Boarding in US Academic Emergency Departments During the COVID-19 Pandemic.

STUDY OBJECTIVE: The first 2 years of the COVID-19 pandemic brought substantial and dynamic changes to emergency department volumes and throughput. The objective of this study was to describe changes in ED boarding among US academic EDs across the duration of the COVID-19 pandemic. METHODS: We conducted a retrospective analysis of monthly data collected from a convenience sample of academic departments of emergency medicine. The study period was from January 2019 to December 2021. The primary outcome was total boarding hours, and secondary outcomes included patient volume stratified by ED disposition. We used multivariable linear panel regression models with fixed effects for individual EDs to estimate adjusted means for 3-month quarters. RESULTS: Of the 73 academic departments of emergency medicine contacted, 34 (46.6%) participated, comprising 43 individual EDs in 25 states. The adjusted mean total boarding hours per month significantly decreased during the second quarter of 2020 (4,449 hours; 95% confidence interval [CI] 3,189 to 5,710) compared to the first quarter of 2019 (8,521 hours; 95% CI 7,845 to 9,197). Beginning in the second quarter of 2021, total boarding hours significantly increased beyond pre-pandemic levels, peaking during the fourth quarter of 2021 (12,127 hours; 95% CI 10,925 to 13,328). CONCLUSIONS: A sustained and considerable increase in boarding observed in selected US academic EDs during later phases of the COVID-19 pandemic may reflect ongoing stresses to the health care system, with potential consequences for patient outcomes as well as clinician well-being.

2017 AAAEM Benchmarking Survey: Comparing Pediatric and Adult Academic Emergency Departments.

OBJECTIVES: The Academy of Administrators in Academic Emergency Medicine Benchmark Survey of academic emergency departments (EDs) was conducted in 2017. We compared operational measures between pediatric and adult (defined as fewer than 5% pediatric visits) EDs based on survey data. Emergency departments in dedicated pediatric hospitals were not represented. METHODS: Measures included: (1) patient volumes, length of stay, and acuity; and 2) faculty staffing, productivity, and percent effort in academics. t Tests were used to compare continuous measures and inferences for categorical variables were made using Pearson χ2 test. RESULTS: The analysis included 17 pediatric and 52 adult EDs. We found a difference in the number of annual visits between adult (median, 66,275; interquartile range [IQR], 56,184-77,702) and pediatric EDs (median, 25,416; IQR, 19,840-29,349) (P < 0.0001). Mean "arrivals per faculty clinical hour" and "total arrivals per treatment space" showed no differences. The proportion of visits (1) arriving by emergency medical services and (2) for behavioral health were significantly higher in adult EDs (both P < 0.0001). The mean length of stay in hours for "all" patients was significantly longer in adult (5.4; IQR, 5.0-6.6) than in pediatric EDs (3.5; IQR, 2.9-4.3; P = 0.017). A similar difference was found for "discharged" patients (P = 0.004). Emergency severity indices, professional evaluation and management codes, and hospitalization rates all suggest higher acuity in adult EDs (all P < 0.0001). There were no differences in mean work relative value units per patient or in the distribution of full time equivalent effort dedicated to academics. CONCLUSIONS: In this cohort, significant differences in operational measures exist between academic adult and pediatric EDs. No differences were found when considering per unit measures, such as arrivals per faculty clinical hour or per treatment space.

Clinical operations of academic versus non-academic emergency departments: a descriptive comparison of two large emergency department operations surveys.

BACKGROUND: Academic and non-academic emergency departments (EDs) are regularly compared in clinical operations benchmarking despite suggestion that the two groups may differ in their clinical operations characteristics. and outcomes. We sought to describe and compare clinical operations characteristics of academic versus non-academic EDs. METHODS: We performed a descriptive, comparative analysis of academic and non-academic adult and general EDs with 40,000+ annual encounters, using the Academy of Academic Administrators of Emergency Medicine (AAAEM)/Association of Academic Chairs of Emergency Medicine (AACEM) and Emergency Department Benchmarking Alliance (EDBA) survey results. We defined academic EDs as primary teaching sites for emergency medicine (EM) residencies and non-academic EDs as sites with minimal resident involvement. We constructed the academic and non-academic cohorts from the AAAEM/AACEM and EDBA surveys, respectively, and analyzed metrics common to both surveys. RESULTS: Eighty and 454 EDs met inclusion criteria for academic and non-academic EDs, respectively. Academic EDs had more median annual patient encounters (73,001 vs 54,393), lower median proportion of pediatric patients (6.3% vs 14.5%), higher median proportion of EMS patients (27% vs 19%), and were more commonly designated as Level I or II Trauma Centers (94% vs 24%). Median patient arrival-to-provider times did not differ (26 vs 25 min). Median length-of-stay was longer (277 vs 190 min) for academic EDs, and left-before-treatment-complete was higher (5.7% vs 2.9%). MRI utilization was higher for academic EDs (2.2% patients with at least one MRI vs 1.0 MRIs performed per 100 patients). Patients-per-hour of provider coverage was lower for academic EDs with and without consideration for advanced practice providers and residents. CONCLUSIONS: Demographic and operational performance measures differ between academic and non-academic EDs, suggesting that the two groups may be inappropriate operational performance comparators. Causes for the differences remain unclear but the differences appear not to be attributed solely to the academic mission.

Hospital Capacity Command Centers: A Benchmarking Survey on an Emerging Mechanism to Manage Patient Flow.

BACKGROUND: Delayed hospital and emergency department (ED) patient throughput, which occurs when demand for inpatient care exceeds hospital capacity, is a critical threat to safety, quality, and hospital financial performance. In response, many hospitals are deploying capacity command centers (CCCs), which co-locate key work groups and aggregate real-time data to proactively manage patient flow. Only a narrow body of peer-reviewed articles have characterized CCCs to date. To equip health system leaders with initial insights into this emerging intervention, the authors sought to survey US health systems to benchmark CCC motivations, design, and key performance indicators. METHODS: An online survey on CCC design and performance was administered to members of a hospital capacity management consortium, which included a convenience sample of capacity leaders at US health systems (N = 38). Responses were solicited through a targeted e-mail campaign. Results were summarized using descriptive statistics. RESULTS: The response rate was 81.6% (31/38). Twenty-five respondents were operating CCCs, varying in scope (hospital, region of a health system, or entire health system) and number of beds managed. The most frequent motivation for CCC implementation was reducing ED boarding (n = 24). The most common functions embedded in CCCs were bed management (n = 25) and interhospital transfers (n = 25). Eighteen CCCs (72.0%) tracked financial return on investment (ROI); all reported positive ROI. CONCLUSION: This survey addresses a gap in the literature by providing initial aggregate data for health system leaders to consider, plan, and benchmark CCCs. The researchers identify motivations for, functions in, and key performance indicators used to assess CCCs. Future research priorities are also proposed.

Operational factors associated with emergency department patient satisfaction: Analysis of the Academy of Administrators of Emergency Medicine/Association of Academic Chairs of Emergency Medicine national survey.

BACKGROUND: Patient satisfaction is a focus for emergency department (ED) and hospital administrators. ED patient satisfaction studies have tended to be single site and focused on patient and clinician factors. Inclusion of satisfaction scores in a large, national operations database provided an opportunity to conduct an investigation that included diverse operational factors. METHODS: We performed a retrospective analysis of the 2019 Academy of Administrators in Academic Emergency Medicine/Association of Academic Chairs of Emergency Medicine (AAAEM/AACEM) benchmarking survey to identify associations between operational factors and patient satisfaction. We identified 59 database variables as potential predictors of Press Ganey likelihood-to-recommend and physician overall scores. Using random forest modeling, we identified the top eight predictors in the models and described their associations. RESULTS: Forty-three (57.3%) academic departments responding to the AAAEM/AACEM survey reported patient satisfaction scores for 78 EDs. Likelihood to recommend ranged from 30.0 to 93.0 (median = 74.8) and was associated with ED length of stay, boarding, use of hallway spaces, hospital annual admissions, faculty base clinical hours, proportion of patients leaving before treatment complete (LBTC), and provider in triage hours per day. Physician overall score ranged from 53.3 to 93.4 (median = 81.9) and was associated with faculty base clinical hours, x-ray utilization, annual ED arrivals, LBTC, use of hallway spaces, arrivals per attending hour, and CT utilization. CONCLUSIONS: ED patient satisfaction was associated with intrinsic and extrinsic factors, some being potentially manageable within the ED but others being relatively fixed or outside the control of ED operations. For likelihood to recommend, patient flow was dominant, with erosion of satisfaction observed with increased boarding and longer LOS. Factors associated with physician overall score were more varied. The use of hallway spaces and base clinical hours greater than 1,500 per year were associated with both lower likelihood-to-recommend and lower physician overall scores.

Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS): development and application of computer modeling to selected National Planning Scenarios for high-consequence events.

Few tools exist that are sufficiently robust to allow manipulation of key input variables to produce casualty estimates resulting from high-consequence events reflecting local or specific regions of concern. This article describes the design and utility of a computerized modeling simulation tool, Electronic Mass Casualty Assessment and Planning Scenarios (EMCAPS), developed to have broad application across emergency management and public health fields as part of a catastrophic events preparedness planning process. As a scalable, flexible tool, EMCAPS is intended to support emergency preparedness planning efforts at multiple levels ranging from local health systems to regional and state public health departments to Metropolitan Medical Response System jurisdictions. Designed around the subset of the National Planning Scenarios with health effects, advanced by the US Department of Homeland Security, the tool's platform is supported by the detailed descriptions and readily retrievable evidence-based assumptions of each scenario. The EMCAPS program allows the user to manipulate key scenario-based input variables that would best reflect the region or locale of interest. Inputs include population density, vulnerabilities, event size, and potency, as applicable. Using these inputs, EMCAPS generates the anticipated population-based health surge influence of the hazard scenario. Casualty estimates are stratified by injury severity/types where appropriate. Outputs are graph and table tabulations of surge estimates. The data can then be used to assess and tailor response capabilities for specific jurisdictions, organizations, and health care systems. EMCAPS may be downloaded without cost from http://www.hopkins-cepar.org/EMCAPS/EMCAPS.html as shareware.

Trends and Characterization of Academic Emergency Department Patient Visits: A Five-year Review.

OBJECTIVES: To meet the unique comparative data needs of academic emergency departments (EDs), we describe the recent 5-year national and regional trends for adult emergency patients' characteristics and operational parameters at academic emergency medical centers. METHODS: Data collected from the recent 5-year period academic year (AY) 2012 through AY 2016 of the Academy of Administrators in Academic Emergency Medicine (AAAEM) and the Association of Academic Chairs of Emergency Medicine (AACEM) Academic Emergency Medicine Benchmarking Survey were analyzed for trends in 1) ED volumes and modes of arrival, 2) triage acuity level, 3) trends in ED professional fee billing, and 4) disposition patterns of ED patients including admission rates and walkouts. The AY spanned the 12-month period of July 1 through June 30. Only primary academic or academic affiliate hospitals data were included. Community and freestanding affiliated EDs were excluded. Institutional-specific data were stratified into four cohorts based on the following annual ED visit volumes: under 40,000, 40,000 to 60,000, 60,000 to 80,000, and over 80,000. Triage acuity levels were based on the Emergency Severity Index (ESI). Professional fee billing was analyzed specifically for CPT codes 99284, 99285, and 99291 (critical care). Left without being seen (LWBS), defined as leaving before a screening examine by a licensed medical provider, and screened and left (SAL), i.e., patients who were screened by a provider, but left before definitive evaluation and management, were similarly evaluated. Total walkouts were defined as the sum of LWBS and SAL. RESULTS: Forty-four primary academic and academic affiliate sites completed the survey for all 5 years. The mean annual patient census increased 13.4% over the study period, with the majority (80%) of sites experiencing volume growth. Acuity/illness severity, measured as ESI 1 and 2, and CPT codes 99284, 99295, and 99291 increased an aggregate 18.2 and 8.4%, respectively. Large-volume hospitals (>60,000-80,000, >80,000) admissions increased by 15 and 21.6%, respectively, primarily due to surge in 2016. Overall emergency medical services (EMS) arrivals increased 7.3% although admissions from EMS remained relatively stable. LWBS rates decreased 19.5%, but total walkouts did not appear to change. CONCLUSION: With a focus on larger academic institutions, differences were noted in the overall increases in volume and acuity. In this survey, participating institutions experienced increased volumes of patients with seemingly higher illness severity. While inroads have been made in LWBS rates, there has not been an overall decrease in total walkouts. The data reported here differed in many aspects compared to other benchmark surveys.

Use of Systems Engineering to Design a Hospital Command Center.

BACKGROUND: In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems. METHODS: A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared. RESULTS: With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays. CONCLUSION: The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas.

Contributions of Academic Emergency Medicine Programs to U.S. Health Care: Summary of the AAAEM-AACEM Benchmarking Data.

OBJECTIVES: The societal contribution of emergency care in the United States has been described. The role and impact of academic emergency departments (EDs) has been less clear. Our report summarizes the results of a benchmarking effort specifically focused on academic emergency medicine (EM) practices. METHODS: From October through December 2016, the Academy of Academic Administrators of Emergency Medicine (AAAEM) and the Association of Academic Chairs of Emergency Medicine (AACEM) jointly administered a benchmarking survey to allopathic, academic departments and divisions of emergency medicine. Participation was voluntary and nonanonymous. The survey queried various aspects of the three components of the tripartite academic mission: clinical care, education and research, and faculty effort and compensation. Responses reflected a calendar year from July 1, 2015, to June 30, 2016. RESULTS: Of 107 eligible U.S. allopathic, academic departments and divisions of emergency medicine, 79 (74%) responded to the survey overall, although individual questions were not always answered by all responding programs. The 79 responding programs reported 6,876,189 patient visits at 97 primary and affiliated academic clinical sites. A number of clinical operations metrics related to the care of these patients at these sites are reported in this study. All responding programs had active educational programs for EM residents, with a median of 37 residents per program. Nearly half of the overall respondents reported responsibility for teaching medical students in mandatory EM clerkships. Fifty-two programs reported research and publication activity, with a total of $129,494,676 of grant funding and 3,059 publications. Median faculty effort distribution was clinical effort, 66.9%; education effort, 12.7%; administrative effort, 12.0%; and research effort, 6.9%. Median faculty salary was $277,045. CONCLUSIONS: Academic EM programs are characterized by significant productivity in clinical operations, education, and research. The survey results reported in this investigation provide appropriate benchmarking for academic EM programs because they allow for comparison of academic programs to each other, rather than nonacademic programs that do not necessarily share the additional missions of research and education and may have dissimilar working environments.

Inpatient disposition classification for the creation of hospital surge capacity: a multiphase study.

BACKGROUND: The ability to provide medical care during sudden increases in patient volume during a disaster or other high-consequence event is a serious concern for health-care systems. Identification of inpatients for safe early discharge (ie, reverse triage) could create additional hospital surge capacity. We sought to develop a disposition classification system that categorises inpatients according to suitability for immediate discharge on the basis of risk tolerance for a subsequent consequential medical event. METHODS: We did a warfare analysis laboratory exercise using evidence-based techniques, combined with a consensus process of 39 expert panellists. These panellists were asked to define the categories of a disposition classification system, assign risk tolerance of a consequential medical event to each category, identify critical interventions, and rank each (using a scale of 1-10) according to the likelihood of a resultant consequential medical event if a critical intervention is withdrawn or withheld because of discharge. FINDINGS: The panellists unanimously agreed on a five-category disposition classification system. The upper limit of risk tolerance for a consequential medical event in the lowest risk group if discharged early was less than 4%. The next categories had upper limits of risk tolerance of about 12% (IQR 8-15%), 33% (25-50%), 60% (45-80%) and 100% (95-100%), respectively. The expert panellists identified 28 critical interventions with a likelihood of association with a consequential medical event if withdrawn, ranging from 3 to 10 on the 10-point scale. INTERPRETATION: The disposition classification system allows conceptual classification of patients for suitable disposition, including those deemed safe for early discharge home during surges in demand. Clinical criteria allowing real-time categorisation of patients are awaited.

Current Status of Gender and Racial/Ethnic Disparities Among Academic Emergency Medicine Physicians.

OBJECTIVE: A 2010 survey identified disparities in salaries by gender and underrepresented minorities (URM). With an increase in the emergency medicine (EM) workforce since, we aimed to 1) describe the current status of academic EM workforce by gender, race, and rank and 2) evaluate if disparities still exist in salary or rank by gender. METHODS: Information on demographics, rank, clinical commitment, and base and total annual salary for full-time faculty members in U.S. academic emergency departments were collected in 2015 via the Academy of Administrators in Academic Emergency Medicine (AAAEM) Salary Survey. Multiple linear regression was used to compare salary by gender while controlling for confounders. RESULTS: Response rate was 47% (47/101), yielding data on 1,371 full-time faculty: 33% women, 78% white, 4% black, 5% Asian, 3% Asian Indian, 4% other, and 7% unknown race. Comparing white race to nonwhite, 62% versus 69% were instructor/assistant, 23% versus 20% were associate, and 15% versus 10% were full professors. Comparing women to men, 74% versus 59% were instructor/assistant, 19% versus 24% were associate, and 7% versus 17% were full professors. Of 113 chair/vice-chair positions, only 15% were women, and 18% were nonwhite. Women were more often fellowship trained (37% vs. 31%), less often core faculty (59% vs. 64%), with fewer administrative roles (47% vs. 57%; all p < 0.05) but worked similar clinical hours (mean ± SD = 1,069 ± 371 hours vs. 1,051 ± 393 hours). Mean overall salary was $278,631 (SD ± $68,003). The mean (±SD) salary of women was $19,418 (±$3,736) less than men (p < 0.001), even after adjusting for race, region, rank, years of experience, clinical hours, core faculty status, administrative roles, board certification, and fellowship training. CONCLUSIONS: In 2015, disparities in salary and rank persist among full-time U.S. academic EM faculty. There were gender and URM disparities in rank and leadership positions. Women earned less than men regardless of rank, clinical hours, or training. Future efforts should focus on evaluating salary data by race and developing systemwide practices to eliminate disparities.

Development of emergency medicine administration in the People's Republic of China.

A collaborative partnership between the Johns Hopkins Hospital, Chaoyang Red Cross Hospital and Chinese Ministry of Health has been established to initiate Emergency Medicine (EM) administrative training in Beijing, China. The Emergency Medical Education and Training Center (EMETC) at Chaoyang Red Cross Hospital was opened as a training facility to foster EM administrative curriculum development and training nationwide. A six-step approach with problem identification, needs assessment, goals and objectives, educational strategies, implementation and evaluation was used to form a locally adapted curriculum. With a train-the-trainers model, the EMETC sponsored several EM administration courses, the first of their kind in China. Since its inception, the EMETC has trained 95 persons from throughout China in EM administration. An EM administration curriculum has been developed and refined. In conclusion, an international partnership between academic hospitals, supported by the local Ministry of Health, to develop a national training facility using this six-step approach may be an attractive strategy for dissemination of EM administration principles.

Understanding estimated worker absenteeism rates during an influenza pandemic.

OBJECTIVES: Published employee absenteeism estimates during an influenza pandemic range from 10 to 40 percent. The purpose of this study was to estimate daily employee absenteeism through the duration of an influenza pandemic and to determine the relative impact of key variables used to derive the estimates. DESIGN: Using the Centers for Disease Control and Prevention's FluWorkLoss program, the authors estimated the number of absent employees on any given day over the course of a simulated 8-week pandemic wave by using varying attack rates. Employee data from a university with a large academic health system were used. Sensitivity of the program outputs to variation in predictor (inputs) values was assessed. Finally, the authors examined and documented the algorithmic sequence of the program. RESULTS: Using a 35 percent attack rate, a total of 47,270 workdays (or 3.4 percent of all available workdays) would be lost over the course of an 8-week pandemic among a population of 35,026 employees. The highest (peak) daily absenteeism estimate was 5.8 percent (minimum 4.8 percent; maximum 7.4 percent). Sensitivity analysis revealed that varying days missed for nonhospitalized illness had the greatest potential effect on peak absence rate (3.1 to 17.2 percent). Peak absence with 15 and 25 percent attack rates were 2.5 percent and 4.2 percent, respectively. CONCLUSIONS: The impact of an influenza pandemic on employee availability may be less than originally thought, even with a high attack rate. These data are generalizable and are not specific to institutions of higher education or medical centers. Thus, these findings provide realistic and useful estimates for influenza pandemic planning for most organizations.

A randomized controlled trial of the effect of service delivery information on patient satisfaction in an emergency department fast track.

OBJECTIVES: The objective was to determine the effect on patient satisfaction of providing patients with predicted service completion times. METHODS: A randomized controlled trial was conducted in an urban, community teaching hospital. Emergency department (ED) patients triaged to fast track on weekdays between October 26, 2009, and December 30, 2009, from 9 am to 5 pm were eligible. Patients were randomized to: 1) usual care (n = 342), 2) provided ED process information (n = 336), or 3) provided ED process information plus predicted service delivery times (n = 333). Patients in group 3 were given an "average" and "upper range" estimate of their waiting room times and treatment times. The average and upper range predictions were calculated from quantile regression models that estimated the 50th and 90th percentiles of the waiting room time and treatment time distributions for fast track patients at the study site based on 2.5 years of historical data. Trained research assistants administered the interventions after triage. Patients completed a brief survey at discharge that measured their satisfaction with overall care, the quality of the information they received, and the timeliness of care. Satisfaction ratings of very good versus good, fair, poor, and very poor were modeled using logistic regression as a function of study group; actual service delivery times; and other patient, clinical, and temporal covariates. The study also modeled satisfaction ratings of fair, poor, and very poor compared to good and very good ratings as a function of the same covariates. RESULTS: Survey completion rates and patient, clinical, and temporal characteristics were similar by study group. Median waiting room time was 70 minutes (interquartile range [IQR] = 40 to 114 minutes), and median treatment time was 52 minutes (IQR = 31 to 81 minutes). Neither intervention affected any of the satisfaction outcomes. Satisfaction was significantly associated with actual waiting room time, individual providers, and patient age. Every 10-minute increase in waiting room time corresponded with an 8% decrease (odds ratio [OR] = 0.92; 95% confidence interval [CI] = 0.89 to 0.95) in the odds of reporting very good satisfaction with overall care. The odds of reporting very good satisfaction with care were lower for several triage nurses and fast track nurses, compared to the triage nurse and fast track nurse who treated the most study patients. Each 10-minute increase in waiting room time was also associated with a 10% increase in the odds of reporting very poor, poor, or fair satisfaction with overall care (OR = 1.10; 95% CI = 1.06 to 1.14). The odds of reporting very poor, poor, or fair satisfaction with overall care also varied significantly among the triage nurses, fast track doctors, and fast track nurses. The odds of reporting very poor, poor, or fair satisfaction with overall care were significantly lower among patients aged 35 years and older compared to patients aged 18 to 34 years. CONCLUSIONS: Satisfaction with overall care was influenced by waiting room time and the clinicians who treated them and not by service completion time estimates provided at triage.

Creation of surge capacity by early discharge of hospitalized patients at low risk for untoward events.

OBJECTIVES: US hospitals are expected to function without external aid for up to 96 hours during a disaster; however, concern exists that there is insufficient capacity in hospitals to absorb large numbers of acute casualties. The aim of the study was to determine the potential for creation of inpatient bed surge capacity from the early discharge (reverse triage) of hospital inpatients at low risk of untoward events for up to 96 hours. METHODS: In a health system with 3 capacity-constrained hospitals that are representative of US facilities (academic, teaching affiliate, community), a variety (N = 50) of inpatient units were prospectively canvassed in rotation using a blocked randomized design for 19 weeks ending in February 2006. Intensive care units (ICUs), nurseries, and pediatric units were excluded. Assuming a disaster occurred on the day of enrollment, patients who did not require any (previously defined) critical intervention for 4 days were deemed suitable for early discharge. RESULTS: Of 3491 patients, 44% did not require any critical intervention and were suitable for early discharge. Accounting for additional routine patient discharges, full use of staffed and unstaffed licensed beds, gross surge capacity was estimated at 77%, 95%, and 103% for the 3 hospitals. Factoring likely continuance of nonvictim emergency admissions, net surge capacity available for disaster victims was estimated at 66%, 71%, and 81%, respectively. Reverse triage made up the majority (50%, 55%, 59%) of surge beds. Most realized capacity was available within 24 to 48 hours. CONCLUSIONS: Hospital surge capacity for standard inpatient beds may be greater than previously believed. Reverse triage, if appropriately harnessed, can be a major contributor to surge capacity.

Hospital-based event medical support for the Baltimore Marathon, 2002-2005.

OBJECTIVES: We present a four-year, cross-sectional epidemiologic description of injuries and illnesses among Baltimore Marathon participants and the evaluation, treatment, and disposition of those conditions by an on-site event medical team led by physicians and staff from an urban, academic emergency department. METHODS: We analyzed data from injuries encountered during the marathon. Subjects presenting to a medical aid station along the course route or at the finish line were defined as "injured or ill" and were prospectively divided into two groups: 1) a brief-encounter group and 2) an extended-encounter group. Data collected included gender, presenting complaint(s), assessment, treatment(s), and disposition. RESULTS: Three percent (N=1,144) of approximately 33,700 total participants over four years presented to medical aid stations during the Baltimore Marathon between 2002 and 2005. Most participants (66%) did not require a full clinical evaluation. Common complaints encountered were dehydration (32%), musculoskeletal injuries (25%), and cutaneous wounds (20%). Transport to the hospital was required for 4% of all injured participants, and 61% returned to the race. CONCLUSIONS: Most injuries/illnesses encountered at the Baltimore Marathon in 2002-2005 were minor, although some were serious enough to require transport to a hospital. The year with the highest average race-day temperature had the highest observed injury rates and the highest number of hospital transports. These results help to improve understanding of the types, severity, and distribution of injuries commonly sustained by marathon participants and may guide decisions regarding the appropriate distribution of emergency medical resources at such events.

Likelihood of reroute during ambulance diversion periods in central Maryland.

OBJECTIVES: To determine the proportion of patients rerouted during ambulance diversion periods and factors associated with reroute. METHODS: A retrospective cohort design was used to examine reroute practices of prehospital providers in central Maryland in 2000. Ambulance transport and diversion data were merged to identify transports that occurred during diversion periods. The proportion of patients rerouted when the closest hospital was on diversion was determined. Generalized estimating equation modeling identified patient, transport, and hospital factors that influenced the likelihood of reroute. RESULTS: Central Maryland hospitals were on diversion 25% of the time in 2000, although it varied by hospital (range of 1-34%). There were 128,165 transports during the study period, of which 18,633 occurred when the closest hospital was on diversion. Of these, only 23% were rerouted. More than half of all transports during a diversion period (53%) occurred when multiple neighboring hospitals were also on diversion. The factors that influenced the likelihood of reroute the most were hospital-related factors. Large volume hospitals and hospitals that spent more time on diversion were less likely to have transports rerouted to them. CONCLUSIONS: Rerouted transports more frequently go to lower volume, less busy hospitals. However, only a small proportion of patients were rerouted. Prehospital providers have limited options because often when one hospital is on diversion, other nearby hospitals are as well. Although ambulance diversion may be an important signal of hospital distress, in this region it infrequently resulted in its intended outcome, rerouting patients to less crowded facilities.