Nathan R. Hoot

Forecasting Emergency Department Crowding: A Discrete Event Simulation

STUDY OBJECTIVE To develop a discrete event simulation of emergency department (ED) patient flow for the purpose of forecasting near-future operating conditions and to validate the forecasts with several measures of ED crowding. METHODS We developed a discrete event simulation of patient flow with evidence from the literature. Development was purely theoretical, whereas validation involved patient data from an academic ED. The model inputs and outputs, respectively, are 6-variable descriptions of every present and future patient in the ED. We validated the model by using a sliding-window design, ensuring separation of fitting and validation data in time series. We sampled consecutive 10-minute observations during 2006 (n=52,560). The outcome measures--all forecast 2, 4, 6, and 8 hours into the future from each observation--were the waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion. Forecasting performance was assessed with Pearsons correlation, residual summary statistics, and area under the receiver operating characteristic curve. RESULTS The correlations between crowding forecasts and actual outcomes started high and decreased gradually up to 8 hours into the future (lowest Pearsons r for waiting count=0.56; waiting time=0.49; occupancy level=0.78; length of stay=0.86; boarding count=0.79; boarding time=0.80). The residual means were unbiased for all outcomes except the boarding time. The discriminatory power for ambulance diversion remained consistently high up to 8 hours into the future (lowest area under the receiver operating characteristic curve=0.86). CONCLUSION By modeling patient flow, rather than operational summary variables, our simulation forecasts several measures of near-future ED crowding, with various degrees of good performance.

The Challenge of Predicting Demand for Emergency Department Services

OBJECTIVES The objective was to develop methodology for predicting demand for emergency department (ED) services by characterizing ED arrivals. METHODS One year of ED arrival data from an academic ED were merged with local climate data. ED arrival patterns were described; Poisson regression was selected to represent the count of hourly ED arrivals as a function of temporal, climatic, and patient factors. The authors evaluated the appropriateness of prediction models by whether the data met key Poisson assumptions, including variance proportional to the mean, positive skewness, and absence of autocorrelation among hours. Model accuracy was assessed by comparing predicted and observed histograms of arrival counts and by how frequently the observed hourly count fell within the 50 and 90% prediction intervals. RESULTS Hourly ED arrivals were obtained for 8,760 study hours. Separate models were fit for high- versus low-acuity patients because of significant arrival pattern differences. The variance was approximately equal to the mean in the high- and low-acuity models. There was no residual autocorrelation (r = 0) present after controlling for temporal, climatic, and patient factors that influenced the arrival rate. The observed hourly count fell within the 50 and 90% prediction intervals 50 and 90% of the time, respectively. The observed histogram of arrival counts was nearly identical to the histogram predicted by a Poisson process. CONCLUSIONS At this facility, demand for ED services was well approximated by a Poisson regression model. The expected arrival rate is characterized by a small number of factors and does not depend on recent numbers of arrivals.

Forecasting Emergency Department Crowding: A Prospective, Real-time Evaluation

OBJECTIVE Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding. DESIGN AND MEASUREMENTS The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future. RESULTS The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R(2) values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86). CONCLUSIONS The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.

Forecasting Emergency Department Crowding: An External, Multicenter Evaluation

STUDY OBJECTIVE We apply a previously described tool to forecast emergency department (ED) crowding at multiple institutions and assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count. METHODS The ForecastED tool was validated with historical data from 5 institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n=52,560) at 4 sites and 10 months (n=44,064) at the fifth. Three outcome measures-the waiting count, occupancy level, and boarding count-were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared with observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured with the median absolute error. RESULTS The tool was successfully used for 5 different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at 4 of 5 sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the median absolute error of the waiting count ranged between 0.6 and 3.1 patients, the median absolute error of the occupancy level ranged between 9.0% and 14.5% of beds, and the median absolute error of the boarding count ranged between 0.9 and 2.8 patients. CONCLUSION The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at 5 external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems.

ACETYLFENTANYL: AN EMERGING DRUG OF ABUSE

BACKGROUND Opioid analgesics are widely used in health care, yet have significant potential for abuse. High doses are associated with potentially fatal respiratory depression, which caused 21,314 deaths in the United States in 2011. Acetylfentanyl, a synthetic opioid agonist closely related to fentanyl, recently emerged as a drug of abuse linked to numerous deaths in North America. CASE REPORT A 36-year-old male developed the habit of using a propylene glycol electronic cigarette filled with acetylfentanyl to aid relaxation. He purchased the drug online in a manner that appeared legal to him, which compromised his insight about the danger of the substance. He had been using the e-cigarette with increasing frequency while on medical leave, and his wife reported finding him weakly responsive on more than one occasion. At approximately 3 am, the family activated 911 for altered mental status. His presentation included respiratory depression, pinpoint pupils, hypoxemia, and a Glasgow Coma Scale score of 6. He responded to serial doses of intravenous naloxone with improvement in his mental status and respiratory condition. Due to the need for repeated dosing, he was placed on a naloxone infusion and recovered uneventfully in intensive care. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Complications from emerging drugs of abuse, like acetylfentanyl, frequently present first to emergency departments. Prompt recognition and treatment can help avoid morbidity and mortality. Acetylfentanyl can be managed effectively with naloxone, although higher than conventional dosing may be required to achieve therapeutic effect.

Stranded on emergency isle: Modeling competition for cardiac services using survival analysis

Patients with cardiovascular disease (CVD) consume a large proportion of inpatient, procedural and emergency services within United States health care system. These patients are major contributors to the steadily increasing demand for health care services nationwide. Unfortunately, economic and legislative factors have resulted in concurrent reductions in hospital system capacity. The resulting imbalance has fallen directly on to the shoulders of emergency departments (ED) in the form of boarding. Boarding refers to the act of holding admitted patients in the ED until an inpatient bed becomes available. Boarding is a barrier to efficient throughput, a major contributor to ED overcrowding and a threat to patient safety. Patients with CVD often use the ED as an entry point to the hospital system. These patients frequently experience long boarding times as a result of hospital wide competition for inpatient resources. The objective of this study is to use survival analysis to determine how demand from competing cardiology admission sources affects access to ED patients requiring inpatient cardiac care. The model reflects bed management policies of the division of cardiology and demonstrates how variability in demand for cardiac services (i.e., surgical, catheterization, telemetry, intensive care) affects ED boarding time for cardiac patients.

Factors Associated with the Likelihood of Hospitalization Following Emergency Department Visits for Behavioral Health Conditions.

OBJECTIVES Behavioral health-related emergency department (ED) visits have been linked with ED overcrowding, an increased demand on limited resources, and a longer length of stay (LOS) due in part to patients being admitted to the hospital but waiting for an inpatient bed. This study examines factors associated with the likelihood of hospital admission for ED patients with behavioral health conditions at 16 hospital-based EDs in a large urban area in the southern United States. METHODS Using Andersens Behavioral Model of Health Service Use for guidance, the study examined the relationship between predisposing (characteristics of the individual, i.e., age, sex, race/ethnicity), enabling (system or structural factors affecting healthcare access), and need (clinical) factors and the likelihood of hospitalization following ED visits for behavioral health conditions (n = 28,716 ED visits). In the adjusted analysis, a logistic fixed-effects model with blockwise entry was used to estimate the relative importance of predisposing, enabling, and need variables added separately as blocks while controlling for variation in unobserved hospital-specific practices across hospitals and time in years. RESULTS Significant predisposing factors associated with an increased likelihood of hospitalization following an ED visit included increasing age, while African American race was associated with a lower likelihood of hospitalization. Among enabling factors, arrival by emergency transport and a longer ED LOS were associated with a greater likelihood of hospitalization while being uninsured and the availability of community-based behavioral health services within 5 miles of the ED were associated with lower odds. Among need factors, having a discharge diagnosis of schizophrenia/psychotic spectrum disorder, an affective disorder, a personality disorder, dementia, or an impulse control disorder as well as secondary diagnoses of suicidal ideation and/or suicidal behavior increased the likelihood of hospitalization following an ED visit. CONCLUSION The block of enabling factors was the strongest predictor of hospitalization following an ED visit compared to predisposing and need factors. Our findings also provide evidence of disparities in hospitalization of the uninsured and racial and ethnic minority patients with ED visits for behavioral health conditions. Thus, improved access to community-based behavioral health services and an increased capacity for inpatient psychiatric hospitals for treating indigent patients may be needed to improve the efficiency of ED services in our region for patients with behavioral health conditions. Among need factors, a discharge diagnosis of schizophrenia/psychotic spectrum disorder, an affective disorder, a personality disorder, an impulse control disorder, or dementia as well as secondary diagnoses of suicidal ideation and/or suicidal behavior increased the likelihood of hospitalization following an ED visit, also suggesting an opportunity for improving the efficiency of ED care through the provision of psychiatric services to stabilize and treat patients with serious mental illness.

Voluntary Medical Incident Reporting Tool to Improve Physician Reporting of Medical Errors in an Emergency Department

Introduction Medical errors are frequently under-reported, yet their appropriate analysis, coupled with remediation, is essential for continuous quality improvement. The emergency department (ED) is recognized as a complex and chaotic environment prone to errors. In this paper, we describe the design and implementation of a web-based ED-specific incident reporting system using an iterative process. Methods A web-based, password-protected tool was developed by members of a quality assurance committee for ED providers to report incidents that they believe could impact patient safety. Results The utilization of this system in one residency program with two academic sites resulted in an increase from 81 reported incidents in 2009, the first year of use, to 561 reported incidents in 2012. This is an increase in rate of reported events from 0.07% of all ED visits to 0.44% of all ED visits. In 2012, faculty reported 60% of all incidents, while residents and midlevel providers reported 24% and 16% respectively. The most commonly reported incidents were delays in care and management concerns. Conclusion Error reporting frequency can be dramatically improved by using a web-based, user-friendly, voluntary, and non-punitive reporting system.