organizational profciency changes, medical planners can manage casualty care assets to account for peaks and troughs of profciency.This study aims to demonstrate the intertheater evacuation learning curve and provide evidence of a peacetime effect.MILITARY MEDICINE, Vol. 00, Month/Month 2023 1Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime EffectIt was hypothesized that intertheater patient movement prof- ciency increased during periods of relatively high activity and declined over periods of relative disuse.MATERIALS AND METHODSAfter institutional review board review and nonhuman research determination, TRANSCOM Regulating and Com- mand &amp; Control Evacuation System (TRAC2ES) data were acquired from U.S. Transportation Command. The data uti- lized were all U.S. Central Command evacuations occurring between January 1, 2003, and December 31, 2022, which includes major U.S. and coalition operations in Iraq and Afghanistan.Any decrease in the monthly average time to evacuation was considered an increase in profciency. Conversely, any increase in time would be a decline in profciency. For pur- poses of the study, priority and urgent evacuations were used to identify periods where profciency was gained or lost. Time to evacuation was measured as the difference between the patient’s “ready date” and the patient’s “actual departure date.” Time to evacuation was reported in days for analysis and graphical purposes, but data fdelity was to the minute. Urgent and priority evacuations were excluded if the differ- ence between dates was negative or greater than 1 month as these times were either impossible or likely secondary to data entry errors. The number excluded declined with progressing years, with the most being in 2003 when 84 out of 1675 urgent and priority evacuations were excluded.Priority evacuations were the most numerous category and therefore explicitly analyzed to determine profciency gains and losses. By analyzing one category, it eliminates any time effecting variables caused by the evacuation category. By doctrine, different categories of evacuations should be <a id="bookmark14"></a>evacuated within specifed periods.<a href="#bookmark15">6</a>Urgent and priority evac- uations have the most pressing evacuation requirements. In contrast, routine or convenience evacuations have the most protracted doctrinal guidelines. During this study period, routine or convenience evacuations were often transported on prescheduled fights, given their relatively low clinical acuity.The confict zone with the most casualties varied between 2004 and 2022, fuctuating from Iraq to Afghanistan. Aircraft travel time between role 4 and each geographic confict area is different, which would affect the difference between the ready date and the actual departure date. The role 4 location in Germany did not change during the study period. Other variables we assumed were adjustable by the military orga- nization. Confict locations were examined in isolation during relative peak casualty-generating activity to determine the rate of change in profciency. Profciency measurements began at the greatest average monthly evacuation time for a spe- cifc area and continued for 40 months for each theater. A 40-month period was selected because it roughly approxi- mated the time periods between visualized major changes in aggregate evacuation times.Peacetime, within this study, existed starting January 1, 2013 (month 121). At this point in time, evacuations consis- tently were below 50 evacuations per month. Measurement of any peacetime effect on evacuation time began on January 1, 2013, and continued until December 31, 2020. The year 2020 was the last full year of the U.S. Military presence in Afghanistan.Average monthly profciency change within each confict area and time period was calculated using linear regression. A test of the interaction between the confict area and time was used to compare profciency gains during periods of peak activity. Results for Iraq and Afghanistan were compared.Downloaded from <a href="https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336bygueston30November2023">https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336 by guest on 30 November 2023</a>The number of evacuations to maintain profciency gains was determined by comparing the period where profciency gains occurred and the period where profciency was lost. The data were split into two time periods, “prior to month 119” and “month 119 to end of study period.” Month 119 corresponds to November 2012. The trend and the combined number of days for priority and urgent evacuations were compared for the two time periods. Data were modeled using an interrupted time series linear regression model. All regression models used Newey–West standard errors to account for autocorrelation and heteroskedasticity.RESULTSThe combined number of priority and urgent evacuations and the average evacuation time are graphically repre- sented <a href="#bookmark16">(Fig. 1)</a>. After month 119 of the study period, all monthly evacuation counts were less than 50. Before month 119, only three months (1, 2, and 117) had fewer than 50 evacuations. Results show a slightly decreasing trend during the period before month 119, but the trend was not statistically signifcant (slope = −0.0.0026 days per month, 95% confdence interval [CI] −0.0055 to 0.0003 days per month, P = .077). Predicted evacuation time did not change signifcantly between months 118 and 119 (estimated change = 0.095 days per month, 95% CI, −0.109 to 0.300, P = .360). However, the trend over time, starting in month 119, was positive and statistically signifcant (slope = 0.0087, 95% CI, 0.0074 to 0.0101, P &lt; .001). This represents a signif- icant difference in time trends between the two periods (dif- ference in slopes = 0.011, 95% CI, 0.008 to 0.014, P &lt; .001). These results suggest that the number of evacuations to main- tain evacuation profciency is 50.As demonstrated in, <a href="#bookmark16">Figure. 1,</a> two prominent peaks corresponded to heightened activity in Iraq and again in Afghanistan. The earliest spike in time to evacuation corre- sponds to increasing activity in Iraq, and the second spike corresponds to Afghanistan. The average time of priority evac- uations for each theater was analyzed respectively. The slopes of the lines indicate a monthly marginal increase of 0.019 days (27.4 min) for evacuations from Iraq (<a href="#bookmark17">Fig. 2)</a> (95% CI, 0.009 to 0.028 days, P &lt; .001) and a monthly marginal increase of 0.010 days (14.4 min) from Afghanistan <a href="#bookmark18">(Fig. 3)</a> (95% CI, 0.003 to 0.016 days, P = .004). These marginal increases are2 MILITARY MEDICINE, Vol. 00, Month/Month 2023<a id="bookmark16"></a>Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime Effect<img src="/media/202408//1724838597.464434.jpeg" />Downloaded from <a href="https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336bygueston30November2023">https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336 by guest on 30 November 2023</a>FIGURE 1. Average monthly evacuation time in days for combined urgent and priority intertheater evacuations originating from the U.S. Central Command area of responsibility from January 1, 2003,to December 31, 2022. The number of total priorities and urgent evacuations corresponding to the time period is also plotted. The spike at approximately month 17 corresponds to increased activity in Iraq, and the spike at approximately month 81 corresponds to increased <a id="bookmark17"></a>activity in Afghanistan.<img src="/media/202408//1724838597.469481.png" />FIGURE 2. Average monthly intertheater priority evacuation time for casualties originating from Iraq from September 2004 through December 2007. The trendline equation is represented with the formula y = −0.019x + 1.047.small, but cumulatively over 40 months represent a prof- ciency gain of 0.76 days (Iraq) and 0.40 days (Afghanistan). Although a signifcant profciency increase was observed in Iraq and Afghanistan during peak activity periods, the rate of increase was signifcantly greater in Iraq (an addi- tional 0.009 days per month, 95% CI, 0.003 to 0.016 days, P = .003).A profciency decline is also present (<a href="#bookmark19">Fig. 4)</a>. The slope of the lines implies a monthly marginal decrease in profciency of 0.008 days (11.5 min) (95% CI, 0.005 to 0.011, P &lt; .001). Over a period of 96 months, this represents a total decline of 0.77 days.DISCUSSIONThe study identifed that the rate intertheater evacuation prof- ciency increased and declined. This study supports the theory of a “peacetime effect” for military medicine. Additionally,the study indicates that intertheater medical evacuation pro- fciency is maintained or improved with 50 evacuations per month.The concept of a learning curve is well-known in business <a id="bookmark20"></a>and psychology.<a href="#bookmark21">7</a><a href="#bookmark22">,8</a>The application of learning curves has also been used by the military. However, application to medical <a id="bookmark23"></a>functions is poorly reported or absent<a href="#bookmark24">.9,10</a> The presence of a curve had been implied in publications showcasing increased survivability of casualties, but the rate of improvement and <a id="bookmark25"></a>decline has not<a href="#bookmark26">.11</a> Given the heterogeneity of casualties and the specifc care provided to each, the marginal increase in the effectiveness of an organization in providing direct care maybe harder to describe or model mathematically. The rates or declines in the performance of health functions in addition to intertheater evacuation would be valuable for planning and training purposes.The applicability of this study to other aspects of expe- ditionary medicine is limited. The profciency gain and lossMILITARY MEDICINE, Vol. 00, Month/Month 2023 3<a id="bookmark18"></a>Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime Effect<img src="/media/202408//1724838597.488908.jpeg" />Downloaded from <a href="https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336bygueston30November2023">https://academic.oup.com/milmed/advance-article/doi/10.1093/milmed/usad353/7275336 by guest on 30 November 2023</a>FIGURE 3. Average monthly intertheater priority evacuation time for casualties originating from Afghanistan from August 2009 through December 2012. <a id="bookmark19"></a>The trendline equation is represented with the formula y = −0.010x + 0.938.<img src="/media/202408//1724838597.496156.png" />FIGURE 4. Average intertheater priority evacuation time for casualties originating from Afghanistan from January 2013 through December 2020. The trendline equation is represented with the formula y = 0.008x + 0.730.slopesidentifed in this study, in the absence of other informa- tion, can form the basis for estimating the learning curves for other military medical functions. Learning curves are likely demonstratable for other aspects of expeditionary military medical care delivery if relevant databases can be identifed and accessed.The 50 evacuations to maintain or gain profciency is an estimate. A weakness of the analysis is that it needs tofully characterize changes over periods of time. This modelassumed a linear trend over the study period, while the fgures provided show several peaks in evacuation time. Examination of the time series plot suggests that evacuation time may have started increasing around month 63 rather than month 119, but this would not correspond with any persistent decrease in the <a id="bookmark27"></a>number of evacuations to which month 119 corresponds<a href="#bookmark28">.12</a>CONCLUSIONThere exists a learning curve for the intertheater evacuationsystem with an associated peacetime effect in which orga-nizational profciency was lost. The number of intertheater evacuations necessary to increase or maintain organizationalprofciency is estimated to be 50 patient movements. The mathematical evidence that there is a learning curve for one expeditionary medical function suggests that similar learningcurves exist for other functions.ACKNOWLEDGMENTSNone declared.FUNDINGNone declared.CONFLICT OF INTEREST STATEMENTNone declared.DATA AVAILABILITYOn request.CLINICAL TRIAL REGISTRATIONNot applicable.4 MILITARY MEDICINE, Vol. 00, Month/Month 2023<a id="bookmark10"></a>Aeromedical Evacuation, the Expeditionary Medicine Learning Curve, and the Peacetime EffectINSTITUTIONAL REVIEW BOARDThe study was determined to be nonhuman research by the U.S. Army Med- ical research and Development Command’s Offce of Research Protections,Institutional Review Board Offce.INSTITUTIONAL ANIMAL CARE AND USE COMMITTEENot applicable.INDIVIDUAL AUTHOR CONTRIBUTION STATEMENTA.H. conceived the study concept, collected and interpreted the data, andwrote the draft; C.O. analyzed the data and contributed to the draft; W.D. <a id="bookmark24"></a>interpreted the data and contributed to the draft; J.G. contributed to the draft;M.H. interpreted the data and contributed to the draft.INSTITUTIONAL CLEARANCEApproved.REFERENCES<a id="bookmark7"></a><a href="#bookmark6">1.</a> Thompson P: The relationship between unit cost and cumulative quan-tity and the evidence for organizational learning-by-doing. 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