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.

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.

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.

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.

Reduction in Emergency Department Presentations in a Regional Health System during the Covid-19 Pandemic.

INTRODUCTION: Nationally, there has been more than a 40% decrease in Emergency Department (ED) patient volume during the coronavirus disease 2019 (Covid-19) crisis, with reports of decreases in presentations of time-sensitive acute illnesses. We analyzed ED clinical presentations in a Maryland/District of Columbia regional hospital system while health mitigation measures were instituted. METHODS: We conducted a retrospective observational cohort study of all adult ED patients presenting to five Johns Hopkins Health System (JHHS) hospitals comparing visits from March 16 through May 15, in 2019 and 2020. We analyzed de-identified demographic information, clinical conditions, and ICD-10 diagnosis codes for year-over-year comparisons. RESULTS: There were 36.7% fewer JHHS ED visits in 2020 compared to 2019 (43,088 vs. 27,293, P<.001). Patients 75+ had the greatest decline in visits (-44.00%, P<.001). Both genders had significant decreases in volume (-41.9%, P<.001 females vs -30.6%, P<.001 males). Influenza like illness (ILI) symptoms increased year-over-year including fever (640 to 1253, 95.8%, P<.001) and shortness of breath (2504 to 2726, 8.9%, P=.002). ICD-10 diagnoses for a number of time-sensitive illnesses decreased including deep vein thrombosis (101 to 39, -61%, P<.001), acute myocardial infarction (157 to 105, -33%, P=.002), gastrointestinal bleeding (290 to 179, -38.3%, P<.001), and strokes (284 to 234, -17.6%, P=0.03). CONCLUSION: ED visits declined significantly among JHHS hospitals despite offsetting increases in ILI complaints. Decreases in presentations of time-sensitive illnesses were of particular concern. Efforts should be taken to inform patients that EDs are safe, otherwise preventable morbidity and mortality will remain a problem.

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.

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.

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.

An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model.

Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacity-related diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.

Accuracy of emergency department triage using the Emergency Severity Index and independent predictors of under-triage and over-triage in Brazil: a retrospective cohort analysis.

BACKGROUND: Emergency department (ED) triage is performed to prioritize care for patients with critical and time-sensitive illness. Triage errors create opportunity for increased morbidity and mortality. Here, we sought to measure the frequency of under- and over-triage of patients by nurses using the Emergency Severity Index (ESI) in Brazil and to identify factors independently associated with each. METHODS: This was a single-center retrospective cohort study. The accuracy of initial ESI score assignment was determined by comparison with a score entered at the close of each ED encounter by treating physicians with full knowledge of actual resource utilization, disposition, and acute outcomes. Chi-square analysis was used to validate this surrogate gold standard, via comparison of associations with disposition and clinical outcomes. Independent predictors of under- and over-triage were identified by multivariate logistic regression. RESULTS: Initial ESI-determined triage score was classified as inaccurate for 16,426 of 96,071 patient encounters. Under-triage was associated with a significantly higher rate of admission and critical outcome, while over-triage was associated with a lower rate of both. A number of factors identifiable at time of presentation including advanced age, bradycardia, tachycardia, hypoxia, hyperthermia, and several specific chief complaints (i.e., neurologic complaints, chest pain, shortness of breath) were identified as independent predictors of under-triage, while other chief complaints (i.e., hypertension and allergic complaints) were independent predictors of over-triage. CONCLUSIONS: Despite rigorous and ongoing training of ESI users, a large number of patients in this cohort were under- or over-triaged. Advanced age, vital sign derangements, and specific chief complaints-all subject to limited guidance by the ESI algorithm-were particularly under-appreciated.

Emergency department resource utilization during Ramadan: distinct and reproducible patterns over a 4-year period in Abu Dhabi.

OBJECTIVES: Emergency Department (ED) patient arrivals vary daily and change considerably during holidays, posing challenges to resource allocation. Ramadan, during which observant Muslims follow a daily fasting period for ∼30 days, could represent a unique annual circumstance that predictably alters ED arrivals in predominantly Muslim populations. Our study examined an adult and pediatric ED in the United Arab Emirates to determine whether arrival patterns and patient characteristics differed during Ramadan. METHODS: Hourly arrivals, census (number of patients in ED at any given time), and visit characteristics were retrospectively compared for Ramadan versus non-Ramadan periods over 4 years (2010-2013). Hourly arrivals and census were plotted using two-way repeated-measures analysis of variance. Differences in characteristics were examined using the χ-test and Wilcoxon rank sum tests. RESULTS: Ramadan adult and pediatric ED arrival patterns differed significantly (P<0.001) from non-Ramadan days, with sharp decreases after the fast was broken around 6 p.m. (sunset), followed by steep increases by 8:30 pm. The median daily adult arrivals were similar [143 (Ramadan) vs. 148 (non-Ramadan); P=0.060], with slightly decreased length-of-stay (7%; P<0.001) during Ramadan. The median daily pediatric arrivals were lower during Ramadan (43 vs. 57; P<0.001), with decreased length-of-stay (20%; P<0.001). Arrival pattern shifts led to significant census redistribution to evening hours. Patient characteristics were similar during both periods. CONCLUSION: A distinct, predictable pattern of arrivals emerged during Ramadan. EDs serving predominantly Muslim populations or anticipating increases in Muslim patients in their catchment region may benefit from advanced planning for efficient distribution of provider hours during Ramadan.

Implementing standardized, inter-unit communication in an international setting: handoff of patients from emergency medicine to internal medicine.

Standardized handoffs may reduce communication errors, but research on handoff in community and international settings is lacking. Our study at a community hospital in the United Arab Emirates characterizes existing handoff practices for admitted patients from emergency medicine (EM) to internal medicine (IM), develops a standardized handoff tool, and assesses its impact on communication and physician perceptions. EM physicians completed a survey regarding handoff practices and expectations. Trained observers utilized a checklist based on the Systems Engineering Initiative for Patient Safety model to observe 40 handoffs. EM and IM physicians collaboratively developed a written tool encouraging bedside handoff of admitted patients. After the intervention, surveys of EM physicians and 40 observations were subsequently repeated. 77.5% of initial observed handoffs occurred face-to-face, with 42.5% at bedside, and in four different languages. Most survey respondents considered face-to-face handoff ideal. Respondents noted 9-13 patients suffering harm due to handoff in the prior month. After handoff tool implementation, 97.5% of observed handoffs occurred face-to-face (versus 77.5%, p = 0.014), with 82.5% at bedside (versus 42.5%, p < 0.001), and all in English. Handoff was streamlined from 7 possible pathways to 3. Most post-intervention survey respondents reported improved workflow (77.8%) and safety (83.3%); none reported patient harm. Respondents and observers noted reduced inefficiency (p < 0.05). Our standardized tool increased face-to-face and bedside handoff, positively impacted workflow, and increased perceptions of safety by EM physicians in an international, non-academic setting. Our three-step approach can be applied towards developing standardized, context-specific inter-specialty handoff in a variety of settings.

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.

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.

Implementing Data Definition Consistency for Emergency Department Operations Benchmarking and Research.

OBJECTIVES: The objective was to obtain a commitment to adopt a common set of definitions for emergency department (ED) demographic, clinical process, and performance metrics among the ED Benchmarking Alliance (EDBA), ED Operations Study Group (EDOSG), and Academy of Academic Administrators of Emergency Medicine (AAAEM) by 2017. METHODS: A retrospective cross-sectional analysis of available data from three ED operations benchmarking organizations supported a negotiation to use a set of common metrics with identical definitions. During a 1.5-day meeting-structured according to social change theories of information exchange, self-interest, and interdependence-common definitions were identified and negotiated using the EDBA's published definitions as a start for discussion. Methods of process analysis theory were used in the 8 weeks following the meeting to achieve official consensus on definitions. These two lists were submitted to the organizations' leadership for implementation approval. RESULTS: A total of 374 unique measures were identified, of which 57 (15%) were shared by at least two organizations. Fourteen (4%) were common to all three organizations. In addition to agreement on definitions for the 14 measures used by all three organizations, agreement was reached on universal definitions for 17 of the 57 measures shared by at least two organizations. The negotiation outcome was a list of 31 measures with universal definitions to be adopted by each organization by 2017. CONCLUSION: The use of negotiation, social change, and process analysis theories achieved the adoption of universal definitions among the EDBA, EDOSG, and AAAEM. This will impact performance benchmarking for nearly half of US EDs. It initiates a formal commitment to utilize standardized metrics, and it transitions consistency in reporting ED operations metrics from consensus to implementation. This work advances our ability to more accurately characterize variation in ED care delivery models, resource utilization, and performance. In addition, it permits future aggregation of these three data sets, thus facilitating the creation of more robust ED operations research data sets unified by a universal language. Negotiation, social change, and process analysis principles can be used to advance the adoption of additional definitions.

Discrete event simulation for healthcare organizations: a tool for decision making.

Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling procedures, and (5) using ancillary resources (e.g., labs, pharmacies). This article describes applicable scenarios, outlines DES concepts, and describes the steps required for development. An original DES model developed to examine crowding and patient flow for staffing decision making at an urban academic emergency department serves as a practical example.

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.

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.

Adverse clinical events during intrahospital transport by a specialized team: a preliminary report.

BACKGROUND: Caring for patients during intrahospital transport is a high-risk activity. Few data exist on the use of specialized transport teams similar to the teams used for out-of-hospital transport. OBJECTIVE: To describe the experience with a dedicated, intra-hospital transport program, to report the rate of clinically significant adverse events, and to examine types of adverse events, interventions provided, and outcomes. METHODS: Patient transports within an academic quaternary-care hospital from November 2007 through April 2008 were retrospectively reviewed. Adverse events were defined as extubation, code team activation, death, sustained arrhythmia, hypoxia exceeding 5 minutes, hypotension exceeding 20% of baseline systolic or diastolic blood pressure and requiring intervention, use of physical restraints, or acute change in mental status. RESULTS: A total of 3383 charts were reviewed (91.8% of all completed transports).The overall rate of adverse events was 1.7% (59 events). Most events were related to hypoxia (25/59) and blood pressure changes (25/59). One extubation and one code team activation occurred. Most interventions involved adjustments to oxygen therapy (22/59) and vasopressor management (18/59). Only 12 (20.3%) of the transports with adverse events were aborted, more often during magnetic resonance imaging (χ(2) = 6.86, df = 1, P = .01) and in older patients (mean [SD], 70.8 [14.2] vs 58.7 [14.9] years; P = .02). CONCLUSIONS: The rate of clinically significant adverse events during patient transport by a specialized team is relatively low. Further studies are needed to compare effectiveness and mortality benefits between intrahospital transport teams and traditional transport teams.

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.