Jeffrey S. Desmond

Emergency Medical Services Outcomes Project I (EMSOP I): Prioritizing Conditions for Outcomes Research☆☆☆★★★

Over the past several years, out-of-hospital EMS have come under increased scrutiny regarding the value of the range of EMS as currently provided. We used frequency data and expert opinion to rank-order EMS conditions for children and adults based on their potential value for the study of effectiveness of EMS care. Relief of discomfort was the outcome parameter EMS professionals identified as having the most potential impact for the majority of children and adults in the top quartile conditions. Future work from this project will identify appropriate severity and outcome measures that can be used to study these priority conditions. The results from the first year of this project will assist those interested in EMS outcomes research to focus their efforts. Furthermore, the results suggest that nonmortality out-come measures, such as relief of discomfort, may be important parameters in determining EMS effectiveness.

Effect of Testing and Treatment on Emergency Department Length of Stay Using a National Database

OBJECTIVES Testing and treatment are essential aspects of the delivery of emergency care. Recognition of the effects of these activities on emergency department (ED) length of stay (LOS) has implications for administrators planning efficient operations, providers, and patients regarding expectations for length of visit; researchers in creating better models to predict LOS; and policy-makers concerned about ED crowding. METHODS A secondary analysis was performed using years 2006 through 2008 of the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationwide study of ED services. In univariate and bivariate analyses, the authors assessed ED LOS and frequency of testing (blood test, urinalysis, electrocardiogram [ECG], radiograph, ultrasound, computed tomography [CT], or magnetic resonance imaging [MRI]) and treatment (providing a medication or performance of a procedure) according to disposition (discharged or admitted status). Two sets of multivariable models were developed to assess the contribution of testing and treatment to LOS, also stratified by disposition. The first was a series of logistic regression models to provide an overview of how testing and treatment activity affects three dichotomized LOS cutoffs at 2, 4, and 6 hours. The second was a generalized linear model (GLM) with a log-link function and gamma distribution to fit skewed LOS data, which provided time costs associated with tests and treatment. RESULTS Among 360 million weighted ED visits included in this analysis, 227 million (63%) involved testing, 304 million (85%) involved treatment, and 201 million (56%) involved both. Overall, visits with any testing were associated with longer LOS (median = 196 minutes; interquartile range [IQR] = 125 to 305 minutes) than those with any treatment (median = 159 minutes; IQR = 91 to 262 minutes). This difference was more pronounced among discharged patients than admitted patients. Obtaining a test was associated with an adjusted odds ratio (OR) of 2.29 (95% confidence interval [CI] = 1.86 to 2.83) for experiencing a more than 4-hour LOS, while performing a treatment had no effect (adjusted OR = 0.84; 95% CI = 0.68 to 1.03). The most time-costly testing modalities included blood test (adjusted marginal effects on LOS = +72 minutes; 95% CI = 66 to 78 minutes), MRI (+64 minutes; 95% CI = 36 to 93 minutes), CT (+59 minutes; 95% CI = 54 to 65 minutes), and ultrasound (US; +56 minutes; 95% CI = 45 to 67 minutes). Treatment time costs were less substantial: performing a procedure (+24 minutes; 95% CI = 20 to 28 minutes) and providing a medication (+15 minutes; 95% CI = 8 to 21 minutes). CONCLUSIONS Testing and less substantially treatment were associated with prolonged LOS in the ED, particularly for blood testing and advanced imaging. This knowledge may better direct efforts at streamlining delivery of care for the most time-costly diagnostic modalities or suggest areas for future research into improving processes of care. Developing systems to improve efficient utilization of these services in the ED may improve patient and provider satisfaction. Such practice improvements could then be examined to determine their effects on ED crowding.

Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments

Crisis-level overcrowding conditions in emergency departments EDs have led hospitals to seek out new patient-flow designs to improve both responsiveness and safety. One approach that has attracted attention and experimentation in the emergency medicine community is a system in which ED beds and care teams are segregated and patients are “streamed” based on predictions of whether they will be discharged or admitted to the hospital. In this paper, we use a combination of analytic and simulation models to determine whether such a streaming policy can improve ED performance, where it is most likely to be effective, and how it should be implemented for maximum performance. Our results suggest that the concept of streaming can indeed improve patient flow, but only in some situations. First, ED resources must be shared across streams rather than physically separated. This leads us to propose a new “virtual-streaming” patient flow design for EDs. Second, this type of streaming is most effective in EDs with 1 a high percentage of admitted patients, 2 longer care times for admitted patients than discharged patients, 3 a high day-to-day variation in the percentage of admitted patients, 4 long patient boarding times e.g., caused by hospital “bed-block”, and 5 high average physician utilization. Finally, to take full advantage of streaming, physicians assigned to admit patients should prioritize upstream new patients, whereas physicians assigned to discharge patients should prioritize downstream old patients.

Forecasting Models of Emergency Department Crowding

OBJECTIVES The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. METHODS From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaikes Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. RESULTS The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. CONCLUSIONS Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.

Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency

Hospital emergency departments (EDs) typically use triage systems that classify and prioritize patients almost exclusively in terms of their need for timely care. Using a combination of analytic and simulation models, we demonstrate that adding an up-front estimate of patient complexity to conventional urgency-based classification can substantially improve both patient safety (by reducing the risk of adverse events) and operational efficiency (by shortening the average length of stay). Moreover, we find that EDs with high resource (physician and/or examination room) utilization, high heterogeneity in the treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most from complexity-augmented triage. Finally, we find that (1) although misclassification of a complex patient as simple is slightly more harmful than vice versa, complexity-augmented triage is relatively robust to misclassification error rates as high as 25p; (2) streaming patients based on complexity information and prioritizing them based on urgency is better than doing the reverse; and (3) separating simple and complex patients via streaming facilitates the application of lean methods that can further amplify the benefit of complexity-augmented triage.

Little's Law Flow Analysis of Observation Unit Impact and Sizing

Expanding hospital capacity by developing an observation unit may be an important strategy in congested hospitals. Understanding the principles for evaluating the potential impact and appropriate sizing of an observation unit is important. The objective of this paper is to contrast two approaches to determining observation unit sizing and profitability, real options, and a flow analysis based on Littles Law. Both methods have validity and use similar data sets. The Littles Law approach has the advantage of providing an estimate of appropriate size for the unit and a natural internal consistency check on data. The benefits of an observation unit can depend critically on assumptions regarding backfill patients, and minor changes in data or assumptions can translate into significant changes in annual financial consequences. Using both the real options and the Littles Law approaches provides some internal consistency checks on data and assumptions. Both are sufficiently simple to be easily mastered and conducted. Using these two simple and accessible methods in parallel for computing the size and financial consequences for an observation unit is recommended.

Academic Radiology and the Emergency Department:: Does It Need Changing?

RATIONAL AND OBJECTIVES The increasing importance of imaging for both diagnosis and management in patient care has resulted in a demand for radiology services 7 days a week, 24 hours a day, especially in the emergency department (ED). We hypothesized the resident preliminary reports were better than generalist radiology interpretations, although inferior to subspecialty interpretations. MATERIALS AND METHODS Total radiology volume through our Level I pediatric and adult academic trauma ED was obtained from the radiology information system. We conducted a literature search for error and discordant rates between radiologists of varying experience. For a 2-week prospective period, all preliminary reports generated by the residents and final interpretations were collected. Significant changes in the report were tabulated. RESULTS The ED requested 72,886 imaging studies in 2004 (16% of the total radiology department volume). In a 2-week period, 12 of 1929 (0.6%) preliminary reports by residents were discordant to the final subspecialty dictation. In the 15 peer-reviewed publications documenting error rates in radiology, the error rate between American Board of Radiology (ABR)-certified radiologists is greater than that between residents and subspecialists in the literature and in our study. However, the perceived error rate by clinicians outside radiology is significantly higher. CONCLUSION Sixteen percent of the volume of imaging studies comes through the ED. The residents handle off-hours cases with a radiology-detected error rate below the error rate between ABR-certified radiologists. To decrease the perceived clinician-identified error rate, we need to change how academic radiology handles ED cases.

Radiologic educationAcademic Radiology and the Emergency Department:: Does It Need Changing?

RATIONAL AND OBJECTIVES The increasing importance of imaging for both diagnosis and management in patient care has resulted in a demand for radiology services 7 days a week, 24 hours a day, especially in the emergency department (ED). We hypothesized the resident preliminary reports were better than generalist radiology interpretations, although inferior to subspecialty interpretations. MATERIALS AND METHODS Total radiology volume through our Level I pediatric and adult academic trauma ED was obtained from the radiology information system. We conducted a literature search for error and discordant rates between radiologists of varying experience. For a 2-week prospective period, all preliminary reports generated by the residents and final interpretations were collected. Significant changes in the report were tabulated. RESULTS The ED requested 72,886 imaging studies in 2004 (16% of the total radiology department volume). In a 2-week period, 12 of 1929 (0.6%) preliminary reports by residents were discordant to the final subspecialty dictation. In the 15 peer-reviewed publications documenting error rates in radiology, the error rate between American Board of Radiology (ABR)-certified radiologists is greater than that between residents and subspecialists in the literature and in our study. However, the perceived error rate by clinicians outside radiology is significantly higher. CONCLUSION Sixteen percent of the volume of imaging studies comes through the ED. The residents handle off-hours cases with a radiology-detected error rate below the error rate between ABR-certified radiologists. To decrease the perceived clinician-identified error rate, we need to change how academic radiology handles ED cases.

Predicting Emergency Department Length of Stay Using Quantile Regression

Objectives: Length of stay (LOS) is an important emergency department (ED) performance measure. The objective of this study was to predict the 10, 50 and 90 percentile of ED LOS using demographic, clinical and temporal characteristics in order to better inform patients and ED staff. Methods: A retrospective cohort study was conducted on one year ED visit data from an academic ED (N=50,824). We estimated the 10, 50 and 90 percentile for three different phases of ED care: waiting time, treatment time and boarding time. We used multivariate quantile regression to model the three phases of ED care separately as a function of patients’ arrival day and time, age, gender, mode of arrival, insurance status, acuity level and chief complaint. Results: The median waiting time was 14 minutes, 191 minutes for median treatment time and 154 minutes for median boarding time. Patients at the 90% waited 7 times longer (98 minutes), took 2.5 times longer to be treated (487 minutes) and boarded 7 times longer (1,122 minutes) compared to patients at the median. Patients’ chief complaint and acuity level were the most important predictors of the three phases of LOS. The adjusted median treatment times for patients with a cardiovascular symptom (202 to 328 minutes depending on acuity level) were longer than patients with any other complaint, regardless of acuity. Of all chief complaints, the longest median boarding times were experienced by patients with a skin problem (177 to 291 minutes depending on acuity level). Day and time of arrival were important predictors of wait time and boarding time as well. The adjusted boarding times were 30% to 100% longer on Mondays compared to Sundays. Conclusions: ED LOS varied significantly among patients in a predictable manner that is largely explained by information available at triage. Providing patients with an expected LOS at triage may result in increased patient satisfaction.

Use of an Administrative Data Set to Determine Optimal Scheduling of an Alcohol Intervention Worker

BACKGROUND Brief alcohol interventions are efficacious in reducing alcohol-related consequences among emergency department (ED) patients. Use of non-clinical staff may increase alcohol screening and intervention; however, optimal scheduling of an alcohol intervention worker (AIW) is unknown. OBJECTIVES Determine optimal scheduling of an AIW based on peak discharge time of alcohol-related ED visits. METHODS Discharge times for consecutive patients with an alcohol-related diagnosis were abstracted from an urban EDs administrative data set from September 2005 through August 2007. Queuing theory was used to identify optimal scheduling. Data for weekends and weekdays were analyzed separately. Stationary independent period-by-period analysis was performed for hourly periods. An M/M/s queuing model, for Markovian inter-arrival time/Markovian service time/and potentially more than one server, was developed for each hour assuming: 1) a single unlimited queue; 2) 75% of patients waited no longer than 30 min for intervention; 3) AIW spent an average 20 min/patient. Estimated average utilization/hour was calculated; if utilization/hour exceeded 25%, AIW staff was considered necessary. RESULTS There were 2282 patient visits (mean age 38 years, range 11-84 years). Weekdays accounted for 45% of visits; weekends 55%. On weekdays, one AIW from 6:00 a.m.-9:00 a.m. (max utilization 42%/hour) would accommodate 28% of weekday alcohol-related patients. On weekends, 5:00 a.m.-11:00 a.m. (max utilization 50%), one AIW would cover 54% of all weekend alcohol-related visits. During other hours the utilization rate falls below 25%/hour. CONCLUSIONS Evaluating 2 years of discharge data revealed that 30 h of dedicated AIW time--18 weekend hours (5:00 a.m.-11:00 a.m.), 12 weekday hours (6:00 a.m.-9:00 a.m.)--would allow maximal patient alcohol screening and intervention with minimal additional burden to clinical staff.