The onset of p53 loss of heterozygosity is differentially induced in various stem cell types and may involve the loss of either allele

p53 loss of heterozygosity (p53LOH) is frequently observed in Li-Fraumeni syndrome (LFS) patients who carry a mutant (Mut) p53 germ-line mutation. Here, we focused on elucidating the link between p53LOH and tumor development in stem cells (SCs). Although adult mesenchymal stem cells (MSCs) robustly underwent p53LOH, p53LOH in induced embryonic pluripotent stem cells (iPSCs) was significantly attenuated. Only SCs that underwent p53LOH induced malignant tumors in mice. These results may explain why LFS patients develop normally, yet acquire tumors in adulthood. Surprisingly, an analysis of single-cell sub-clones of iPSCs, MSCs and ex vivo bone marrow (BM) progenitors revealed that p53LOH is a bi-directional process, which may result in either the loss of wild-type (WT) or Mut p53 allele. Interestingly, most BM progenitors underwent Mutp53LOH. Our results suggest that the bi-directional p53LOH process may function as a cell-fate checkpoint. The loss of Mutp53 may be regarded as a DNA repair event leading to genome stability. Indeed, gene expression analysis of the p53LOH process revealed upregulation of a specific chromatin remodeler and a burst of DNA repair genes. However, in the case of loss of WTp53, cells are endowed with uncontrolled growth that promotes cancer.

Medication waste reduction in pediatric pharmacy batch processes

OBJECTIVES

To inform pediatric cart-fill batch scheduling for reductions in pharmaceutical waste using a case study and simulation analysis.

METHODS

A pre and post intervention and simulation analysis was conducted during 3 months at a 205-bed children's center. An algorithm was developed to detect wasted medication based on time-stamped computerized provider order entry information. The algorithm was used to quantify pharmaceutical waste and associated costs for both preintervention (1 batch per day) and postintervention (3 batches per day) schedules. Further, simulation was used to systematically test 108 batch schedules outlining general characteristics that have an impact on the likelihood for waste.

RESULTS

Switching from a 1-batch-per-day to a 3-batch-per-day schedule resulted in a 31.3% decrease in pharmaceutical waste (28.7% to 19.7%) and annual cost savings of $183,380. Simulation results demonstrate how increasing batch frequency facilitates a more just-in-time process that reduces waste. The most substantial gains are realized by shifting from a schedule of 1 batch per day to at least 2 batches per day. The simulation exhibits how waste reduction is also achievable by avoiding batch preparation during daily time periods where medication administration or medication discontinuations are frequent. Last, the simulation was used to show how reducing batch preparation time per batch provides some, albeit minimal, opportunity to decrease waste.

CONCLUSIONS

The case study and simulation analysis demonstrate characteristics of batch scheduling that may support pediatric pharmacy managers in redesign toward minimizing pharmaceutical waste.

Adult respiratory distress syndrome following cardiac surgery

BACKGROUND

Severe lung injury with the development of acute respiratory distress syndrome (ARDS) is a serious complication of cardiac surgery. The aim of this study was to determine the incidence, risk factors, and mortality of ARDS following cardiac surgery.

METHODS

We retrospectively analyze data in the period between January 2005 and March 2013.

RESULTS

Of 6069 patients who underwent cardiac surgery during the study period, 37 patients developed ARDS during the postoperative period. The incidence of ARDS was 0.61%, with a mortality of 40.5% (15 patients). Multivariate regression analysis identified previous cardiac surgery, complex cardiac surgery, and more than three transfusions with packed red blood cells (PRBC) were independent predictors for developing ARDS.

CONCLUSIONS

ARDS remains a serious, but very rare complication associated with significant mortality. In our study, previous cardiac surgery, complex cardiac surgery, and more than three transfusions of PRBC were independent predictors for the development of ARDS.

Critical resources for hospital surge capacity: an expert consensus panel

Background: Hospital surge capacity (HSC) is dependent on the ability to increase or conserve resources. The hospital surge model put forth by the Agency for Healthcare Research and Quality (AHRQ) estimates the resources needed by hospitals to treat casualties resulting from 13 national planning scenarios. However, emergency planners need to know which hospital resource are most critical in order to develop a more accurate plan for HSC in the event of a disaster. Objective: To identify critical hospital resources required in four specific catastrophic scenarios; namely, pandemic influenza, radiation, explosive, and nerve gas. Methods: We convened an expert consensus panel comprised of 23 participants representing health providers (i.e., nurses and physicians), administrators, emergency planners, and specialists. Four disaster scenarios were examined by the panel. Participants were divided into 4 groups of five or six members, each of which were assigned two of four scenarios. They were asked to consider 132 hospital patient care resources- extracted from the AHRQ's hospital surge model- in order to identify the ones that would be critical in their opinion to patient care. The definition for a critical hospital resource was the following: absence of the resource is likely to have a major impact on patient outcomes, i.e., high likelihood of untoward event, possibly death. For items with any disagreement in ranking, we conducted a facilitated discussion (modified Delphi technique) until consensus was reached, which was defined as more than 50% agreement. Intraclass Correlation Coefficients (ICC) were calculated for each scenario, and across all scenarios as a measure of participant agreement on critical resources. For the critical resources common to all scenarios, Kruskal-Wallis test was performed to measure the distribution of scores across all scenarios. Results: Of the 132 hospital resources, 25 were considered critical for all four scenarios by more than 50% of the participants. The number of hospital resources considered to be critical by consensus varied from one scenario to another; 58 for the pandemic influenza scenario, 51 for radiation exposure, 41 for explosives, and 35 for nerve gas scenario. Intravenous crystalloid solution was the only resource ranked by all participants as critical across all scenarios. The agreement in ranking was strong in nerve agent and pandemic influenza (ICC= 0.7 in both), and moderate in explosives (ICC= 0.6) and radiation (ICC= 0.5). Conclusion: In four disaster scenarios, namely, radiation, pandemic influenza, explosives, and nerve gas scenarios; supply of as few as 25 common resources may be considered critical to hospital surge capacity. The absence of any these resources may compromise patient care. More studies are needed to identify critical hospital resources in other disaster scenarios.

Nutrient intake in the GEICO multicenter trial: the effects of a multicomponent worksite intervention

BACKGROUND/OBJECTIVES

To assess the effects on macro- and micronutrient intake of a nutrition intervention program in corporate settings across the United States.

SUBJECTS/METHODS

Two hundred and ninety-two individuals who were overweight or had type 2 diabetes were recruited from 10 sites of a US insurance company. Two hundred and seventy-one participants completed baseline diet recalls, and 183 participants completed dietary recalls at 18 weeks. Sites were randomly assigned to an intervention group (five sites) or to a control group (five sites) for 18 weeks. At intervention sites, participants were asked to follow a low-fat vegan diet and attend weekly group meetings. At control sites, participants continued their usual diets. At baseline and 18 weeks, participants completed 2-day diet recalls. Between-group differences in changes in nutrient intake were assessed using an analysis of covariance.

RESULTS

Compared with those in the control group, intervention-group participants significantly reduced the reported intake of total fat (P=0.02), saturated (P=0.006) and monounsaturated fats (P=0.01), cholesterol (P=0.009), protein (P=0.03) and calcium (P=0.02), and increased the intake of carbohydrate (P=0.006), fiber (P=0.002), β-carotene (P=0.01), vitamin C (P=0.003), magnesium (P=0.04) and potassium (P=0.002).

CONCLUSIONS

An 18-week intervention program in a corporate setting reduces intake of total fat, saturated fat and cholesterol and increases the intake of protective nutrients, particularly fiber, β-carotene, vitamin C, magnesium and potassium. The reduction in calcium intake indicates the need for planning for this nutrient.

A multicenter randomized controlled trial of a plant-based nutrition program to reduce body weight and cardiovascular risk in the corporate setting: the GEICO study

Background/objectives:

To determine the effects of a low-fat plant-based diet program on anthropometric and biochemical measures in a multicenter corporate setting.

Subjects/methods:

Employees from 10 sites of a major US company with body mass index ⩾25 kg/m² and/or previous diagnosis of type 2 diabetes were randomized to either follow a low-fat vegan diet, with weekly group support and work cafeteria options available, or make no diet changes for 18 weeks. Dietary intake, body weight, plasma lipid concentrations, blood pressure and glycated hemoglobin (HbA_1C) were determined at baseline and 18 weeks.

Results:

Mean body weight fell 2.9 kg and 0.06 kg in the intervention and control groups, respectively (P<0.001). Total and low-density lipoprotein (LDL) cholesterol fell 8.0 and 8.1 mg/dl in the intervention group and 0.01 and 0.9 mg/dl in the control group (P<0.01). HbA_1C fell 0.6 percentage point and 0.08 percentage point in the intervention and control group, respectively (P<0.01).

Among study completers, mean changes in body weight were −4.3 kg and −0.08 kg in the intervention and control groups, respectively (P<0.001). Total and LDL cholesterol fell 13.7 and 13.0 mg/dl in the intervention group and 1.3 and 1.7 mg/dl in the control group (P<0.001). HbA_1C levels decreased 0.7 percentage point and 0.1 percentage point in the intervention and control group, respectively (P<0.01).

Conclusions:

An 18-week dietary intervention using a low-fat plant-based diet in a corporate setting improves body weight, plasma lipids, and, in individuals with diabetes, glycemic control.

Influenza Forecasting with Google Flu Trends

Objective

We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model.

Introduction

Each year, influenza results in increased Emergency Department crowding which can be mitigated through early detection linked to an appropriate response. Although current surveillance systems, such as Google Flu Trends, yield near real-time influenza surveillance, few demonstrate ability to forecast impending influenza cases.

Methods

Forecasting models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004 – 2011) divided into training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear, and autoregressive methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. Models were developed and evaluated through statistical measures of global deviance and log-likelihood ratio tests. An additional measure of forecast confidence, defined as the percentage of forecast values, during an influenza peak, that are within 7 influenza cases of the actual data, was examined to demonstrate practical utility of the model.

Results

A generalized autoregressive Poisson (GARMA) forecast model integrating previous influenza cases with Google Flu Trends information provided the most accurate influenza case predictions. Google Flu Trend data was the only source of external information providing significant forecast improvements (p = 0.00002). The final model, a GARMA intercept model with the addition of Google Flu Trends, predicted weekly influenza cases during 4 out-of-sample outbreaks within 7 cases for 80% of estimates (Figure 1).

Conclusions

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

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.

Influenza forecasting with Google Flu Trends

BACKGROUND

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.

METHODS

Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.

RESULTS

A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.

CONCLUSIONS

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Abstract P157: Factors Influencing Time-Dependent Quality Indicators for Suspected ACS Patients in the ED

Background: Rapid risk stratification and timely treatment are critical to favorable outcomes for acute coronary syndrome (ACS) patients. This study examines five time-dependent quality indicators (QIs) for unstable angina (UA) / non-ST elevation (NSTEMI) patients in the ED. Our objective was to determine patient and system factors that influence QIs (see Table1) for timeliness for ED UA/NSTEMI patients.

Methods: A retrospective, cohort study was conducted at an academic medical center ED over a 2 year period. The cohort consisted of all patients (N=12,544) aged 24 years or older suspected of having ACS as defined by receiving an electrocardiogram (ECG) and at least one cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service utilization, staffing provisions, equipment availability and metrics characterizing ED and hospital crowding on time-dependent QIs.

Results: UA/NSTEMI patient QI times and percentage adherence to defined performance standards are displayed in Table 1. Table 2 shows the factors that most influenced ED performance on each QI, a description of how each factor changed, and the resultant change in QI time interval. Average change in indicator time was calculated using the full Cox regression model created for each indicator, thus adjusting for all other significant factors.

Quality Indicator Definition Performance Standard Time: Median (IQR) min % Adherence 1. ECG read out time (Suspected of UA/NSTEMI - ECG and Biomarker) Arrival time to ECG read out time < = 10 min 13 (7 - 32) 42% 2. Lab turn-around-time Physician biomarker order to lab report time < = 60 min 70 (53 - 96) 37% 3. Therapeutic
 __turn-around-time (overall) Physician biomarker order to first anti-ischemic medication administration Not defined 30 (14 - 66) – 3b. Therapeutic
 __turn-around-time (Anti-platelet)) Physician biomarker order to first anti-ischemic medication administration Not defined 173 (71 - 317) – 3c. Therapeutic
 __turn-around-time (Anti-thrombin) Physician biomarker order to first anti-ischemic medication administration Not defined 30 (14 - 65) – 4. Boarding time Disposition decision time to inpatient admit time for admitted patients < = 120 min 198 (97 - 496) 32% 5. ED length of stay Arrival time to discharge (disposition decision for inpatients) Not defined 247 (156 - 392) –

Quality Indicator Factor Change in Factor Hazard Ratio Median Ä in Minutes (%) 1. ECG read out time Chief Complaint
 Chief Complaint
 Acuity
 Acuity Non-chest pain related compared to chest pain
 Chest pain related compared to chest pain
 Level 1 compared to levels 3 to 5
 Level 2 compared to levels 3 to 5 0.35
 0.58
 1.83
 1.36 17.0
 5.0
 -2.4
 -1.4 2. Lab turn-around-time Cardiologist in ED
 Time of Day
 Time of Day
 Point of Care Testing
 Team Triage Cardiologist present in ED compared to not present
 Night compared to day
 Evening compared to day
 Point of care compared to standard lab
 Team triage compared to not active 0.74
 0.84
 0.89
 1.15
 1.12 8.0
 4.5
 3.0
 -3.0
 -2.4 3. Therapeutic turn-around-time Stress Test
 Chief Complaint
 Chief Complaint
 Non-portable Chest
 X-ray Stress test prior to med administration compared to none
 Non-chest pain related compared to chest pain
 Chest pain related compared to chest pain
 Non-portable chest X-ray prior to med administration compare to none 0.41
 0.60
 0.72
 0.82 163.8
 67.7
 39.2
 20.5 4. ED Boarding Time Med Administration
 Patient Insurance
 Patient Insurance
 Time of Day
 Patient Insurance
 Telemetry Occupancy
 Time of Day
 Acuity
 Cardiology consult placeHoliday
 Portable Chest X-Ray
 Acuity
 Med Administration Med administration before disposition compared to no med administration
 Self pay compared to commercial
 Medicaid compared to commercial
 Night Compared to day
 Medicare compared to commercial
 10% increase in telemetry occupancy
 Evening compared to day
 Level 1 compared to levels 3 to 5
 Consultant used compared to none
 placeHoliday compare to non holiday
 Portable Chest X-ray before disposition compared to no X-ray
 Level 2 compared to levels 3 to 5
 Med administration after disposition
 Compared to no med administration 1.09
 0.84
 0.81
 0.84
 0.88
 0.28
 0.97
 3.83
 1.37
 1.33
 1.19
 1.15
 0.77 63.3
 48.4
 39.6
 38.4
 27.8
 27.7
 6.7
 -135.5
 -48.1
 -44.1
 -29.5
 -24.1
 -14.6 5. ED length of stay Stress Test
 Point of Care Testing
 Age 45 - 54 Years
 Age 35 - 44 Years
 Chief Complaint
 Time of Day
 Admitted Chief Complaint
 ED chest pain occupancy Stress test prior to disposition decision compared to none
 Point of care compared to standard lab
 45 - 54 years compared to > 64 years
 35 -44 years compared to > 64 years
 Not chest pain-related compared to chest pain-related
 Evening
 Admitted compared to discharged
 Chest pain-related compare to not related to chest pain Percent increase in ED chest pain/neurology load 0.36
 0.78
 0.81
 0.81
 0.87
 0.88
 1.70
 1.11
 1.01 171.0
 30.2
 26.0
 25.2
 17.6
 15.0
 -50.5
 -10.7
 -2.9

Conclusions: The natural variability (not amenable to change) of patient and clinical factors and the artificial variability created by man-made processes and systems (amenable to change) both significantly influenced QI times for our ED UA/NSTEMI cohort. ECG time was the only QI influenced solely by patient factors.

Designed to fail: how computer simulation can detect fundamental flaws in clinic flow

Discrete-event simulation can be used as an effective tool for healthcare administrators to "test" various operational decisions. The recent growth in hospital outpatient volumes and a constrained financial environment make discrete-event simulation a cost-effective way to diagnose inefficiency and create and test strategies for improvement. This study shows how discrete-event simulation was used in an adult medicine clinic within a large, tertiary care, academic medical center. Simulation creation steps are discussed, including information gathering, process mapping, data collection, model creation, and results. Results of the simulation indicated that system bottle-necks were present in the medication administration and check-out steps of the clinic process. The simulation predicted that matching resources to excessive demand at appropriate times for these bottleneck steps would reduce patients' mean time in the system (i.e., visit time) from 124.3 (s.d. +/- 65.7) minutes to 87.0 (s.d. +/- 36.4) minutes. Although other factors may affect real-world operations of a clinic, discrete-event simulation allows healthcare administrators and clinic operational decision makers to observe the effects of changing staffing and resource allocations on patient wait and throughput time. Discrete-event simulation is not a cure-all for clinic throughput problems, but can be a strong tool to provide evidentiary guidance for clinic operational redesign.

Evaluating the effects of increasing surgical volume on emergency department patient access

AIM

To determine how increases in surgical patient volume will affect emergency department (ED) access to inpatient cardiac services. To compare how strategies to increase cardiology inpatient throughput can either accommodate increases in surgical volume or improve ED patient access.

METHODS

A stochastic discrete event simulation was created to model patient flow through a cardiology inpatient system within a US, urban, academic hospital. The simulation used survival analysis to examine the relationship between anticipated increases in surgical volume and ED patient boarding time (ie, time interval from cardiology admission request to inpatient bed placement).

RESULTS

ED patients boarded for a telemetry and cardiovascular intensive care unit (CVICU) bed had a mean boarding time of 5.3 (median 3.1, interquartile range 1.5-6.9) h and 2.7 (median 1.7, interquartile range 0.8-3.0) h, respectively. Each 10% incremental increase in surgical volume resulted in a 37 and 33 min increase in mean boarding time to the telemetry unit and CVICU, respectively. Strategies to increase cardiology inpatient throughput by increasing capacity and decreasing length of stay for specific inpatients was compared. Increasing cardiology capacity by one telemetry and CVICU bed or decreasing length of stay by 1 h resulted in a 7-9 min decrease in average boarding time or an 11-19% increase in surgical patient volume accommodation.

CONCLUSIONS

Simulating competition dynamics for hospital admissions provides prospective planning (ie, decision making) information and demonstrates how interventions to increase inpatient throughput will have a much greater effect on higher priority surgical admissions compared with ED admissions.

Does diet really affect acne?

Acne vulgaris has anecdotally been attributed to diet by individuals affected by this skin condition. In a 2009 systematic literature review of 21 observational studies and 6 clinical trials, the association between acne and diet was evaluated. Observational studies, including 2 large controlled prospective trials, reported that cow's milk intake increased acne prevalence and severity. Furthermore, prospective studies, including randomized controlled trials, demonstrated a positive association between a high-glycemic-load diet, hormonal mediators, and acne risk. Based on these findings, there exists convincing data supporting the role of dairy products and high-glycemic-index foods in influencing hormonal and inflammatory factors, which can increase acne prevalence and severity. Studies have been inconclusive regarding the association between acne and other foods.

Shifting Toward Balance: Measuring the Distribution of Workload Among Emergency Physician Teams

STUDY OBJECTIVE

The objective of this investigation is to determine time-dependent workload patterns for emergency department (ED) physician teams across work shifts. A secondary aim was to demonstrate how ED demand patterns and the timing of shift changes influence the balance of workload among a physician team.

METHODS

Operational measurements of an adult ED were collected from a clinical information system to characterize physician workload patterns during all current work shifts. Plots of patient load versus time were developed for each physician shift, in which patient load was defined as the number of patients a physician simultaneously managed at a point in time. Patient-load curves for each shift were superimposed during 24 hours to display how patient load was distributed among a team of physicians.

RESULTS

Resident shift changes during daily peak occupancy periods caused patient load imbalances so that residents on a particular shift consistently managed a disproportionate number of patients (mean 9.4 patients; 95% confidence interval [CI] 6.7 to 12.1 patients) compared with other residents on duty (mean 3.4 patients; 95% CI 2.1 to 4.7 patients).

CONCLUSION

Physician patient load patterns and ED demand patterns should be taken into consideration when physician shift times are scheduled so that patient load may be balanced among a team. Real-time monitoring of physician patient load may reduce stress and prevent physicians from exceeding their safe capacity for workload.

System complexity as a measure of safe capacity for the emergency department

OBJECTIVES

System complexity is introduced as a new measure of system state for the emergency department (ED). In its original form, the measure quantifies the uncertainty of demands on system resources. For application in the ED, the measure is being modified to quantify both workload and uncertainty to produce a single integrated measure of system state.

METHODS

Complexity is quantified using an information-theoretic or entropic approach developed in manufacturing and operations research. In its original form, complexity is calculated on the basis of four system parameters: 1) the number of resources (clinicians and processing entities such as radiology and laboratory systems), 2) the number of possible work states for each resource, 3) the probability that a resource is in a particular work state, and 4) the probability of queue changes (i.e., where a queue is defined by the number of patients or patient orders being managed by a resource) during a specified time period.

RESULTS

An example is presented to demonstrate how complexity is calculated and interpreted for a simple system composed of three resources (i.e., emergency physicians) managing varying patient loads. The example shows that variation in physician work states and patient queues produces different scores of complexity for each physician. It also illustrates how complexity and workload differ.

CONCLUSIONS

System complexity is a viable and technically feasible measurement for monitoring and managing surge capacity in the ED.