ORIGINAL ARTICLE
Load index model: An advanced tool to support decision making during mass-casualty incidents Bruria Adini, PhD, Limor Aharonson-Daniel, PhD, and Avi Israeli, MD, MPH BACKGROUND: In mass-casualty events, accessing information concerning hospital congestion levels is crucial to improving patient distribution and optimizing care. The study aimed to develop a decision support tool for distributing casualties to hospitals in an emergency scenario involving multiple casualties. METHODS: A comprehensive literature review and structured interviews with 20 content experts produced a shortlist of relevant criteria for inclusion in the model. A ‘‘load index model’’ was prepared, incorporating results of a modified Delphi survey of 100 emergency response experts. The model was tested in three simulation exercises in which an emergency scenario was presented to six groups of senior emergency managers. Information was provided regarding capacities of 11 simulated admitting hospitals in the region, and evacuation destinations were requested for 600 simulated casualties. Of the three simulation rounds, two were performed without the model and one after its presentation. Following simulation experiments and implementation during a real-life security threat, the efficacy of the model was assessed. RESULTS: Variability between experts concerning casualties’ evacuation destinations decreased significantly following the model’s introduction. Most responders (92%) supported the need for standardized data, and 85% found that the model improved policy setting regarding casualty evacuation in an emergency situation. These findings were reaffirmed in a real-life emergency scenario. CONCLUSION: The proposed model improved capacity to ensure evacuation of patients to less congested medical facilities in emergency situations, thereby enhancing lifesaving medical services. The model supported decision-making processes in both simulation exercises and an actual emergency situation. (J Trauma Acute Care Surg. 2015;78: 622Y627. Copyright * 2015 Wolters Kluwer Health, Inc. All rights reserved.) KEY WORDS: Mass-casualty event; hospital congestion; patient evacuation; decision making; load index.
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mergencies are often characterized by massive casualty evacuation to hospitals.1,2 The hospitals themselves may be disabled by the disaster, in which case patients may have to be transferred to other medical facilities, as occurred following Hurricane Sandy.2 To avoid overloading and long waiting times in emergency departments (EDs), operating rooms (ORs), and intensive care units (ICUs), decisions regarding evacuation destinations must be carefully weighed based on the severity of injuries, the overall duration of the evacuation operation, mean evacuation times, and available hospital resources.3 Studies have shown that extended waiting times in EDs and high ED occupancy rates are frequently inevitable4 because of imbalance between needs and resources.4,5 Not only does hospital congestion affect EDs, but it also places a heavy burden on operating theaters and recovery rooms.5,6 Patient flow and waiting times are common indicators of quality of care and should therefore be carefully considered in the effort to provide efficient medical services.3,7 It has been
Submitted: August 13, 2014, Revised: October 22, 2014, Accepted: October 23, 2014. From the PREPARED Center for Emergency Response Research (B.A., L.A.-D.), and Department of Emergency Medicine (B.A., L.A.-D.), Leon and Mathilde Recanati School of Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva; Hadassah (A.I.), Hebrew University Medical Center; and Ministry of Health (A.I.), Jerusalem, Israel. Address for reprints: Bruria Adini, PhD, Department of Emergency Medicine, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel PO Box 653, Beer Sheva, Israel 8410501; email: [email protected]. DOI: 10.1097/TA.0000000000000535
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suggested that increasing hospital capacity and smoothing inpatient discharges may reduce overcrowding,8,9 but such measures often prove unfeasible or insufficient. Moreover, interventions that focus primarily on EDs have been found to have limited success.7,10 To facilitate the complex process of patient-to-hospital allocation in routine and mass-casualty events, efforts have been made to develop decision support tools for first responders and managers of emergency situations. The Mass Casualty Patient Allocation Model by Amram et al.11 provides information regarding locations of hospitals, driving times to medical facilities, trauma services offered, and hospital capacities. The Uniform Core Criteria for Mass Casualty Triage aim to ensure interoperability and standardization.12 Another method uses surge control measures to cater for the needs of patient evacuations and restore normal operations in the admitting hospitals.13 Although significant efforts have been made to create tools to support the casualty evacuation process and although recent advances in communication systems have facilitated situation awareness regarding needs and resources, it seems that to date, no structured mechanism has been developed that is capable of providing policy and decision makers with current information regarding hospital congestion levels. The aims of the present study were (1) to develop a dynamic and flexible ‘‘load index model’’ (LIM), capable of providing vital information regarding the loads of potential admitting hospitals, in accordance with the needs of changing circumstances; (2) to use this model as a support tool for decision makers, emergency J Trauma Acute Care Surg Volume 78, Number 3
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J Trauma Acute Care Surg Volume 78, Number 3
Adini et al.
of emergency operation centers, and military medical officers. The experts were requested to list data relevant to calculating such an index within their own domain and to participate in a modified Delphi process, with the aim of achieving consensus among them with regard to the parameters to be included in the index and to the formulas offered. Each of parameters was rated by the experts participating in the Delphi process on a 5-point Likert scale ranging from complete disagreement to strong agreement. Consensus was defined as agreement among 75% or more of the experts. The experts were further asked to estimate the relative impact of each admission/treatment site on the hospital’s overall capacity to manage a mass-casualty event. Their answers were expressed as fractions adding up to one.
Phase III: Simulating Model Use in Emergency Scenarios
Figure 1. Development of the LIM.
managers, and directors of operation centers during primary and secondary evacuation of casualties to hospital.
MATERIALS AND METHODS The study was exempted from a need of an approval from the ethics committee because no patients were involved. It was thus approved by the Ministry of Health.
Phase 1: Development of the LIM Figure 1 depicts the process of developing the LIM. Initially, an extensive literature review was conducted to identify issues known to affect workload and capacity for treatment in emergency scenarios. This step led to the definition of casualty treatment sites, characteristics that affect surge capacity, potential bottlenecks that might arise in hospitals in the process of admitting and treating casualties during emergencies, as well as their impacts. Subsequently, 20 content experts in health care management associated with hospital management and with the Israeli Ministry of Health and emergency management authorities were interviewed in structured interviews. These interviews enabled to define parameters that may be presumed to influence on the load levels of hospitals during emergencies that can be used to calculate the level of congestion. A formula for calculation of the load index was then proposed.
The LIM was modified in light of the considerations mentioned earlier and then tested in two independent simulation exercises as described in Figure 2. In each round of the exercise, an emergency scenario was presented to six groups of senior personnel experienced in emergency management, including decision making and managing casualty evacuation during past emergency situations. Among the experts, two were formerly director-generals of the Ministry of Health, three were hospital managers, two were senior emergency medical services officers, and three were senior military medical evacuation officers. In both rounds, information was provided as to the capacities of 11 simulated admitting hospitals located in the region, their current hospitalization loads in all medical departments, availability of staff, and the general situation in the field. Each group was asked to determine the evacuation destinations of 600 simulated casualties. In the baseline task, their suggestions were drawn from their knowledge and expertise. The referral destinations indicated by the different expert groups were compared to determine variability and level of agreement in decision making. Following the simulation exercise, the experts were presented with the recommendations generated by LIM and requested to comment on the similarity/ variability of their evacuation plans as compared with those suggested by the model.
Phase II: Model Verification The proposed LIM was disseminated to 100 emergency management experts, including hospitals’ managers, directors
Figure 2. Simulation of casualty allocation using the LIM.
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A third simulation exercise was conducted based on the same components as the previous exercises, with one difference; that is, before proceeding to define evacuation destinations, the experts were given the relative congestion levels of the 11 admitting hospitals as established a priori using LIM, as well as the model’s recommendation regarding evacuation destinations for the 600 casualties. Six expert groups participated in this repeat exercise, and they were once again requested to determine casualty evacuation destinations. The recommendations of the six groups were compared for variability and mutual agreement. Following the exercises, the participating experts were asked to respond to a questionnaire about their perceptions concerning the need for a support mechanism and their opinion of LIM.
Phase IV: Real-time Application of the LIM Decision Support Tool The LIM tool was used in the real-life security threat situation that developed in Southern Israel between 14 and 21 of November 2012 at the time of operation Pillar of Defense. During that period, more than 1,500 missiles were launched toward a civilian population. The Ministry of Health deployed a think tank consisting of six senior experts in emergency management (one was a former director-general of the ministry, two others were former surgeon-generals of the Israel Defense Forces, and one was an academic from the field of emergency medicine). The think tank was asked to determine policy regarding casualty evacuation destinations using LIM, and the resulting decisions were disseminated daily to the first responders. At the end of the operation, the members of the think tank were questioned as to the value and contribution of LIM to their decision-making process.
Statistical Analysis One-way analysis of variance was used to identify variance between recommendations of the different expert groups before and following their introduction to the LIM. The assumptions of the test were not violated.
RESULTS Load Parameters Subsequent to a literature review and interviews with 20 emergency management experts, nine parameters were defined as relevant to calculating hospital congestion levels. These parameters were disseminated to 100 content experts for evaluation (response rate, 55%). Five of the nine were described by more than 75% of the experts as highly or very highly relevant to determining hospital load levels. The parameters that calculate congestion levels and the degree of consensus among experts for each of these parameters are presented in Table 1 (the five that garnered high rates of consensus are in bold). The content experts were also requested to assess availability of data. The data required to calculate all nine parameters were characterized as highly availableVor obtainable subject to allocation of manpower for the taskVby more than 90% of the experts responding. The percentage of experts who perceived the data as highly available is presented in Table 1. The literature review led to the identification of four treatment sites that impact hospital congestion levels, a result confirmed by subsequent interviews. The relative importance of each of these sites, as ranked in the modified Delphi process by more than 75% of the respondents, was as follows: (1) EDs, 0.1; (2) hospital wards, 0.2; (3) ORs, 0.3; and (4) ICUs, 0.4.
Formula for Load Index Calculation Based on the recommendations of the content experts, a formula was developed for calculating the relative congestion in a given hospital. The relative congestion is calculated by the sum of the five load parameters, each of which is calculated as follows: the index value for the given hospital, divided by the highest value for that index reported for the set of admitting hospitals, multiplied by the relative impact of the index on the overall capacity of the hospital: sum of parameters = [hospital’s index value / highest hospital index value] relative importance of index [site].
TABLE 1. Parameters Used to Calculate Congestion Levels and Perceptions of Content Experts Regarding Their Availability and Relevance for Inclusion in LIM
No. 1 2 3 4 5 6 7 8 9
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Index
Relevance
Availability
Description
Highly or Very Highly Relevant (%), n = 36
Necessary Data Highly Available (%), n = 36
Occupancy of ORs/surge capacity of ORs Severe/moderate casualties admitted to surgical departments in the last 24 h/no. physicians Severe/moderate casualties admitted to surgical departments in the last 24 h/no. nurses Severe/moderate casualties admitted to surgical departments in the last 24 h/ICU beds Severe/moderate patients hospitalized in surgical departments/no. ICU physicians Severe/moderate patients hospitalized in surgical departments/no. ICU nurses Severe/moderate patients hospitalized in surgical departments/no. surgeons Severe/moderate patients hospitalized in surgical departments/no. nurses Severe/moderate patients hospitalized in surgical departments/no. computed tomographic machines
84 84 75 84 80 75 78 71 43
70 52 53 65 62 60 49 52 51
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Variability Between Experts With Regard to Simulation Exercises Before and After Use of LIM One-way analysis of variance identified significant variance between the recommendations of the different expert groups participating in the simulation exercises before their introduction to the LIM (MSE for casualty evacuation destinations, 984.39; p = 0.04). After the model was introduced, disparities between expert groups decreased significantly and ceased to be significant (mean square error [MSE], 133.96; p 9 0.05); that is, the use of LIM led to a sevenfold reduction in variance (F = 984.39 / 133.36 = 7.38). The allocation of casualties to each of the 11 hospitals by the different expert groups before and after the adoption of the LIM is presented in Tables 2 and 3, respectively. In the preuse phase of the LIM, as noted, the recommendations of different expert groups differed significantly between all teams (no similarity was found between any of the teams’ evacuation recommendations); in the postuse phase of the LIM, in contrast, no significant differences were found between four of the six teams (their recommendations were found to be similar). Examination of the characteristics of the expert groups revealed that the two outlier teams included professionals who were less senior in position and experience relative to the other groups. Accordingly, it was decided to repeat the statistical analysis after excluding the two groups. The repeat analysis gave an MSE of 52.18; this suggests that variability was 18 times greater before the introduction of the model than afterward (F = 984.39 / 52.18 = 18.65).
How Content Experts Perceive LIM The perceptions of the content experts regarding whether LIM facilitated the allocation of evacuation destinations were assessed. Almost all (92%) agreed that there is a high-to-moderate need for a standardized, data-based national support tool for decision making regarding patient evacuation, while 82.5% maintained that the model contributed or highly contributed to the decision-making process. Similarly, 85% considered that the model is applicable to policy setting regarding casualty evacuation in an emergency situation. In the initial simulation exercises, in which the experts were presented with the recommendations of the LIM only after they had TABLE 2. Casualty Allocation by Expert Groups Before the Adoption of Recommendations of LIM Hospital 1 2 3 4 5 6 7 8 9 10 11 Total
Team 1
Team 2
Team 3
Team 4
Team 5
Team 6
90 30 70 40 60 20 10 60 120 30 70 600
60 40 30 60 20 60 85 30 100 95 20 600
125 60 100 5 80 15 75 40 85 5 10 600
60 130 10 10 30 10 115 120 100 10 5 600
55 40 90 55 60 20 30 60 80 60 50 600
75 15 40 50 45 75 100 30 50 80 40 600
TABLE 3. Casualty Allocation by Expert Groups After Adoption of Recommendations of LIM Hospital 1 2 3 4 5 6 7 8 9 10 11 Total
Team 1
Team 2
Team 3
Team 4
Team 5
Team 6
25 28 72 26 31 95 44 63 94 46 76 600
30 30 30 30 80 85 50 60 90 55 60 600
30 40 52 25 40 84 45 60 65 60 99 600
36 40 70 55 40 83 56 60 60 50 50 600
25 28 70 26 31 95 44 65 94 46 76 600
25 30 70 26 31 95 44 63 94 46 76 600
Variability was 18 times greater before the use of LIM than after the adoption of the model (F = 984.39 / 52.18 = 18.65).
determined evacuation destinations based entirely on their own expertise, 50% of the experts believed that their proposals were highly or very highly similar to those suggested by the model. In the final simulation exercise, in which the recommendations generated by LIM were presented to the emergency managers before the process of defining destinations, 88% reported a high or very high similarity between their recommendations and those of the model.
Actual Implementation: Using LIM During an Emergency During the security threat period episode in November 2012 in Southern Israel, approximately 420 casualties were evacuated to medical facilities, including 6 severely, 9 moderately, 100 mildly injured and more than 300 experiencing acute stress reactions. Six additional patients died. The LIM was used as a support mechanism tool to determine daily casualty evacuation policy. Nine hospitals designated as admitting facilities were instructed to provide twice daily reports concerning surge capacities and planned activities of the ORs, the number and severity of casualties admitted in the last 24 hours to the ED, the number of severe and moderate casualties occupying the surgical wards, and the number of physicians available in the trauma wards as well as in the ICUs. A report concerning availability of physicians was required on the first day only, and hospital administrators were instructed to provide this information again only in case of changes. Based on these data, the LIM was used to calculate relative congestion levels for each hospital and to generate recommendations for primary evacuation destinations for potential casualties. These recommendations were presented to the expert think tank during sessions devoted to determining evacuation policy for the next 12 hours. Following implementation of LIM during operation Pillar of Defense, members of the think tank were asked to assess the contribution of the model to their decision-making process regarding evacuation policy. All six members reported that the model contributed significantly to their ability to delineate patient evacuation policy, and they recommended
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its adoption as an integral support mechanism for emergency management nationally.
DISCUSSION Casualty evacuation during emergencies is a challenging task with direct impact on patient outcomes.14,15 Effective policy setting and decision making regarding evacuation destinations depend crucially, first, on collection and aggregation of relevant data and, second, on appropriate analysis of the influence of these data on hospital congestion levels.3,4 Any imbalance between needs and immediately deployable resources during emergencies may cause overcrowding of vital admitting and treatment sites, such as EDs, ORs, or ICUs.4,9 To date, casualty evacuation policy has mostly relied on the expertise of first responders or emergency managers.12,13 However, decision making regarding the allocation of patients is a complex process, and response managers often find themselves unable to dedicate adequate resources to the task.15 The present study uncovered significant disparities between decisions concerning evacuation destinations, yet all were taken by emergency management experts well qualified to lead casualty evacuation processes. This fortifies the need to base policy making on a structured decision support mechanism rather than on individual expertise or experience.16 The various data relating to hospital occupancy and availability levels, including fatigue of personnel and diminished resources, must be subjected to in-depth analysis,17 which is no simple task in the absence of such a mechanism. Similarly, the dynamic changes that characterize medical facilities during emergencies, together with the fluctuating needs of the patients, call for the aggregation and analysis of large quantities of information in a short period, a process that could be expedited by appropriate decision support tools. It has long been recognized that casualty evacuation policy has to take into account a range of essential factors, such as severity of injuries, the given hospital’s resources, and available means of evacuation.2,3 Nevertheless, until recently, no model has considered the influence of each of these factors and the interactions between them on the overall decisionmaking process. A recent publication describes the SAVE SeverityAdjusted Victim Evacuation] model.15 This model bears several similarities to the LIM. First, it explicitly considers multiple hospitals while recommending evacuation decisions. Second, it takes into account the condition of the casualties. It also takes into account the treatment capacities at the hospitals. Lastly, it considers ambulance availability (based on when and where transport was dispatched). The model showed good results in a computerized simulation. However, it has not yet been introduced to the decision makers and managers of emergency scenarios, and the chances that such a complex and theoretical tool will be adopted are poor. Barriers to implementation of operations research models in health care, including alienation between the field and the academy, have been described before. Brailsford18 claims that, although operational research models have been applied to a range of health care challenges since the 1960s, these models are not widely accepted and used. 626
The LIM proposed here has the advantage that it was developed by emergency managers for emergency managers, those who will be using the tool in the future. LIM provides policy makers with a structured validated decision support tool that calculates the relative impact of each factor on the congestion level of the hospitals, based on actual situation awareness. The surge capacity is defined accordingly, facilitating a standardized evidence-based decision-making process concerning patient evacuation. In conformity with earlier studies,19 four hospital admission and treatment sites were identified in the present study as impacting surge capacity, namely, EDs, hospitalization wards, ORs, and ICUs. The differences found here in the relative impact of each site on the hospital’s overall capacity support earlier studies,20 which state that ICUs play a key role in the response to major disasters. Interestingly, the content experts who participated in the modified Delphi process did not regard nurses as playing a dominant role in congestion levels perhaps because they were not perceived as a potential bottleneck that should be monitored and considered.17 Contrariwise, physicians were seen to be a crucial factor that must be integrated in the model, most probably reflecting the expectation that the latter could be in shortage in scenarios with multiple casualties. The LIM is designed to serve as a decision support mechanism to assist in the formulation of casualty evacuation policies. It must be emphasized that this tool, effective as it may be, cannot serve as sole determinant of the decision-making process.16 Three significant elements should also be considered when evacuating casualties, namely, specific types of injuries (such as head or burn wounds); geographic locations of the casualties in relation to the medical facilities (some patients need urgent care and will not survive evacuation to a distant hospital); and the desire to keep family members together during the evacuation process, particularly if children are involved. In its present form, the LIM does not allow for these specificities, which will be incorporated in future versions.
CONCLUSION Evacuation of casualties in an emergency situation is a complex and challenging task necessitating simultaneous calculation of numerous factors. Our findings suggest that the LIM decision support model significantly improves the capacity of emergency managements to implement and coordinate evacuation measures, thereby ensuring that patients will be evacuated to less congested medical facilities better suited to providing lifesaving medical services in emergencies. LIM was tested in both simulation exercises and an actual emergency situation and found to contribute positively to policy setting and decision-making processes. LIM provides vital and timely information in accordance with the constantly changing characteristics of emergency situations. Further studies are recommended to ascertain the model’s sensitivity and measure its efficacy and impact in diverse types of emergency scenarios and for diverse levels of emergency management (e.g., first responders’ operation centers vs. national emergency management teams). It is also recommended that future guidelines include the standard of critical mortality, * 2015 Wolters Kluwer Health, Inc. All rights reserved.
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which should be used as an index of evacuation accuracy as well as triage. ACKNOWLEDGMENT We thank the content experts in emergency management that enabled to define the LIM and the parameters on which it is consisted. We also thank all the senior personnel who participated in the simulation exercises. Without their expertise, we would not have been able to develop and validate this important decision support tool.
AUTHORSHIP B.A conducted the literature review, designed the study, collected and analyzed the data, and wrote the manuscript. L.A.-D. assisted in the data analysis and reviewed the manuscript. A.I. was coresearcher in designing the study and collection (as well as analysis) of the data; he actively reviewed the manuscript, and approved its submission.
DISCLOSURE The authors declare no conflicts of interest.
REFERENCES 1. Bosch X. Beyond 9/11: health consequences of the terror attacks outside the USA. Intern Emerg Med. 2012;7:159Y161. 2. Redlener I, Reilly MJ. Lessons from SandyVpreparing health systems for future disasters. N Engl J Med. 2012;367:2269Y2271. 3. Wang Y, Luangkesom KL, Shuman L. Modeling emergency medical response to a mass casualty incident using agent based simulation. Socioecon Plann Sci. 2012;46(4):281Y290. 4. Black D, Pearson M. Average length of stay, delayed discharge, and hospital congestion. Br Med J. 2002;325:610Y611. 5. Wong HJ, Morra D, Caesar M, Carter MW, Abrams H. Understanding hospital and emergency department congestion: an examination of inpatient admission trends and bed resources. CJEM. 2010;12(1):18Y26. 6. Schoenmeyr T, Dunn PF, Gamarnik D, Levi R, Berger DL, Daily BJ, Levine WC, Sandberg WS. A model for understanding the impacts of demand and capacity on waiting time to enter a congested recovery room. Anesthesiology. 2009;110(6):1293Y1304.
7. Dhar S, Michel R, Balavenkatesh K. Improving visit cycle time using patient flow analysis in a high-volume inner-city hospital-based ambulatory clinic serving minority New Yorkers. J Healthc Qual. 2011;33(2):23Y28. 8. Harris A, Sharma A. Access block and overcrowding in emergency departments: an empirical analysis. Emerg Med J. 2010;27(7):508Y511. 9. Wong HJ, Wu RC, Caesar M, Abrams H, Morra D. Smoothing inpatient discharges decreases emergency department congestion: a system dynamics simulation model. Emerg Med J. 2010;27(8):593Y598. 10. Howell E, Bessman E, Kravet S, Kolodner K, Marshall R, Wright S. Active bed management by hospitalists and emergency department throughput. Ann Intern Med. 2008;149(11):804Y811. 11. Amram O, Schuurman N, Hedley N, Hameed SM. A web-based model to support patient-to-hospital allocation in mass casualty incidents. J Trauma Acute Care Surg. 2012;72(5):1323Y1328. 12. Lerner EB, Cone DC, Weinstein ES, Schwartz RB, Coule PL, Cronin M, Wedmore IS, Bulger EM, Mulligan DA, Swienton RE, et al. Mass casualty triage: an evaluation of the science and refinement of a national guideline. Disaster Med Public Health Prep. 2011;5:129Y137. 13. Lee YH, Seo H, Rasheed F, Kim KS, Kim SH, Park I. Surge capacity evaluation of an emergency department in case of mass casualty. Software Engineering, Business Continuity and Education. 2011;257:522Y531. 14. Kragh J, San Antonio J, Simmons J, Simmons JW, Mace JE, Stinner DJ, White CE, Fang R, Aden JK, Hsu JR, et al. Compartment syndrome performance improvement project is associated with increased combat casualty survival. J Trauma Acute Care Surg. 2013;74(1):259Y263. 15. Dean MD, Nair SK. Mass-casualty triage: distribution of victims to multiple hospitals using the SAVE model. Eur J Operational Res. 2014; 238:363Y373. 16. Sinclair H, Doyle EEH, Johnston DM, Paton D. Decision-making training in local government emergency management. Int J Emerg Serv. 2012;1(2): 159Y174. 17. Adams L, Berry D. Who will show up? Estimating ability and willingness of essential hospital personnel to report to work in response to a disaster. Online J Issues Nurs. 2012;17:2. 18. Brailsford S. Overcoming the barriers to implementation of operations research simulation models in healthcare. Clin Invest Med. 2005;28(6):312Y315. 19. Wheeler DS, Geis G, Mack EH, LeMaster T, Patterson MD. High-reliability emergency response teams in the hospital: improving quality and safety using in situ simulation training. BMJ Qual Saf. 2013;22:507Y514. 20. Flabouris A, Jeyadoss J, Field J, Soulsby T. Association between emergency department length of stay and outcome of patients admitted either to a ward, intensive care or high dependency unit. Emerg Med Australas. 2013;25:46Y54.
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Load index model: An advanced tool to support decision making during mass-casualty incidents Bruria Adini, PhD, Limor Aharonson-Daniel, PhD, and Avi Israeli, MD, MPH BACKGROUND: In mass-casualty events, accessing information concerning hospital congestion levels is crucial to improving patient distribution and optimizing care. The study aimed to develop a decision support tool for distributing casualties to hospitals in an emergency scenario involving multiple casualties. METHODS: A comprehensive literature review and structured interviews with 20 content experts produced a shortlist of relevant criteria for inclusion in the model. A ‘‘load index model’’ was prepared, incorporating results of a modified Delphi survey of 100 emergency response experts. The model was tested in three simulation exercises in which an emergency scenario was presented to six groups of senior emergency managers. Information was provided regarding capacities of 11 simulated admitting hospitals in the region, and evacuation destinations were requested for 600 simulated casualties. Of the three simulation rounds, two were performed without the model and one after its presentation. Following simulation experiments and implementation during a real-life security threat, the efficacy of the model was assessed. RESULTS: Variability between experts concerning casualties’ evacuation destinations decreased significantly following the model’s introduction. Most responders (92%) supported the need for standardized data, and 85% found that the model improved policy setting regarding casualty evacuation in an emergency situation. These findings were reaffirmed in a real-life emergency scenario. CONCLUSION: The proposed model improved capacity to ensure evacuation of patients to less congested medical facilities in emergency situations, thereby enhancing lifesaving medical services. The model supported decision-making processes in both simulation exercises and an actual emergency situation. (J Trauma Acute Care Surg. 2015;78: 622Y627. Copyright * 2015 Wolters Kluwer Health, Inc. All rights reserved.) KEY WORDS: Mass-casualty event; hospital congestion; patient evacuation; decision making; load index.
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mergencies are often characterized by massive casualty evacuation to hospitals.1,2 The hospitals themselves may be disabled by the disaster, in which case patients may have to be transferred to other medical facilities, as occurred following Hurricane Sandy.2 To avoid overloading and long waiting times in emergency departments (EDs), operating rooms (ORs), and intensive care units (ICUs), decisions regarding evacuation destinations must be carefully weighed based on the severity of injuries, the overall duration of the evacuation operation, mean evacuation times, and available hospital resources.3 Studies have shown that extended waiting times in EDs and high ED occupancy rates are frequently inevitable4 because of imbalance between needs and resources.4,5 Not only does hospital congestion affect EDs, but it also places a heavy burden on operating theaters and recovery rooms.5,6 Patient flow and waiting times are common indicators of quality of care and should therefore be carefully considered in the effort to provide efficient medical services.3,7 It has been
Submitted: August 13, 2014, Revised: October 22, 2014, Accepted: October 23, 2014. From the PREPARED Center for Emergency Response Research (B.A., L.A.-D.), and Department of Emergency Medicine (B.A., L.A.-D.), Leon and Mathilde Recanati School of Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva; Hadassah (A.I.), Hebrew University Medical Center; and Ministry of Health (A.I.), Jerusalem, Israel. Address for reprints: Bruria Adini, PhD, Department of Emergency Medicine, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel PO Box 653, Beer Sheva, Israel 8410501; email: [email protected]. DOI: 10.1097/TA.0000000000000535
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suggested that increasing hospital capacity and smoothing inpatient discharges may reduce overcrowding,8,9 but such measures often prove unfeasible or insufficient. Moreover, interventions that focus primarily on EDs have been found to have limited success.7,10 To facilitate the complex process of patient-to-hospital allocation in routine and mass-casualty events, efforts have been made to develop decision support tools for first responders and managers of emergency situations. The Mass Casualty Patient Allocation Model by Amram et al.11 provides information regarding locations of hospitals, driving times to medical facilities, trauma services offered, and hospital capacities. The Uniform Core Criteria for Mass Casualty Triage aim to ensure interoperability and standardization.12 Another method uses surge control measures to cater for the needs of patient evacuations and restore normal operations in the admitting hospitals.13 Although significant efforts have been made to create tools to support the casualty evacuation process and although recent advances in communication systems have facilitated situation awareness regarding needs and resources, it seems that to date, no structured mechanism has been developed that is capable of providing policy and decision makers with current information regarding hospital congestion levels. The aims of the present study were (1) to develop a dynamic and flexible ‘‘load index model’’ (LIM), capable of providing vital information regarding the loads of potential admitting hospitals, in accordance with the needs of changing circumstances; (2) to use this model as a support tool for decision makers, emergency J Trauma Acute Care Surg Volume 78, Number 3
Copyright © 2015 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
J Trauma Acute Care Surg Volume 78, Number 3
Adini et al.
of emergency operation centers, and military medical officers. The experts were requested to list data relevant to calculating such an index within their own domain and to participate in a modified Delphi process, with the aim of achieving consensus among them with regard to the parameters to be included in the index and to the formulas offered. Each of parameters was rated by the experts participating in the Delphi process on a 5-point Likert scale ranging from complete disagreement to strong agreement. Consensus was defined as agreement among 75% or more of the experts. The experts were further asked to estimate the relative impact of each admission/treatment site on the hospital’s overall capacity to manage a mass-casualty event. Their answers were expressed as fractions adding up to one.
Phase III: Simulating Model Use in Emergency Scenarios
Figure 1. Development of the LIM.
managers, and directors of operation centers during primary and secondary evacuation of casualties to hospital.
MATERIALS AND METHODS The study was exempted from a need of an approval from the ethics committee because no patients were involved. It was thus approved by the Ministry of Health.
Phase 1: Development of the LIM Figure 1 depicts the process of developing the LIM. Initially, an extensive literature review was conducted to identify issues known to affect workload and capacity for treatment in emergency scenarios. This step led to the definition of casualty treatment sites, characteristics that affect surge capacity, potential bottlenecks that might arise in hospitals in the process of admitting and treating casualties during emergencies, as well as their impacts. Subsequently, 20 content experts in health care management associated with hospital management and with the Israeli Ministry of Health and emergency management authorities were interviewed in structured interviews. These interviews enabled to define parameters that may be presumed to influence on the load levels of hospitals during emergencies that can be used to calculate the level of congestion. A formula for calculation of the load index was then proposed.
The LIM was modified in light of the considerations mentioned earlier and then tested in two independent simulation exercises as described in Figure 2. In each round of the exercise, an emergency scenario was presented to six groups of senior personnel experienced in emergency management, including decision making and managing casualty evacuation during past emergency situations. Among the experts, two were formerly director-generals of the Ministry of Health, three were hospital managers, two were senior emergency medical services officers, and three were senior military medical evacuation officers. In both rounds, information was provided as to the capacities of 11 simulated admitting hospitals located in the region, their current hospitalization loads in all medical departments, availability of staff, and the general situation in the field. Each group was asked to determine the evacuation destinations of 600 simulated casualties. In the baseline task, their suggestions were drawn from their knowledge and expertise. The referral destinations indicated by the different expert groups were compared to determine variability and level of agreement in decision making. Following the simulation exercise, the experts were presented with the recommendations generated by LIM and requested to comment on the similarity/ variability of their evacuation plans as compared with those suggested by the model.
Phase II: Model Verification The proposed LIM was disseminated to 100 emergency management experts, including hospitals’ managers, directors
Figure 2. Simulation of casualty allocation using the LIM.
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A third simulation exercise was conducted based on the same components as the previous exercises, with one difference; that is, before proceeding to define evacuation destinations, the experts were given the relative congestion levels of the 11 admitting hospitals as established a priori using LIM, as well as the model’s recommendation regarding evacuation destinations for the 600 casualties. Six expert groups participated in this repeat exercise, and they were once again requested to determine casualty evacuation destinations. The recommendations of the six groups were compared for variability and mutual agreement. Following the exercises, the participating experts were asked to respond to a questionnaire about their perceptions concerning the need for a support mechanism and their opinion of LIM.
Phase IV: Real-time Application of the LIM Decision Support Tool The LIM tool was used in the real-life security threat situation that developed in Southern Israel between 14 and 21 of November 2012 at the time of operation Pillar of Defense. During that period, more than 1,500 missiles were launched toward a civilian population. The Ministry of Health deployed a think tank consisting of six senior experts in emergency management (one was a former director-general of the ministry, two others were former surgeon-generals of the Israel Defense Forces, and one was an academic from the field of emergency medicine). The think tank was asked to determine policy regarding casualty evacuation destinations using LIM, and the resulting decisions were disseminated daily to the first responders. At the end of the operation, the members of the think tank were questioned as to the value and contribution of LIM to their decision-making process.
Statistical Analysis One-way analysis of variance was used to identify variance between recommendations of the different expert groups before and following their introduction to the LIM. The assumptions of the test were not violated.
RESULTS Load Parameters Subsequent to a literature review and interviews with 20 emergency management experts, nine parameters were defined as relevant to calculating hospital congestion levels. These parameters were disseminated to 100 content experts for evaluation (response rate, 55%). Five of the nine were described by more than 75% of the experts as highly or very highly relevant to determining hospital load levels. The parameters that calculate congestion levels and the degree of consensus among experts for each of these parameters are presented in Table 1 (the five that garnered high rates of consensus are in bold). The content experts were also requested to assess availability of data. The data required to calculate all nine parameters were characterized as highly availableVor obtainable subject to allocation of manpower for the taskVby more than 90% of the experts responding. The percentage of experts who perceived the data as highly available is presented in Table 1. The literature review led to the identification of four treatment sites that impact hospital congestion levels, a result confirmed by subsequent interviews. The relative importance of each of these sites, as ranked in the modified Delphi process by more than 75% of the respondents, was as follows: (1) EDs, 0.1; (2) hospital wards, 0.2; (3) ORs, 0.3; and (4) ICUs, 0.4.
Formula for Load Index Calculation Based on the recommendations of the content experts, a formula was developed for calculating the relative congestion in a given hospital. The relative congestion is calculated by the sum of the five load parameters, each of which is calculated as follows: the index value for the given hospital, divided by the highest value for that index reported for the set of admitting hospitals, multiplied by the relative impact of the index on the overall capacity of the hospital: sum of parameters = [hospital’s index value / highest hospital index value] relative importance of index [site].
TABLE 1. Parameters Used to Calculate Congestion Levels and Perceptions of Content Experts Regarding Their Availability and Relevance for Inclusion in LIM
No. 1 2 3 4 5 6 7 8 9
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Index
Relevance
Availability
Description
Highly or Very Highly Relevant (%), n = 36
Necessary Data Highly Available (%), n = 36
Occupancy of ORs/surge capacity of ORs Severe/moderate casualties admitted to surgical departments in the last 24 h/no. physicians Severe/moderate casualties admitted to surgical departments in the last 24 h/no. nurses Severe/moderate casualties admitted to surgical departments in the last 24 h/ICU beds Severe/moderate patients hospitalized in surgical departments/no. ICU physicians Severe/moderate patients hospitalized in surgical departments/no. ICU nurses Severe/moderate patients hospitalized in surgical departments/no. surgeons Severe/moderate patients hospitalized in surgical departments/no. nurses Severe/moderate patients hospitalized in surgical departments/no. computed tomographic machines
84 84 75 84 80 75 78 71 43
70 52 53 65 62 60 49 52 51
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Variability Between Experts With Regard to Simulation Exercises Before and After Use of LIM One-way analysis of variance identified significant variance between the recommendations of the different expert groups participating in the simulation exercises before their introduction to the LIM (MSE for casualty evacuation destinations, 984.39; p = 0.04). After the model was introduced, disparities between expert groups decreased significantly and ceased to be significant (mean square error [MSE], 133.96; p 9 0.05); that is, the use of LIM led to a sevenfold reduction in variance (F = 984.39 / 133.36 = 7.38). The allocation of casualties to each of the 11 hospitals by the different expert groups before and after the adoption of the LIM is presented in Tables 2 and 3, respectively. In the preuse phase of the LIM, as noted, the recommendations of different expert groups differed significantly between all teams (no similarity was found between any of the teams’ evacuation recommendations); in the postuse phase of the LIM, in contrast, no significant differences were found between four of the six teams (their recommendations were found to be similar). Examination of the characteristics of the expert groups revealed that the two outlier teams included professionals who were less senior in position and experience relative to the other groups. Accordingly, it was decided to repeat the statistical analysis after excluding the two groups. The repeat analysis gave an MSE of 52.18; this suggests that variability was 18 times greater before the introduction of the model than afterward (F = 984.39 / 52.18 = 18.65).
How Content Experts Perceive LIM The perceptions of the content experts regarding whether LIM facilitated the allocation of evacuation destinations were assessed. Almost all (92%) agreed that there is a high-to-moderate need for a standardized, data-based national support tool for decision making regarding patient evacuation, while 82.5% maintained that the model contributed or highly contributed to the decision-making process. Similarly, 85% considered that the model is applicable to policy setting regarding casualty evacuation in an emergency situation. In the initial simulation exercises, in which the experts were presented with the recommendations of the LIM only after they had TABLE 2. Casualty Allocation by Expert Groups Before the Adoption of Recommendations of LIM Hospital 1 2 3 4 5 6 7 8 9 10 11 Total
Team 1
Team 2
Team 3
Team 4
Team 5
Team 6
90 30 70 40 60 20 10 60 120 30 70 600
60 40 30 60 20 60 85 30 100 95 20 600
125 60 100 5 80 15 75 40 85 5 10 600
60 130 10 10 30 10 115 120 100 10 5 600
55 40 90 55 60 20 30 60 80 60 50 600
75 15 40 50 45 75 100 30 50 80 40 600
TABLE 3. Casualty Allocation by Expert Groups After Adoption of Recommendations of LIM Hospital 1 2 3 4 5 6 7 8 9 10 11 Total
Team 1
Team 2
Team 3
Team 4
Team 5
Team 6
25 28 72 26 31 95 44 63 94 46 76 600
30 30 30 30 80 85 50 60 90 55 60 600
30 40 52 25 40 84 45 60 65 60 99 600
36 40 70 55 40 83 56 60 60 50 50 600
25 28 70 26 31 95 44 65 94 46 76 600
25 30 70 26 31 95 44 63 94 46 76 600
Variability was 18 times greater before the use of LIM than after the adoption of the model (F = 984.39 / 52.18 = 18.65).
determined evacuation destinations based entirely on their own expertise, 50% of the experts believed that their proposals were highly or very highly similar to those suggested by the model. In the final simulation exercise, in which the recommendations generated by LIM were presented to the emergency managers before the process of defining destinations, 88% reported a high or very high similarity between their recommendations and those of the model.
Actual Implementation: Using LIM During an Emergency During the security threat period episode in November 2012 in Southern Israel, approximately 420 casualties were evacuated to medical facilities, including 6 severely, 9 moderately, 100 mildly injured and more than 300 experiencing acute stress reactions. Six additional patients died. The LIM was used as a support mechanism tool to determine daily casualty evacuation policy. Nine hospitals designated as admitting facilities were instructed to provide twice daily reports concerning surge capacities and planned activities of the ORs, the number and severity of casualties admitted in the last 24 hours to the ED, the number of severe and moderate casualties occupying the surgical wards, and the number of physicians available in the trauma wards as well as in the ICUs. A report concerning availability of physicians was required on the first day only, and hospital administrators were instructed to provide this information again only in case of changes. Based on these data, the LIM was used to calculate relative congestion levels for each hospital and to generate recommendations for primary evacuation destinations for potential casualties. These recommendations were presented to the expert think tank during sessions devoted to determining evacuation policy for the next 12 hours. Following implementation of LIM during operation Pillar of Defense, members of the think tank were asked to assess the contribution of the model to their decision-making process regarding evacuation policy. All six members reported that the model contributed significantly to their ability to delineate patient evacuation policy, and they recommended
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its adoption as an integral support mechanism for emergency management nationally.
DISCUSSION Casualty evacuation during emergencies is a challenging task with direct impact on patient outcomes.14,15 Effective policy setting and decision making regarding evacuation destinations depend crucially, first, on collection and aggregation of relevant data and, second, on appropriate analysis of the influence of these data on hospital congestion levels.3,4 Any imbalance between needs and immediately deployable resources during emergencies may cause overcrowding of vital admitting and treatment sites, such as EDs, ORs, or ICUs.4,9 To date, casualty evacuation policy has mostly relied on the expertise of first responders or emergency managers.12,13 However, decision making regarding the allocation of patients is a complex process, and response managers often find themselves unable to dedicate adequate resources to the task.15 The present study uncovered significant disparities between decisions concerning evacuation destinations, yet all were taken by emergency management experts well qualified to lead casualty evacuation processes. This fortifies the need to base policy making on a structured decision support mechanism rather than on individual expertise or experience.16 The various data relating to hospital occupancy and availability levels, including fatigue of personnel and diminished resources, must be subjected to in-depth analysis,17 which is no simple task in the absence of such a mechanism. Similarly, the dynamic changes that characterize medical facilities during emergencies, together with the fluctuating needs of the patients, call for the aggregation and analysis of large quantities of information in a short period, a process that could be expedited by appropriate decision support tools. It has long been recognized that casualty evacuation policy has to take into account a range of essential factors, such as severity of injuries, the given hospital’s resources, and available means of evacuation.2,3 Nevertheless, until recently, no model has considered the influence of each of these factors and the interactions between them on the overall decisionmaking process. A recent publication describes the SAVE SeverityAdjusted Victim Evacuation] model.15 This model bears several similarities to the LIM. First, it explicitly considers multiple hospitals while recommending evacuation decisions. Second, it takes into account the condition of the casualties. It also takes into account the treatment capacities at the hospitals. Lastly, it considers ambulance availability (based on when and where transport was dispatched). The model showed good results in a computerized simulation. However, it has not yet been introduced to the decision makers and managers of emergency scenarios, and the chances that such a complex and theoretical tool will be adopted are poor. Barriers to implementation of operations research models in health care, including alienation between the field and the academy, have been described before. Brailsford18 claims that, although operational research models have been applied to a range of health care challenges since the 1960s, these models are not widely accepted and used. 626
The LIM proposed here has the advantage that it was developed by emergency managers for emergency managers, those who will be using the tool in the future. LIM provides policy makers with a structured validated decision support tool that calculates the relative impact of each factor on the congestion level of the hospitals, based on actual situation awareness. The surge capacity is defined accordingly, facilitating a standardized evidence-based decision-making process concerning patient evacuation. In conformity with earlier studies,19 four hospital admission and treatment sites were identified in the present study as impacting surge capacity, namely, EDs, hospitalization wards, ORs, and ICUs. The differences found here in the relative impact of each site on the hospital’s overall capacity support earlier studies,20 which state that ICUs play a key role in the response to major disasters. Interestingly, the content experts who participated in the modified Delphi process did not regard nurses as playing a dominant role in congestion levels perhaps because they were not perceived as a potential bottleneck that should be monitored and considered.17 Contrariwise, physicians were seen to be a crucial factor that must be integrated in the model, most probably reflecting the expectation that the latter could be in shortage in scenarios with multiple casualties. The LIM is designed to serve as a decision support mechanism to assist in the formulation of casualty evacuation policies. It must be emphasized that this tool, effective as it may be, cannot serve as sole determinant of the decision-making process.16 Three significant elements should also be considered when evacuating casualties, namely, specific types of injuries (such as head or burn wounds); geographic locations of the casualties in relation to the medical facilities (some patients need urgent care and will not survive evacuation to a distant hospital); and the desire to keep family members together during the evacuation process, particularly if children are involved. In its present form, the LIM does not allow for these specificities, which will be incorporated in future versions.
CONCLUSION Evacuation of casualties in an emergency situation is a complex and challenging task necessitating simultaneous calculation of numerous factors. Our findings suggest that the LIM decision support model significantly improves the capacity of emergency managements to implement and coordinate evacuation measures, thereby ensuring that patients will be evacuated to less congested medical facilities better suited to providing lifesaving medical services in emergencies. LIM was tested in both simulation exercises and an actual emergency situation and found to contribute positively to policy setting and decision-making processes. LIM provides vital and timely information in accordance with the constantly changing characteristics of emergency situations. Further studies are recommended to ascertain the model’s sensitivity and measure its efficacy and impact in diverse types of emergency scenarios and for diverse levels of emergency management (e.g., first responders’ operation centers vs. national emergency management teams). It is also recommended that future guidelines include the standard of critical mortality, * 2015 Wolters Kluwer Health, Inc. All rights reserved.
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which should be used as an index of evacuation accuracy as well as triage. ACKNOWLEDGMENT We thank the content experts in emergency management that enabled to define the LIM and the parameters on which it is consisted. We also thank all the senior personnel who participated in the simulation exercises. Without their expertise, we would not have been able to develop and validate this important decision support tool.
AUTHORSHIP B.A conducted the literature review, designed the study, collected and analyzed the data, and wrote the manuscript. L.A.-D. assisted in the data analysis and reviewed the manuscript. A.I. was coresearcher in designing the study and collection (as well as analysis) of the data; he actively reviewed the manuscript, and approved its submission.
DISCLOSURE The authors declare no conflicts of interest.
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