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Live tissue versus simulation training for emergency procedures: Is simulation ready to replace live tissue?

Published:August 06, 2016DOI:https://doi.org/10.1016/j.surg.2016.04.044

      Background

      Training of emergency procedures is challenging and application is not routine in all health care settings. The debate over simulation as an alternative to live tissue training continues with legislation before Congress to banish live tissue training in the Department of Defense. Little evidence exists to objectify best practice. We sought to evaluate live tissue and simulation-based training practices in 12 life-saving emergency procedures.

      Methods

      In the study, 742 subjects were randomized to live tissue or simulation-training. Assessments of self-efficacy, cognitive knowledge, and psychomotor performance were completed pre- and post-training. Affective response to training was assessed through electrodermal activity. Subject matter experts gap analysis of live tissue versus simulation completed the data set.

      Results

      Subjects demonstrated pre- to post-training gains in self-efficacy, cognitive knowledge, psychomotor performance, and affective response regardless of training modality (P < .01 each). With the exception of fluid resuscitation in the psychomotor performance domain, no statistically significant differences were observed based on training modality in the overall group. Risk estimates on the least pretest performance subgroup favored simulation in 7 procedures. Affective response was greatest in live tissue training (P < .01) and varied by species and model. Subject matter experts noted significant value in live tissue in 7 procedures. Gap analysis noted shortcomings in all models and synergy between models.

      Conclusion

      Although simulation has made significant gains, no single modality can be identified definitively as superior. Wholesale abandonment of live tissue training is not warranted. We maintain that combined live tissue and simulation-based training add value and should be continued. Congressional mandates may accelerate simulation development and improve performance.
      The training of medical personnel in high-acuity, low-frequency, life-saving procedural interventions is challenging. An ideal model for training does not exist presently, and the debate between live tissue (LT) and models of inanimate simulation continues. Research designed to compare the educational effectiveness of LT versus simulation training is difficult to perform, and the time needed to assess long-term impacts of training interventions coupled with the fluid landscape of simulation development contributes to a paucity of information on which to base best practice.
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      Political pressures about the use of LT in medical training are substantial and have resulted in legislation limiting effectively the use of funds for LT training in the Department of Defense, despite current training practices relying heavily on this modality.

      HR 3172. 113th Congress. S.1550. September 25, 2013. Battlefield Excellence through Superior Training Practices Act (BEST).

      National Defense Authorization Act 2013, Sect 736. Report on strategy to transition to use of human-based methods for certain medical training.

      Medical modeling and simulation technology, while advancing rapidly, still present technical challenges for faithful replication of human anatomy, physiology, and pathology. LT use does not lend itself to repetitive training or extensive throughput and carries with it complex regulatory requirements, extensive life-cycle and logistical support, and the physiologically confounding effects of general anesthesia. The Advanced Trauma Life Support course developed by the American College of Surgeons shifted from LT to simulation-based surgical skills training in the early 2000s; this shift occurred despite an American College of Surgeons position statement classifying animals as “an indispensable element of biomedical research, education, and teaching… and that, wherever feasible, alternatives to the use of live animals should be developed and employed,” further highlighting the friction between abandonment of animal models used traditionally and the wholesale adoption of simulation that persists in medical education and training today.
      Statement on the Use of Animals in Research, Education, and Teaching Bulletin of the American College of Surgeons.
      The mandated transition from LT to simulation-based skills training in the military has exposed strong views from advocates of both LT and simulation, with the potential for training models to change without solid evidence to guide best practice.
      Given the current state of medical simulation technology, the importance of effective training for optimal patient outcomes and the disagreement about the superiority of either LT or simulation-based training environments, the University of Missouri Combat Casualty Training Consortium (MU CCTC) was established to investigate the comparative effectiveness of LT and simulation-based training across a spectrum of 12 emergency trauma procedures.

      Methods

      The MU CCTC represents a national coalition of subject matter experts (SMEs) encompassing the areas of battlefield/trauma surgery, surgical education, prehospital/battlefield medical care and training, educational practice and design, statistical analysis, and simulator design. The primary goal of the study was to identify best training practices and modalities to decrease preventable mortality on the battlefield and in civilian practice. This multiacademic and industry effort hypothesized that relevant differences in self-efficacy, cognitive performance (COG), psychomotor performance (PSY), and affective response (AFR) would be observed between subjects trained with LT versus simulation in 12 life-saving emergency procedures (Table I). In 11 of the 12 procedures (P1−P11), the research design randomized subjects into LT or simulation training arms. For procedure P12 (nerve agent casualty), subjects were randomized into 3 training groups: LT, simulation, or a high-resolution video of the LT training exercise. Additionally, procedure P12 in the PSY assessments were separated into 3 subgroups representing the varying presentations of nerve agent exposure (Fig 1).
      Table ILive tissue, simulation, video training groupings, subjects, simulators, animal models, and electrodermal activity affective results
      Training groupIDProcedureNSimulatorAnimal modelVideoAffective response
      TestingTrainingFindingP value
      All742AllAllAllNHP videoLT>SIM>VID<.001
      Group 1P1Adult intubation147SimuLab TraumaMan

      Laerdal Airway Management Manikin
      Trauma FX AirwayPlus Lifecast; intubation

      amputation
      CaprineNALT = SIM.892
      P2Surgical airway
      P3Chest seal
      P4Needle thoracostomy
      P5Tube thoracostomy
      Group 2P6Intraosseous device194MATT Trauma FX. KGS-TFX-HEMO-1

      KGS-TFX-APL-R
      Tom000C Custom GSW T.O.M ManPorcineNALT > SIM.004
      P7Casualty resuscitation
      P8CAT tourniquet
      P9Hemostatic dressing
      P10Amputation management
      Group 3P11Pediatric intubation207CAE Healthcare PediaSim2TruCorp

      AirSim
      FerrettNALT = SIM.418
      Group 4P12Nerve agent casualty194CAE Healthcare METIMan

      prehospital
      Laerdal

      SimMan 3G
      NHPNHP videoLT>SIM>VID<.001
      Simulab, Seattle Washington; ITTS – Innovative Tactical Training Solutions – Crestwood, Kentucky; CAE Healthcare – Sarasota, Florida; KGS – Kforce, Inc, Fairfax, Virginia; Laerdal – Stavanger, Norway; Trucorp – Belfast, Ireland.
      CAT, Combat application tourniquet; ID, procedural identification; LT, live tissue animal training; NA, not applicable; NHP, non-human primate; P, procedure.
      Standardization of training was achieved through scripted curricula. LT and inanimate simulation models were selected based on consensus input from the consortium (Table I, Table II). To isolate the effect of each training modality, subject performance was assessed in a controlled setting without external stressors. Four animal models were utilized, and related procedures were grouped for sequential performance in both training and testing. Group 1 consisted of procedures P1–P5, group 2 included procedures P6–P10, and groups 3 and 4 each contained a single procedure, P11 and P12, respectively (Table I). This study design was based on logistical considerations and a desire to limit overall LT use. All training and testing was performed in a single day.
      Table IILive tissue versus simulation medic training: Results and training recommendations
      ID, Procedural identification; ND, no statistically significant differences; P, procedure.
      The subjects comprised a heterogeneous population of both military and civilian medic volunteers. Self-efficacy was measured through surveys administered pre- and post-training on a 10-point Likert scale. COG was measured by multiple choice assessments given pre- and post-training. PSY was scored by trained observers utilizing standardized checklists composed of readily identifiable and observable decomposed steps for each procedure. These assessments were procedure-specific, scored dichotomously, and designed to capture each subject's ability to perform a related action. PSY was analyzed in 2 ways: the total number of steps completed and the total number of critical steps completed. Step criticality was determined by consensus of the SMEs.
      To account for potential inter-rater variation in the scoring of PSY assessment checklists, inter-rater concordance was used as a measure of consistent judgment between raters within each procedural grouping. Observational concordance was aided by strict definition of successful completion of each decomposed, readily identifiable, and observable item in the PSY performance checklist. Concordance between individual raters was achieved and documented via repeated scripted performances with planned omissions for rater training, both prior to the start of the study and mid-study to assure sustainment.
      AFR to the training modality was quantified by measuring the electrodermal activity (EDA) of each subject before and during training using the Affectiva Q-Sensor (Affectiva, Waltham, MA). This device was worn on the anterior wrist of the non-dominant hand and allowed for unobstructed free range of motion. EDA was sampled at 8 Hz via 2 12-mm electrodes. Matlab (MathWorks, Natick, MA) was used to analyze time-synched raw data. Baseline and training periods were extracted from each EDA time series. For each subject, the change in EDA response from baseline to training was quantified via the fractional change (FC) defined as FC = (training point estimate − baseline point estimate [BPE])/BPE. The training point estimate was taken as the maximum EDA response recorded in the training period after application of a moving window average to filter pressure and motion artifact. The BPE was taken as the 10% trimmed mean of the baseline period data. Fractional change was found to be skewed positively, and data were normalized using a log transformation prior to statistical analysis.
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      A wearable sensor for unobtrusive, long-term assessment of electrodermal activity.
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      Practicality of using galvanic skin response to measure intraoperative physiologic autonomic activation in operating room team members.
      Gap analysis of each training modality, LT and simulation, by procedure was performed by industry SME members of the MU CCTC. These members were not engaged directly in the research conduct and performed the analysis through direct observation and survey of research teams. For each procedure, PSY checklist steps were analyzed for their ability to be performed consistently without limitations during the pre- and post-testing and training exercises. For each modality, steps were labeled as either equivalent, unable to be accomplished, or accomplished uniquely by either LT or simulation. Based on the proportion of steps accomplished uniquely by modality, gap analysis results were identified as either representing synergy between modalities, favorable to LT, or favorable to simulation.
      Data analysis focused on pre- to post-training differences of assessment instrument scores in the self-efficacy, COG, and PSY domains and on the EDA fractional change in the affective domain. Subjects were evaluated as a whole and then divided into high and low performing groups based on pre-training PSY scores. Customary descriptive statistics were calculated. For inferential statistics, general statistical assumptions were appraised for all data, including normality in the scales, linearity in the scores, homogeneity of variance, sphericity, homogeneity of regression, and absence of outliers in the score distributions. Scales were measured at a nominal level of measurement or above, and when summed, interval level was assumed. Outliers were explored with both logistic and ordinary least squares regression by regressing expected normal values onto observed values with a criterion of ±1 standard deviation. Additionally, to identify extreme values not judged to be outliers, z-scores were calculated using criterion ±3 z-score units. These data lead to risk analyses focusing primarily on odds ratios. In addition to these correlational approaches, inferential statistics included t tests, analysis of variance, and analysis of covariance (G-Power version 3.1, Kiel, Germany; SPSS version 22, Armonk, New York; SAS version 9.4, Cary, North Carolina).
      To complement the research design, highly experienced senior special operations medics underwent structured interviews to evaluate their perceptions of LT and simulation as training tools. Overall responses were categorized as supporting either LT or simulation when responses were >80% in favor, mixed when responses were between 60% and 79%, and equivalent when responses were in the 40–59% range in regards to LT or simulation. Responses to directed questions exploring limitations and benefits of each modality were categorized and reported as a percentage.
      All human subject use was approved by both local Institutional Review Boards and the Human Research Protections Office of the United States Army Medical Research and Materiel Command (USAMRMC). Prior to participation and data collection, written informed consent was obtained from each subject. All animal use was approved by both local Animal Care and Use Committees and the Animal Care and Use Review Office of the USAMRMC.

      Results

      In the study, 742 subjects had complete data sets for analysis. Age ranged from 18–64 years (32 years, mean age), with 459 (62%) males and 412 (55.5%) military participants; 384 (52%) were randomized to LT, 358 (48%) were randomized to simulation, and 64 (8.6%) were randomized to video for the nerve agent casualty P12 group 4 only. Of the 742 subjects, 238 (32.1%) claimed human experience in at least 1 of the procedures, and 322 (43.4%) claimed no experience in any of the procedures in either simulated or clinical environments. For the 12 procedures tested, all subjects demonstrated assessment gains from pre- to post-training in self-efficacy, COG, and PSY performance, regardless of training modality (P < .01 each). Similarly, subjects demonstrated increases in EDA fractional change (P < .01) from the baseline to training conditions, demonstrating the study format captured changes in performance effectively in all assessment domains, and the training design was effective in achieving those gains. Inter-rater concordance for evaluation of PSY performance ranged from 89.2−7.3 % throughout the study (Table III).
      Table IIIInter-rater concordance for psychomotor steps evaluation
      Training groupProcedure IDsInter-rater concordance
      Pre-studyMid-study
      Group 1P1−P589.3 %97.3 %
      Group 2P6−P1089.2 %91.6 %
      Group 3P1195.3 %92.6 %
      Group 4P1296.7 %94.6 %
      ID, Procedural identification; P, procedure.
      When all subjects were analyzed, no statistically significant differences in self-efficacy or COG were noted based on LT versus simulation training in any of the 12 procedures. Although there were more gains in those subjects with less prior knowledge or experience as demonstrated by pre-training testing and survey, randomization to LT or simulation-based training did not influence changes in self-efficacy and COG performance in this 1-day testing and training model. No differences were seen with PSY performance in 11 of the 12 procedures based on training modality. Only procedure P7, casualty resuscitation, favored inanimate simulation training for PSY (Table II).
      Cognizant that the level of training and experience may play a role in the effectiveness of a given training modality, subjects were classified further into high and low performance groups based solely on pretraining PSY performance. For each procedure, analysis of variance, risk estimates, and odds ratios were calculated for all steps and the subset of critical steps. High performers demonstrated no differences in PSY based on training modality by either analysis of variance or risk estimate analysis. Low performers, while demonstrating no differences by analysis of variance, demonstrated differences by risk estimates and odds ratios based on training modality favoring simulation for 7 of the procedures (P1, 2, 4, 5, 7–9) when evaluating all steps and for 3 of the procedures (P1, 4, 5) when evaluating only the critical steps (Table II).
      AFR to each LT and simulation training exercise was evaluated for each of the 4 procedural groupings (Table I). LT training across all modalities demonstrated a greater AFR as measured by EDA FC than that seen with simulation (P < .001); however, further evaluation of each LT model demonstrated differences between AFR. The porcine model utilized in procedures P6−P10, and the non-human primate model utilized in procedure P12, demonstrated greater AFR (P = .004 and P < .001, respectively) when compared with simulation training. The caprine and ferret models, utilized in procedures P1−P5 and P11, respectively, did not demonstrate differences between LT and simulation with respect to AFR. The additional video training modality utilized in procedure P12 only allowed for an additional comparison among VID, simulation, and LT training, with both LT and simulation having greater AFR than VID. Due to the methodology of collection of EDA data and the training curricula utilized, analysis of AFR was limited to training group and specific training modalities and was not specific to an individual procedure (Table I, Table II).
      Gap analysis of each procedure in the PSY checklist demonstrated significant shortcomings in both the LT and simulation models used in this study for faithful replication of the steps necessary for successful completion. In 11 of the 12 procedures evaluated (P1−P11), significant synergy was demonstrated in 7 (P1, 3–6, 8, and 11), LT was favored in 3 (P2, 7, and 10), and simulation was favored in 1 (P1). In the nerve agent casualty model with its 3 distinct presentations (P12), 2 of the presentations favored simulation, while 1 favored a synergistic approach to training. No single model was able to accomplish 100% of the steps in the PSY checklist for any of the procedures studied, with failure to accomplish rates varying from 8–50% (Table II, Table IV).
      Table IVGap analysis by percent decomposed steps accomplished by procedure
      Training groupProcedureSteps,

      N
      % (N) steps equally accomplished LT or SIM% (N) steps unable to be accomplished by modality% (N) steps uniquely accomplished by modalityResults
      No.NameLTSIMLTSIM
      Group 1P1Adult intubation2868% (19)14% (4)11% (3)7% (2)11% (3)Synergistic
      P2Surgical airway2070% (14)10% (2)30% (6)20% (4)0.00% (0)LT
      P3Chest seal743% (3)29% (2)43% (3)29% (2)15% (1)Synergistic
      P4Needle thoracostomy1040% (4)30% (3)30% (3)30% (3)30% (3)Synergistic
      P5Tube thoracostomy2148% (10)33% (7)24% (5)14% (3)24% (5)Synergistic
      Group 2P6Intra-osseous device1377% (10)15% (2)8% (1)8% (1)15% (2)Synergistic
      P7Casualty resuscitation850% (4)25% (2)50% (4)25% (2)0.00% (0)LT
      P8CAT tourniquet771% (5)14% (1)14% (1)14% (1)14% (1)Synergistic
      P9Hemostatic dressing786% (6)14% (1)0.00% (0)0.00% (0)14% (1)SIM
      P10Amputation management967% (6)22% (2)33% (3)11% (1)0.00% (0)LT
      Group 3P11Pediatric intubation2854% (15)39% (11)14% (4)7% (2)21% (6)Synergistic
      Group 4P12Nerve agent casualty, mild130.00% (0)100% (13)23% (3)0.00% (0)62% (8)SIM
      Nerve agent casualty, moderate130.00% (0)100% (13)23% (3)0.00% (0)69% (9)SIM
      Nerve agent casualty, severe1354% (7)38% (5)54% (7)38% (5)23% (3)Synergistic
      CAT, Combat application tourniquet; P, procedure; synergistic, both LT and SIM demonstrate uniquely accomplished steps.
      Twenty-five senior US Army Special Operations medics underwent voluntary structured interviews. All participants demonstrated extensive experience in all training environments under investigation (LT and simulation), with 96% reporting experience in these modalities as both students and instructors and 100% having combat experience. All but 1 (96%) reported translation of skills P1–P11 from training to the care of combat-related injuries.
      The majority of respondents thought that the LT training was superior to SIM for 7 of the 12 procedures surveyed (P2, 4, 5, 7, 9, 10, and 12). Chest seal placement and tourniquet application (P3 and P8) garnered equal favor for both modalities, suggesting equivalence. Adult and pediatric intubation as well as insertion of the intra-osseous device (P1, 6, and 11) produced a mixed response (Table II). All participants thought LT training should be used in combat medic training, while 96% thought simulation also offered substantial benefit. Limitations and benefits were noted for both LT and simulation modalities by respondents (Table V). Simulators were noted commonly to be of appropriate size and weight, allowed for repeatability, and improved familiarization with equipment and steps producing muscle memory. In contrast, simulators were thought to provide an inaccurate and linear response to treatment, produced no sense of urgency, lacked the appropriate tactile feedback, and provided no visceral response to the trainee. Live tissue was noted most commonly to build confidence, instilling both a sense of urgency and a visceral response in the trainee to a model that can expire with more realistic responses to treatment and better tactile response. Reported limitations of LT were the non-human anatomy, logistic difficulty, and the need for anesthesia and veterinary medicine, thus making decision-making processes inconsistent with human medicine. Public perception also was noted by respondents as a benefit of simulation and a limitation of LT.
      Table VStructured interview live tissue versus simulation: Limitations and benefits
      N = 25Limitations% responseBenefits% response
      SimulationInaccurate/linear response to treatment84Appropriate size and weight: easy logistics96
      No sense of urgency: not taken seriously84Repeatability92
      Lacks appropriate feel/tactile response80Familiarization: muscle memory48
      No visceral response: no stress inoculation70Public perception20
      Live tissueInaccurate anatomy84Confidence: feel/tactile response92
      Not appropriate size weight: logistics difficult52Accurate/nonlinear response to treatment84
      Anesthetized/veterinary medicine50Instills sense of urgency: focuses training84
      Public perception28Visceral response: stress inoculation72

      Discussion

      LT always has been used in medical education. Although modern medical simulators came into use in the 1960s, the current climate of medical simulation has gained notable ground with advances in technology, materials, and cost-reduction in the past 25 years.
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      Even with these advances, LT remains an integral part of medical education, and wholesale adoption of the modern simulator for all medical training has not occurred. Few studies comparing directly LT to simulation exist.
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      “Political sensitivities” of all aspects (ethical, social, financial, etc) have played a major role in the move to mandate decreased use of LT for trauma training, especially for military medical personnel.
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      Petition for enforcement. In re: training of Air Force Special Operations Forces at Joint Base Lewis McChord. Physicians Committee for Responsible Medicine. 2013. https://assets.documentcloud.org/documents/700761/pigs.pdf.

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      In our head-to-head, single-day comparison of LT and simulation in a controlled environment without external stressors, we found no differences in the domains of COG or self-efficacy. Interestingly, those subjects with the greatest opportunities for PSY gain showed no benefit from LT; however, 7 procedures demonstrated some benefit from simulation training, suggesting simulation may be superior to LT for the introduction of a new procedure to the novice learner. Although these results are compelling, additional results from our comprehensive analysis (including the affective domain, gap analysis, and SME structured interviews) do not allow us to support wholesale change to paradigms of simulation-only based training.
      AFR measured by EDA represents the emotional response to training and learner engagement. This study represents the first novel application of EDA measurement to medical education for the purpose of quantifying AFR to training. Both the porcine and non-human primate models were dynamic in nature, both in their interaction with the learner and response to treatment, while the ferret and caprine models were static in their presentations to the learner, similar to simulation. These model differences may account for the observation that some but not all LT models induced greater AFR than simulation and may suggest a development strategy for future generation simulation to have a greater impact on learner engagement.
      Gap analysis demonstrated substantial and relevant shortcomings in both LT and simulation with no model achieving all educational goals and human-like performance for any of the 12 tested procedures. Many training programs utilize a combined approach of both LT and simulation for the introduction of new medical skills. We suspect that this combined training methodology allows local expertise to incorporate the most effective parts of LT and simulation, so that current technologic gaps are minimized, and training value is maximized.
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      SME structured interviews demonstrated significant bias for LT while supporting the use of simulation in a combined training model as described above. It is unclear whether this was secondary to the rigors of training for medical care in the austere environment, perceived historic training success, or visceral response to training providing psychologic preparation for providing care under duress. It is clear that current training with combined LT and simulation is effective on the modern battlefield in terms of improved injury survival.
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      Our analysis has limitations. The subject group was heterogeneous and demonstrated variance in age, prior training, and experience. Gains in those with prior experience or understanding were minimal and thus made significant comparisons of gains difficult to achieve. The pre- and post-training testing was performed with inanimate simulation, which may have allowed for additional learning and may have favored simulation outcomes. Each test occurred on a single day, so comments on retention based on LT or simulation cannot be made. The selection of simulators was based on commonly utilized training paradigms and did not evaluate all simulation technology directly nor technology which is/was in development in this rapidly progressing field. While EDA is an established method for quantifying emotional responses to defined stimuli, the use of EDA estimates of baseline and training points to enable the quantification of affective response to training environment as a fractional change represents a novel technique of EDA analysis.
      Traditional medical education of “see one, do one, teach one” has changed with advances in technology and changes in practice and patient expectations.
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      Training environments have grown through the use of LT and simulation prior to direct patient interaction.
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      The passion and “politics” around the use of LT is substantial, and none more so than that seen in the training of combat casualty care.
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      • Balsam M.J.
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      Petition for enforcement. In re: training of Air Force Special Operations Forces at Joint Base Lewis McChord. Physicians Committee for Responsible Medicine. 2013. https://assets.documentcloud.org/documents/700761/pigs.pdf.

      • Martinic G.
      The use of animals in live-tissue trauma training and military medical research.
      These types of political issues, while important and relevant to the general population, should not drive best practice because the consequences are too high, most notably in high acuity, low occurrence events. Without the ideal model, educators must choose from those available, and mitigate the negative and accentuate the positive aspects of each. As responsible educators, we must refine, decrease, replace, and whenever possible respect the use of LT protocols for training.

      Animal Welfare Act, Amendment of Title XVII, Subtitle F, Sections 1751−1759, US Statutes at Large 99:50, 1985.

      As efforts are made to decrease the use of LT, there should be recognition that LT and simulation offer differing benefits to learners for some skills. The combined use of LT and SIM allows curricula to maximize the benefits of both training modalities and lead to the best opportunities for success in the future. Congressionally mandated development programs and ever advancing technology hold promise to continue to decrease and may eliminate ultimately LT in medical and surgical education.
      Although simulation has made widespread substantial gains in education, no single training modality can yet be identified definitively as superior for any of the 12 emergency trauma skills we evaluated. Wholesale abandonment of LT is not warranted and should be addressed with caution. Combined LT and simulation-based training adds value to training outcomes. Congressional funding may accelerate simulation development, improve performance, and move medical and surgical education closer to LT-free training environments, but we are not there yet.
      Views expressed are those of the authors alone and do not represent the views of the US Government or the Department of Defense. University of Missouri Combat Casualty Training Consortium Investigators: Col. Jeff Bailey, MD, FACS, Walter Reed Army Medical Center, Bethesda, MD; Col. (retired) Warren Dorlac, MD, FACS, USAF CSTARS, Cincinnati, OH; Rob Shotto, Penn State University Simulation Center, Hershey, PA; Jack Norfleet, USA RDECOM/ARL-STTC, TX; Tim Coakley, MD, USN Deputy Force Surgeon, VA; Mark Bowyer, MD, FACS, USUHS National Capital Simulation Center, Bethesda, MD; Bousseau Murray, MD, Penn State University Department of Anesthesia, Hershey, PA; Mark Shapiro, MD, FACS, Duke University Department of Surgery, Durham, NC; Roberto Manson, MD, Duke University Department of Surgery, Durham, NC; Al Moloff, MD, Information Visualization and Innovative Research Inc, Sarasota, FL; Deborah Burgess, MD, The Salus Group, San Antonio, TX; Robert Hester, PhD, University of Mississippi, Jackson, MS; William Lewandowski, 3D Systems Corporation, Rock Hill, SC; Waymon Armstrong, Engineering and Computer Simulations, Inc, Orlando, FL; Jack McNeff, Simulaids Corp, Saugerties, NY; Jan Cannon-Bowers, PhD, Joanne Hardeman, MPA, Jenny Guido, MD, Cole Giering, BS, Robert Rohrlack, BS, Jessica Acosta, BS, Raj Patel, BS, Zachary Green, BS, University of South Florida Center for Advanced Medical Simulation and Learning, Tampa, FL; Ronald Roan, MD, Adam Robinett, MD, Scott Snyder, PhD, Bharat Soni, PhD, Dale Davis, BSN, Lina Rodriquez, BS, BSN, Phillip Shum, MS, University of Alabama-Birmingham, Birmingham Alabama; Steve Osterlind, PhD, Chris Cooper, MD, Rindi Uhlich, MD, Christina Stephan, MD, PhD, John Tucker, MS, University of Missouri, Columbia MO; John Anton, MS, Ray Shuford, BS, Catherine Strayhorn, BS, Emily Anton, BS, Nadine Baez, BS, Erin Honold, BS, Information Visualization and Innovative Research (IVIR), Sarasota, FL.

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      Linked Article

      • Comment on: Live tissue versus simulation training for emergency procedures: Is simulation ready to replace live tissue?
        SurgeryVol. 161Issue 5
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          Barnes et al1 are to be commended for their efforts in comparing live tissue training (LT) to simulation training for determining the most effective way to teach emergency medical procedures to military personnel. Their conclusion that “Wholesale abandonment of live tissue training is not warranted,” however, seems unduly weighted by a puzzling bias favoring LT rather than objective results gained from their own data, which show “no differences” between the 2 training methods in the cognitive performance and self-efficacy of the study participants, “no benefit” in psychomotor performance from LT, and “some benefit from simulation training, suggesting simulation may be superior to LT for the introduction of a new procedure to the novice learner.”
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